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Source: https://newzmarker.com/feed

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<title>The Road Less Paved: An Introduction to Gravel Biking – Steven Rindner</title>
<link>https://newzmarker.com/the-road-less-paved-an-introduction-to-gravel-biking-steven-rindner.html</link>
<dc:creator><![CDATA[Baylor Edgar]]></dc:creator>
<pubDate>Thu, 11 Apr 2024 07:05:37 +0000</pubDate>
<category><![CDATA[Business]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19914</guid>
<description><![CDATA[In the ever-evolving world of cycling, a new trend has emerged, blurring the lines between road biking and mountain biking: gravel biking. This adventurous style of cycling combines the thrill of exploration with the physical challenge of traversing unpaved terrains, offering riders a unique way...]]></description>
<content:encoded><![CDATA[In the ever-evolving world of cycling, a new trend has emerged, blurring the lines between road biking and mountain biking: gravel biking. This adventurous style of cycling combines the thrill of exploration with the physical challenge of traversing unpaved terrains, offering riders a unique way to experience the great outdoors. Gravel biking, characterized by its versatility and the freedom it affords, is quickly gaining popularity among cycling enthusiasts looking for a new kind of adventure. With this article <a href="https://stevenrindner.wordpress.com/">Steven Rindner</a> delves into the essence of gravel biking, its appeal, the equipment involved, and tips for those ready to embark on this gritty journey.
What is Gravel Biking?
Gravel biking refers to riding on surfaces that are not paved, including gravel roads, dirt trails, forest paths, and more rugged landscapes. Unlike road biking, which is confined to smooth asphalt, or traditional mountain biking, which often tackles technical off-road trails, gravel biking occupies a sweet spot in between, inviting cyclists to explore less-traveled paths with a mix of terrains.
The Appeal of Gravel Biking
The allure of gravel biking lies in its versatility and the sense of adventure it invokes. It encourages riders to venture beyond the confines of paved roads and experience more remote, scenic landscapes typically inaccessible to road bikes. This style of biking appeals to those seeking solitude, natural beauty, and the challenge of adapting to varying surface conditions. Additionally, gravel biking fosters a strong sense of community among riders who share a passion for exploring and pushing their limits.
Choosing the Right Equipment
Gravel bikes, the steeds of choice for this discipline, are specifically designed to handle a variety of surfaces with stability and comfort. They resemble road bikes but are equipped with features optimized for durability and performance on rough terrains, such as:
– Wider Tires: Offering increased grip and cushioning on uneven surfaces, wider tires are a hallmark of gravel bikes. They provide stability and confidence on loose or slippery terrain.
– Robust Frame: Gravel bike frames are built to withstand the rigors of off-road riding, made from materials that offer an optimal balance of strength, weight, and durability.
– Gearing: Gravel bikes typically have a wide range of gears to tackle steep climbs and fast descents, ensuring riders can maintain an efficient pace regardless of the terrain.
– Disc Brakes: Offering superior stopping power in varied conditions, disc brakes are essential for the unpredictable surfaces encountered in gravel biking.
Tips for Gravel Biking Beginners
<ol style="text-align: justify;">
<li>Start Slow: Begin with shorter rides on familiar gravel paths to acclimate to the handling and feel of a gravel bike on unpaved surfaces.</li>
</ol>
<ol style="text-align: justify;" start="2">
<li>Plan Your Route: Research and plan your routes in advance, taking into consideration the terrain, distance, and any potential weather impacts. GPS devices and mapping apps that cater to off-road cycling can be invaluable tools.</li>
</ol>
<ol style="text-align: justify;" start="3">
<li>Stay Prepared: Pack essentials such as water, snacks, a basic repair kit, and a first aid kit. Being self-sufficient is crucial when venturing into less accessible areas.</li>
</ol>
<ol style="text-align: justify;" start="4">
<li>Focus on Comfort: Invest in quality cycling gear designed for off-road riding. Padded shorts, gloves, and appropriate eyewear can significantly enhance comfort and protection.</li>
</ol>
<ol style="text-align: justify;" start="5">
<li>Join a Community: Connect with local gravel biking groups or online communities. These groups can provide route recommendations, advice, and the opportunity to participate in group rides or events.</li>
</ol>
Conclusion
Gravel biking opens up a world of adventure and exploration, allowing cyclists to immerse themselves in the beauty of untouched landscapes. It’s a celebration of the freedom, resilience, and joy that comes with pushing through uncharted paths. Whether you’re a seasoned cyclist seeking a new challenge or a newcomer drawn to the call of the wild, gravel biking offers an exhilarating way to explore the world on two wheels. With the right preparation and a spirit of adventure, the gravel paths less traveled await to unfold their stories beneath your tires.
]]></content:encoded>
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<item>
<title>DIY Moving vs. Professional Movers: Navigating the Best Path for Your Relocation – Safe Ship Moving Services</title>
<link>https://newzmarker.com/diy-moving-vs-professional-movers-navigating-the-best-path-for-your-relocation-safe-ship-moving-services.html</link>
<dc:creator><![CDATA[Baylor Edgar]]></dc:creator>
<pubDate>Thu, 11 Apr 2024 06:56:43 +0000</pubDate>
<category><![CDATA[Business]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19912</guid>
<description><![CDATA[When the time comes to relocate, one of the most pivotal decisions you’ll face is whether to embark on a DIY move or to enlist the services of professional movers. This choice not only impacts your moving budget but also the overall stress and success...]]></description>
<content:encoded><![CDATA[When the time comes to relocate, one of the most pivotal decisions you’ll face is whether to embark on a DIY move or to enlist the services of professional movers. This choice not only impacts your moving budget but also the overall stress and success of your move. Both options have their merits and drawbacks, influenced by factors such as cost, convenience, time, and the complexity of the move. This article weighs the pros and cons of DIY moving versus hiring professional movers, providing insights to help you make an informed decision tailored to your unique moving needs. Here is what pros like <a href="http://www.safemovingservicesreviews.com/">Safe Ship Moving Services</a> say.
DIY Moving: Embracing Autonomy and Affordability
Opting for a DIY move often stems from the desire to save money or the preference for having direct control over every aspect of the moving process.
Pros:
<ol style="text-align: justify;">
<li>Cost-Effective: The most compelling advantage of a DIY move is the potential for cost savings. By bypassing professional mover fees, you primarily face expenses for rental trucks, packing materials, and perhaps some pizza for the friends who help you out.</li>
</ol>
<ol style="text-align: justify;" start="2">
<li>Flexible Scheduling: A DIY move offers more flexibility in scheduling. You can decide when to start and how to pace your move, which can be particularly advantageous for local relocations or if you have a tight schedule.</li>
</ol>
<ol style="text-align: justify;" start="3">
<li>Complete Control: When you move yourself, you have total control over the packing, loading, and transportation of your belongings. This can be reassuring for those who prefer to handle their items personally. </li>
</ol>
Cons:
<ol style="text-align: justify;">
<li>Physical and Emotional Stress: The physical demands of packing, lifting, and moving boxes and furniture can be significant, leading to exhaustion and stress, especially without proper equipment or assistance.</li>
</ol>
<ol style="text-align: justify;" start="2">
<li>Time-Consuming: DIY moves often take longer than anticipated. Without the efficiency of experienced movers, you might find yourself spending more time packing and transporting your belongings.</li>
</ol>
<ol style="text-align: justify;" start="3">
<li>Risk of Damage: Without professional packing and moving techniques, there’s a higher risk of damaging your belongings or injuring yourself during the move. </li>
</ol>
Professional Movers: Ensuring Efficiency and Peace of Mind
Hiring professional movers brings expertise, efficiency, and peace of mind to the moving process, albeit at a higher cost.
 Pros:
<ol style="text-align: justify;">
<li>Efficiency and Expertise: Professional movers bring experience and skills to ensure your belongings are safely packed, loaded, and transported. Their efficiency can significantly reduce the time and hassle involved in moving.</li>
</ol>
<ol style="text-align: justify;" start="2">
<li>Less Physical Strain: Entrusting the heavy lifting to a professional team can save you from the physical exhaustion and potential injuries associated with moving heavy items.</li>
</ol>
<ol style="text-align: justify;" start="3">
<li>Additional Services: Many moving companies offer a range of services, including packing, unpacking, and temporary storage, which can further alleviate the stress of moving. </li>
</ol>
Cons:
<ol style="text-align: justify;">
<li>Higher Cost: The most significant drawback of hiring professional movers is the cost. Full-service moves can be expensive, though many find the price justifiable for the convenience and reliability provided.</li>
</ol>
<ol style="text-align: justify;" start="2">
<li>Less Flexibility: Working with a moving company means adhering to their schedule. This can sometimes result in less flexibility in choosing your moving date or time.</li>
</ol>
<ol style="text-align: justify;" start="3">
<li>Impersonal: Some may find the experience of having strangers handle personal belongings a bit unsettling, though this is often mitigated by hiring reputable, professional services. </li>
</ol>
Making the Decision
The choice between a DIY move and hiring professional movers depends on several personal factors:
– Budget: If minimizing costs is your priority, a DIY move may be more appealing. However, consider the hidden costs, such as time, packing materials, and potential damages.
– Scale and Complexity of the Move: For large or complex moves, especially those involving long distances, the expertise of professional movers can be invaluable.
– Physical Ability and Availability of Help: Assess your physical capacity and whether you have reliable friends or family to assist with a DIY move.
– Personal Preferences: Consider how much personal involvement and control you desire in the moving process and weigh it against the appeal of a hassle-free, professionally managed move.
Conclusion
Both DIY moving and hiring professional movers have their place, dictated by individual needs, preferences, and circumstances. By carefully considering the pros and cons of each option, you can choose the path that best aligns with your relocation goals, ensuring a move that’s as smooth and stress-free as possible. Whether you roll up your sleeves or opt for the professionals, the key to a successful move lies in thorough planning and preparation.
]]></content:encoded>
</item>
<item>
<title>The Debt Ceiling and Technological Innovation: Navigating Future Frontiers – Kavan Choksi</title>
<link>https://newzmarker.com/the-debt-ceiling-and-technological-innovation-navigating-future-frontiers-kavan-choksi.html</link>
<dc:creator><![CDATA[Baylor Edgar]]></dc:creator>
<pubDate>Thu, 11 Apr 2024 06:37:01 +0000</pubDate>
<category><![CDATA[Business]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19910</guid>
<description><![CDATA[In the dynamic intersection of finance and technology, the U.S. debt ceiling emerges not just as a fiscal constraint but as a catalyst for innovation within the realm of public finance management and economic forecasting. This unique perspective reveals how the challenges posed by the...]]></description>
<content:encoded><![CDATA[In the dynamic intersection of finance and technology, the U.S. debt ceiling emerges not just as a fiscal constraint but as a catalyst for innovation within the realm of public finance management and economic forecasting. This unique perspective reveals how the challenges posed by the debt ceiling debates could drive technological advancements, leading to more sophisticated tools for managing national debt, enhancing transparency, and fostering a deeper understanding of economic policies’ implications. By exploring the potential for technological innovation spurred by the complexities of the debt ceiling, we unveil a future where technology and fiscal policy evolve in tandem to address some of the most pressing economic challenges. Let’s see what pros like <a href="https://marketbusinessnews.com/kavan-choksi-provides-his-perspective-on-chinas-economic-rebound/330708/">Kavan Choksi</a> think.
Technological Responses to Fiscal Constraints
The recurrent nature of debt ceiling crises highlights the need for more effective tools to manage national debt and fiscal policies. In response, financial technologists and economists are increasingly turning to advanced technologies such as blockchain, big data analytics, and artificial intelligence (AI) to reimagine public finance management. These technologies offer the promise of greater efficiency, security, and transparency in how government obligations are tracked, managed, and projected.
<ol style="text-align: justify;">
<li>Blockchain for Transparency and Efficienc: Blockchain technology, with its decentralized and immutable ledger system, could revolutionize how government debts and obligations are recorded and managed. By enabling a transparent, tamper-proof record of transactions, blockchain can enhance trust in public finance management and streamline operations to reduce administrative costs.</li>
</ol>
<ol style="text-align: justify;" start="2">
<li>Big Data Analytics for Economic Forecasting: The vast amounts of economic data generated daily hold critical insights into fiscal health and the potential impacts of hitting the debt ceiling. Big data analytics can process this information in real-time, providing policymakers with actionable intelligence to make more informed decisions regarding debt management and spending priorities.</li>
</ol>
<ol style="text-align: justify;" start="3">
<li>AI and Machine Learning for Scenario Planning: AI and machine learning algorithms can analyze historical data and current economic indicators to model potential outcomes of various debt ceiling scenarios. This capability allows for more sophisticated scenario planning, helping policymakers and the public understand the potential short- and long-term consequences of debt ceiling decisions. </li>
</ol>
Enhancing Public Engagement and Education
Beyond improving fiscal management, technological innovations can play a vital role in demystifying the debt ceiling for the general public. Interactive platforms and visualization tools can make complex economic concepts more accessible, fostering greater public engagement with fiscal policy discussions. By leveraging technology to educate and involve citizens, policymakers can build broader consensus and support for sustainable fiscal strategies.
Future Frontiers: Blockchain Voting and Digital Currencies
Looking ahead, the challenges associated with the debt ceiling could inspire even more radical innovations. For instance, blockchain technology could facilitate more direct public participation in fiscal policy decisions through secure digital voting mechanisms. Similarly, the exploration of digital currencies by central banks could offer new ways to manage national debt levels and financing needs, potentially redefining the very nature of public finance in the digital age.
Conclusion
The challenges posed by the U.S. debt ceiling, while rooted in fiscal policy, present an unexpected frontier for technological innovation. By catalyzing advancements in blockchain, big data analytics, AI, and beyond, these fiscal constraints could inadvertently drive the development of more sophisticated, transparent, and efficient tools for public finance management. As we navigate the complexities of the modern economy, the synergy between technology and fiscal policy offers a promising path forward, transforming challenges into opportunities for innovation and progress. In this evolving landscape, the debt ceiling becomes not just a limit but a launchpad for reimagining the future of economic governance.
]]></content:encoded>
</item>
<item>
<title>The Federal Reserve: Navigating Business Impacts – Kavan Choksi</title>
<link>https://newzmarker.com/the-federal-reserve-navigating-business-impacts-kavan-choksi.html</link>
<dc:creator><![CDATA[Baylor Edgar]]></dc:creator>
<pubDate>Thu, 11 Apr 2024 06:21:37 +0000</pubDate>
<category><![CDATA[Business]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19907</guid>
<description><![CDATA[The Federal Reserve, often referred to simply as “the Fed,” stands as the central bank of the United States, wielding substantial influence over the nation’s monetary policy, financial regulation, and economic stability. Its decisions and actions ripple through various sectors of the economy, impacting businesses...]]></description>
<content:encoded><![CDATA[The Federal Reserve, often referred to simply as “the Fed,” stands as the central bank of the United States, wielding substantial influence over the nation’s monetary policy, financial regulation, and economic stability. Its decisions and actions ripple through various sectors of the economy, impacting businesses of all sizes and industries. In this article, we will explore the multifaceted role of the Federal Reserve and its implications for businesses, providing insights into how businesses can navigate the dynamic economic landscape shaped by the Fed’s policies and operations. Let’s see the thoughts of people like <a href="https://businessgrowsteps.com/kavan-choksi-business-consultant-talks-about-the-ways-to-increase-sales-in-retail/">Kavan Choksi</a>.
Monetary Policy and Interest Rates
One of the primary tools at the Federal Reserve’s disposal is monetary policy, particularly the setting of interest rates. Through adjustments to the federal funds rate, the Fed aims to influence borrowing costs, consumer spending, and investment activity. For businesses, changes in interest rates can have significant implications for financing decisions, capital investments, and overall economic activity. When the Fed raises interest rates, borrowing becomes more expensive, potentially slowing down business expansion and investment projects. Conversely, lowering interest rates can stimulate borrowing and investment, boosting economic growth and business activity.
Market Sentiment and Investor Confidence
The Federal Reserve’s communication and guidance also play a crucial role in shaping market sentiment and investor confidence. Markets closely scrutinize the Fed’s statements, policy actions, and economic projections to gauge its stance on monetary policy and the overall health of the economy. Businesses must monitor these developments to anticipate market reactions and adjust their strategic priorities accordingly. Positive signals from the Fed can bolster investor confidence, encouraging investment and risk-taking behavior among businesses. On the other hand, unexpected policy shifts or uncertainties may lead to market volatility and cautious business sentiment.
Regulatory Oversight and Compliance
Beyond its monetary policy mandate, the Federal Reserve is tasked with regulating and supervising financial institutions to ensure their safety and soundness. Businesses operating in regulated industries or relying on financial services must navigate the Federal Reserve’s regulatory framework and compliance requirements. From capital adequacy standards to risk management practices, businesses must adhere to regulatory guidelines set forth by the Fed to maintain operational resilience and regulatory compliance. Failure to comply with regulatory requirements can result in fines, reputational damage, and legal repercussions, underscoring the importance of robust governance and risk management practices.
Economic Outlook and Strategic Planning
The Federal Reserve’s economic projections and assessments provide valuable insights into the broader economic landscape, informing businesses’ strategic planning processes. By incorporating these insights into their strategic decision-making, businesses can adapt to evolving market conditions, identify growth opportunities, and mitigate risks associated with economic uncertainties. For example, businesses may adjust their expansion plans or investment priorities in response to the Fed’s economic forecasts and inflation expectations. Additionally, businesses may leverage the Fed’s guidance to optimize their financing strategies, capital allocation decisions, and risk management practices.
Conclusion
In conclusion, the Federal Reserve’s decisions and actions have far-reaching implications for businesses, influencing interest rates, market sentiment, regulatory compliance, and strategic planning. Businesses must stay informed about the Fed’s policies and operations to navigate the dynamic economic landscape effectively. By understanding the Fed’s role and its impact on the economy, businesses can adapt their strategies, seize opportunities, and mitigate risks to achieve sustainable growth and success in a rapidly changing business environment.
]]></content:encoded>
</item>
<item>
<title>Embracing Community: The Ascendance of Multifamily Living – Kanat Sultanbekov</title>
<link>https://newzmarker.com/embracing-community-the-ascendance-of-multifamily-living-kanat-sultanbekov.html</link>
<dc:creator><![CDATA[Baylor Edgar]]></dc:creator>
<pubDate>Thu, 11 Apr 2024 05:48:04 +0000</pubDate>
<category><![CDATA[Business]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19905</guid>
<description><![CDATA[In today’s ever-evolving real estate landscape, multifamily living has emerged as a dominant force, reshaping urban environments and transforming the way people live, work, and interact with their surroundings. From bustling city centers to suburban neighborhoods, the rise of multifamily living reflects shifting demographics, changing...]]></description>
<content:encoded><![CDATA[In today’s ever-evolving real estate landscape, multifamily living has emerged as a dominant force, reshaping urban environments and transforming the way people live, work, and interact with their surroundings. From bustling city centers to suburban neighborhoods, the rise of multifamily living reflects shifting demographics, changing lifestyles, and evolving preferences among renters and homeowners alike. Let’s explore the trends and insights driving the surge in multifamily living and the transformative impact it’s having on communities worldwide. Let’s see what pros like <a href="https://www.tumblr.com/blog/kanatsultanbekov2022">Kanat Sultanbekov</a> say.
Urbanization and Population Growth
One of the primary drivers behind the rise of multifamily living is urbanization—a global trend characterized by the migration of people from rural areas to cities in search of economic opportunities, cultural experiences, and urban amenities. As cities continue to grow and urban populations swell, demand for housing in urban centers has surged, leading to the proliferation of multifamily developments such as apartment complexes, condominiums, and mixed-use buildings.
Changing Demographics and Lifestyles
Demographic shifts, including changes in household composition, generational preferences, and lifestyle choices, have also fueled the rise of multifamily living. Millennials, in particular, are driving demand for multifamily housing, drawn to the convenience, affordability, and sense of community offered by apartment living in urban environments. Additionally, empty nesters and retirees are downsizing from single-family homes to multifamily residences, seeking maintenance-free living and access to amenities.
Convenience and Connectivity
Multifamily living offers residents unparalleled convenience and connectivity, with easy access to urban amenities, public transportation, and cultural attractions. From walkable neighborhoods and vibrant dining and entertainment districts to proximity to employment centers and educational institutions, multifamily developments provide residents with a wealth of lifestyle options and opportunities for social engagement and enrichment.
Amenities and Lifestyle Enhancements
Today’s multifamily developments are redefining the concept of apartment living, offering residents an array of amenities and lifestyle enhancements designed to enhance their quality of life. From state-of-the-art fitness centers and rooftop lounges to co-working spaces and pet-friendly facilities, multifamily communities cater to the diverse needs and preferences of modern residents. Amenities such as package lockers, smart home technology, and concierge services further enhance the resident experience, making multifamily living an attractive option for renters and homeowners alike.
Sustainability and Green Living
With growing awareness of environmental issues and a shift towards sustainable living, multifamily developments are embracing green building practices and eco-friendly design principles. LEED-certified buildings, energy-efficient appliances, and sustainable landscaping are becoming standard features in many multifamily communities, appealing to environmentally conscious residents and reducing carbon footprints.
Technology Integration
Advancements in technology are revolutionizing the multifamily living experience, with smart home technology, mobile apps, and digital platforms transforming how residents interact with their living spaces and communities. From remote-controlled thermostats and keyless entry systems to online rent payments and community forums, technology is streamlining operations, enhancing security, and fostering communication within multifamily communities.
Community and Social Connection
At its core, multifamily living is about more than just a place to reside—it’s about building community and fostering social connection. Shared amenities, communal spaces, and organized events and activities create opportunities for residents to connect, collaborate, and build relationships with their neighbors. Whether it’s a rooftop barbecue, a fitness class, or a community garden project, multifamily living fosters a sense of belonging and camaraderie among residents, enriching their lives and enhancing their overall well-being.
Conclusion
The rise of multifamily living represents a paradigm shift in urban and suburban development, offering a dynamic blend of convenience, community, and connectivity for residents of all ages and backgrounds. As cities continue to grow and lifestyles evolve, multifamily living will remain a cornerstone of urban development, providing a vibrant and sustainable housing solution for generations to come. By embracing the principles of community, connectivity, and sustainability, multifamily developments are shaping the cities of tomorrow and enriching the lives of residents worldwide.
]]></content:encoded>
</item>
<item>
<title>Bot Names: How to Name Your Chatbot +What We’ve Learned</title>
<link>https://newzmarker.com/bot-names-how-to-name-your-chatbot-what-we-ve.html</link>
<dc:creator><![CDATA[]]></dc:creator>
<pubDate>Wed, 21 Feb 2024 12:53:22 +0000</pubDate>
<category><![CDATA[Artificial intelligence]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19888</guid>
<description><![CDATA[GOOGL Stock: AI Human Imagery Mishaps Generate Angst For Google Investor’s Business Daily This way, you’ll know who you’re speaking to, and it will be easier to match your bot’s name to the visitor’s preferences. It only takes about 7 seconds for your customers to...]]></description>
<content:encoded><![CDATA[<h1>GOOGL Stock: AI Human Imagery Mishaps Generate Angst For Google Investor’s Business Daily</h1>

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" width="301px" alt="chatbot name"/>
This way, you’ll know who you’re speaking to, and it will be easier to match your bot’s name to the visitor’s preferences. It only takes about 7 seconds for your customers to make their first impression of your brand. So, make sure it’s a good and lasting one with the help of a catchy bot name on your site.

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<h3>A U.S. Politician Is Robocalling Voters With an AI Chatbot Named ‘Ashley’ – VICE</h3>
A U.S. Politician Is Robocalling Voters With an AI Chatbot Named ‘Ashley’.
Posted: Tue, 12 Dec 2023 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiUmh0dHBzOi8vd3d3LnZpY2UuY29tL2VuL2FydGljbGUvNGEzcHZ3L3NoZW1haW5lLWRhbmllbHMtY29uZ3Jlc3MtYWktY2hhdGJvdC1hc2hsZXnSAQA?oc=5' rel="nofollow">source</a>]
</div>
Naming a chatbot makes it more natural for customers to interact with a bot. Simultaneously, a chatbot name can create a sense of intimacy and friendliness between a program and a human. Here are 8 tips for designing the perfect chatbot for your business that you can make full use of for the first attempt to adopt a chatbot.

<h2>Creative Chatbot Names</h2>

Organizing and facilitating a design workshop can be challenging. But by stringing together the right people and plan, product design workshops will become an important part of your team’s process. Product improvement is the process of making meaningful product changes that result in new customers or increased benefits for existing customers. Speaking our searches out loud serves a function, but it also draws our attention to the interaction. A study released in August showed that when we hear something vs when we read the same thing, we are more likely to attribute the spoken word to a human creator. As the resident language expert on our product design team, naming things is part of my job.

<a href="https://www.metadialog.com/blog/cute-chatbot-name/">
<figure><img 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alt='chatbot name' class='aligncenter' style='display:block;margin-left:auto;margin-right:auto;' width='404px'/></figure>
</a>
Scientific research has proven that a name somehow has an impact on the characteristic of a human, and invisibly, a name can form certain expectations in the hearer’s mind. A mediocre or too-obvious chatbot name may accidentally make it hard for your brand to impress your buyers at first glance. Uncover some real thoughts of customer when they talk to a chatbot. Talking to or texting a program, a robot or a dashboard may sound weird.

<h2>How to Name Your Chatbot Within 15 Minutes?</h2>

Unless your chatbot knows the answers to a majority of employee queries, naming it “AskMeAnything” might not be a smart idea. While naming your chatbot, try to keep it as simple as you can. People tend to relate to names that are easier to remember. You need to respect the fine line between unique and difficult, quirky and obvious. Industry-specific chatbot names can showcase your business’s deep knowledge and dedicated service.

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" width="308px" alt="chatbot name"/>
If you’ve ever had a conversation with Zo at Microsoft, you’re likely to have found the experience engaging. Using a name makes someone (or something) more approachable. Customers having a conversation with a bot want to feel heard. But, they also want to feel comfortable and for many people talking with a bot may feel weird.

These are perfect for the technology, eCommerce, entertainment, lifestyle, and hospitality industries. Your chatbot’s alias should align with your unique digital identity. Whether playful, professional, or somewhere in between,  the name should truly reflect your brand’s essence. At

 Userlike,

 we offer an

 AI chatbot

 that is connected to our live chat solution so you can monitor your chatbot’s performance directly in your Dashboard. This helps you keep a close eye on your chatbot and make changes where necessary — there are enough digital assistants out there

 giving bots a bad name.

A bank or

 real estate chatbot

 may need to adopt a more professional, serious tone. Once you’ve outlined your bot’s function and capabilities,

 consider your business, brand and customers. Ideally, your chatbot should be an extension of your company. If you have a marketing team, sit down with them and bring them into the brainstorming process for creative names. Your team may provide insights into names that you never considered that are perfect for your target audience. You have the perfect chatbot name, but do you have the right ecommerce chatbot solution?

As a writer and analyst, he pours the heart out on a blog that is informative, detailed, and often digs deep into the heart of customer psychology. He’s written extensively on a range of topics including, marketing, AI chatbots, omnichannel messaging platforms, and many more. Praveen Singh is a content marketer, blogger, and professional with 15 years of passion for ideas, stats, and insights into customers. An MBA Graduate in marketing and a researcher by disposition, he has a knack for everything related to customer engagement and customer happiness. If you want your bot to make an instant impact on customers, give it a good name.

As you present a digital assistant, human names are a great choice that give you a lot of freedom for personality traits. Even if your chatbot is meant for expert industries like finance or healthcare, you can play around with different moods. Conversations need personalities, and when you’re building one for your bot, try to find a name that will show it off at the start. For example, Lillian and Lilly demonstrate different tones of conversation.

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" width="307px" alt="chatbot name"/>
A chatbot name that is hard to pronounce, for customers in any part of the world, can be off-putting. For example, Krishna, Mohammed, and Jesus might be common names in certain locations but will call to mind religious associations in other places. Siri, for example, means something anatomical and personal in the language of the country of Georgia. Wherever you hope to do business, it’s important to understand what your chatbot’s name means in that language. Doing research helps, as does including a diverse panel of people in the naming process, with different worldviews and backgrounds. You could also look through industry publications to find what words might lend themselves to chatbot names.

You want to design a chatbot customers will love, and this step will help you achieve this goal. If you use Google Analytics or something similar, you can use the platform to learn who your audience is and key data about them. You may have different names for certain audience profiles and personas, allowing for a high level of customization and personalization.

Your main goal is to make users feel that they came to the right place. So if customers seek special attention (e.g. luxury brands), go with fancy/chic or even serious names. As you scrapped the buying personas, a pool of interests can be an infinite source of ideas. For travel, a name like PacificBot can make the bot recognizable and creative for users. What is the expected result from a conversation with a bot?

Certain names for bots can create confusion for your customers especially if you use a human name. To avoid any ambiguity, make sure your customers are fully aware that they’re talking to a bot and not a real human with a robotic tone of voice! The next time a customer clicks onto your site and starts talking to Sophia, ensure your bot introduces herself as a chatbot.

The move came after Gemini users produced pictures of Black Founding Fathers in American history as well as other imagery. Check our ultimate collection of the best chatbot names that will help with your success. And if you manage to find some good chatbot name ideas, you can expect a sharp increase in your customer engagement for sure. This is how you can customize the bot’s personality, find a good bot name, and choose its tone, style, and language. Sometimes a bot is not adequately built to handle complex questions and it often forwards live chat requests to real agents, so you also need to consider such scenarios. Another factor to keep in mind is to skip highly descriptive names.

<ul>
<li>Dimitrii, the Dashly CEO, defined the problem statement that we need a bot to simplify our clients’ work right now.</li>
<li>Soliciting and acting upon feedback might sound like a cumbersome process and a detour from your launch timeline.</li>
<li>A study found that 36% of consumers prefer a female over a male chatbot.</li>
<li>However, if the bot has a catchy or unique name, it will make your customer service team feel more friendly and easily approachable.</li>
<li>This will depend on your brand and the type of products or services you’re selling, and your target audience.</li>
</ul>
You can start by giving your chatbot a name that will encourage clients to start the conversation. It will also make them feel more connected with your brand. MarketSmith will be performing technical updates on March 2nd from 10pm to March 3rd at 10PM ET on the desktop and mobile platforms. You may experience intermittent downtime, slowness and limited functions during this time. If you have any questions, email our MarketSurge team at [email protected]. With REVE Chat, you can sign up here, get step-by-step instructions on how to create and how to name your chatbot in simple steps.

While there’s no strict right or wrong, your decision can significantly shape the user’s interaction with the bot. Subconsciously, a bot name partially contributes to improving brand awareness. It was only when we removed the bot name, took away the first person pronoun, and the introduction that things started to improve. To help combat climate change, many companies are setting science-based emissions reduction targets. Learn more about these efforts and the impact they can have on the planet. ChatBot covers all of your customer journey touchpoints automatically.

A robotic name will help to lower the high expectation of a customer towards your live chat. Customers will try to utilise keywords or simple language in order not to “distract” your chatbot. Brand owners usually have 2 options for chatbot names, which are a robotic name and a human name. An example of this would be “Customer Agent” or “Tips for Cat Owners” which tells you what your bot is able to converse in but there’s nothing catchy about their names. By being creative, you can name your customer service bot, “Ask Becky” or “Kitty Bot” for cat-related products or services. A name helps to build relationship even if it’s with a bot.

Hope that with our pool of chatbot name ideas, your brand can choose one and have a high engagement rate with it. Should you have any questions or further requirements, please drop us a line to get timely support. These relevant names can create a sense of intimacy, thus, boosting customer engagement and time on-site. If your bot is designed to support customers with information in the insurance or real estate industries, its name should be more formal and professional.

So, whether you want your bot to be smart, witty, intelligent, or friendly, all will be dependent on the chatbot scripts you write and outline you prepare for the bot. For other similar ideas, read our post on 8 Steps to Build a Successful Chatbot Strategy. Plus, whatever name for bot your choose, it has to be credible so that customers can relate to that. With so many different types of chatbot use cases, the challenge for you would be to know what you want out of it. And if your bot has a cold or generic name, customers might not like it and it may dilute their experience to some extent.

<h2>Tips on How to Name a Chatbot</h2>

It’s in our nature to

 attribute human characteristics

to non-living objects. Customers will automatically assign a chatbot a personality if you don’t. If you want your bot to represent a certain role, I recommend taking control. A clever, memorable bot name will help make your customer service team more approachable. Finding the right name is easier said than done, but I’ve compiled some useful steps you can take to make the process a little easier. Here are a few examples of chatbot names from companies to inspire you while creating your own.

Once you have a clearer picture of what your bot’s role is, you can imagine what it would look like and come up with an appropriate name. Knowing your bot’s role will also define the type of audience your chatbot will be engaging with. This will help you decide if the name should be fun, professional, or even wacky.

But, if you follow through with the abovementioned tips when using a human name then you should avoid ambiguity. There are a number of factors you need to consider before deciding on a suitable bot name. Down below is a list of the best bot names for various industries.

Creating a chatbot is a complicated matter, but if you try it — here is a piece of advice. You can also use our Leadbot campaigns for online businesses. You may give a gendered name, not only to human bot characters. You may provide a female or male name to animals, things, and any abstractions if it suits your marketing strategy. We tend to think of even programs as human beings and expect them to behave similarly.

Here, it makes sense to think of a name that closely resembles such aspects. However, naming it without keeping your ICP in mind can be counter-productive. Different chatbots are designed to serve different purposes. You can foun additiona information about <a href="https://www.cravingtech.com/ai-customer-service-all-you-need-to-know.html">ai customer service</a> and artificial intelligence and NLP. While a chatbot is, in simple words, a sophisticated computer program, naming it serves a very important purpose.

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width="300px" alt="chatbot name"/>
Read our post on 10 Must-have Chatbot Features That Make Your Bot a Success can help with other ways to add value to your chatbot. In this post, we will discuss some useful steps on how to name a bot and also how to make the entire process easier. An HR chatbot will do its job, regardless of whether or not you name it. Therefore, both the creation of a chatbot and the choice of a name for such a bot must be carefully considered. Only in this way can the tool become effective and profitable. There’s a variety of chatbot platforms with different features.

You have brainstormed, sifted, innovated, and finally, selected your chatbot’s name. Thus, eliminating the high risks of user disengagement or potential legal disputes. Despite our best intentions, there are pitfalls that we can inadvertently land in, potentially causing roadblocks in our chatbot’s success story.

It presents a golden opportunity to leave a lasting impression and foster unwavering customer loyalty. So far in the blog, most of the names you read strike out in an appealing way to capture the attention of young audiences. But, if your business prioritizes factors like trust, reliability, and credibility, then opt for conventional names. A 2021 survey shows that around 34.43% of people prefer a female virtual assistant like Alexa, Siri, Cortana, or Google Assistant. To truly understand your audience, it’s important to go beyond superficial demographic information. You must delve deeper into cultural backgrounds, languages, preferences, and interests.

<div style='border: grey dotted 1px;padding: 12px;'>
<h3>From Bard to Gemini: Google’s ChatGPT Competitor Gets a New Name and a New App – CNET</h3>
From Bard to Gemini: Google’s ChatGPT Competitor Gets a New Name and a New App.
Posted: Fri, 09 Feb 2024 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiZ2h0dHBzOi8vd3d3LmNuZXQuY29tL3RlY2gvZnJvbS1iYXJkLXRvLWdlbWluaS1nb29nbGVzLWNoYXRncHQtY29tcGV0aXRvci1nZXRzLWEtbmV3LW5hbWUtYW5kLWEtbmV3LWFwcC_SAQA?oc=5' rel="nofollow">source</a>]
</div>
Just as biological species are carefully named based on their unique characteristics, your chatbot also requires a careful process to find the perfect name. In a nutshell, a proper chatbot name is a cornerstone for simplifying the user experience and bridging knowledge gaps, preparing the ground for loyal and satisfied customers. However, improving your customer experience must be on the priority list, so you can make a decision to build and launch the chatbot before naming it. Keep in mind that an ideal chatbot name should reflect the service or selling product, and bring positive feelings to the visitors. Names provoke emotions and form a connection between 2 human beings.

They can also spark interest in your website visitors that will stay with them for a long time after the conversation is over. Large language models understand the way that humans write and speak. They allow users to interact with AI systems without the need to understand or write algorithms. Google last week stopped allowing users of its Gemini chatbot technology to generate images of humans.

If you’re as excited as we are about how chatbots can grow your business, you can get started right here. This might be due to novelty — we might become more comfortable with the virtual, more trusting of it (though this year’s headlines haven’t given us much to trust). But despite the hundreds of movies we’ve made and books we’ve written about robots, introducing personality into technology might not be the way we become more comfortable. The digital tools we make live in a completely different psychological landscape to the real world. There is no straight line from a tradesman’s hammer he can repair himself, to a chatbot designed and built by a design team somewhere in California (or in Dublin, in our case). When we began iterating on a bot within our messaging product, I was prepared to brainstorm hundreds of bot names.

<ul>
<li>Once you’ve decided on your bot’s personality and role, develop its tone and speech.</li>
<li>While there’s no strict right or wrong, your decision can significantly shape the user’s interaction with the bot.</li>
<li>The hardest part of your chatbot journey need not be building your chatbot.</li>
<li>Read moreFind out how to name and customize your Tidio chat widget to get a great overall user experience.</li>
<li>Even Slackbot, the tool built into the popular work messaging platform Slack, doesn’t need you to type “Hey Slackbot” in order to retrieve a preprogrammed response.</li>
</ul>
Chatbots aren’t human, but those interacting with it are and they will lend it a persona if you fail to give it one. When interacting with anyone or anything, humans tend to lend it certain human traits and your HR chatbot will be no exception to this rule. Be it a way of asserting control over it or developing a feeling of belonging and trust towards it, by naming an object we humans attempt to make it one of us.

Use our tips to get you started once you’ve built your bot. Contact us at Botsurfer for all your bot building requirements and we’ll assist you with humanizing your chatbot while personalizing it for all your business communication needs. If you’re still wondering about chatbot names, check out these reasons why you should give your bot a unique name. Using cool bot names will significantly impact chatbot engagement rates, especially if your business has a young or trend-focused audience base. Industries like fashion, beauty, music, gaming, and technology require names that add a modern touch to customer engagement. Creative chatbot names are effective for businesses looking to differentiate themselves from the crowd.

Also, remember that your chatbot is an extension of your company, so make sure its name fits in well. Let’s have a look at the list of bot names you can use for inspiration. I’m Pat Walls and I created Starter Story – a website dedicated to helping people start businesses. We interview entrepreneurs from around the world about how they started and grew their businesses. From there, you can create a shortlist based on the words that resonate best with you and follow the naming guidelines above. The first that come to mind for me are Alexa, Google, Nike, Apple – each unique in their own way (hence, easy to remember) , less than six characters and easy to spell.

Make your bot approachable, so that users won’t hesitate to jump into the chat. As they have lots of questions, they would want to have them covered <a href="https://www.metadialog.com/blog/cute-chatbot-name/">chatbot name</a> as soon as possible. The mood you set for a chatbot should complement your brand and broadcast the vision of how the pain point should be solved.

It’s not to say that any of these feelings are wrong, but it’s important to ensure that they are in line with your values and mission. Your business name has the power to evoke certain emotions and thoughts from your customer. Before your customer goes to your website or speaks to you, the name of your business should spark some initial thoughts in their brain as to what you’re all about. It requires considerable effort and resources which makes it feel complex. You should always focus on finding the name relevant to your brand or branding.

With these swift steps, you can have a shortlist of potential chatbot names, maximizing productivity while maintaining creativity. As a matter of fact, there exist a bundle of bad names that you shouldn’t choose for your chatbot. A bad bot name will denote negative feelings or images, which may frighten or irritate your customers. A scary or annoying chatbot name may entail an unfriendly sense whenever a prospect or customer drop by your website. User experience is key to a successful bot and this can be offered through simple but effective visual interfaces. You also want to have the option of building different conversation scenarios to meet the various roles and functions of your bots.
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</item>
<item>
<title>What Is Machine Learning? MATLAB & Simulink</title>
<link>https://newzmarker.com/what-is-machine-learning-matlab-simulink.html</link>
<dc:creator><![CDATA[]]></dc:creator>
<pubDate>Wed, 14 Feb 2024 10:11:04 +0000</pubDate>
<category><![CDATA[Artificial intelligence]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19892</guid>
<description><![CDATA[Deep learning vs machine learning For example, the algorithm can pick up credit card transactions that are likely to be fraudulent or identify the insurance customer who will most probably file a claim. The training is provided to the machine with the set of data...]]></description>
<content:encoded><![CDATA[<h1>Deep learning vs machine learning</h1>

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" width="308px" alt="how machine learning works"/>
For example, the algorithm can pick up credit card transactions that are likely to be fraudulent or identify the insurance customer who will most probably file a claim. The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns.

<ul>
<li>If you’re looking at the choices based on sheer popularity, then Python gets the nod, thanks to the many libraries available as well as the widespread support.</li>
<li>This phase can be divided into several sub-steps, including feature selection, model training, and hyperparameter optimization.</li>
<li>Whether or not AGI emerges, AI of the future will be embedded everywhere and will touch every part of society, from smart devices to loan applications to phone apps.</li>
<li>Then, they’ll have the computer build a model to categorize MRIs it hasn’t seen before.</li>
</ul>
In this example, a sentiment analysis model tags a frustrating customer support experience as “Negative”. A great base for getting started on Machine Learning theory and learning how to use Python tools to create models. Programmers do this by writing lists of step-by-step instructions, or algorithms. Those algorithms help computers identify patterns in vast troves of data.

For example, the algorithm can identify customer segments who possess similar attributes. Customers within these segments can then be targeted by similar marketing campaigns. Popular techniques used in unsupervised learning include nearest-neighbor mapping, self-organizing maps, singular value decomposition and k-means clustering.

<h2>Languages</h2>

While machine learning systems practice pattern recognition on historical data, symbolic systems only require an expert to define the problem space in terms of symbols, propositions, and rules. In reality, AI is programmed by humans to complete tasks and offer predictions. AI can mimic intelligence, but it cannot independently learn like a person. The goal of AI engineers today is to make machines think more like humans and less like machines.

And they’re already being used for many things that influence our lives, in large and small ways. Machines make use of this data to learn and improve the results and outcomes provided to us. These outcomes can be extremely helpful in providing valuable insights and taking informed business decisions as well.

It completed the task, but not in the way the programmers intended or would find useful. For example, Google Translate was possible because it “trained” on the vast amount of information on the web, in different languages. Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. For doing this, the machine learning algorithm considers certain assumptions about the target function and starts the estimation of the target function with a hypothesis.

To give an idea of what happens in the training process, imagine a child learning to distinguish trees from objects, animals, and people. Before the child can do so in an independent fashion, a teacher presents the child with a certain number of tree images, complete with all the facts that make a tree distinguishable from other objects of the world. Such facts could be features, such as the tree’s material (wood), its parts (trunk, branches, leaves or needles, roots), and location (planted in the soil).

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" width="307px" alt="how machine learning works"/>
The researchers found that no occupation will be untouched by machine learning, but no occupation is likely to be completely taken over by it. The way to unleash machine learning success, the researchers found, was to reorganize jobs into discrete tasks, some which can be done by machine learning, and others that require a human. With the growing ubiquity of machine learning, everyone in business is likely to encounter it and will need some working knowledge about this field.

<h2>Advantages of AI: Using GPT and Diffusion Models for Image Generation</h2>

Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of not having enough labeled data for a supervised learning algorithm. Machine <a href="https://www.metadialog.com/blog/how-does-ml-work/">how machine learning works</a> learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics.

<ul>
<li>In this method, we simply find the k points closest to the new point and assign its label to be the mode (the most commonly occurring class) of these k points.</li>
<li>Learn how to leverage artificial intelligence within your business to enhance productivity and streamline resolutions.</li>
<li>Unstructured data may also be qualitative instead of quantitative, making it even harder to analyze.</li>
<li>On the other hand, regression models are used to predict a range of output variables, such as sales revenue or costs.</li>
<li>A machine learning tool in the hands of an asset manager that focuses on mining companies would highlight this as relevant data.</li>
<li>They are also able to predict when equipment will break down and send alerts before it happens.</li>
</ul>
Machine learning is playing a pivotal role in expanding the scope of the travel industry. Rides offered by Uber, Ola, and even self-driving cars have a robust machine learning backend. Every industry vertical in this fast-paced digital world, benefits immensely from machine learning tech. Some known classification algorithms include the Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm, and Support Vector Machine Algorithm. Explore the ideas behind ML models and some key algorithms used for each. IBM watsonx is a portfolio of business-ready tools, applications and solutions, designed to reduce the costs and hurdles of AI adoption while optimizing outcomes and responsible use of AI.

Deep learning algorithms attempt to draw similar conclusions as humans would by constantly analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks. Deep learning is a subset of machine learning, which is a subset of artificial intelligence. Artificial intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine learning represents a set of algorithms trained on data that make all of this possible. In the training phase, a data scientist supplies some input data and describes the expected output using historical information.

Thus, a pattern exists across the people who already purchased the product and the future buyers of the product. But, with the rising inflation, it’s not too easy to figure within the budget. This happens because the shopkeeper changes the quantity and price of a product fairly often. You can foun additiona information about <a href="https://www.cravingtech.com/ai-customer-service-all-you-need-to-know.html">ai customer service</a> and artificial intelligence and NLP. It takes tons of effort, research and time to update the list for each change. Please keep in mind that the learning rate is the factor with which we have to multiply the negative gradient and that the learning rate is usually quite small.

Enterprise machine learning gives businesses important insights into customer loyalty and behavior, as well as the competitive business environment. Machine Learning is, undoubtedly, one of the most exciting subsets of Artificial Intelligence. It completes the task of learning from data with specific inputs to the machine. It’s important to understand what makes Machine Learning work and, thus, how it can be used in the future.

Categorical data inherently simplifies data by reducing the number of data points. To give a simple example, if one variable is the weight of a patient and the other variable is the height of a patient, then the relationship between these variables can be found by running regression analysis on a set of patients. If your data has a numerical range of values, like income, age, transaction size, or similar, it’s quantitative. If, on the other hand, there are categories, like “Yes,” “Maybe,” and “No,” it’s categorical.

Deep learning is one of the most powerful machine learning techniques available today and it can be used to develop advanced AI applications. It requires a readable syntax as well as specialized programming resources in order to make use of its full capabilities. When definite goals and objectives are clearly established before testing the models, it becomes easier to measure how well the models are performing against the established criteria. To make sure your solution is effective, it’s important to spend time with your data scientists so that they can properly validate the model output and make any necessary adjustments before deploying the models.

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eSi7VU1lLU+G932Kryq6XiKK9WtCn7UaiyoEhyDkeaLIcngqOtdUJGYGeU+JwMqBy2KtSq57hZPJIP8AmKUfI0nseEm45OVE5ZK0pbCDuxTGp80+d2KY1AzkBInjcibyR7/mQXXMduKC0lTTQhKN+YJRJA45R2OJPJXJOTMoMpC284Hoo9PaBxbyCtKzk8iwSTLJTeSdJrTdgnSv6mX/AAg8fmjIsfmjJAAugZOFxdBwchC5I8sO1u3zRkVhJzlEqamCmj8SWUN+ikSSEzgV+yTqZ4KNgfUytY3BJ57cKCr9Y2ykGGTiQjuAqJqXVlRdJ/Bp3ObCQQQmzkoxefgXOYs9aUUZ91hkxkGJo/TC13o3F/tezb2Cy60s8S10Yx/uI8/+ULYOi1JLLXSuHZvA/Rcz6ead7LHwzG6dUUbhpm10VO57WtY3JwpJtE+I7XtwfsjW+P3aQhowR381JzyCseJG+S6uMnsb7nJ1MY2GDYcjGOyMIGhrmvGQRkAeZHknjIXZ7Kv9QzdKfSNdJZZCyt+HbwM7Nw3Y+uCrMqyoUpVpcJZLFGm6tRRRU+nWlbbTWKShqqeGrbHI/Bc3I+Yq2XW22mwWKW4Q0lNBHHGS5scYjG7+9x5qtdNrvSiwQbqkEv8AnJAznJz+qktWXaO92eqsjXNZTzwujL2u55HlnzXk9SrGblPw3lf5PS6EIwjFvwjy9o32l9S0PWPUdBDp10emaemkiZVgkOEzXtAc7Jxh3xDjnsqXB136gV3XC4U9TpOSTSF3eIqYilLnMBwPGDhnOHZyF6Xs+ibbd6J9q/Z0dZGyna4yOo2tfIWDG5xaB2z+qf1ejKiw26NlDbPAo25aWxs8MN9S3nIPqVoR0Km9uVt+yo3LuqSljf8A4Lb0untmptLQPjIk8J72bmuyWFjiMg+hx2VhuMdVDCYmluzBy4gZx+GFnGjbnY9E0TKGxSmHZIXObI8vBLiXHJd35J7qVvHUWB9M+aSJsUkWXSsY7cHRns4f6+izt0tzR93BgXtKW819smEbhva0+XceaceyHLBpfStVYr5M2CW63J1RQhx/tdzQNo+vwqA6w6nbep3QUrvgkyGjHmn+k9NXOvuOmRUUr4IrLUx1TJQSN0jgMfyP5rqOj3b6dKEn/c1/yYNzRjcVXHG6PTM9LuALQDkc47ZUZUUWScn9FbX0rnRtcGBuQOAOyYVFv7nbyvVHQTjqKVvaPdyKbVUYBLc/ooerhwcYVyrKF248KCraPaTkKvVhpWGWJ2ySyip1sRwfhVfubNkEsmfkGcK4VdPlpyFWb3TkQSNaMb2nKoTo8NGdVotM8+9Yo4Wiy1TR+8niqC8574ewLNXycDhaB1bqMss1O85kiZUN/Dc1ZxI92Byqd+kqrwZV69NVpCnifRJuldk4RN7vVcJzyqCk1wV4VNL3OmQgZwknASZ3DyXC5x4yiucWjhNlOXySd5EZcKRjmOb4e7I7qn3Wi8CTxWNxjyV9lOWnPmq9eoA5pIATNTGSesRsVTy0H1CsWQ5uQVTLZI6Koc1pwG8q0U05dECDyUmWM1yDu+YojnkHGEHuIPdEJyclINy2cd2KYznBJT5NKlrcHhKt3gQj3P8Ai7IJKVxDyAgr6nLAEsgM+S6WkDK6zv8AguUfBlAbuzzlSFJjccJknlEr1q0OjyWSm/hTtM6VzTgjyTxvxnDVe3Re8ICUaRt5KTwkKiupaUZnma0J6awA8y08E8fRRNy1BbrcXNlf8v8Ai5yoa861giBipXAk/CC0fqqNW1s9XO980pduOQEjmkskaTyW2r6gy7y2ljw0dj6qv3rU90uQaS8tjPBAJyopdPLcJndctiQ45wOS53fnkpIOAOQAfohIMjA8lyJjjIOybJ7N/gJexnu61UZbbaGQA/FTx8f+ELX+jDTHXzNBPOOFmFmANqoQ4Hinj8v8IWs9Go2m6PI9f9AsP03T1X8n+Gcz05/zLN0giDju4yeVINgYOGY/BJUsQ2t48lIwU+O4XbRg2tkdO+ROOBuMYCitVW+WstEtNG5rBJhpcf4ckZOfJWKOEE9kw1ZQTyaarTTNcZBG4gM5Pljj80txRlUtKkccxZZs59uvCT4yeUtO3uaw36rscjyfdqmeMDPBAcSCPwXdQdVLDp2tiprjO11VPzBTHgbfXHmoO/NdaNU1N0j8RzDKHfvGlpOWnccHnGQeVplxsWnL4bPrSO20klypm+PTzSQNf4O5uCPx/ReUUrdUpYqo9CpV41ofY/LHGk//AGnanomV2ltCXiVkg/cve3wGSefAc5vGPPsl9RRdVLfb/fbzoO5NM7/Cic+sgbukH8DQX58vL0VqsXU+ajp2i43yWifAP3ZEkYYD67cccZRNU9WKaa3QmC9sne5+5zhOAPuAwtOc/Va1ONHTsxyVy56XJYPM2s9Z6/omCafQ92lZlpkfDGJMBwBZhw+bgg/ZT0hvFNY47neIfc/eKZ+2J7sP2ugc7BB47jkK60Hh1dcbhMWOYHbwzbti+hx9BgKk9X9ROqnxW2jY14ALQW/4z8WPsOPxVStpbwh1RSh9z54Mfq2S1VV47nuLQYhycjJI4+69K6SvVoltdHbrhSiKsrqyGKjIk52tIw7b6d+VlWhtL01/1nZtMtldFJcK6GMua0EtcDuzz6YXqXSHRq9WvUtPetT1FvqW2yQyU7KZrg6R/YOcHcN4A4C3en9LuL90pR9kWmULaooSk3yXWSgLm8RbR6DnP1TSW2uOPgP5K1so24wQNx5PHmuC2OfnaxesxrwbeS5QhTX/AFOSiVlp2ku2ZH2VXuluOThhxz5LVqu0yBpDo1VbrbRh+G/dSOEay+3c0Pp6daP8Mym40pYSAqvdKcvBGCcrQL1SbC7hVG4RNAKybmjp5MG/ttB4o9pS5V9g1JaoYcCPw5yQfP4mrN6LWdJIGsqSGvPqcLUPbMgjpr/p5/8AFNT1H6OavOrJADkNyf5Ll7qo3Vkmcbe1dVdmrw1UFS0PgeCD9co5OO5wsyorrX03DZ3Buc91OUerpWfBUt3Aefqq2UVsouB+UkJNNqG4wVrP3L8/fhOeyM5DKOkt2EEjKjbhDvjyBlSD2OyEhUMcWYCjlyLlFJqGGGrc4HaPyypi3VOWD4v1TK9UrgS4Dt9UhbKofJnkJ+URrksxduAOFxJwuLmAlKJJPYkOHsUynJ55T0kYPPkmM5HKIhki5f7Q/dBCUjxCgn5YZROO+UorO/4LhcSMIAkdlhtZRlCieUSZNJPdOaeoipxul7Kza7PA6PJZaUBoHxZSz6qGna57pWggdsqnV+qWwRuZTvAcWkDPqqxVXetqnnxKh34HC0nNMvLhFnvms5A/wKFuCMhzt3/6VWqblXV73NqahzgfIcJq9xznzPdFEgacnuk1IBdsUUYJaTnvyk3ZLtwH4IniOPmusf8AF8WMKMAwJPcYRXPwcYXZJAcbcIhJJyUAcPJyujORg4wQVxdb3RL2SCXskfQyhEb7dQNZFt20sWee/wAAWsdFadrrk8k45Pl9Asts7G/s6jP/AGaH/gC1no4dt1cB2P8AyWZ6ZWq/kl8M5C0cvq9vk3emhwAMZwFJwRtPCa0OHNbu9E/haByu4pS07M61yFIoRu45TyNoDCNvOfouU7GjJTmGMyeXmr0XiBBqk+Wecvao0VU+70uvKeidJTRM9xuJjbkxsccxynHkDkH0VI6SPqKqP3F9Wwxb9sW7sWH5SPovZc1sorhRT224U7KimqmGKaKQbmvYe4I9F4K67F3s39SP2Tp+eX9iVlOyup2nn3cO4MWT5By4P1H0vEneU/8AK/8Ah0/Rup6UqMzdbh0d0vfp3TVNPSCYDDnbc7lyl6H9PrMBWPhgMkY4wPh/JefqD207BTUZhqK5rJcfE7dnlV29e21Q+DLHT15la/yaudp0J6U0dHO9pp+49DaxpLJQwMFK7MbeDjhefNe3uwWuqfUy1TW+Ec43Z/X1WPat9q253YSsoIp3Nf5l30Wc0d6vutK0+/Pk2OeHbD27qxTs2t2ipX6ip7RfB6/9ii4za968OuVXEPdLdRTy0475k3Bu/wC+F7+fSgvPOAfXuvAXsW1tBoTX9RX122KFlpmOSccg5P8AJer9Oe0TonUNS6Jlc3aw7d23avTfT1hXrWSqW8G4ooQq1IXGJfs1Ono2ue1o5JPorZQaciMbScZIBPwrP7HrnTdXVMfBcYng/wCILX7VW01ZSxy0xa9paOQ7PkoOpyubbClFol6vUr0FGSTSZXrjpeJ0Ty3Gf8qzDUlsFO+Ru3HfyW9zBm0jHJGBwsg6illLVvieQMNzwn9DvalWt22S+nOo1Kld05MwzUsTWF/Hks+uhxl2OxzhX3VFW2SR+0jGFnd6qAxrtp9VqX1SOTa6tWpttRZ4r9saqdV6nsTC3aI4KjHOe7mLz41u3zW8+1dKKjUVme/5vCnHH+ZqwvY1cjefbUefJ51XkpVZNAZ2/FGXAAOyNuYO/wDNUyEVp66rpBmGYjClqPVs7cR1Tcj+9u8/yUE75Sk/wTovAGg0l9o5wB4oye/PZPnyRyDMTw7IWYse5jtzHEFWSwXrbiKV4JHdI3lgSN5h3tcMY4VZhzTVBYec857K2VMoqGndjJHGFVrg3w6j4u2UgIsNDMHR4KcySNZ9VCUVWGNAylauvDRwQgk1Icy1bWZ5/VR9RcGjJH80wmrHOPzd0xqJXDOTwnxGyeR5JWAvJB/VBQjphuPKCcNNBQQQ7nCxDPCvk8NpUTX17wS0PJx9Uvc6tsYMbXc454UMN0nxkd1eoU8LWTU6ercDsuO5wz90R+AMjGV1zn4II/VJqwWgE+pXPh+i48E4wi7HeiAFEM7ecZQCBIHJQAN27yx+CC5vb6roIPIQAoAMDgIOADSQFwObgcoOcCMAofta/AS9kj6LWRgdbKP/ALtD/wAAWr9HmBt4cCsosf8A1ZRf91h/4AtS6TOcLwAB5rI9LVl/qD/TORs/6v8Ayeh6RoDRgAcJ8G47Nx+CZUJJwT6KRDmnHxAZ9ThegKcXsjqZeRxAD6J9BtyA3H2CjKm40dspJautq4oIYhmSR7wGtHqSsE6t+2RoLQ9plZom4QahvbnmJkLGP8Bh9Xv24I/ykqdtKH3MiSy0j0v7xFFl0jmNaPMkALxx7Z9BZNZaytkVNW0VSXW50RYx4cQWyZAJWJ03tC9XusevKCwal1XLS2usnEbqKiAhjx6Ej4jnt5Ky3GywWjVfuTYC0ujwcuLv1yVxvqHqtHQ7WO7fn4Ol6F02VSX1E+E8Y+TzJfOkFS6tmZBEWEvJHoRyq/UdGroXAhrj9cL1JqG1upq+KqdGRGTtz3Tq1S2ZsEsdfBHG9gONx75XLUridKKib76ZRbbaPLND0lqaUeJUNLj55bkK86N0QKSdzi1rsDg7c4V8usjJYpnxsDWucQ0BPtK2yWOGeR0eMEHuFI7ubWMifR047YLd0atdul1pHRXKugo430j2l8rwwOL3AYyvTsvs+aQjoY3UsUe7G7xY+wz6HyXjCspKe5X2no5yS2bLQMkZP3ClK7rZ1L9na5Utqtd+nvNjuERl/Z9wcZRAf8BPIH4nsF3/AKX9VVLC3hZRnp3/APJRvredOp9VH2pYPTc3RiqtczZrRe6uDB4PiEgfqrJY7l1X0pTyUtr1M2oZyG+Mzdt9CMqC6bdatN9UdN011tNwpm1r4mvqaIPAfC89xt8x9lOz3abuMYJIHrx9F2N11hVV/FSmn8mtY+o40aSpXEIzX/cslRv/AFm9qTSAkrWajt90pw4u8Oe3swG+m5hyPxys0077YmoNf6snsGqrW+ir2PDHDG1js5+XP2Ww3GtNYx9PMxr2PBBa5eZ+sOlaXTeo6LWFthbG+GYeIWjGR55+yx6t7Qi80oKL/BPdddsovXa0oU5Y3cV/6Nwu1Y+UGQOcQ4ZHKqF5IdGTnuEtbNSMu9jp6pjh8gbwP/Xqmdc7MD2O+cknCxrq4c3ycvc3juJN5PF/tTx7dQWc5/3dRx/4mrEFuPtVtP8AWaz8fLBPn/zNWHg4BVO/kpVFgw6u02/k5keq5gHyCIWuyeEYOAGCeVTIzriNp5RGYzyib2+qG9vqgBR+M8LjHOjduY4tPqDgom9vqug7uyAJakvNVBhs3xg+ZOcI1Y5lbHvaQCDn6qM3t9Vx0zmDMfJPB+yAA2ucyQsyfh47rtRWF/8AGfzTCpdghzec90ydUfEeUqWQJcT7sc5KRn8Q5+YhJ0D2vPdTMVOyRhz2wnpYFSy8FVlL/EOC5BWpumKypHiw0xcw9jkD+ZQSk3ZXyWUPBOMI2Q34j5JIHByizyhsZLuAsZLLwZBD3OQSTkjzTaMhrACUWadr5XYPZJF2TkLSprEMIu04pRDucOeUmSB3SeSDnK7w/wCF3A+icPOl4H1Q8Qei4WNaPhJP3K4xoc3JQAouOGRhdQQAmWEfVHaCByjA48giPe/dwB+SADILgDzjslRG0+ZQ+H+htRtQZ9FbH/1ZRf8Adof0bj/RaZ0rfi+BnIOM5xwsvss2y0UUjmkj3eEHH1aD/qjXHq7F088SS20zKm4OZhucnwj6kAhcp6eq9rqbb4wzkrL+qX7PZNXqKz6btzrrfrhDRUcQ+OeVwa0f81hHUn20NNWu31FD09ppLpW+GWmtdHiFhPmCe68ja+6ra26gyY1NfJqiJriWwNJbG0/5c/zVQ99kO+FhwxvZo7Lu/qcrKOre7LXqTqLrjVkz6rUGqLhVCpyXMMxawAnttHGFTZDLLUxkO2thOBt80u6pMjRGwDZjbnzR4YCGeI05A4x5lQSqTk8tjoxUXlBLLd6mxaqor7Ads1JMyUY4BA7j8V6luVdT3O70d2i+IVELX5BzjPYZXlWqpnVAdVBpa2L5h5lbt0NuVLqLT81kqa0CuoHNMO543SxeRH2WB1q310+6lujpfT12qNSVKX9xqFysLbjbA4QjdgHPfH1VCu2m6epa6OaQNkaRzg+S0a23CWF7qSY52t258sZUNqCSmic+QRswO/C5mjJ8HXVI7ZZnlfZgyniiY5rt7wBhqmooYaKkechu4dvsF2GaOtk8ZkRLInbvh7cJjeHzSyCmb8JJzwOcKXiQzSmiArHTRXu31MTCd0oa37k8KhdetTsvGv8A9meO3/oujZTHHIEhJLv9FrOt6Fmk9JR3ure0OpfjbuPL5McBoXmCqdU3W51F2rhumqZDI5/94n/0Fu9KpOc+6/BzPXK2iHZ+SStWpai01TZ7JVy0VVGQQ+neWdvXHdbjof2ptaWanjo7/TsusLDzI7iXH39VhNHb4Q0OEY3EnnzUgwOjaW+Flp4Jz5LpI1JN5bOaU5LY9w6S6vaI1pRRTU16ggrn4zTTvDHNPpz3SPVGyRXzTlVF4QJMZOe+D5FeHzMYZmS0gMckZBa5hwc/dah01623qwNms2pp5q6gqOGPldvdEec49R/yTZybY+M21gu3SW/ySwz2CqkzNSuMYz5geavlfWwQszUStYcc5Kwir1bQaV1V+3bfJFLQXDPxg/Ifr6FQmquq899mMFLIY2jPbJJ/VMf3bMMtFK9qCtpq7UlqfTyhw8GYEjy+Jv8AyWJ4GcblZeoN4luNzj94Lw9oIaHA/wDryVU5znKSrsyKTct2KkY88pN3zFAOIXCcnKjECeGfVDwz6o4GThcz8RCAC+GfVGYNpXURzyDjhAB1xxwF1Ff2/FADaeMlhdlRR7n7qXn3eGQ3CiXghxBSp4AVpJzA4Z5yVPUNzAkja3zcAcqto8UzoXNkz8h3cp2XhsTLTWDeLfHG6kjc0sALe2EFEWeufPbKeWMjDmBBQd2RtxowaWxFggHJCjbrOdm1rsfF5fYqQkeImlzjjCrdxnEkp54yqVsm3lo5ags8iAaCXHHmjduyQ3t9URz3Z47LRLkRU90EiOeQnDCA3JQPCoAkcArkxBIwitcAMEoAOXOweUeEkj4uURvcJRwJGAgArnEOOCutkAGHDJRCCO66yEudvwgBXccZBSXivbzuKcYw3HoFyNp3B2OAh8P9CSxoZ9B7ZWx09lgle4gMoonAeWfDCw281z66uq6uqneHvldg57jyV61tf3WfRNLBA4eLU00TD9GFoyVjdRcIRHukmeA7yb/F9VgdCt9M6laS+TE6XQWuVVjqolgyf3zjyigkEuZE/cfMnhNmTjwhI0ERActPmj00plGG85XRrg2xZhlZ8Tow1rTk4807ZNM0B0Ya4O5DfP8ABMJHtyGHJ9W4PKXj94ijLWxOw45Ax2CUUdGoqXAgwkB3cO7pO31Vz0ndob/ZKgwTxuBbtPB9QR5hIuqJomgvaR911tXu+WmO4d3f3kk4xnHTJbCapRacHhm+aQ60WrUMkbK/Zb65+PEjJzG/HGW+nfsr5eLXNX0zJ6cB8coznyK8cSNjc1+IgyQnknnaPUY/BS1m1vrLTZbDQauubImjiMSbmj8HLDuekRm82+x1Np6jlGCjcrOD1JTW0W6mFMImsMmc7R9VV5NVaX0/fZa/UdYI20bSWx43PmcOQA3y54WG3Pq71LuMPup1FV+C7jLNsbv0UVSy1QldPW1HjzyjBkJ3E5+6ZQ6NVW9Zofd+pKbWLeO5aeqPUC89TbyHSNFLbqQkwUrDw3/ET/ESPyVI2kFsRGRHwPon872Rkue7A7JtDVmEyGM53Y/1W5RpRoR0xOar1qlaWuq8tjinicwCYk7T5JGpd87mnGcnhEdNJOMhuSUIopMjLeT9VKQjUF73YLjlKxkxHJJJ7j6I0/7uTD8AprLM2P4ieEAJ30PqbTPG15DIxv254yqXo/Vf7LvAqLi93ggc55z9VcKqRpYGOOBI0j8Fm11oTT1M8bmHax7h+GU9YwKnk07rtbdPO03ovVdkDhJfYq6Soz2BjkYxoH4ErHVYbrfJ6/SWn7BU8fsuWtdH/kldG7H5h35KumJocSfNV3nyOq4zsdQQwB2R2uaABlIMAQAMgJPHOVze31Qc5pBGUAceSDwUXv3RQMErqAO7neqBJPcou5vbK6eyACvJ7Z4TCqa0OJACeRuALgT3SdTDvJdjugCOXCMtIPbCcGJ+D8PkkmsySx/GRhAq5RoGmb1HFZ4IyPl4QVQpK6oo4BBE3LQgkwi4qn5LdcpSyJzSc5Crkzi5+T2UndKgk44URI89+FBbJxjhmVSi48gQXGknuuqwWANO1pylWuBbhNy88jhGjkPbAQApJ5IiMXA/NwuHGeOyAO+LgfKUBI8nGClI2xO7o4jiHLeSgBJpcfmSjd2OCF0sBXQMDAQAUvfyMoMkLRycrpYCc8obG+ZPPCR/gTGdmeidf6jdXVdLRQvd4MFLEw57E7R2VOnkbK1rQex802q66WUxFzg47AclJMn+Z0hw1ozwmUKKpQ0xK1Gl2XjwS4mBpCNw4SlOyeaVopZQzPb1Vfpa81rJY6d3LXY5UtReCySM1ImfA17XTNikbHI6MHLg1zgWtcW5w5wIBwSCOFZ4RaJunppy7ZLOfE+4SpNxjLmMm3BmMnOcD1Xra2ewTZdQ6o0zqPS/UC5z9Hr3p2W93DUdaaZtfQOi5fC4CIRNyHsI3MO3w5gflBMZpz2cfZrtGgLT1U6l9YtWW7TmvLw+2aObSUlOZ5KYPe2KWoPgOG92x0mQxjWNIBDjkpNSA8qS+9NeH1LS4PdgeWfzR45pnMwSwE/w/wAQ/BerNQew1bdLR9bG37W13qD02slLfbDJBFBGyvjmhqpMVLXMech1OGkxlmSHHAyAKdoX2Zum2pOjHSzrLrDX100/FrrWUmn7r4lTSQ0lvpGxVzhJG+SI7ZN9JGMvc5uHO47YNSAwQujdSOmdE8OxzkfVM6n3h8gdSwl2QOMcr17ob2UvZ5u/SCw9Udb9X9Z0NNq3Ulw09aTb4KOaGZ0dfVwUvLaV/L4qZr3PyG7icYBAErpj+j70vF1E6iaU6hdR9ROtmkqKiutrq7HSwe8T0NQ2U4lifFKTK0wubiMAOxkAZ2g1IDxP4dSSfFaI3j5mk9vunEcQEYnEh+F2CM9nLT+u2genGhupOl+nnSK+6wu9Rf4WiYaothoZ4Z5ZhFBtaYIDscS74iH/ACkBbvqH2RfZn0ld7n0qv/tBXi29R7dZm3aonrKOEWuJ7mbmRkeGCQeCIhL4hB4POEakB49bDNURvleQGMHYnkpNroGR7nvaN7g0c55PlwvTWhvZz6K6f6Rab6x+0Z1bu+n49YVclPZ6exQiSKGMFwZJI58UhduawvyWsDQ4AgkFLas9iel0hcOvlFf9Z3OePplogaz03NSMp2NuDZIK97WVbHMd8slDsPhlmfiIxkBpqQHmIbYHhrzgk9gCf5Lkz3Nje/dwDheub/7AdPbNPdJdW6Y1HfrnZtZzWuDVAm93M1qFayNzZqYtiA2BznMw8PIL2HOAcsqP2N+mFhunVHU3VLqRfbb076fXptkgkp6aKS43GUxU790jmxFo5m2NayPLie7dvxGpAeRXljozJucXg8Z80194aJWiVri3zWr+0d096Y9L79ppvS3qDJquxaltxuEHvQArrfgNzHUtaxgG7xGlvAc0tc1wzgnJmmObxDN8LG5wR34Sp5ASrpA95cxwDYwDz5/RRlwpX128xxty9oHIXZnbwGBxLXuzkd8KxaToqe41s0E8oYGDIOe5ToxctkOpRevHyZzqG2z2yCg8dv8AaMeRjy5//ahXOB7K/wDV+2y2ye0xOna9jo5cBp+re6z5NqJwbi+RZQcW0/AEEEQvIOOFEMCgZOF0tI5K435gjv7figBNBBFc8g44QAoQzHCI44CK1xyMnhB7wTtCAEw07so7zubgIjnEdl1pyMpUsgJSfCDlNtri/dhPXsDlxkGXY2pdLAIxp2jhBPm0w29kEaWAvVSh5OeSmb+34rszhu7pNRwjpWBqjg6CR2KDXOLsEriCcOAe6Mzv+CTc4NByeUVku44KAF5PJdZ8oRNpPkjtGG4KADNIaQScDPKD6umY7InA+/Zc3FvxNAJHIBGQVqfSa5WSvtkun6mhpxW0HxwvMTcyQuPf7g/ogDMmuJG8TteHenku+8wR/DK+MO/xPwVb+pejJ7ZqOmqrBQ7Y7s8R+FFGAyKoOMNyPI53f+FaJQWHTWjtMsdV08Egoo/Eqppo2kySAfEckHu5AGGGdmNwLdvfOcjCTFZSPO19UwD6d1puhdLWzVNRUa3vdvZ4FRUSPpKMACINbwHFvbPB/EK61eo9JWyojs813o6WbcGNpsbS0+QOOAgCiRzxSNiwdw8JvPrwmT64F0tMSSe4+yLWXANq5mZGGyv/AJprWVrJo/heCeycpYGuOWP7IWMpJjt+IOxlTEcxc3gFxHxAA4zjnGfwUf0t0rfNfa7tmh9NQQSXi+VMdHRsqJvDZ4jhkFzvIL1Xd/6Ob2nrFaK6+19t0o2lt9NLVTOhvW5/hxsLnbQIxl2AcDPdGochO0e0RoyyewVq7ohPrSem1TW3h8dBbo4n5dbpHwPkYx4GwMOaj4c+Zb2OFedC9TfZh6pez/006Z9a+pFy0Veel90bco2sp3GG4hpeRh3hvBDg8Ajh7SD685vpf2CPaL1XpCm1rQ6as0MNbSNrKO21lxZBc6iNzdzTHFhzQSOfjla76KhdMeiWvurmvanpbpq1041HbhUmppK+UU5pzTuDZWOcc/EHOxj6fimgetme2t0Q6hdWOrmnNVXG7WzQWv8AT1Bp+h1HHTEvjdTsqWve+MglrZBVHY8gjMXxAbli3tMdSeh9H7Pmg/ZY6GaurtZ01g1A+91t4qYfDjA21GIx8LQXOdVuwWjDWsGSdwVGo/Z86kS6f1xqGDS0UMHTy4C2X2CqqmxysndjbsjIw5pDmuz5hy0S/f0fHtG6dsNx1DdrZpgUVspJayp2XoFwijYXOwDGOcNPmgC8dIPba0f0B9nbpJoKzXeKpvdDq2sg1bbBRySSQ2Saqr5TJG8jaHDxKVwAOTnGOSr9oj2tfZ8snWbrFqef2iKv9na3pra+zXB1BM+W2OjhkikhjY6MsAjf8bQW4xJ8WXAk+fLB/R7e0hq2xWzVFvtmm20N3o4a+jNRd27mwzRtewObjvtdgqL0F7BvtBdR9IW/X+k7JYJrddI5Jqf3i5tZM7a9zC0DacHc3aMuaMAEnCAC+0l1J0HeOuui+p+ies126o11FSw+/T11HFSupmUtV40ELWsiiaQ5zpCXHsSvYdF7Z3suae1Lfut9N1hukT9R0FL+0NHm0F07quCLw4iCWBwOBt/tAzIznzXgnRvQDqjqHq1P0UGm223WsIkfPQXGdkLIWtj8TcZBkOBZghwyCCCFoWvfYQ9pDQWmq3Wt9sun5KO2sD5YqK4tnqHjeGNZHGWZe454aOSTgd0AaE3q/wCy37THRLSGiuuPUC89Obpo+61NfDC2ISiphc+XZ4c2x4JdE/nABDw4Yc0hzpjX3to9H9fVXtLV8VxqbfR6n6bR6Q0rJUUsgddqmOmugdtAB8MGWsYG7j2weM4GTXb2A/actmkZNVT6QstU2np/e57RDdvFuTYu+XQGPYTgH4WyE8eSovRHoBr32g7hdLV07ht1T+yI4Z7gayo8ARQvc4AtzyTmN425Hyp+kD1np/29+mGmNddMLFR6jfXaOm0TR2fVUnuUmbXcKYAwStBGS5pMjXFo7Oaedgwhp72tvZ71peurGhdSdSK3TFuvWsqfU+nNVU9AZIX7I6VzRte0ljhLTn4XN2uaRg5WAdTfY76x6J1/pfQLdKSxy6rqP2ZabnLWwupaufYXua5zS4Q7GtcQ1wy4AkdlVdd+y31e6c6K1N1Bvtqtf7H0ndTaLoaC4NmfBNlgLjGACW/HGc8YBBTWsAX/APpAvaQ0H1zufTzTnT7UUuqY9Iw1TrpqB1GKdtVUztgaAxoAH+4eXYAbkgDOzI8uV07o6R0DDh0j8N/1W4ae9i7rrq6DRk9jtWm6Z+vLXLe7LT1N1EcrqKNsT3TPaGO2gtnhOQCcyNz3UR169k7rL7Olgt2s+qcNgbbLlcG2qm/Zl0NRIal0Ukoywsb8O2F4Lucbh6pVLAGOB4o4nc5kd+7A+iVsrqt9ZFBSPcZnnDgPLHkm0W1wlrph8znMYM5wWkgnPnyCnujqsUerLfO5wOZgXn7hTU56dx9OWJpkV1GhrzW0TbiZmubG4jd2GQ0lUt01NnwxN8Q7rZvaMo66eC1X2iYwUdP4kdQGjcW7i3a4j08k40Fa7ZJoq1iW10suYMvc6MZJ3Z5ymV5qc3JeR9xT7dZpPJiG8H5H7h6pN08IJDqiMEdwe6t/TvR0OqbzXPrx/sNBK+MQgBgkeXHEZI7tHc/datPdNH6Q8GirKmjtvwjw4muAO36D0URCeeoZI5yHx1DCB5DzS5APdbvfdI6a1pbRNHT07Znx5p66nZ8QJ7fEOXN+6wBzKiiq57ZVNZvppHRPc3zc044+/dACrwAeEUtB7hEf5IzPlCAE1zAB3AcpWThpB9E2II7hAHNxJOT2SrPlCS2kuBASuQOCU6PIjeBWNoIJI7I8fzBOLe0OY5vqCE49yb6p5LGnlZCM+UII/uI+qCBe1+SIf8TshBBBRERwuA7oA5GUSY7RlCKQFgJQAWfuixDLlyeQ57I8Ayc/RADkPAGOUBI0u2gFEQHDtyAFHAlpAxnHGU+td7qLBdIL7RwtdNTO3Oycb48fEzH2TAPJOMI3HdwyBg49UAelbfWUF6t9Lc6XZJDLG2eIuGdriAcD6jOPzWW9ZtXS1kzdKUcoMUZ8esI4D3gfC38PNW/pO0O0Vb8k/A+UD6APOB+CxnV5LtZXkHzq5ASgDaulOz+oVpOTy1/xY/8AyScrCtRF8uoL1Jvc7/bqnLnHncHO5+nC2fpHdKWq0nBaGHM9ve6KVoOXbS5xa/HfHxYKib30VNyvdVcoL26GGsqHTPh8HO3ceQHdvPzQBUxVRyRRGSMBxiYQQfLHYpL4SctIAQlo2wzOiZUtcxuGg+fHCRlZBEN0tY1o9PNADqj2MrmPJPbJG8jkdnDGCCF7b9qOWao/o9vZ5hoZJZJqirkiZsk+JwNNNwSfU4x6d14Upa2OW4xwURdMNpc9wHygdyVZaQiV8RqXzyxQnLYTUPDBn5hgHjIJHHqhAfXDV/S/WnVb2v8ApH7UXT69WyfpjatMRRV9ey5NjEbgax5HheYcKqHyIBjdu27RnFvZQ1FZNW/0iXUy9adq6ea0Vrbu+nqoHDw5W5iaXtI4cMtyD5914SMs81NPAy519PT1RBlp4JzHC8jPeMDYe5PbuSTnKQhaymhjpaLNOIyS18T3NcHEgkgg8cjsOPLCk0oD6rV/U7SPW32ONbdXbFBS0F7vlJQU2pqZmGeHdaWeOKQuI5OW7C0kDLNp75AmvbY6QXfqNpq/z2P2e9O6krjpyaKn1XWX9tJUW4ta52I4fDJftGcfG3P0XyRjtzWUYo46qpjjLS2RrJS0SZxy4eZ+HAz2BIHdKyxTujkabpX7X8ub4+R9uQeEmlAfW3XfTKt6kdD+m1DQ9B7B1Aqho2mpmVl0vwoDbJH0cQaWN2P8Ulwz92DlZRF7PPVvql7JPs8aZ0HNQ0Vz0XeffLrLU3EU7KaKGWZjyHMDjJh2RgAk8ZA5XzmggqBGc3S4xgNDWNbVOAaARjA7DAGBjGEGU7WHbHWVrGc/A2oc1uT3O0Yb9cYS6UB6K/pHNXaT177Ul3fpa8xXCC1aforXV1FJI1zRVsM0jw17TjhkkYcQeCCCAQtl9sS/0di6seyLf7pVNZbbdDa7hXSOcHNighrLfLK92TggRtcc8+a8LQwQUsBZTRMYHjDjjLj68n18/VJmGnEzqyZ8s8gYWtbJI5zWj0aCcN5xwOOBwjSgPrDYelmsNJe2fr32o9QXa003TK7aci90uhusZieBS00Za5hd8IDoXvBPw4kB3ZJA83f0eNBP1Cb7R9t026F82p9O1ENsMu2NmaiSubECTy1uXtyDwAASOy8SyzVtTAbe+6VwoTyKT3mQwA4xkRk7eRjPHPnlce4U+33Z8sJ3BxdFIWEgeRI7/ilA+tFoEfSqx9DOkGvK+0wavqtcuroLXSVYqXUtMIqp+G4A2ta0tbwNuXBrSVm+jLraepftN+1B7JOr6vZaeoO642yTI/dVsdPDHMB6uLPAkA9KcnzXzQfHIJxVPmlmma3aJZHlz84wDu7jHxY2kcuJTOepko3uqIKyoZUyODnTCUh+cEHkeuefXt24TZAfVzSfUui1d/ScHRNhkhjs3TzQFZp+kpIsNjila+lfK1o+XIJaz7ReWF5z/pBuj100jZtL64b7Pemenlngu9TR1c1q1EK6S5TzsDoHSRCNmwNEM3PxY8fC8M09XUe+u93rqtpdnxJm1DxKTg93g55zz68Z7KUmkqaxgZVXeskYHbwyape9m7GAcE4zjI/EpgEg+SWaMvqGsZG0uEbGHJ4JHPAHl9UxbWS0tQysj4eyUSfgPJdiy3J8UPycnHAzjyHkkJjuc7y5KkXAGlaQ1TTdQrXWQV1GHNhd7tI1/Y7gc/hjKl9M20WewNtQdkULXU+f7zQ55af/ACuA/BY7oHV40nqiSnrJALfcHhk/GAx3Ia/8yAfoSt3cGFpMZGHj4vuex/I8KGMdKwhW222zP+jDWe6X15B3ftaQD/yhQmpNLjVnVSW0VVdLCxtvjmJaNxO3+FvHATjo3fqaO4XixS4bNNWPqoufnwdrmj6gDP4qwap6d1d9vn9Y7TqGW1VRgEO5jXElo9ccpwhwdNp4rUyww60usdE13wxxhrMA9xnAP6rGL7bP2DqCts4m8ZtNM5niFoDn9iCT5nBXo21UjtP2ZsN4uktW6nbvkqp+HHHlyvOmqLlHd9TXG6U4/c1FQ50f1GAM/ogBtkEDCUZ8oSYGGhGLy1owEAJSSHeM+q494wO/dJh5lOSMYRw0POCe3KADsGG5JCTMjS8tGV2UloDQk2M/jzyU6PIjWR/Q1HhcHz9E+ZWN3dyoWMFrs7j3Tljnbu6eTQk8Er7431KCjdzvVBA7Uxrvd6rrHEnkomR6hcJ7YPn5KIgDyAHgpEgNOB2SzjkBFeW7ccZQAk/4hkrkUpa7k8IhJyuDugB+1wcMhd2vPIPH2ScRGMZGSOyUJ2nBOD9UADsul7sHlczlAgg7TwcbsfT1QBL2vXur7HRst1ou3gU8RJaz3eJ+CTk8uaT3UNPU1lxuE1fWS+JPUPMkj9oG5x88DgfglImhwLmtyPUBHwBjjGe31QAvRVlwtdZHcbPVyUtTGMGRh7jzBHYj6FWBnVzW9NCY3z0MmMYc6my93PcAHCrAPoUZrTIBnJb+ico5zITGZKJa5tKXGqLKjEjnSMa92xm0ZIz2XBoSZ0ZqKuUxtaQ0h3zfTj6rTornHBQwxtDTmJm3Hn8PkmFvjdf7zBFI0mCGRrpQBnPPAWQrlqf3G5cW9C0pqpPyal0T9nWmh6fX7UVbTxvud0oJmUcLx/ZtDcg/crzvCyppppqWrZskhd4b2f3SO/6r6GaJq42abZHHt2iBwGPTC8HdS6ylOvr42nZgGqdjb2+q1IPKTMSq094+RpHPhmAUYSEHLThRbKghoycJWOQvOfFc0fQZCmGRe5LRVEx4L/0CWZUMlaWnzTCAOPILnfXCM0EEHBCB47kmc5wEku0AYHAQJhjw/dvb5pFsoYc8O8ueUlVVjWsxgDIQA4lrIi390XhvkAMhN3SOwTnhRxuL28BpwjxV7T3I5QA5M7RyQkfe43E5HZIzTmY7Wt+vATSeqjpo3Pc9oI8iUAPaqrYyHLeCqxcbi1ziBy7Jx90zuF2qq17mQuAb5YTV8UkcYfLndjPPmU2fA2XBP2yx3A0j7jTgyNPL2+iWhe5zQXDGTjBHZWHpFdIKsyWWo2OAOcOcMuyrtqDpiypd71SwGLufhHCz/qYUpYqM16fT5XVNTpcmaA7WfCMIsFvut1kMNrpjI7zf/CPurKOnWoXTe7+7F8chAD3AgDnyK0Ok07S6ftEVnpW7p3n969ozl3n+qdVvYpfwgt+lz1NVzzvqOwVdrqKc1exz6tryQzkNxwU+p+ouu6Cmioqe+kQwNDIwaeJxDR2yS3J/Eq49Z7Sy0yWfI2mWOUkEY82/6rNJBkZwp6D7lFTKVzS7NVxG8PvTZ/fIZnxz7y/xGfC4OJyeQrbTdW9dUDGxPlpaprBtBqKYHP4tcCVV+QPRdG49sn7KQrkjfteat1NEI7ncnCCQn9xCzwmj7+Z/EqDji28HtgY/BOQ0Yy0DGM8IryMeSAOsJI58lyVxAwD5IMkDRhBwc87g0kHtwgAga1vYIzCGkk+iABByRwkpWmQgNP5IAOXCQ/ZADHAQbHsCI4/EcFAC1O3xJGtPmcKTbbyW5aOfVRdM4CRuTjn/AFVshLHQR7SCcc4T47k1KGpEP+zpfVBTe0/3f0QTyTtfkpK45xaMhGAycIPj47+ahKoI5Mg7iuOe3cUk9hBGHIDgcnKAAUB3QXCduDjzQBatD6YGrbs62SVr6aOOB0znMj3nIIA4/FXlnQykIwdVVG7nI91YP5rMbNerpZKn360VjqafYWFwaHAtPcYI+i1zpLqi/alfdYL5WtqRB4BY7ww0jdkeX+VADcdDKdpBOpKsem6ljAP5qmnQl1/rbUaPtJ96fT4e6qeA1rI3DIJ2/wAla+qms9RacvtPSWeuEMMtIJdromPw4PPmR9FJdHpqm50N2vdxnM1ZVVoZJKAGkhsbSBx5fF2+iAG1N0ToGw7a/UNc+YN3uNO1jGfgHNJUPqXpBXWqlmr7HcXVcULd8kMzQZXAebS0AH7YXeqWtdR2vU0lpttylpYKaFhHhYBc7kgkkHj6K/dPr1X6h0tSXK5va+ctfE9waBvLSRuP3HkgDAYZmSgOBL84J45V00P07/rhbJbxLdnUmyodCImQteMNwe5+4VY1PTMt2sLxS0uGMbWyYAGAA52cAfTdj8FrXRgn+qVRtGT77LgevwtSS4aGyzyilftyqLRQsnMkzHmKMjg7RwOB24Wj6Uov2TDC7c500zmvfnyz/wD4qzZNJQWuvfX1jd024lrcduTyp+uuMlLTy1EZw8DIPphcneXcateMYvZFHqPUXdThD+1HqmwXM0OiJKoSBro4Hd/svC2oas1d5uNwl+eWofID9C7Cv1m9oK6stVbpqtgkc7YWMcCMEeqze6OFQ1v8BkcST37nK6qG8VguuSklpORS+I0cp4yTa0AFRIbJA8RnluMh3ZKPrRGPiwPu7CljyCTTJaOR4O7xiAfIJX3twcGknlQkV1jPDwWj1zlOve4peQ7anjyYjcSckjH3RnxMlHJHCiWVQAx4uUjV3QU4AaJJHHyaEASklNEBtyMhMTG0E4IyOyYyXvbyYJs/5U3lvDMENhl3OHGW+aAHtVVvpmEtOT245UDUVU08ga45DvRHdUVUz/hY4E+qeUVs+MyuZgnCAGtNbvi3beCkb6wwNDDjO3I/JWplKxjA8tx9FUNTyufWbduA3jumyAV0ZeH2rUdLO1+1j3hrj9V6ws91grKQOlla8MAJAPdeMWSvhe2VhAc0gg+i2rSmuw+3Qxzu2ODAOD82PVY/UaDq4kje6NcRpNxbNlmroql7nNYGMjGGgJvQFlTVGWU8BVylurTQmpa7O9ucZSNBqinp3Hc15J7hUFtsjVnNOTKr7RU7I6yySFhcBBO1ue2/c3A/TKiKTow2ss8Fwl1LIJZafxWNFO3AcRkDOc4yprrBVUt+0TUV4YBJb5oHM5ycudsPP2d+it9lA/qzSMdzuoowPodvdbtvFqkk/By98067wZNpHpjT6p0/BdxqOdj5XOZIxlOwhjmk8HJ74A/NVTUVrfpe91FnlmdJ4O0se5uC9jhkOwOyunQi9vgra+wyu3RTg1bGk/xtOHY+42/ki9crLMLrbLzTRGQ1bBRktHd4cdo/HkKcqCWkulsmqLM29TXmSkZO53gwsga74GnaOT5lFtXSuluuor1aodRymG1SRxCVsDS57nDJHfGBhanE2HRuk2tncGx2qka703PDcH8z/NUHoTUy179Q18shc6eaGRwP953iE/6IAWHQumP/ANUVf/8AFYVE6q6Q0+nrBVXyC/zzupQC+OSAAOycfw8jyV61jQ9QaqtgfpC509NTiLErZQ3Jfk88g+WFm2v6nqRZ7cy36pusU1LcA5oELWDJbg8kNygCjCfc0gZwlIGkuyPRJUsJcMk4CcGVsXDW5QByR7R8JSJXXu3nOMIqADxcSsP+IKz0Mo8LIPKqwO059OVNW+oDowC7CNUlwS02kiY8V3qgmfit/wDufogk1zJNSKwSQMhcbIScPPCJk+pXEpWDSFpI2oqCCABtf38kCAe6HiH0/Rc3g8cBAC8ByCtV6E/+9Xv7Uv8AxPWTxHvgq79Nda2rR0lzN2ZO5tXHGYzEwEgt3eeUAPeuG0ant+exoeft4jlZeiVSwafudKBmaOtEmPUOjA/mwrP+pGrKPWV8hrbTFMyGCn8H980Ak5zxhE0tqm46Qr/faU+LFJhk9MSQJGjkOB8ncn80AT3VnT96qdYPrKW1VVTTzRRkyQwueBjgjgcdwtE6a26ss+j6Slr6d0TxvkeHd2BzieeAmMHWnRs8QfM+tpH4GY30+7B88Fp5+6gNV9aaJ1vmoNMwzzTVAw6pkbsYwHvhvclAFA1fUsqda3eWB4cx9fI0H12cH9QtY6KuJ0pOP+2y/wDCxYpAzxpPGc5xL3F5c7uSe5P1PmtE6e9QrHpWxyW27Q1njOqXSZhiBbtc0Ack+rf1TJ7JsRvCbLnUODHnJJ5d5/VM5gyqhkikadpHzZ7InvDqs7YNxjd8e9x52nkKD1XqSGz0wp6YnxXcHlcTSoTr3GmPycym6tXRFDB+lojNU1dLF4mwc4PKY1AjdI1kxwwFPrDrB8dM+kdD8cnzOAUfNHUv4MbHZ9RldvCDjFR+DooQ0xSzwLS07ZWBzZhtHZIxNp4D/tMAqGk/kuNa0Ya4SA+gBwlmF2fA92Bc7kPa7GB6YUkU8j9Q7jt1qrqdxhDW47DPZJS6dp2gtp537yPhz6ojqKqgbtpnBjvPPCNHd6lpHv48P/EOMJ4aho6wXSJxMUrXHtgjCFPFd6B5c+lZK49j6KTN7tzGjNcHE+Xmkv21T5OwPePUNQGo7TsuFXzUU0ULT/EME/klG0bH7w5jTtcAOEg+7nG6noZ3P9cDB/NN/wBo3XktoHAnnzQOJB9BEBkRAc+QRhHHEN0jg0BRgrb3Idvu4bnzyu1VufURj3mukidj5dx5/BACtddqOLLWOBx5qKuFpFxovfo2neTwc+RRBpwPcf8ApA7f8Sn7C6Gma63Vb2PYeGuz+SRrIGZ1FNNC98UncHjhTFguk1BmJ0fiNdgA4+VXyt0fbal/j7W/F9ByuwaUt1OD+7Hxf4VDOGXhj4TdN5Ryg1s+ipxT1EZkicMDAx+CTqtTFrd9E8kuGdnfb9E5ksdO1myONpaOeW+aa/sEMeXhgbk54b3Vd0IZ4Li6jVSwNbrefH0BfI6qTD6iqpooW/aTcf0atis3/wAOUP8A3OL/AIV5/wBZxyRClp2Zax+5xj7AnONxHqMnlaLbusGmqex01vqIa4TRUogJbC3G4RgA53Z7qxCOlYKLk5Nyl5Mt0heXad1Fb70D8EM+JB/gJLTn6Ydn8F6KvdngvDLa7YJYqerjqW59Wjg/yXmGL94fCLQ1shO447ArYdN9Y7JbrBSUF8ZWCspwI3uYze17RwCDnjjCcIPOuF8ZRWCktDJMTXGTLm55EbPX7k4/BRnQAAwXzjGHwYx9nqh9QdXR6v1CbhSB4pY4mxU7ZvmA/iP0OVL9LteWjRkVwjvMc7hV+C6Mwtz8ocDn80AafrLVOr7FXQ0+ndKm6wyRb3yBrjsdkjbwfTBWY9QrrrTU9FBPfdKy2ymoS95e2J2C52G8knsryOt+ijz4Fy/CIf6lROrerOmr1puvtVuhrhNVxOjb4kYA8sZ5wgDK2z7GFrMDIwg0B3J7pGl7fHzj1S7pAB8I/IIADmgDIRAchd3F3dcQADjHPZcbXmn+FrsNRMndyfNI1DQQTgIHJ4JFt0aRncgoBxIOASggXUSaABPZcJwMoMkAPIQMDbHeiGx3ou+M1FNQ0HGP1QAYMJ4IRJWsY365RTVj+5+qRkkc7nH4IAcU7gcgFL7Gfxd0lBTzsjbMYzh6VJzygA8MbWODj65ToSRu4J/RNh2RmfMgAzoYnEly57vD6/ojoIA5G5sZIzwOy4/bI4DPBKBZk5yg1u05748k2azFpDamdDxya7PVx2S1RuJaZHRNYGk9zjCr79L0tVUtuWpaiRrJACyFoJOPLsiltRX1UNbK4FkIBEbhkc8qQnmoK6bxKmaXdnGN3Ab6D9VF0+xVvHWluzOtKcaCcpcsbVJs9P8AubdR7WM4ae5ITR9wp2gh1vcPqHZUhU2W3TND7fVvjJ5wTlQdW6QOfDUQuhHk8OytIsrd5Di40D5C3BjIHmco7DQ1LssrDG8cAgFQcrcPGYw5p/i7FOoaOgc395LK1x/uoe5I3hEtLFXD4GVjXgdnEd01kD5P3bhvDuC4jsuts5NP4sFdI4Ds0901IuUTg4uBwc9k3ShmpjussRZI0yNb8vGAmMglpXBvGPoU4u2pZTmJw2Oc0N75woplUx0YAy4/xHKNKHp5RINqZHtL2zEAdx6INqa2VjvDmcQAVGipYxrmF+C76dk9oZzGAGjk9j6JjHqT4D+LXNiLhUlhHIJCTe251DGTyVW/vnywn2PFOJXbieOAi1BJ2UVKwPlGcgOQlnZDm8IjZJJWu2uD3keYdhKQSNa4O8KUOyCPTKkaeyTygeM4Rk98jKLLRRwOI8Vziw/nhO0NcjdTLvbP9poacuI3BvbKdGAN7hUSiulZSuwyXjy+isNv1Uw/u61vph2UjpN7oFNeSWfC3PZFkhBbgA9k4gq6GtIME4JPklXxCP5g78BlVpQkmPTyZp1Bh8GqonOGMxuA/NVBrNzjnsrz1N2unt4AxmN/6EKk/Jz3ynAcMe0ZHZIy8jAR5JCeBwk/ugBJ4jj5J5+yDQ2T7Ik5BOM90pE3a3OUAKRxNwV3YRwBwg123ySgORlABImuaOQjoofk4wjIACCCKX4OMIAIe6JKC5hAR1x3ylAEZICHEFBGm/tCggB8XOIwMIha891KOpmNBdsbx9Ek+IOGGtAP0Ci7qeyJeyyP8N3quGEk5yVIspHOGUq2h+HnCMsOyyNMBxxnKcW+2S1lQyIDucn7Jw+He8RMGC444Vz0xZ2wgTPY0u24yRynxGSjpGt1tcNHaQA07mDjKp7ey1DUFF7zbXxjG7BOVl0jXMkcw5BBIThooHHICUBIOQkm9wlEAG3uR2kkZKTQyR2KADF5BKAkcD5Iq604OcZ4P8ksfchJe0vMUrzC3Bxw3t58BEmmDXglrc4TU1BbFG7cQAxpPPfhcbWQ1TgyVu0dtwHKvR2RUwnux74jahobFOGOA5AKaVck7dombva38yuy2ogeLb5XOLue/K4yskZ+7uEQBPnhDSFGckIc18scg3Y4YfP7Jix7t/xOew+YypKooxKRPRP+Jpz37BR8pjLy2qy2Y9iPMKMct3uOYqipiBEcri13ryuyVUkgILzz6JriSMbS48fVO6RlDXStb4vhAnBPogWSGNTmZwLmgkeeERschcMNAYO+ApieyytfuMzC0dtoxkfVIPhx8DcDHfHmgWPASChZUluyP4R8znIPj90eON4z/D6J1TFzI/Ce4ti+nGV1z4mtIIDYvJ2OUmEOGkElbVFwp8xNzhzz/C3zKf04hii8WnGGg4MruHyEfySDaja3YWYaezB/M+qN4UjyHPeSCOMnsELbgSTeBWW4STj929wb257ojcyjDiSjD3SIbZCAUk+QudtpxyThpCVtvkjywPY5h+iDWl/4JVtKxo3V00mR/C1xGUb3ynAMUVPt/wAR7lKm0I9wQ+PEP3cr28+RUhS6iuluwJpzMzsAeSAojdI048R3r3RxKCMEZPqk5HKTQhre7RXQ0MkTC0xseCD9SquXE91J305kiP0KiXuwBzhV6qSexZhvHJx/f8ElI8tGQg+UNOO6Rc4uJOThMAIWgndzwnUfyhGp4mys27Rk+eEd9I+NucnCACLocQMBc2lvc5QQB0dx90ouYb9F1AASb/mKUQIb3OEAJE4GUmZAQQEsKd9WcRktAPOEYWp8Z3F+Ua1HkfGm5EW9oLiSgpttuYQCWN/JBHdj8DuyySNOPPsue7xjkH9Es6QbTwk/EHoqhbClm3skZnt290rJK0N5TMv8VxjAPHmnReGRznFLckLJB4tUJZBjBGFeqAMYzvzhVS0wiPAODnCtNH8v4KaDyinnI8cGyRuaTwRgrOdTULaSrMjPXK0GQkRuIKo2q3h8oBPOU8CAJJOT5rrSAclFQQApvb6ob2+qTQQApvb6ozHgPaQfMJFKNYQ4c+YTorcSW8cFnhfFUNEMoyC0c+iLIHUTw17iWn5T9E0D3Mc0tOMBO46yOdpp54yWkcH0Ks5yVtLQo28uoz+7PKWdcqOsO2pjIB747qKq6QxjLHhw8kiHGSMlp2O8soFSZJzUcQBloqtzSOS0+ijnVIqCaarG3HIf3SQq5YuC/P2RnNp6qMvLXB44HKTUiQ4DNSuG4+NGfl54S8TWfBK05BPCYse6HMD8uZ5jzS7N8U23duae2PJCeQJyCcOZsJ5Aymk8nhEuPb6d/wAkhDO5su3k5GEnPWSxylsTG7u25w7JQDTVpa3z/LlEZ4s5DpZuByAiRt+LfuLpD3Lk6cynpm79pLwMjnjKBMoVhkji5Djn7FdmrMABpySmb650w2bQOUaJv8TyPok1IUUYx8x3TDDD55TkTxULf3T88ccFNJ6gRtw3Jx5JONr3DfK7IPIHojUgHRqDUne7uuFxaQBnlEY9gGAOV1pmmO2BmWebijORkk2xUSlgw4/kco8R+LcfPlJinZSnc52X9+TwiGVznl2e5yjKQmlkXqGVrPBJPO13/EoAybypPUBL3Qc+Tv5qKa3b5qCq1J5RZhtHDOoIIJgD2gkZua3dzlTTYhNHg9s5VapZAyRvHmrJRPy0c5TJfJNS4wNaihePlbx90xfE+M4c3GFYyzc3tlNZaQOJ+FR91rZEjoOW5BozO/4J5LRPaCm/gSR/FjPkpdcSDtyXgI8EngJSlgfVSmDb2QYx73BoYeVN0FEIoWvON5zk/imykmLGEsnG0jKRmGenKQe3e/6J7UO3AjH0SMULi/lRSeS54OMhG0IJ4GADBagmgMnfKUmgggR8DeqJGPsiUQyclBBBUr+0sVv+Zv3CstH8v4IIKelwQx4F5f7Jyz3VZIqeD5j+SCCkHEQOyCCCAAggggDo7pVvzD7hBBPiBLO7j/KuniIkd8oIKaPBHLkWozvb8XPHmmFX8MwA4CCCViDUn4ylqQn3hozwggogDVwAq5APUfyStD8TCTycIIJ8QHFMM1Iz6f6otQB7weEEE8Dg4lP4ItWTgcoIJGR+RvH8wTo/KEEFESCDSTVEE5GAnbuGnHogggBoXEEYJ7gKWm/cwARfCNueEEFJHgCKdLI9ri55JzhOYjljCfQIIJj5BEJfPmg/yu/motBBQkoEEEEAJs+YKx2wnA+yCCbP2ktLkmIgC3kLjwNx4CCCrI0o8BJ2NwfhHZNtjf7oQQQV3wdiYzxW/CFIvADsAYHCCCBseRrJ84+4S0IG/sOyCCB44wPQIIIIA//Z" width="300px" alt="how machine learning works"/>
This accuracy allows you to assess the risk of insuring an individual based on their past claims history and use this information to correctly price your premiums. While we’ll explore some of the top applications of machine learning across a number of industries, the academic world is also using AI, largely for research in areas such as biology, chemistry, and materials science. If your dataset is too large, it becomes difficult to explore and understand what the data is telling you. This is particularly the case with big data in the order of many gigabytes, or even terabytes, which cannot be analyzed with regular tools like Excel or even typical Python Pandas code.

<h2>Q.4. What is the difference between Artificial Intelligence and Machine learning ?</h2>

The most common application in our day to day activities is the virtual personal assistants like Siri and Alexa. For the sake of simplicity, we have considered only two parameters to approach a machine learning problem here that is the colour and alcohol percentage. But in reality, you will have to consider hundreds of parameters and a broad set of learning data to solve a machine learning problem. These devices measure health data, including heart rate, glucose levels, salt levels, etc. However, with the widespread implementation of machine learning and AI, such devices will have much more data to offer to users in the future.

No-code AI tools don’t require any IT work or coding, so hospitals can save money and improve the quality of care they provide. Today’s AI trading is a form of automated trading that uses algorithms to find patterns in the market and make trades. AI traders can also be used to optimize portfolios with respect to risk and return objectives and are often used in trading organizations. For example, a 1986 New York Times article titled “Wall Street’s Tomorrow Machine” discussed the use of computers for evaluating new trading opportunities. The credit default rate problem is difficult to model due to its complexity, with many factors influencing an individual’s or company’s likelihood of default, such as industry, credit score, income, and time.

Machine learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and likely will become a pillar of our future civilization. Just connect your data and use one of the pre-trained machine learning models to start analyzing it.

Questions should include how much data is needed, how the collected data will be split into test and training sets, and if a pre-trained ML model can be used. As the volume of data generated by modern societies continues to proliferate, machine learning will likely become even more vital to humans and essential to machine intelligence itself. The technology not only helps us make sense of the data we create, but synergistically the abundance of data we create further strengthens ML’s data-driven learning capabilities. The early stages of machine learning (ML) saw experiments involving theories of computers recognizing patterns in data and learning from them. Today, after building upon those foundational experiments, machine learning is more complex.

In real life, the process we’d follow would be to look at several product reviews describing qualities about the model we are considering purchasing. For example, if we see that the reviews mostly consists of words like “good,” “great,” “excellent” etc. then we’d conclude that the webcam is a good product and we can proceed to purchase it. Whereas if the words like “bad,” “not good quality,” “poor resolution,” then we conclude that it is probably better to look for another webcam.

Continuous data, on the other hand, refers to data that can meaningfully be broken down into smaller units, or placed on a scale, like a customer’s income, an employee’s salary, or the dollar size of a financial transaction. One use-case for unstructured data is to analyze reviews and comments on social media, both from your own company and from competitors, to inform competitive strategy. Unstructured data can be difficult to process and understand because it’s messy and in a variety of formats. Unstructured data may also be qualitative instead of quantitative, making it even harder to analyze.

Business requirements, technology capabilities and real-world data change in unexpected ways, potentially giving rise to new demands and requirements. These prerequisites will improve your chances of successfully pursuing a machine learning career. For a refresh on the above-mentioned prerequisites, the Simplilearn YouTube channel provides succinct and detailed overviews. Machine learning operations (MLOps) is the discipline of Artificial Intelligence model delivery. It helps organizations scale production capacity to produce faster results, thereby generating vital business value. Now that you know what machine learning is, its types, and its importance, let us move on to the uses of machine learning.

Machine learning algorithms are smart programs that can predict output values based on input data. Typically, an algorithm uses given input data and training data to build a model, which then makes predictions or decisions. By using this method, ML algorithms arrive at more accurate predictions and better decision-making. Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions.

Consider taking Simplilearn’s Artificial Intelligence Course which will set you on the path to success in this exciting field. Traditionally, data analysis was trial and error-based, an approach that became increasingly impractical thanks to the rise of large, heterogeneous data sets. Machine learning provides smart alternatives for large-scale data analysis. Machine learning can produce accurate results and analysis by developing fast and efficient algorithms and data-driven models for real-time data processing.

<h2>Blockchain meets machine learning</h2>

We cannot predict the values of these weights in advance, but the neural network has to learn them. Deep learning’s artificial neural networks don’t need the feature extraction step. The layers are able to learn an implicit representation of the raw data directly and on their own. All recent advances in artificial intelligence in recent years are due to deep learning. Without deep learning, we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri.

In 2023, ML applications will include medical image analysis and image classification, fraud detection, facial recognition, and speech recognition. Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item’s target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.

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<h3>Graphic: How machines learn – Artificial intelligence – Financial Times</h3>
Graphic: How machines learn – Artificial intelligence.
Posted: Wed, 19 Jul 2023 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiP2h0dHBzOi8vd3d3LmZ0LmNvbS9jb250ZW50LzY2OTJiYTczLWZhNzEtNDVkYi04ODFjLTBiZTIyMTQ5NWIzYdIBAA?oc=5' rel="nofollow">source</a>]
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The dimensions of a weight matrix result from the sizes of the two layers that are connected by this weight matrix. The input layer has the same number of neurons as there are entries in the vector x. At the majority of synapses, signals cross from the axon of one neuron to the dendrite of another. All neurons are electrically excitable due to the maintenance of voltage gradients in their membranes.

<h2>Transfer learning</h2>

Essentially, by digesting past queries to find patterns in terms of content, AI can learn how to classify new tickets more accurately and efficiently. This means that with time, AI-based ticket classification will become an integral part of any organization’s customer service strategy. Akkio’s API can help any organization that needs accurate credit risk models in a fraction of the time it would take to build them on their own. Akkio makes it easy to build a model that predicts the likelihood of default based on data from the past.

With Akkio, machine learning operations are standardized, streamlined, and automated in the background, allowing non-technical users to have access to the same caliber of features as industry experts. One of these concerns is overfitting, which happens when a model tries to predict every individual input that it might get instead of just being able to predict certain patterns in the data. On the other hand, regression models are used to predict a range of output variables, such as sales revenue or costs. Lead scoring is a crucial part of any marketing campaign because it helps you focus your time and resources on the potential customers that are most likely to become paying customers. In other words, an accurate lead scoring model helps you go where the money is.

In the majority of supervised learning applications, the ultimate goal is to develop a finely tuned predictor function h(x) (sometimes called the “hypothesis”). The Natural Language Toolkit (NLTK) is possibly the best known Python library for working with natural language processing. It can be used for keyword search, tokenization and classification, voice recognition and more. With a heavy focus on research and education, you’ll find plenty of resources, including data sets, pre-trained models, and a textbook to help you get started. While artificial intelligence and machine learning are often used interchangeably, they are two different concepts. It is also likely that machine learning will continue to advance and improve, with researchers developing new algorithms and techniques to make machine learning more powerful and effective.

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width="308px" alt="how machine learning works"/>
A thorough discussion of neural networks is beyond the scope of this tutorial, but I recommend checking out previous post on the subject. What we usually want is a predictor that makes a guess somewhere between 0 and 1. In a cookie quality classifier, a prediction of 1 would represent a very confident guess that the cookie is perfect and utterly mouthwatering.

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<h3>Next Big Thing: Understanding how machine learning actually works – Cosmos</h3>
Next Big Thing: Understanding how machine learning actually works.
Posted: Fri, 25 Aug 2023 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiR2h0dHBzOi8vY29zbW9zbWFnYXppbmUuY29tL3NjaWVuY2UvbWF0aGVtYXRpY3MvbWFjaGluZS1sZWFybmluZy1tb2RlbHMv0gEA?oc=5' rel="nofollow">source</a>]
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The field is vast and is expanding rapidly, being continually partitioned and sub-partitioned into different sub-specialties and types of machine learning. Association rule-learning is a machine learning technique that can be used to analyze purchasing habits at the supermarket or on e-commerce sites. It works by searching for relationships between variables and finding common associations in transactions (products that consumers usually buy together). This data is then used for product placement strategies and similar product recommendations. For example, facial recognition technology is being used as a form of identification, from unlocking phones to making payments. For example, UberEats uses machine learning to estimate optimum times for drivers to pick up food orders, while Spotify leverages machine learning to offer personalized content and personalized marketing.

<a href="https://www.metadialog.com/blog/how-does-ml-work/">
<figure><img 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' alt='how machine learning works' class='aligncenter' style='display:block;margin-left:auto;margin-right:auto;' width='404px'/></figure>
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It also enables insurers to respond faster to a changing insurance market, which provides a critical edge against competitors that are still relying on outdated techniques like regression modeling in Excel. The result is an improved customer experience that translates into higher sales volume and happier shareholders. In the past, the industry relied on outdated modeling techniques that often led to under- or over-pricing claims. AI has been shown to be highly accurate when it comes to predicting future claims costs.

It is of the utmost importance to collect reliable data so that your machine learning model can find the correct patterns. The quality of the data that you feed to the machine will determine how accurate your model is. If you have incorrect or outdated data, you will have wrong outcomes or predictions which are not relevant. It takes the positive aspect from each of the learnings i.e. it uses a smaller labeled data set to guide classification and performs unsupervised feature extraction from a larger, unlabeled data set. The machine learning model aims to compare the predictions made by itself to the ground truth. The goal is to know whether it is learning in the right direction or not.

Best of all, retailers don’t need any data scientists or AI specialists to deploy predictive models – no-code AI automatically powers recommendations with no coding required. Unfortunately, even if you have a good understanding of your customers’ behaviors and preferences, it is not easy to predict which rewards will incentivize them most effectively. While your neighborhood coffee shop might offer a free coffee for every fifth visit, the scale and complexity of loyalty programs are orders of magnitude greater for large, data-driven firms. Today’s lead scoring is powered by machine learning that leverages any historical data, whether from Salesforce, Snowflake, Google Sheets, or any other source, to predict the likelihood a given lead will convert. Machine learning can help in reducing readmission risk via predictive analytics models that identify at-risk patients. By feeding in historical hospital discharge data, demographics, diagnosis codes, and other factors, medical professionals can calculate the probability that the patient will have a readmission.

Almost any task that can be completed with a data-defined pattern or set of rules can be automated with machine learning. This allows companies to transform processes that were previously only possible for humans to perform—think responding to customer service calls, bookkeeping, and reviewing resumes. Deep learning and neural networks are credited with accelerating progress in areas such as computer vision, natural language processing, and speech recognition. Typical results from machine learning applications usually include web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these are the by-products of using machine learning to analyze massive volumes of data.

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width="305px" alt="how machine learning works"/>
“John” and “pizza” are symbols, while “eat” is the relationship between these two objects/symbols. Another goal of AI researchers today is to make AI behave more like humans. This is particularly challenging, as behavior is thought of as the joint product of predisposition and environment, which are entirely different concepts between people and machines.
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</item>
<item>
<title>5 Best Shopping Bots Examples and How to Use Them</title>
<link>https://newzmarker.com/5-best-shopping-bots-examples-and-how-to-use-them.html</link>
<dc:creator><![CDATA[]]></dc:creator>
<pubDate>Thu, 01 Feb 2024 11:22:57 +0000</pubDate>
<category><![CDATA[Artificial intelligence]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19886</guid>
<description><![CDATA[Best 25 Shopping Bots for eCommerce Online Purchase Solutions As an online vendor, you want your customers to go through the checkout process as effortlessly and swiftly as possible. Fortunately, a shopping bot significantly shortens the checkout process, allowing your customers to find the products...]]></description>
<content:encoded><![CDATA[<h1>Best 25 Shopping Bots for eCommerce Online Purchase Solutions</h1>

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" width="309px" alt="bot to purchase items online"/>
As an online vendor, you want your customers to go through the checkout process as effortlessly and swiftly as possible. Fortunately, a shopping bot significantly shortens the checkout process, allowing your customers to find the products they need with the click of a button. Many customers hate wasting their time going through long lists of irrelevant products in search of a specific product. Augmented Reality (AR) chatbots are set to redefine the online shopping experience. Imagine being able to virtually “try on” a pair of shoes or visualize how a piece of furniture would look in your living room before making a purchase. With shopping bots, customers can make purchases with minimal time and effort, enhancing the overall shopping experience.

<ul>
<li>By eliminating any doubt in the choice of product the customer would want, you can enhance the customer’s confidence in your buying experience.</li>
<li>For merchants, Operator highlights the difficulties of global online shopping.</li>
<li>You can’t base your shopping bot on a cookie cutter model and need to customize it according to customer need.</li>
<li>AI assistants can automate the purchase of repetitive and high-frequency items.</li>
<li>Thus far, we have discussed the benefits to the users of these shopping apps.</li>
<li>You will find a product list that fits your set criteria on the new page.</li>
</ul>
The dashboard leverages user information, conversation history, and events and uses AI-driven intent insights to provide analytics that makes a difference. Cart abandonment is a significant issue for e-commerce businesses, with lengthy processes making customers quit before completing the purchase. Shopping bots can cut down on cumbersome forms and handle checkout more efficiently by chatting with the shopper and providing them options to buy quicker. If you have ever been to a supermarket, you will know that there are too many options out there for any product or service.

This means fewer steps to complete a purchase, reducing the chances of cart abandonment. They can also scout for the best shipping options, ensuring timely and cost-effective delivery. Furthermore, with the rise of conversational commerce, many of the best shopping bots in 2023 are now equipped with chatbot functionalities. This allows users to interact with them in real-time, asking questions, seeking advice, or even getting styling tips for fashion products. A shopping bot is a software program that can automatically search for products online, compare prices from different retailers, and even place orders on your behalf. Shopping bots can be used to find the best deals on products, save time and effort, and discover new products that you might not have found otherwise.

<h2>Best Shopping Bots For Online Shoppers</h2>

Moreover, shopping bots can improve the efficiency of customer service operations by handling simple, routine tasks such as answering frequently asked questions. This frees up human customer service representatives to handle more complex issues and provides a better overall customer experience. Sephora’s shopping bot app is the closest thing to the real shopping assistant one can get nowadays.

<ul>
<li>Certainly empowers businesses to leverage the power of conversational AI solutions to convert more of their traffic into customers.</li>
<li>The first step in creating a shopping bot is choosing a platform to build it on.</li>
<li>One way that shopping bots are helping customers is by providing a faster and more convenient way to shop online.</li>
<li>Because you need to match the shopping bot to your business as smoothly as possible.</li>
<li>They ensure an effortless experience across many channels and throughout the whole process.</li>
<li>Plus, about 88% of shoppers expect brands to offer a self-service portal for their convenience.</li>
</ul>
Look for a bot developer who has extensive experience in RPA (Robotic Process Automation). Make sure they have relevant certifications, especially regarding RPA and UiPath. You can foun additiona information about <a href="https://www.cravingtech.com/ai-customer-service-all-you-need-to-know.html">ai customer service</a> and artificial intelligence and NLP. Be sure and find someone who has a few years of experience in this area as the development stage is the most critical. It’s not merely about sending texts; it’s about crafting experiences. And with A/B testing, you’re always in the know about what resonates.

Outside of a general on-site bot assistant, businesses aren’t using them to their full potential. Troubleshoot your sales funnel to see where your bottlenecks lie and whether a shopping bot will help remedy it. Their shopping bot has put me off using the business, and others will feel the same. This is important because the future of e-commerce is on social media. Online stores can be uninteresting for shoppers, with endless promotional materials for every product. However, you can help them cut through the chase and enjoy the feeling of interacting with a brick-and-mortar sales rep.

<h2>Benefits for Online and In-store Merchants</h2>

In these scenarios, getting customers into organic nurture flows is enough for retailers to accept minor losses on products. Fairness is one of the most important predictors of loyalty to ecommerce brands. This means if you’re not the sole retailer selling a certain item, shoppers will move to retailers where they feel valued. Online shopping bots let bot operators hog massive amounts of product with no inconvenience—they just sit at their computer screen and let the grinch bots do their dirty work.

Users can set up notifications for when a particular item goes on sale or when a new product is launched. Additionally, these bots can be integrated with user accounts, allowing them to store preferences, sizes, and even payment details securely. This results in a faster checkout process, as the bot can auto-fill necessary details, reducing the hassle of manual data entry. As AI and machine learning technologies continue to evolve, shopping bots are becoming even more adept at understanding the nuances of user behavior. In the vast realm of e-commerce, even minor inconveniences can deter potential customers. The modern consumer expects a seamless, fast, and intuitive shopping experience.

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" width="302px" alt="bot to purchase items online"/>
In this scenario, the multi-layered approach removes 93.75% of bots, even with solutions that only manage to block 50% of bots each. The key to preventing bad bots is that the more layers of protection used, the less bots can slip through the cracks. When a true customer is buying a PlayStation from a reseller in a parking lot instead of your business, you miss out on so much. From harming loyalty to damaging reputation to skewing analytics and spiking ad spend—when you’re selling to bots, a sale’s not just a sale. The releases of the PlayStation 5 and Xbox Series X were bound to drive massive hype. It had been several years since either Sony or Microsoft had released a gaming console, and the products launched at a time when more people than ever were video gaming.

Well, those days are long gone, thanks to the evolution of shopping bots. The bot continues to learn each customer’s preferences by combining data from subsequent chats, onsite shopping habits, and H&M’s app. Whether you are a seasoned online shopper or a newbie, a shopping bot can be a valuable tool to help you find the best deals and save money. That’s where you’re in full control over the triggers, conditions, and actions of the chatbot. It’s a bit more complicated as you’re starting with an empty screen, but the interface is user-friendly and easy to understand.

Beyond just chat, it’s a tool that revolutionizes customer service, offering lightning-fast responses and elevating user experiences. And with its myriad integrations, streamlining operations is a cinch. The digital age has brought convenience to our fingertips, but it’s not without its complexities. From signing up for accounts, navigating through cluttered product pages, to dealing with pop-up ads, the online shopping journey can sometimes feel like navigating a maze.

Unlike human agents who get frustrated handling the same repeated queries, chatbots can handle them well. Further, there are many reasons to use an online ordering and shopping bot. Let’s discuss some of the reasons why you should use an online ordering and shopping bot for your business. By managing your traffic, you’ll get full visibility with server-side analytics that helps you detect and act on suspicious traffic.

If you’re looking to increase sales, offer 24/7 support, etc., you’ll find a selection of 20 tools. The usefulness of an online purchase bot depends on the user’s needs and goals. Some buying bots automate the checkout process and help users secure exclusive deals or limited products.

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<h3>Shopping bots help buyers snatch in-demand items before they sell out – Boing Boing</h3>
Shopping bots help buyers snatch in-demand items before they sell out.
Posted: Mon, 22 Nov 2021 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMibGh0dHBzOi8vYm9pbmdib2luZy5uZXQvMjAyMS8xMS8yMi9zaG9wcGluZy1ib3RzLWhlbHAtYnV5ZXJzLXNuYXRjaC1pbi1kZW1hbmQtaXRlbXMtYmVmb3JlLXRoZXktc2VsbC1vdXQuaHRtbNIBcGh0dHBzOi8vYm9pbmdib2luZy5uZXQvMjAyMS8xMS8yMi9zaG9wcGluZy1ib3RzLWhlbHAtYnV5ZXJzLXNuYXRjaC1pbi1kZW1hbmQtaXRlbXMtYmVmb3JlLXRoZXktc2VsbC1vdXQuaHRtbC9hbXA?oc=5' rel="nofollow">source</a>]
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On the front-end they give away minimal value to the customer hoping on the back-end that this shopping bot will get them to order more frequently. A shopper tells the bot what kind of product they’re looking for, and NexC quickly uses AI to scan the internet and find matches for the person’s request. Then, the bot narrows down all the matches to the top three best picks. They’ll send those three choices to the customer along with pros and cons, ratings and reviews, and corresponding articles. Alternatively, the chatbot has preprogrammed questions for users to decide what they want. This bot is the right choice if you need a shopping bot to assist customers with tickets and trips.

<h2>What are shopping bots?</h2>

You can either do a text-based search or upload pictures of the apparel you like. However, the AI doesn’t ask further questions, unlike other tools, so you’ll have to follow up yourself. Compared to other tools, this AI showed results the fastest both in the chat and shop panel. The only issue I noticed is that it starts showing irrelevant results <a href="https://www.metadialog.com/blog/online-shopping-bots/">bot to purchase items online</a> when you try to be too specific, and sometimes it shows 1 or 2 unrelated results alongside other results. There are several e-commerce platforms that offer bot integration, such as Shopify, WooCommerce, and Magento. These platforms typically provide APIs (Application Programming Interfaces) that allow you to connect your bot to their system.

Generally, customers don’t want to spend time scrolling through irrelevant products. But the shopping bot offers customized recommendations, which helps customers get the product they are searching for. Online ordering and shopping bots make the shopping experience more personalized and offer suggestions for purchases. When you hear “online shopping bot”, you’ll probably think of a scraping bot like the one just mentioned, or a scalper bot that buys sought-after products.

It is easy to install and use, and it provides a variety of features that can help you to improve your store’s performance. Manifest AI is a GPT-powered AI shopping bot that helps Shopify store owners increase sales and reduce customer support tickets. It can be installed on any Shopify store in 30 seconds and provides 24/7 live support. Shopping bots are virtual assistants on a company’s website that help shoppers during their buyer’s journey and checkout process.

Of course, this cuts down on the time taken to find the correct item. With fewer frustrations and a streamlined purchase journey, your store can make more sales. The more advanced option will be coded to provide an extensive list of language options for users. This helps users to communicate with the bot’s online ordering system with ease.

Depending on your country’s legal system, shopping bots may or may not be illegal. In some countries, it is illegal to build shopping bot systems such as chatbots for online shopping. Shopping carts provide shoppers with personalized options for purchase. Customer chats become eCommerce tools to find suitable products according to what they need. Moreover, they simplify customers’ billing process, reducing cart abandonment.

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" width="300px" alt="bot to purchase items online"/>
When choosing a platform, it’s important to consider factors such as your target audience, the features you need, and your budget. Keep in mind that some platforms, such as Facebook Messenger, require you to have a Facebook page to create a bot. Here’s an overview of how to make a buying bot that buys products online automatically. In the TechFirst podcast clip below, Queue-it Co-founder Niels Henrik Sodemann explains to John Koetsier how retailers prevent bots, and how bot developers take advantage of P.O.

<h2>How to Make Your Shopify Website More Mobile-Friendly</h2>

First, you miss a chance to create a connection with a valuable customer. Hyped product launches can be a fantastic way to reward loyal customers and bring new customers into the fold. Shopping bots sever the relationship between your potential customers and your brand. In the frustrated customer’s eyes, the fault lies with you as the retailer, not the grinch bot.

Headquartered in San Francisco, Intercom is an enterprise that specializes in business messaging solutions. In 2017, Intercom introduced their Operator bot, ” a bot built with manners.” Intercom designed their Operator bot to be smarter by making the bot helpful, restrained, and tactful. The end result has the bot understanding the user requirement better and communicating to the user in a helpful and pleasant way. The Kompose bot builder lets you get your bot up and running in under 5 minutes without any code. Bots built with Kompose are driven by AI and Natural Language Processing with an intuitive interface that makes the whole process simple and effective.

And with its multi-channel integration, customer retention is a breeze. With its advanced NLP capabilities, it’s not just about automating conversations; it’s about making them personal and context-aware. Think of purchasing movie tickets or recharging your mobile – Yellow.ai has got you covered.

<a href="https://www.metadialog.com/blog/online-shopping-bots/">
<figure><img 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' alt='bot to purchase items online' class='aligncenter' style='display:block;margin-left:auto;margin-right:auto;' width='403px'/></figure>
</a>
Furthermore, shopping bots can integrate real-time shipping calculations, ensuring that customers are aware of all costs upfront. In-store merchants, on the other hand, can leverage shopping bots in their digital platforms to drive foot traffic to their physical locations. Moreover, with the integration of AI, these bots can preemptively address common queries, reducing the need for customers to reach out to customer service. This not only speeds up the shopping process but also enhances customer satisfaction.

<h2>How to use a bot to buy online</h2>

In fact, a recent survey showed that 75% of customers prefer to receive SMS messages from brands, highlighting the need for conversations rather than promotional messages. Unfortunately, shopping bots aren’t a “set it and forget it” kind of job. They need monitoring and continuous adjustments to work at their full potential. Most of the chatbot software providers offer templates to get you started quickly. All you need to do is pick one and personalize it to your company by changing the details of the messages. This is more of a grocery shopping assistant that works on WhatsApp.

<img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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" width="300px" alt="bot to purchase items online"/>
Sometimes even basic information like browser version can be enough to identify suspicious traffic. Bots will even take a website offline on purpose, just to create chaos so they can slip through undetected when the website comes back online. To get a sense of scale, consider data from Akamai that found one botnet sent more than 473 million requests to visit a website during a single sneaker release. Bots can skew your data on several fronts, clouding up the reporting you need to make informed business decisions.

No two customers are the same, and Whole Foods have presented four options that they feel best meet everyone’s needs. Thanks to messaging apps, humans are becoming used to text chat as their main form of communication. If you don’t accept PayPal as a payment option, they will buy the product elsewhere. If you don’t offer next day delivery, they will buy the product elsewhere. As we move towards a more digitalized world, embracing these bots will be crucial for both consumers and merchants. But, if you’re leaning towards a more intuitive, no-code experience, ShoppingBotAI, with its stellar support team, might just be the ace up your sleeve.

The bot lets you create customized reports, goals, and challenges within Slack using your CRM data. I don’t know about your sales team, but at HubSpot, it’s always a celebration when the customer sends the signed contract. Most reps try to avoid counting a deal as “won” before this moment — they’ve been burned too many times. Online ordering bots will require extensive user testing on a variety of devices, platforms, and conditions, to determine if there are any bugs in the application. You can focus on strategizing and executing your next marketing campaign by delegating certain tasks to automated bots.

<div style='border: grey dotted 1px;padding: 10px;'>
<h3>Officials once again try to ban bots from buying up online goods – Mashable</h3>
Officials once again try to ban bots from buying up online goods.
Posted: Tue, 30 Nov 2021 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiRmh0dHBzOi8vbWFzaGFibGUuY29tL2FydGljbGUvc3RvcHBpbmctZ3JpbmNoLWJvdHMtYWN0LW9ubGluZS1wdXJjaGFzZXPSAQA?oc=5' rel="nofollow">source</a>]
</div>
The few seconds it takes to set it up will allow Faqbot to help your customers while you get some rest. Here’s a list of bot software you can use to automate parts of the marketing process, so you can spend less time on repetitive tasks and more time running your business. However, there are certain regulations and guidelines that must be followed to ensure that bots are not used for fraudulent purposes. When integrating your bot with an e-commerce platform, make sure you test it thoroughly to ensure that everything is working correctly. This includes testing the product search function, adding products to cart, and processing payments. Once you’ve designed your bot’s conversational flow, it’s time to integrate it with e-commerce platforms.

Denial of inventory bots are especially harmful to online business’s sales because they could prevent retailers from selling all their inventory. By holding products in the carts they deny other shoppers the chance to buy them. What often happens is that discouraged shoppers turn to resale sites and fork over double or triple the sale price to get what they couldn’t from the original seller. Operator lets its users go through product listings and buy in a way that’s easy to digest for the user.

In today’s fast-paced world, consumers value efficiency more than ever. The longer it takes to find a product, navigate a website, or complete a purchase, the higher the chances of losing a potential sale. This not only speeds up the transaction but also minimizes the chances of customers getting frustrated and leaving the site. In the vast ocean of e-commerce, finding the right product can be daunting. They can pick up on patterns and trends, like a sudden interest in sustainable products or a shift towards a particular fashion style. This allows them to curate product suggestions that resonate with the individual’s tastes, ensuring that every recommendation feels handpicked.

BargainBot seeks to replace the old boring way of offering discounts by allowing customers to haggle the price. The bot can strike deals with customers before allowing them to proceed to checkout. It also comes with exit intent detection to reduce page abandonments.

They may use search engines, product directories, or even social media to find products that match the user’s search criteria. Once they have found a few products that match the user’s criteria, they will compare the prices from different retailers to find the best deal. And what’s more, you don’t need to know programming to create one for your business. All you need to do is get a platform that suits your needs and use the visual builders to set up the automation.

Rather than providing a ready-built bot, customers can build their conversational assistants with easy-to-use templates. You can create bots that provide checkout help, handle return requests, offer 24/7 support, or direct users to the right products. Actionbot acts as an advanced digital assistant that offers operational and sales support.

<img decoding="async" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' src="https://www.metadialog.com/wp-content/uploads/2023/07/the-result-of-integration-2-1.webp" width="306px" alt="bot to purchase items online"/>
These will quickly show you if there are any issues, updates, or hiccups that need to be handled in a timely manner. The money-saving potential and ability to boost customer satisfaction is drawing many businesses to AI bots. Payment processing providers who provide secure payment processing services.
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<title>7 AI-Based Chatbots for Your SaaS Support by Shivanshu Gupta MEVE The Publication Jan, 2024</title>
<link>https://newzmarker.com/7-ai-based-chatbots-for-your-saas-support-by.html</link>
<dc:creator><![CDATA[]]></dc:creator>
<pubDate>Thu, 11 Jan 2024 09:10:33 +0000</pubDate>
<category><![CDATA[Artificial intelligence]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19896</guid>
<description><![CDATA[9 Best SaaS Customer Service Chatbot Software Platforms In 2022 Smartloop is one of chatbot software companies with a product for building lead generation and sales chatbots in Facebook Messenger that also connects with their live chat tool. AlphaChat is a chatbot software platform allowing...]]></description>
<content:encoded><![CDATA[<h1>9 Best SaaS Customer Service Chatbot Software Platforms In 2022</h1>
<img decoding="async" class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' src="https://www.metadialog.com/wp-content/uploads/2022/06/power-of-chatbots-2.webp" width="305px" alt="saas chatbot"/>
Smartloop is one of chatbot software companies with a product for building lead generation and sales chatbots in Facebook Messenger that also connects with their live chat tool. AlphaChat is a chatbot software platform allowing anyone to build smart AI bots for automating their SaaS customer service. Aside from Natural Language Understanding, the bots are capable of authenticating users with deep automations.
Intercom provides custom chatbots for sales, marketing, and support to customers in your business. It also offers live chat options along with integration options for eCommerce and social media platforms. Bots’ efficiency depends on the reliability of the systems that run them. Chatbots developed on top of the AI’s platform benefit from the AI’s ability to gather, analyze, and learn from data in other systems.
Such tools serve their purpose, but the challenge is ensuring they work cohesively to guide users. This demonstrates the need for a learning curve every time there’s an update. But, it can become a deterrent for some, leading to reduced platform engagement or even abandonment. SaaS businesses continuously go through updates, feature additions, and interface changes. Every modification challenges the onboarding process for new and returning users.
<h2>How do I train a SaaS AI chatbot for my specific needs?</h2>
DataRobot provides an AI platform that automates the data science process from data to actionable insights. It offers features like machine learning, model monitoring, and decision intelligence, catering to industries like banking, healthcare, and marketing. A SaaS business and the whole SaaS industry offer tools that leverage AI to offer services like data analytics, customer service automation, marketing automation, and more.
Here, we see chatbots being used to engage with people on social networks. Examples of this include Facebook messenger apps, messaging apps, and other platforms dedicated specifically to text communication. Generally speaking, these chatbots aren’t very sophisticated compared to other models. Their sole function is to simply deliver messages back and forth between two parties. With it, businesses can offer free trials to prospective customers and see how much interest exists among potential buyers.
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" width="302px" alt="saas chatbot"/>
Since your expertise lies in programming, you could potentially land a position writing code for a chatbot. Then again, you could also apply for roles involving design or content creation. Whatever path you choose, freelancing is definitely another viable alternative to earning money with your passion. Some universities and colleges offer courses related to AI development and machine learning.
<h2>The Revolution of AI in the SaaS Industry</h2>
For WordPress users or those considering a migration, say a Drupal migration to WordPress, these AI tools offer seamless WordPress integration. Thus, users can effortlessly add a chatbot to their WordPress site, either by adding a custom code or using a plugin. High-quality chatbot platforms collect all this data and store it in customer profiles. But with that, make sure to give the option of chatting with a customer service representative to your customers if your Chatbot cannot resolve the customer queries. It is a tool that helps you to create amazing Chatbots using drag and drop builder. It also allows applying pre-existing templates along with customization.
For instance, you can find tutorials detailing how to build a chatbot from scratch. Additionally, you can explore blogs discussing AI trends, chatbot news and best practices, or forums filled with fellow enthusiasts sharing ideas and experience. Further, with the help of AI, your chatbot can provide your agent with suggested responses, knowledge base articles, and other resources. An agent will already be registered into your support platform and have all the appropriate context when they take up a problematic assistance request from a bot discussion. With this consolidated hub, you can monitor how your bot influences agent productivity and other metrics critical to your support operations. Never let a consumer drop out because they couldn’t reach a human being when they needed help.
Most importantly, it provides seats for multiple team members to work and collaborate. Also, there are 95 language options to have your sources and ask questions. With the possibility of adding a widget to your website, Chatbase allows you to create chats through integrations and API. When we change our perspective to the benefits, we can clearly see that Fin aims for faster resolution, easy monitoring, and human agent interruption when necessary. Besides, you can check out the resources that LivePerson creates and have more knowledge about generative AI. Sign up for our newsletter to get the latest news on Capacity, AI, and automation technology.
<ul>
<li>Create customized conversation forms with multiple field types to collect information through a chatbot like a name, Email, Location, Geocode and several other.</li>
<li>A negative experience is asking your platform to do ten things it can’t do.</li>
<li>We work on delivering the best customer engagement platform at the best prices possible.</li>
<li>With continued improvements, we could eventually see bots become indistinguishable from actual human beings.</li>
<li>Companies typically employ engineers skilled in developing tools used by millions of daily active users worldwide.</li>
</ul>
Another benefit of AI is that they can adapt to changes in environment and learning new skills through exposure to training datasets. A few chatbot agencies specialize in developing intelligent agents using machine learning algorithms. Examples of AI chatbots include IBM Watson, Salesforce Einstein, and Microsoft Azure Cognitive Services.
From many AI chatbot SaaS tools, we have chosen the most useful ones for SaaS businesses. Also, there are more reasons for SaaS platforms may want to use AI chatbots. SaaS businesses give importance to consistency and timing, AI chatbots are top-tier necessities. Although many different businesses can use chatbots, SaaS businesses tend to need and use them more.
To summarize, there are multiple considerations to take into consideration when creating a chatbot. Next, you need to determine which one suits your particular business needs. Finally, you need to ensure that your chatbot has the capability required to fulfill your business objectives. Only then can you begin marketing your new tool to generate leads and increase sales. Keep reading below to discover the benefits of using a whitelabel chatbot for your business. Search engines are full of resources helping people find products and services offering AI development assistance.
Ada also offers sophisticated analytics and reporting tools to assist businesses in enhancing the functionality of their chatbots. Businesses can build unique chatbots for web chat, Facebook Messenger, and WhatsApp with BotStar, a powerful AI-based chatbot software solution. BotStar also offers sophisticated analytics and reporting tools to assist organizations in enhancing their chatbots’ success. Businesses may build unique chatbots for Facebook Messenger with Chatfuel, a well-liked AI-powered chatbot software solution. Moreover, Chatfuel offers sophisticated analytics and reporting tools to assist organizations in enhancing the functionality of their chatbots. Chatbots have become essential to customer service for software-as-a-service (SaaS) companies.
It refers to determining whether a potential customer has a need or interest in your product and can afford to buy it. In conclusion, to say that AI chatbots are revolutionizing the B2B landscape would be an understatement. Driven by superior automation and engagement prowess, they are being extensively used to drive customer satisfaction, engagement, and revenue. They incorporate a level of sophistication that enables them to understand complex demands, respond to queries, and even learn to predict customer needs from continual interactions. In summary, AI has much to offer in web development, from enhancing user experience to improving website design.
These sophisticated chatbot cloud-based tools increase customer satisfaction while decreasing organizational costs. This guide will explain what a chatbot SaaS is, its benefits, how to use it, and which AI-based chatbot software is the best on the market. AI chatbots are an essential tool for SaaS startups looking to increase their signups and maximize their website traffic. By leveraging the power of AI, startups can engage with their visitors in real time, providing them with the information they need to make a purchase decision.
<h2>Benefits of AI Chatbots in SaaS to Elevate Business Success</h2>
Ada.cx is another AI-powered chatbot support system but here you will get more than just a chatbot, you can also automate the policies and documentation of your SaaS form here. “The tool is the best. It allows to create bots for Facebook, Web and WhatsApp.” Deliver personalized, omnichannel experiences at scale on WhatsApp, web, Facebook Messenger, or connect through API. Engage visitors with ChatBot’s quick responses and personalized greetings, fueled by your data.
Software as a service (SaaS) companies use chatbots to provide automated customer service to their customer base. Offering instantaneous answers through customer support chatbots on their website, SaaS companies increase self-service rates and require less human support in their operations. A better alternative is finding a company that specializes in creating white label chatbots based off another platform. These providers usually provide turnkey services that include everything needed to launch your chatbot including training sessions, ongoing support, branding elements, and much more.
Giving employees work that is both hard and personally fulfilling may boost engagement and retention. By analyzing market trends, user behavior, and other relevant factors, AI algorithms can adjust pricing dynamically to maximize revenue and stay competitive. This ensures that the pricing structure remains optimal and aligned with market conditions.
This will allow them to expand their operations with minimal disruption to their clientele, freeing up resources to invest in building long-term partnerships. AI can analyze customer data and behavior to determine the probability of a lead converting into a client, hence providing a score to that lead. This scoring comes in handy for the sales team, as it helps prioritize leads and focus efforts on those with the highest conversion likelihood. In summary, it’s clear how AI helps create a more compelling, personalized, and satisfying experience for customers. In the next part of this series, we will delve into how AI is boosting sales and marketing and shaping efficient management of resources. AI is making team coordination more efficient, assisting projects to be completed on time and according to plan.
Well, because it was built specifically to handle group chats and voice messages. And since it’s based on Signal Protocol, it doesn’t collect information about its users. WhatsApp is great for small businesses, startups, and individuals alike. It offers excellent integration capabilities along with numerous tools designed especially for marketers. On the plus side, it gives you tremendous control over your career, allowing you to shape it according to your preferences.
So the question for you is this- Are you doing enough in utilizing the full potential of AI conversational Platform for your business? AI conversational platform is highly beneficial for the SaaS firms as it will help them proactively engage with the visitors around the clock. It can precisely target the segments you need with the right offer at the right time and place.
<a href="https://www.metadialog.com/saas/">
<figure><img src='https://www.metadialog.com/wp-content/uploads/2022/06/power-of-chatbots-2.webp' alt='saas chatbot' class='aligncenter' style='display:block;margin-left:auto;margin-right:auto;' width='406px'/></figure>
</a>
As interfaces become exponentially more simplified how do you differentiate your company. The answer to this question must be found in changing our preconceptions of what a user experience is and the subconscious ego that goes along with that. You can foun additiona information about <a href="https://www.cravingtech.com/ai-customer-service-all-you-need-to-know.html">ai customer service</a> and artificial intelligence and NLP. One of the things I used to hate in my previous companies was this need to “educate our clients”.
<h2>Service/Guide</h2>
SaaS companies deal with huge volumes of sensitive customer data, making them prime targets for cyber threats and data breaches. With AI’s cognitive anomaly detection capabilities, these threats can be identified and dealt with proactively rather than reactively. In the Train Milly section, you can test the unanswered or “improvement needed” questions and update the knowledge base until you are satisfied with the responses. Ask for a Gleen AI demonstration or build a generative AI chatbot at no cost with Gleen AI. Any generative AI SaaS chatbot system will have a back-end interaction with an LLM. This guarantees a smooth customer experience while leveraging your existing IT investments.
Here in this blog, we are listing down the top ten Chatbots tools that will boom in 2021. With that, know the requirements and objectives that you want to accomplish using these AI-powered chatbots. Few factors that should be considered on selecting chatbots are response time, function and functionality, etc. By considering such concerns, businesses in different sectors, including lifestyles, healthcare, and eCommerce, use AI’s innovative technology, which we call Chatbots. Creating an Alexa skill provides customers with a voice-activated interface for asking questions, making purchases, and interacting with your brand’s content.
The initial impression a platform leaves on its users can set the tone for lifelong customer relationships. Onboarding isn’t just a first step; it’s a crucial touchpoint that can make or break how users stick with the software. Adobe’s tools are a go-to for designers and photographers seeking enhanced productivity. Their generative AI tool Firefly allows users to recolor, extend images, alter images with text inputs, and more.
<h2>Now, all you need is a smart Chatbot!</h2>
One solution is to simply hire more agents and train them to assist your customers, but there is a better way.
They can recognize and respond to certain words or phrases because they have been programmed with rules and reactions. Developing a unique chatbot flow is similar to conducting split testing. After that, it compares the user’s intent to a set of rules and potential replies to provide the most appropriate <a href="https://www.metadialog.com/saas/">saas chatbot</a> action. The chatbot enhances its linguistic abilities using a combination of machine learning and interaction with human users. It also collects information from other sources to better serve its users. However, the potential of AI chatbots extends far beyond just increasing signups.
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" width="303px" alt="saas chatbot"/>
In contrast, basic chatbot technology advances the discussion via bot-prompted keywords or UX components like Facebook Messenger’s recommended answers. The superior intelligence of today’s chatbots results from their use of cutting-edge artificial intelligence technologies. Imagine your team focusing on hot leads and new hypotheses generation when it answers FAQs, schedules demos, offers trials, shares lead magnets, etc. The rise in customer satisfaction, sales, lead conversions, and instant communication are just some of the chatbot SaaS benefits.
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<h3>SaaS platform Dukaan hires AI chatbot, fires 90% of support staff – DTNEXT</h3>
SaaS platform Dukaan hires AI chatbot, fires 90% of support staff.
Posted: Tue, 11 Jul 2023 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiamh0dHBzOi8vd3d3LmR0bmV4dC5pbi9uZXdzL2J1c2luZXNzL3NhYXMtcGxhdGZvcm0tZHVrYWFuLWhpcmVzLWFpLWNoYXRib3QtZmlyZXMtOTAtb2Ytc3VwcG9ydC1zdGFmZi03MjM2MzDSAW5odHRwczovL3d3dy5kdG5leHQuaW4vYW1wL25ld3MvYnVzaW5lc3Mvc2Fhcy1wbGF0Zm9ybS1kdWthYW4taGlyZXMtYWktY2hhdGJvdC1maXJlcy05MC1vZi1zdXBwb3J0LXN0YWZmLTcyMzYzMA?oc=5' rel="nofollow">source</a>]
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Customers connect with brands via email, live chat, social media, and messengers. This factor plays a vital role in increasing conversion rates and fostering robust customer engagement. It is preferred and used by all kinds of businesses, but the list of its clients also includes big brands of the world like Adidas, LEGO, etc. Like its name, Chatbots are the bots that work as representatives in your absence to deal with your clients or potential customers. Chatbots are capable of having human-like conversations from initial to final discussion with the prospect. If you go out of your way to help a customer, they will be more likely to support what you’re selling.
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" width="304px" alt="saas chatbot"/>
Implementing chatbots is much cheaper than hiring and training human resources. A human can attend to only one or two customers at a time, but a chatbot can engage with thousands of customers simultaneously. Chatbots don’t just talk with your customers, they also let you analyze conversations and gather valuable insights.
An effective AMB strategy for SaaS provides the client with helpful information at each stage of the buying process. If you’re trying to broaden your horizons and try something new, a chatbot built on ABM principles is a fantastic choice. The combination of AI in SaaS solutions will continue to enhance business efficiencies, drive customer satisfaction, and boost sales and revenue. It’s an exciting time for innovators, developers, and businesses ready to leap into this burgeoning field and seize the opportunities that AI-powered SaaS solutions promise.
AI-powered chatbots like Ideta offer immediate customer response irrespective of the time of day. Or needs assistance with data import on the weekend, chatbots can be at their rescue. Their Artificial Design Intelligence (ADI) tool allows users to craft personalized websites based on their preferences. It offers customer content suggestions and simplifies the website creation process. Companies, rendering on-demand services, like cab service, are increasingly relying on bot-type apps.
Chatbots can augment the onboarding process by suggesting features for them to try or recommend self-service content that might be useful. Without a chatbot, the typical customer behavior when encountering a problem is to search for an answer online before turning to your support representative. This interaction requires customers to wait for a representative to become available, whereas a chatbot has been configured to provide instant answers. SendPulse provides a free option that lets you test out their chatbot creation tool. Once you finish configuring your chatbot settings, you can begin interacting with it right away.
The best part about these programs is that they’re able to do this at scale while requiring little maintenance from business owners. So if you want to get ahead of your competitors and start boosting sales today, then read our guide below. A chatbot for SaaS (Software as a Service) is an AI-powered virtual assistant designed to streamline interactions between SaaS providers and their customers. Verloop is a conversational sales and marketing platform plus automated customer support and engagement platform. With SaaS firms who want to set-up the Chatbot for getting qualified leads, Verloop is a good option with an easy to use drag and drop interface. You can train your bot, answer FAQs, generate leads and connect them to sales representatives.
This frees support agents to focus on more critical, revenue-driving initiatives while the chatbot handles tier 0 and 1 inquiries. An AI chatbot support platform like Capacity can help automate time-consuming tasks that take too much time for your team. Typically, these are automated versions of existing chatbots built to mimic conversations occurring on popular social networking sites. Because they were originally created to interact with humans, they typically feature rudimentary artificial intelligence capabilities and rely heavily on keywords inputted by the user. However, this approach has proven successful enough to warrant further development.
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<item>
<title>What is Natural Language Processing?</title>
<link>https://newzmarker.com/what-is-natural-language-processing.html</link>
<dc:creator><![CDATA[]]></dc:creator>
<pubDate>Wed, 10 Jan 2024 07:01:54 +0000</pubDate>
<category><![CDATA[Artificial intelligence]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19894</guid>
<description><![CDATA[Natural Language Processing With Python’s NLTK Package IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP. If you’re analyzing a single text, this can help you see which words show up near each...]]></description>
<content:encoded><![CDATA[<h1>Natural Language Processing With Python’s NLTK Package</h1>

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" width="300px" alt="natural language programming examples"/>
IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP.

If you’re analyzing a single text, this can help you see which words show up near each other. If you’re analyzing a corpus of texts that is organized chronologically, it can help you see which words were being used more or less over a period of time. If you’d like to learn how to get other texts to analyze, then you can check out Chapter 3 of Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit. Chunking makes use of POS tags to group words and apply chunk tags to those groups. Chunks don’t overlap, so one instance of a word can be in only one chunk at a time. For example, if you were to look up the word “blending” in a dictionary, then you’d need to look at the entry for “blend,” but you would find “blending” listed in that entry.

Instead of needing to use specific predefined language, a user could interact with a voice assistant like Siri on their phone using their regular diction, and their voice assistant will still be able to understand them. Natural Language Processing has created the foundations for improving the functionalities of chatbots. One of the popular examples of such chatbots is the Stitch Fix bot, which offers personalized fashion advice according to the style preferences of the user. The rise of human civilization can be attributed to different aspects, including knowledge and innovation.

What comes naturally to humans is challenging for computers in terms of unstructured data, absence of real-word intent, or maybe lack of formal rules. Social listening powered by AI tasks like NLP enables you to analyze thousands of social conversations in seconds to get the business intelligence you need. It gives you tangible, data-driven insights to build a brand strategy that outsmarts competitors, forges a stronger brand identity and builds meaningful audience connections to grow and flourish. Read on to get a better understanding of how NLP works behind the scenes to surface actionable brand insights. Plus, see examples of how brands use NLP to optimize their social data to improve audience engagement and customer experience.

We’ll be there to answer your questions about generative AI strategies, building a trusted data foundation, and driving ROI. Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs. Today, NLP has invaded nearly every consumer-facing product from fashion advice bots (like the Stitch Fix bot) to AI-powered landing page bots. With Stitch Fix, for instance, people can get personalized fashion advice tailored to their individual style preferences by conversing with a chatbot. Now that we’ve explored the basics of NLP, let’s look at some of the most popular applications of this technology. Business lawyer focusing on start-ups, technology and sustainability.

<a href="https://www.metadialog.com/blog/examples-of-nlp/">
<figure><img 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' alt='natural language programming examples' class='aligncenter' style='display:block;margin-left:auto;margin-right:auto;' width='405px'/></figure>
</a>
Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity. If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page. To better understand the applications of this technology for businesses, let’s look at an NLP example. You can foun additiona information about <a href="https://www.cravingtech.com/ai-customer-service-all-you-need-to-know.html">ai customer service</a> and artificial intelligence and NLP. For example, if you’re on an eCommerce website and search for a specific product description, the semantic search engine will understand your intent and show you other products that you might be looking for.

<h2>examples of Natural Language Processing you use every day without noticing</h2>

OpenAI’s GPT-2 is an impressive language model showcasing autonomous learning skills. It can generate coherent paragraphs and achieve promising results in various tasks, making it a highly competitive model. ChatGPT-3 is a transformer-based NLP model renowned for its diverse capabilities, including translations, question answering, and more. With recent advancements, it excels at writing news articles and generating code. What sets ChatGPT-3 apart is its ability to perform downstream tasks without needing fine-tuning, effectively managing statistical dependencies between different words. The model’s remarkable performance is attributed to its extensive training on over 175 billion parameters, drawing from a colossal 45 TB text corpus sourced from various internet sources.

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" width="307px" alt="natural language programming examples"/>
Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables machines to understand human language. The main intention of NLP is to build systems that are able to make sense of text and then automatically execute tasks like spell-check, text translation, topic classification, etc. Companies today use NLP in artificial intelligence to gain insights from data and automate routine tasks. NLP has evolved since the 1950s, when language was parsed through hard-coded rules and reliance on a subset of language. The 1990s introduced statistical methods for NLP that enabled computers to be trained on the data (to learn the structure of language) rather than be told the structure through rules.

<h2>Examples of Natural Language Processing in Action</h2>

Search engines leverage NLP to suggest relevant results based on previous search history behavior and user intent. In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. One of the algorithms it implements is called Semi-structured Statement Extraction. We can use it to search the parse tree for simple statements where the subject is “London” and the verb is a form of “be”.

Conversation analytics provides business insights that lead to better patient outcomes for the professionals in the healthcare industry. Use customer insights to power product-market fit and drive loyalty. Improve quality and safety, identify competitive threats, and evaluate innovation opportunities. Likewise, NLP is useful for the same reasons as when a person interacts with a generative AI chatbot or AI voice assistant.

For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurately translate text. NLP customer service implementations are being valued more and more by organizations.

<h2>Data Structures and Algorithms</h2>

Being able to create a shorter summary of longer text can be extremely useful given the time we have available and the massive amount of data we deal with daily. RoBERTa, short for the Robustly Optimized BERT pre-training approach, represents an optimized method for pre-training self-supervised NLP systems. Built on BERT’s language masking strategy, RoBERTa learns and predicts intentionally hidden text sections. As a pre-trained model, RoBERTa excels in all tasks evaluated by the General Language Understanding Evaluation (GLUE) benchmark.

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<h3>Top 10 companies advancing natural language processing – Technology Magazine</h3>
Top 10 companies advancing natural language processing.
Posted: Wed, 28 Jun 2023 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiW2h0dHBzOi8vdGVjaG5vbG9neW1hZ2F6aW5lLmNvbS90b3AxMC90b3AtMTAtY29tcGFuaWVzLWFkdmFuY2luZy1uYXR1cmFsLWxhbmd1YWdlLXByb2Nlc3NpbmfSAQA?oc=5' rel="nofollow">source</a>]
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That’s why grammar and spell checkers are a very important tool for any professional writer. They can not only correct grammar and check spellings but also suggest better synonyms and improve the overall readability of your content. And guess what, they utilize natural language processing to provide the best possible piece of writing! The NLP algorithm is trained on millions of sentences to understand the correct format. That is why it can suggest the correct verb tense, a better synonym, or a clearer sentence structure than what you have written.

You would think that writing a spellchecker is as simple as assembling a list of all allowed words in a language, but the problem is far more complex than that. Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used. Also, for languages with more complicated morphologies than English, spellchecking can become very computationally intensive. Natural language processing can be used for topic modelling, where a corpus of unstructured text can be converted to a set of topics.

<ul>
<li>The major downside of rules-based approaches is that they don’t scale to more complex language.</li>
<li>Transformers are able to represent the grammar of natural language in an extremely deep and sophisticated way and have improved performance of document classification, text generation and question answering systems.</li>
<li>The study of natural language processing has advanced significantly.</li>
<li>Many of the unsupported languages are languages with many speakers but non-official status, such as the many spoken varieties of Arabic.</li>
</ul>
The first chatbot was created in 1966, thereby validating the extensive history of technological evolution of chatbots. Just like any new technology, it is difficult to measure the potential of NLP for good without exploring <a href="https://www.metadialog.com/blog/examples-of-nlp/">natural language programming examples</a> its uses. Most important of all, you should check how natural language processing comes into play in the everyday lives of people. Here are some of the top examples of using natural language processing in our everyday lives.

It defines a way in which computers and languages interact with each other. Its main aim is to understand human speech, process it, and then generate the output as the same form of input. It can do this with the help of various artificial intelligence, machine learning, and deep learning models.

NLP is a subset of AI that helps machines understand human intentions or human language. Some examples are chatbots and voice assistants like Siri and Alexa. Rules-based approaches often imitate how humans parse sentences down to their fundamental parts. A sentence is first tokenized down to its unique words and symbols (such as a period indicating the end of a sentence). Preprocessing, such as stemming, then reduces a word to its stem or base form (removing suffixes like -ing or -ly). The resulting tokens are parsed to understand the structure of the sentence.

Many languages don’t allow for straight translation and have different orders for sentence structure, which translation services used to overlook. With NLP, online translators can translate languages more accurately and present grammatically-correct results. This is infinitely helpful when trying to communicate with someone in another language.

It simply composes sentences by simulating human speeches by being unbiased. There are calls that are recorded for training purposes but in actuality, they are recorded to the database for an NLP system to learn and improve services in the future. This is also one of the natural language processing examples that are being used by organizations from the last many years.

But at the same time, it is difficult to track queries for every customer. These chatbots are the outcome of natural language processing in AI (Artificial Intelligence). These solutions can ensure that the service providers relate to their customers and provide solutions to their queries instantly. Natural Language Processing is becoming increasingly important for businesses to understand and respond to customers.

Gmail, for instance, uses NLP to create “smart replies” that can be used to automatically generate a response. By extracting meaning from written text, NLP allows businesses to gain insights about their customers and respond accordingly. “According to the FBI, the total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. Insurance fraud affects both insurers and customers, who end up paying higher premiums to cover the cost of fraudulent claims.

RNNs have been around for some time, but newer models, like the long–short-term memory (LSTM) model, are also widely used for text processing and generation. Statistical methods for NLP are defined as those that involve statistics and, in particular, the acquisition of probabilities from a data set in an automated way (i.e., they’re learned). This method obviously differs from the previous approach, where linguists construct rules to parse and understand language. In the statistical approach, instead of the manual construction of rules, a model is automatically constructed from a corpus of training data representing the language to be modeled. As can be seen, NLP uses a wide range of programming languages and libraries to address the challenges of understanding and processing human language. The choice of language and library depends on factors such as the complexity of the task, data scale, performance requirements, and personal preference.

However, there is still a lot of work to be done to improve the coverage of the world’s languages. Facebook estimates that more than 20% of the world’s population is still not currently covered by commercial translation technology. In general coverage is very good for major world languages, with some outliers (notably Yue and Wu Chinese, sometimes known as Cantonese and Shanghainese). Post your job with us and attract candidates who are as passionate about natural language processing. “The decisions made by these systems can influence user beliefs and preferences, which in turn affect the feedback the learning system receives — thus creating a feedback loop,” researchers for Deep Mind wrote in a 2019 study.

In many applications, NLP software is used to interpret and understand human language, while ML is used to detect patterns and anomalies and learn from analyzing data. With an ever-growing number of use cases, NLP, ML and AI are ubiquitous in modern life, and most people have encountered these technologies in action without even being aware of it. The main benefit of NLP is that it improves the way humans and computers communicate with each other.

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width="306px" alt="natural language programming examples"/>
Autocorrect relies on NLP and machine learning to detect errors and automatically correct them. “One of the features that use Natural Language Processing (NLP) is the Autocorrect function. This feature works on every smartphone keyboard regardless of the brand. Natural language processing plays a vital part in technology and the way humans interact with it. Though it has its challenges, NLP is expected to become more accurate with more sophisticated models, more accessible and more relevant in numerous industries.

Insurers can use NLP to try to mitigate the high cost of fraud, lower their claims payouts and decrease premiums for their customers. NLP models can be used to analyze past fraudulent claims in order to detect claims with similar attributes and flag them. On the other hand, NLP can take in more factors, such as previous search data and context. Conversation analytics provides business insights that lead to better CX and business outcomes for technology companies.

While the terms AI and NLP might conjure images of futuristic robots, there are already basic examples of NLP at work in our daily lives. CallMiner is the global leader in conversation analytics to drive business performance improvement. By connecting the dots between insights and action, CallMiner enables companies to identify areas of opportunity to drive business improvement, growth and transformational change more effectively than ever before.

<ul>
<li>NLP Development Services are of diverse types such as summarization, text generation from speech, conversion of speech into text, etc.</li>
<li>Social listening powered by AI tasks like NLP enables you to analyze thousands of social conversations in seconds to get the business intelligence you need.</li>
<li>Every day, humans exchange countless words with other humans to get all kinds of things accomplished.</li>
<li>These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams.</li>
<li>But over time our NLP models will continue to get better at parsing text in a sensible way.</li>
</ul>
NLP algorithms generate summaries by paraphrasing the content so it differs from the original text but contains all essential information. It involves sentence scoring, clustering, and content and sentence position analysis. NLP with programming languages such as Python can help us to translate the text into different languages. These machine translations can help us to communicate with people living in different countries. By bringing NLP into the workplace, companies can analyze data to find what’s relevant amidst the chaos, and gain valuable insights that help automate tasks and drive business decisions.

NLP can be simply integrated into an app or a website for a user-friendly experience. The NLP integrated features like autocomplete, autocorrection, spell checkers located in search bars can provide users a way to find & get information in a click. In any of the cases, a computer- digital technology that can identify words, phrases, or responses using context related hints.

The different examples of natural language processing in everyday lives of people also include smart virtual assistants. You can notice that smart assistants such as Google Assistant, Siri, and Alexa have gained formidable improvements in popularity. The voice assistants are the best NLP examples, which work through speech-to-text conversion and intent classification for classifying inputs as action or question.
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</item>
<item>
<title>The Essential Guide To RV Repair: Fixing Common Issues And Keeping Your Adventure Rolling</title>
<link>https://newzmarker.com/the-essential-guide-to-rv-repair-fixing-common-issues-and-keeping-your-adventure-rolling.html</link>
<dc:creator><![CDATA[Baylor Edgar]]></dc:creator>
<pubDate>Sat, 30 Dec 2023 11:35:50 +0000</pubDate>
<category><![CDATA[Automotive]]></category>
<guid isPermaLink="false">https://newzmarker.com/?p=19849</guid>
<description><![CDATA[1. Understanding Common RV Issues When you embark on a journey in your RV, it’s essential to be prepared for any potential issues that may arise. Understanding common RV problems will not only help you identify them when they occur but also allow you to...]]></description>
<content:encoded><![CDATA[<h2 style="text-align: justify;">1. Understanding Common RV Issues</h2>
When you embark on a journey in your RV, it’s essential to be prepared for any potential issues that may arise. Understanding common RV problems will not only help you identify them when they occur but also allow you to take preventive measures to avoid costly repairs.
<h3 style="text-align: justify;">Diving Into the World of RV Repairs</h3>
RV repairs can be intimidating, especially if you have limited knowledge of the inner workings of your vehicle. However, with a little bit of insight, you can tackle many common issues yourself. By learning the basics, you can save time and money by avoiding unnecessary trips to the mechanic and ensure your adventure is not derailed by unexpected breakdowns.
<h3 style="text-align: justify;">Identifying the Most Common RV Problems</h3>
Before you hit the road, it’s crucial to familiarize yourself with the most common issues RV owners face. By being aware of these problems, you can take proactive steps to prevent them or quickly address them when they occur. Some of the most prevalent issues include electrical problems, plumbing leaks, and mechanical failures.
<h3 style="text-align: justify;">Why Prevention Is Key to Avoiding Costly Repairs</h3>
Prevention is the key to avoiding costly RV repairs. Regular maintenance and proactive measures can help extend the lifespan of your RV and keep you on the road for longer. By following a checklist and performing routine inspections, you can catch minor problems before they become major repairs.
<h2 style="text-align: justify;">2. DIY RV Repair: Tools and Techniques</h2>
Being able to perform DIY repairs on your RV is not only empowering but also saves you money and time. Equipping yourself with the right tools and honing some basic repair techniques will ensure you can handle common issues that may arise during your adventures.
<h3 style="text-align: justify;">Essential Tools Every RV Owner Should Have</h3>
Before you start tackling any RV repairs, it’s essential to have a well-stocked toolbox. Some essential tools every RV owner should have include a set of screwdrivers, adjustable wrenches, pliers, a multimeter, electrical tape, and various sizes of nuts and bolts. Additionally, having a portable air compressor and a tire repair kit can be a lifesaver in case of a flat tire.
<h3 style="text-align: justify;">Step-by-Step Guide to DIY RV Repairs</h3>
When it comes to DIY RV repairs, having a step-by-step guide can make the process much easier. Whether you’re repairing an electrical issue, fixing a plumbing leak, or troubleshooting a mechanical problem, following a systematic approach will ensure you don’t miss any crucial steps. Online resources, RV repair manuals, and video tutorials can provide detailed instructions for specific repairs you may encounter.
<h3 style="text-align: justify;">Mastering Basic RV Repair Techniques</h3>
Mastering basic <a href="https://www.coachspecialists.com/">RV repair</a> techniques will give you the confidence to handle a wide range of issues. From changing fuses and replacing light fixtures to fixing a leaky faucet or repairing a damaged awning, understanding these fundamental skills will empower you to keep your RV in top shape. Practice makes perfect, so don’t be afraid to get your hands dirty and tackle small repairs as they arise.
<h2 style="text-align: justify;">3. Troubleshooting Electrical and Plumbing Problems</h2>
Electrical and plumbing problems are some of the most common issues RV owners face. Understanding how these systems work and being able to troubleshoot and fix problems will ensure a smooth and enjoyable journey.
<h3 style="text-align: justify;">Demystifying RV Electrical Systems: A Beginner’s Guide</h3>
RV electrical systems can be complex, but with a basic understanding, you can tackle many common electrical issues. Start by familiarizing yourself with the main components, including the batteries, converter, fuse box, and circuit breakers. Knowing how to test and replace fuses, troubleshoot faulty wiring, and address power surges will help you keep the lights on and the appliances running smoothly.
<h3 style="text-align: justify;">Common Plumbing Issues and How to Fix Them</h3>
Plumbing leaks and clogs can quickly turn a relaxing RV trip into a stressful situation. Understanding common plumbing issues, such as leaky faucets, blocked drains, and malfunctioning toilets, will enable you to address these problems promptly. Learning how to use plumbing tools like a plunger and a drain snake, as well as knowing when to call a professional plumber, will help keep your RV’s water system in optimal condition.
<h3 style="text-align: justify;">Solving Wiring Problems: Tips and Tricks</h3>
If you notice electrical issues in your RV, there’s a good chance it could be a wiring problem. Frayed or damaged wires can lead to short circuits, malfunctions, and even fires. Knowing how to identify and repair wiring problems will ensure the safety and reliability of your RV. Learning how to properly strip and connect wires, use electrical tape, and test for continuity can save you from potential disasters on the road.
<h2 style="text-align: justify;">4. Keeping Your RV in Top Shape: Maintenance Tips and Tricks</h2>
Maintaining your RV is crucial for prolonging its lifespan and preserving its value. By implementing regular maintenance routines and following some essential cleaning hacks, you can keep your RV in top shape and ready for your next adventure.
<h3 style="text-align: justify;">The Importance of Regular RV Maintenance</h3>
Regular RV maintenance is essential for preventing unexpected breakdowns and costly repairs. Implementing a maintenance schedule that includes inspecting the tires, checking the fluid levels, servicing the generator, and cleaning the roof will help you catch potential problems early and keep your RV running smoothly. Additionally, scheduling professional inspections and servicing at least once a year is recommended to ensure all systems are functioning correctly.
<h3 style="text-align: justify;">Essential RV Cleaning Hacks for a Sparkling Adventure</h3>
Cleaning your RV doesn’t have to be a tedious task. With a few essential cleaning hacks, you can efficiently maintain a sparkling living space while on the road. Using multipurpose cleaners, organizing storage compartments, and implementing regular deep cleaning routines will ensure your RV remains fresh and inviting throughout your travels.
<h3 style="text-align: justify;">How to Extend the Lifespan of Your RV: Maintenance Tips</h3>
If you want to maximize the lifespan of your RV, proper maintenance is key. In addition to regular inspections, there are several maintenance tips you can follow to ensure your RV stands the test of time. These include properly storing your RV during the off-season, maintaining the exterior, checking the seals and weatherstripping, and keeping up with routine maintenance tasks like changing filters, lubricating moving parts, and servicing the appliances.
By understanding common RV issues, equipping yourself with the right tools, mastering DIY repair techniques, troubleshooting electrical and plumbing problems, and implementing regular maintenance and cleaning routines, you can keep your adventure rolling without being derailed by unexpected repairs. With a little knowledge and preparation, you’ll be ready to hit the road and enjoy the freedom and excitement that RV travel has to offer.
<h2 style="text-align: justify;">FAQ</h2>
Question: What are some common RV issues? – Some common RV issues include electrical problems, plumbing leaks, and mechanical failures.
Question: How can I prevent costly RV repairs? – Regular maintenance and proactive measures can help prevent costly RV repairs. It is important to follow a checklist, perform routine inspections, and address minor problems before they become major repairs.
Question: What tools should I have for DIY RV repair? – Essential tools for DIY RV repair include screwdrivers, adjustable wrenches, pliers, a multimeter, electrical tape, various sizes of nuts and bolts, a portable air compressor, and a tire repair kit.
Question: Are there step-by-step guides available for DIY RV repairs? – Yes, there are online resources, RV repair manuals, and video tutorials that provide step-by-step instructions for specific DIY RV repairs.
Question: How can I troubleshoot electrical problems in my RV? – To troubleshoot electrical problems in your RV, start by familiarizing yourself with the main components of the electrical system. Learn how to test and replace fuses, troubleshoot faulty wiring, and address power surges.
Question: How can I fix common plumbing issues in my RV? – Common plumbing issues in RVs include leaky faucets, blocked drains, and malfunctioning toilets. You can fix these problems by using plumbing tools like a plunger and a drain snake. If necessary, you may need to call a professional plumber.
Question: What are some maintenance tips for keeping my RV in top shape? – Some maintenance tips for keeping your RV in top shape include inspecting the tires, checking fluid levels, servicing the generator, cleaning the roof, and scheduling professional inspections and servicing at least once a year.
Question: How can I extend the lifespan of my RV? – To extend the lifespan of your RV, you can follow maintenance tips such as properly storing the RV during the off-season, maintaining the exterior, checking seals and weatherstripping, and keeping up with routine maintenance tasks like changing filters, lubricating moving parts, and servicing appliances.
<h2 style="text-align: justify;">Useful Resources:</h2>
<ul style="text-align: justify;">
<li><a href="https://www.rvrepairclub.com/">RV Repair Club</a></li>
<li><a href="https://www.rvgeeks.com/">RV Geeks</a></li>
<li><a href="https://www.rv.com/">RV.com</a></li>
<li><a href="https://www.doityourselfrv.com/">Do It Yourself RV</a></li>
<li><a href="https://www.rvusa.com/">RVUSA</a></li>
<li><a href="https://www.rv.net/">RV.Net</a></li>
<li><a href="https://www.rvtravel.com/">RV Travel</a></li>
<li><a href="https://www.rvlife.com/">RV Life</a></li>
</ul>

]]></content:encoded>
</item>
</channel>
</rss>

If you would like to create a banner that links to this page (i.e. this validation result), do the following:

Download the "valid RSS" banner.
Upload the image to your own server. (This step is important. Please do not link directly to the image on this server.)
Add this HTML to your page (change the image src attribute if necessary):

<a href="http://www.feedvalidator.org/check.cgi?url=https%3A//newzmarker.com/feed"><img src="valid-rss-rogers.png" alt="[Valid RSS]" title="Validate my RSS feed" /></a>

If you would like to create a text link instead, here is the URL you can use:

http://www.feedvalidator.org/check.cgi?url=https%3A//newzmarker.com/feed

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