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		<title>Insurance Chatbots AI Solutions for the Insurance Industry</title>
		<link>https://futurewingsvisa.in/insurance-chatbots-ai-solutions-for-the-insurance/</link>
		<comments>https://futurewingsvisa.in/insurance-chatbots-ai-solutions-for-the-insurance/#respond</comments>
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		<description><![CDATA[Insurance AI Chatbots Technology Trends, Conversational AI in Insurance Chatbots free up your staff to focus on important tasks, reducing response times and improving efficiency. Our chatbots work around the clock, handling an infinite number of queries with a one-time investment. Let us help you leverage conversational and generative AI in meaningful ways across multiple [&#8230;]]]></description>
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" width="305px" alt="insurance bots"/></p>
<p><p>Chatbots free up your staff to focus on important tasks, reducing response times and improving efficiency. Our chatbots work around the clock, handling an infinite number of queries with a one-time investment. Let us help you leverage conversational and generative AI in meaningful ways across multiple use cases.</p>
</p>
<p><p>Planning to use a chatbot as another channel to push spam to customers is entirely unwise. Customers are tired of ‘push’ marketing, and such an investment would not bring desired results yet can significantly affect customer satisfaction. Instead, bots should be used as a new channel for developing conversational, interactive connections with the target audience and existing customers.</p>
</p>
<p><p>It helps users with tasks such as finding the right insurance product and comparing different policies. In 2022, PolicyBazaar also launched an AI-Enabled WhatsApp bot for the purpose of settling health insurance claims. A chatbot can help customers get a quote for an insurance policy or purchase a policy directly. This makes the process of buying insurance much easier and more convenient for clients.</p>
</p>
<ul>
<li>With a WhatsApp insurance chatbot, companies can easily offer solutions and services previously in human agents and support staff.</li>
<li>Since chatbots are channel-agnostic, insurance companies can be where their customers want them to be.</li>
<li>Third, apps cannot provide end-to-end assistance that simplifies life for insurance customers.</li>
</ul>
<p><p>The integration of chatbots is expected to grow, making them an integral part of the insurance landscape, driven by their ability to enhance customer experience and operational efficiency. Chatbots can facilitate insurance payment processes, from providing reminders to assisting customers with transaction queries. By handling payment-related queries, chatbots reduce the workload on human agents and streamline financial transactions, enhancing overall operational efficiency. Customers often have specific questions about policy coverage, exceptions, and terms. Insurance chatbots can offer detailed explanations and instant answers to these queries.</p>
</p>
<p><p>It is predicted that insurance AI chatbots will take over 95% of customer conversations, including telephone and online conversations. The insurance chatbots will be so advanced that customers will be unable to ‘spot the bot’. Insurance chatbots collect information about the finances, properties, vehicles, previous policies, and current status to provide advice on suggested plans and insurance claims. They can also push promotions and upsell and cross-sell policies at the right time. There is a wide variety of potential use cases for chatbots in the insurance industry.</p>
</p>
<p><p>Many insurance firms lack the internal skills required to develop and implement chatbots. This often leads to a reliance on external vendors which can be expensive and may not always result in the best chatbot solution. American insurance provider State Farm has a chatbot called “Digital Assistant”.</p>
</p>
<p><h2>FAQ Support</h2>
</p>
<p><p>This comprehensive guide explores the intricacies of insurance chatbots, illustrating their pivotal role in modernizing customer interactions. From automating claims processing to offering personalized policy advice, this article unpacks the multifaceted benefits and practical applications of chatbots in insurance. This article is an essential read for insurance professionals seeking <a href="https://chat.openai.com/">https://chat.openai.com/</a> to leverage the latest digital tools to enhance customer engagement and operational efficiency. An insurance chatbot powered by artificial intelligence is a virtual assistant capable of communicating with clients via instant messaging platforms, websites, or mobile applications. Insurance chatbots are designed to comprehend and address customer inquiries promptly and precisely.</p>
</p>
<p><p>Only when bots cross-check the damage, they notify the bank or the agents for the next process. Regardless of the industry, there’s always an opportunity to upsell and cross-sell. After they are done selling home insurance or car insurance, they can pitch other products like life insurance or health insurance, etc.</p>
</p>
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" width="302px" alt="insurance bots"/></p>
<p><p>Traditional claims processing requires employees to  manually gather and transfer information from multiple documents. When you consider how chatbots and automation can help, this number seems ludicrous. Chatbots can detect inconsistencies in a claim, report fraudulent details and reduce the processing times for validating death certificates by cross referencing government websites. Haven Life <a href="https://www.metadialog.com/blog/insurance-chatbots/">insurance bots</a> Insurance, a startup funded by MassMutual, uses chatbot technology to calculate life insurance needs and offer customers quotes for monthly rates based upon the chosen plan. Tour &amp; travel firms can use AI systems to effectively deal with the changing post-pandemic insurance needs and scenarios. They can use AI risk-modeling to assess risk in real-time and adjust policy offerings accordingly.</p>
</p>
<p><p>The company’s website features an AI chatbot that helps users request quotes, find the right insurance product, place claims, and more. This insurance chatbot example also comes with a search function and the “current status” update displaying agent availability. Each FAQ question is answered with a foolproof step-by-step guide along with CTA buttons, enabling users to file claims in minutes.</p>
</p>
<p><h2>Contextualizing Conversations</h2>
</p>
<p><p>It does not stop there, automation is also providing faster claims administration. Collecting feedback is crucial for any business, and chatbots can make this process seamless. They can solicit feedback on insurance plans and customer service experiences, either during or after the interaction. This immediate feedback loop allows insurance companies to continuously improve their offerings and customer service strategies, ensuring they meet evolving customer needs.</p>
</p>
<div style='border: grey dotted 1px;padding: 15px;'>
<h3>Global Insurance Brand sees Strong AI Business results with Verint Bots &#8211; Martechcube</h3>
<p>Global Insurance Brand sees Strong AI Business results with Verint Bots.</p>
<p>Posted: Tue, 25 Jun 2024 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiogFBVV95cUxNWGtoRG9KdDBVOW52S000QUJrWVhYckVOS0tCWU4tTkwyTXd6VnZLWlZBeng0OS1SMldlTEx0UHFxVnhtTVFWdERDOTdBNEJXSWRUWVVQczJRZG5rc3VWUlV0RUZ6QXZ1UG10amFBcHJyYzJPMnhlRzhaYkRYcnYtNDRwVzE4ZUtzQ09XbHhKdzRxdU9mTzRGRjY5VHp0eTN4RWfSAaIBQVVfeXFMTVhraERvSnQwVTludktNNEFCa1lYWHJFTktLQllOLU5MMk13elZ2S1pWQXp4NDktUjJXZUxMdFBxcVZ4bU1RVnREQzk3QTRCV0lkVFlVUHMyUWRua3N1VlJVdEVGekF2dVBtdGphQXBycmMyTzJ4ZUc4WmJEWHJ2LTQ0cFcxOGVLc0NPV2x4Snc0cXVPZk80RkY2OVR6dHkzeEVn?oc=5' rel="nofollow">source</a>]</p>
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<p><p>Are you in search of a solution that understands your insurance business and ensures data security and compliance? The process of receiving and processing claims can take a lot of time in insurance which ends up frustrating the customers. They have to wait to get in touch with a representative to fill out a form and send documents. Considering the time and effort that goes into claiming, this should be one of the first activities you should consider automating to improve customer service in the insurance sector. Indian insurance marketplace PolicyBazaar has a chatbot called “Paisa Vasool”.</p>
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<p><p>There’s no need to connect to a third party chatbot provider — everything you need is already available. But a unique aspect of their page is a bold banner advertising their chatbot as an instant support channel. Or you can have your chatbot automatically send a survey through email or directly in the chat box after the conversation ends.</p>
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<p><p>This interactive approach simplifies decision-making for customers, offering personalized recommendations akin to a knowledgeable advisor. For instance, Yellow.ai’s platform can power chatbots to dynamically adjust queries based on customer responses, ensuring a tailored advisory experience. By automating routine inquiries and tasks, chatbots free up human agents to focus on more complex issues, optimizing resource allocation. This efficiency translates into reduced operational costs, with some estimates suggesting chatbots can save businesses up to 30% on customer support expenses. The ability to communicate in multiple languages is another standout feature of modern insurance chatbots. This multilingual capability allows insurance companies to cater to a diverse customer base, breaking down language barriers and expanding their market reach.</p>
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<p><p>You can use artificial intelligence assistants, such as chatbots, to automate various service tasks. These ways range from handling insurance claims to accessing the user database. Overall, insurance chatbots enhance the payment experience for policyholders, offering convenience, security, and peace of mind in managing their insurance premiums. In essence, insurance chatbots can be viewed as versatile virtual assistants capable of helping all customers and stakeholders involved in the insurance ecosystem. The insurance industry is driven by escalating needs to fast-track digital transformation as customers expect personalized and easy to navigate services. IBM watsonx generative AI assistants enable frictionless self-service, supporting customers to effortlessly select the right policy, file claims or pay bills.</p>
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<p><p>But they only do that after they’ve gauged the spending capacity and the requirements of the customer instead of blindly selling them other products. You can foun additiona information about <a href="https://techunwrapped.com/metadialog-ai-how-to-use-ai-to-deliver-better-customer-service/">ai customer service</a> and artificial intelligence and NLP. Smart Sure provides flexible insurance protection for all home appliances and wanted to scale its website engagement and increase its leads. It deployed a WotNot chatbot that addressed the sales queries and also covered broader aspects of its customer support.</p>
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<p><h2>Enhanced customer engagement</h2>
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<p><p>These chatbots offer immediate and accurate information on insurance products, policy specifics, and claims processing. One of the most significant advantages of insurance chatbots is their ability to offer uninterrupted customer support. Unlike human agents, chatbots don’t require breaks or sleep, ensuring customers receive immediate assistance anytime, anywhere. This round-the-clock availability enhances customer satisfaction by providing a reliable communication channel, especially for urgent queries outside regular business hours. An insurance chatbot automates these aspects to provide fast, relevant answers via an easy-to-use conversational interface that reduces customers’ stress and enhances brand experiences. Can you imagine the potential upside to effectively engaging every customer on an individual level in real time?</p>
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<p><p>Customers can use the bot to submit details about their claim, such as the incident date, description, and relevant documentation. It can also be deployed across multiple digital touchpoints, including your company website, app, and messaging platforms like SMS or WhatsApp. Let’s explore how these digital assistants are revolutionizing the insurance sector. Schedule a personal demonstration with a product specialist to discuss what watsonx Assistant can do for your business or start building your AI assistant today, on our free plan. 60% of insurers&nbsp;expect nontraditional products to generate revenue on par with traditional products. 80% of the Allianz’s&nbsp;most frequent customer requests are fielded by IBM watsonx Assistant in real time.</p>
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<p><p>A life insurance chatbot or chatbot for health insurance can enhance customer experience is a powerful reason for insurance companies to add it to their customer communications stack. Nonetheless, insurance chatbots also deliver many other advantages that make them worth the (minimal) investment required. The insurance industry is experiencing a digital renaissance, with chatbots at the forefront of this transformation. These intelligent assistants are not just enhancing customer experience but also optimizing operational efficiencies.</p>
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<p><p>When the conversation is over, the bot asks you whether your issue was resolved and how you would rate the help provided. Users can also leave comments to specify what exactly they liked or didn’t like about their support experience, which should help GEICO create an even better chatbot. When integrated with your business toolkit, a chatbot can facilitate the entire policy management cycle. Your customers can turn to it to apply for a policy, update account details, change a policy type, order an insurance card, etc.</p>
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<p><p>This not only saves insurance companies money but also helps maintain a fair and trustworthy insurance ecosystem for all customers. AI insurance company chatbots are ideal solutions for delivering world-class, self-service, and omnichannel customer experience at a low cost. In addition, AI chatbots are amazingly versatile and suitable for numerous use cases in the insurance sector. Customers can interact with insurance chatbots at any time to find policy details, pay premiums, make claims, update their accounts, initiate renewals, etc.</p>
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<h3>In Constant Battle With Insurers, Doctors Reach for a Cudgel: A.I. &#8211; The New York Times</h3>
<p>In Constant Battle With Insurers, Doctors Reach for a Cudgel: A.I..</p>
<p>Posted: Tue, 16 Jul 2024 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMikAFBVV95cUxQbHJaYmVFYUxSU2RIZkhteEJNbTNTV1NRcFBTRFAzLXZmNmJLblpoSjZmZHVuYjQ5d29jYk5RRmxhQXBjYnNwcGpDVXk5SEZ4SVFVTnYzSm5rUGJfbV9pOFZCVTNEZENreUV1OTlLYVU4RGg4c04tYmxSQ3gxVWExcVU0Nk9jQU5ndm5nMURLQlk?oc=5' rel="nofollow">source</a>]</p>
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<p><p>Every customer that wants quick answers to insurance-related questions can get them on chatbots. You can also program your chatbots to provide simplified answers to complex insurance questions. Another chatbot use case in insurance is that it can address all the challenges potential customers face with the lack of information.</p>
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<p><p>The chatbot also assists in processing claims quickly, ensuring a smooth and hassle-free experience for customers. Lemonade’s chatbot has significantly reduced the time it takes for customers to get insured and receive claim payouts. AI chatbots are expected to generate cost savings of almost $1.3 billion by 2023 in the insurance industry.</p>
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<p><p>Another simple yet effective use case for an insurance chatbot is feedback collection. Chatbots create a smooth and painless payment process for your existing customers. You just need to add a contact form for users to fill before talking to the bot. You also don’t have to hire more agents to increase the capacity of your support team — your chatbot will handle any number of requests.</p>
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<p><h2>Claims Filing</h2>
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<p><p>Using a dedicated AI-based FAQ chatbot on their website has helped AG2R La Mondiale improve customer satisfaction by 30%. Chatbots gather a wide range of client information and have quick access to it. Let&#8217;s say a client asks an insurance chatbot about their car insurance policy. The chatbot should be able to understand the question and provide the client with the relevant information. Whether it’s a one-time payment or setting up recurring payments, chatbots facilitate seamless transactions, offering maximum convenience.</p>
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<p><p>The user can then either type their request or select one from a list of options. Insurers can use AI solutions to get help with data-driven tasks such as customer segmentation, opportunity targeting, and qualification of prospects. Chatbots can take away all the hassles that customers often face with insurance. With an AI-powered bot, you can put the support on auto-pilot and ensure quick answers to virtually every question or doubt of consumers. Bots can help you stay available round-the-clock, cater to people with information, and simplify everything related to insurance policies.</p>
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<p><p>For example, a health insurance chatbot can find the best insurance product based on their age, gender, medical history, current health status, and several other factors. Chatbots have begun a new chapter in insurance, offering unparalleled efficiency, personalized customer service, and operational agility. Their ability to adapt, learn, and provide tailored solutions is transforming the insurance landscape, making it more accessible, customer-friendly, and efficient. As we move forward, the continuous evolution of chatbot technology promises to enhance the insurance experience further,  paving the way for an even more connected and customer-centric future. Insurance chatbots are redefining customer service by automating responses to common queries. This shift allows human agents to focus on more complex issues, enhancing overall productivity and customer satisfaction.</p>
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<p><p>In insurance, a chatbot should ideally connect with all internal systems but that might be a tall order. Definitely, they need to connect with key systems to get the most value in bot-customer interactions. For example, they should integrate with document management tools, policy management software, CRM systems to track customer interactions and feed into sales pipelines. Finally, conversational AI bots will also need to connect with claims software. The most successful insurance chatbots will be the ones that will drive a conversation perfectly mimicking a human agent.</p>
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<p><p>This process not only captures potential customers’ details but also gauges their interest level and insurance needs, funneling quality leads to the sales team. Claims processing is traditionally <a href="https://play.google.com/store/apps/datasafety?id=pl.edu.pg.chatpg&amp;hl=cs&amp;gl=US">Chat GPT</a> a complex and time-consuming aspect of insurance. Chatbots significantly simplify this process by guiding customers through claim filing, providing status updates, and answering related queries.</p>
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<p><p>In fact, there are specific chatbots for insurance companies that help acquire visitors on the website with smart prompts and remove all customer doubts effectively. Successful insurers heavily rely on automation in customer interactions, marketing, claims processing, and fraud detection. GEICO’s virtual assistant, Kate, is designed to help customers with various insurance-related tasks. Some examples include accessing policy information, getting answers to frequently asked questions, and changing their coverage. Kate’s ability to provide instant assistance has enhanced GEICO’s customer service and reduced the need for customers to call or email support teams for basic inquiries.</p>
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<p><p>Policyholders can use your chatbot to verify policy details/terms, request assistance with coverage adjustments, or seek help with other tasks such as filing a claim (more on this below). This makes it much quicker and easier for users to access the information they need for their specific situation, creating a convenient and personalised customer experience. In an industry where data security is paramount, AI chatbots ensure the secure handling of sensitive customer information, adhering to strict compliance and privacy standards.</p>
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width="301px" alt="insurance bots"/></p>
<p><p>They improve customer service and offer a unique perspective on how technology can reshape traditional business models. The best insurance chatbots focus on the customer, their needs, wants, behaviors and preferences. Powered by Artificial Intelligence, these virtual bots engage with customers in a friendly, natural way that puts them at ease. They “listen” to customers, simplify tasks, provide customized advice, and help them save time and effort. You can also scale support through an insurance chatbot across channels and consolidate chats under a single platform.</p>
</p>
<p><p>These are just a few examples of how chatbots can be used to improve the customer experience. Chatbots provide non-stop assistance and can upsell and cross-sell insurance products to clients. Even with advanced, AI-powered insurance chatbots, there will still be cases that require human assistance for a satisfactory resolution. By digitally engaging visitors on your company website or app, insurance chatbots can provide guidance that’s tailored to their needs.</p>
</p>
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width="305px" alt="insurance bots"/></p>
<p><p>In addition, the best insurance chatbots make it easy to enable a new language, so the insurer doesn’t have to rebuild a new chatbot every time they want to expand their geographical reach. Geico uses a virtual assistant to greet customers and offer help with insurance products or policy questions. By asking qualifying questions, the virtual assistant can learn the customer’s needs and then recommend suitable plans. This is most effective for simpler plans like travel insurance and auto insurance where an embedded chatbot can take a customer through the entire insurance purchase journey themselves.</p>
</p>
<p><p>Staff that was once working on tedious, repetitive work can now focus on more strategic tasks that take human-level thinking. Zurich Insurance uses its chatbot, Zara, to assist customers in reporting auto and property claims. Zara can also answer common questions related to insurance policies and provide advice on home maintenance.</p>
</p>
<p><p>AXA Chat asks the user what they need help with, offers explanations of difficult topics and links relevant pages. A chatbot can also help customers close their accounts and make sure all charges are paid in full. If you haven’t done it yet, we also highly recommend using our post “4-step formula for calculating your chatbot ROI”</p>
</p>
<p> to determine how much you can save and earn by using a chatbot.</p></p>
]]></content:encoded>
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		</item>
		<item>
		<title>The 7 Most Important AI Programming Languages</title>
		<link>https://futurewingsvisa.in/the-7-most-important-ai-programming-languages/</link>
		<comments>https://futurewingsvisa.in/the-7-most-important-ai-programming-languages/#respond</comments>
		<pubDate>Wed, 26 Mar 2025 13:26:51 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[AI News]]></category>

		<guid isPermaLink="false">http://futurewingsvisa.in/?p=1939</guid>
		<description><![CDATA[15 Best Free Online AI Courses for Beginners Julia is a newer language that has been gaining traction in the AI community. It’s designed to combine the performance of C with the ease and simplicity of Python. Julia’s mathematical syntax and high performance make it great for AI tasks that involve a lot of numerical [&#8230;]]]></description>
				<content:encoded><![CDATA[<p><h1>15 Best Free Online AI Courses for Beginners</h1>
</p>
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" width="305px" alt="best programming languages for ai"/></p>
<p><p>Julia is a newer language that has been gaining traction in the AI community. It’s designed to combine the performance of C with the ease and simplicity of Python. Julia’s mathematical syntax and high performance make it great for AI tasks that involve a lot of numerical and statistical computing. Its relative newness means there’s not as extensive a library ecosystem or community support as for more established languages, though this is rapidly improving.</p>
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<h3>I used ChatGPT to write the same routine in 12 top programming languages. Here&#8217;s how it did &#8211; ZDNet</h3>
<p>I used ChatGPT to write the same routine in 12 top programming languages. Here&#8217;s how it did.</p>
<p>Posted: Fri, 08 Mar 2024 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMivAFBVV95cUxNVkJSNjhVeThXRmM1clJueGtfdUZXS0NjR1c4b1ZYQWdmU203Z3RQUTNTM2s5djFpYzdleEk0WG5yTTRSTlNKSTdzTjF2aWhncG9WM09oOG1XUklEWkNGNHVoRDBWMm5IdVJBc3RndkxTN0o1QlJyYldSVFA0cm85bDdvclFGeGtlUWRQM25TLWtNcjBWWE9yUHRyWXN6SmwxRkx5RFM0SzROZVFYcXlzQTdWLWZtdThyeFE3cw?oc=5' rel="nofollow">source</a>]</p>
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<p><p>JavaScript, traditionally used for web development, is also becoming popular in AI programming. With the advent of libraries like TensorFlow.js, it’s now possible to build and train ML models directly in the browser. However, JavaScript may not be the best choice for heavy-duty AI tasks that require high performance and scalability. Lisp (also introduced by John McCarthy in 1958) is a family of programming languages with a long history and a distinctive, parenthesis-based syntax. Today, Lisp is used in a variety of applications, including scripting and system administration. This library combines the functionalities of Matlab and the NumPy library from Python.</p>
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<p><h2>Want to build intelligent applications?</h2>
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<p><p>This post provides insights into the most effective languages for creating advanced artificial intelligence systems. If your professional interests are more focused on data analysis, you might consider learning Julia. This relatively new programming language allows you to conduct multiple processes at once, making it valuable for various uses in AI, including data analysis and building AI apps. AI is an essential part of the modern development process, and knowing suitable AI programming languages can help you succeed in the job market.</p>
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<p><p>As AI continues to shape our world, learning the best programming languages is essential for anyone interested in artificial intelligence development. By mastering the top programming languages such as Python, Java, JavaScript, and R, you can enhance your AI skills and stay competitive in the industry. These languages offer unique features and capabilities for different AI tasks, whether it’s machine learning, natural language processing, or data visualization. R ranked sixth on the 2024 Programming Language Index out of 265 programming languages.</p>
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<p><p>In this post, we’re going to dive deep into the world of AI programming languages. We’ll break down which ones matter most, what makes them important, and how you can leverage them to your advantage. Whether you’re a hiring manager assembling a world-class AI team, or a developer eager to add cutting-edge skills to your repertoire, this guide is your roadmap to the key languages powering AI. For hiring managers looking to future-proof their tech departments, and for developers ready to broaden their skill sets, understanding AI is no longer optional — it’s essential. Without these, the incredible algorithms and intricate networks that fuel AI would be nothing more than theoretical concepts. As Python’s superset, Mojo makes it simple to seamlessly integrate different libraries like NumPy, matplotlib, and programmers&#8217; own code into the Python ecosystem.</p>
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<p><p>R is a favorite among statisticians, data scientists, and researchers for its precise statistical tools. Python is the most popular language for AI because it’s easy to understand and has lots of helpful tools. You can easily work with data and make cool graphs with libraries like NumPy and Pandas. Another advantage to consider is the boundless support from libraries and forums alike. If you can create desktop apps in Python with the Tkinter GUI library, imagine what you can build with the help of machine learning libraries like NumPy and SciPy. It’s primarily designed to be a declarative programming language, which gives Prolog a set of advantages, in contrast to many other programming languages.</p>
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<p><h2>The 5 best programming languages for AI development</h2>
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<p><p>The list of AI-based applications that can be built with Prolog includes automated planning, type systems, theorem proving, diagnostic tools, and expert systems. R is used in so many different ways that it cannot be restricted to just one task. Prolog (general core, modules) is a logic programming language from the early ’70s that’s particularly well suited for artificial intelligence applications. Its declarative nature makes it easy to express complex relationships between data. Prolog is also used for natural language processing and knowledge representation. Julia is a high-performance programming language that is focused on numerical computing, which makes it a good fit in the math-heavy world of AI.</p>
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<p><p>The programming world is undergoing a significant shift, and learning artificial intelligence (AI) programming languages appears more important than ever. In 2023,  technological research firm Gartner revealed that up to 80 percent of organizations will use AI in some way by 2026, up from just 5 percent in 2023 [1]. Scala enables deploying machine learning into production at high performance.</p>
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<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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" width="305px" alt="best programming languages for ai"/></p>
<p><p>It is easy to learn, produces clear, streamlined code, and works well for new as well as experienced developers. With a clearly defined syntax and simple English keywords, Python is highly readable, and easy to learn. R is the go-to language for statistical computing and is widely used for data science applications. You can foun additiona information about <a href="https://hardwaretimes.com/metadialog-ai-how-to-use-ai-to-deliver-better-customer-service/">ai customer service</a> and artificial intelligence and NLP. It shines when you need to use statistical techniques for AI algorithms involving probabilistic modeling, simulations, and data analysis. R’s ecosystem of packages allows the manipulation and visualization of data critical for AI development. The caret package enhances machine learning capabilities with preprocessing and validation options.</p>
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<p><p>In fact, Python has become the &#8220;language of AI development&#8221; over the last decade—most AI systems are now developed in Python. Google’s ‘Machine Learning Crash Course’ offers a comprehensive introduction to machine learning. We’ve also taken the time to answer the question “what is an AI coding assistant? ”, along with a detailed breakdown of how they can help students, beginner developers, and experienced professionals. This is one of the newest AI coding assistants in our list, and JetBrains offers it for their suite of professional IDEs, including Java IDEs like IntelliJ IDEA, PyCharm for Python, and more.</p>
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<p><p>As it turns out, there’s only a small number of programming languages for AI that are commonly used. These languages have many reasons why you may want to consider another. A language like Fortran simply doesn’t have many AI packages, while C requires more lines of code to develop a similar project. A scripting or low-level language wouldn’t be well-suited for AI development. For statistical analysis and data science, Julia provides DataFrames to work with datasets and perform common data manipulations.</p>
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<p><p>It’s compatible with Java and JavaScript, while making the coding process easier, faster, and more productive. As new trends and technologies emerge, other languages may rise in importance. For developers and hiring managers alike, keeping abreast of these changes and continuously updating skills and knowledge are vital. Due to its efficiency and capacity for real-time data processing, C++ is a strong choice for AI applications pertaining to robotics and automation. Numerous methods are available for controlling robots and automating jobs in robotics libraries like roscpp (C++ implementation of ROS).</p>
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<p><p>Scala also features a toolset for writing concurrent applications that can easily scale and process real-time streams of data. With its Write Once Run Anywhere (WORA) mentality, Java was one of the first languages to create platform-independent software. Thanks to Java virtual machine (JVM), compiled Java programs can run across different <a href="https://chat.openai.com/">https://chat.openai.com/</a> chip architectures and operating systems. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.</p>
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<p><p>Some of the most popular AI frameworks have been developed using C++. It’s not all sunshine with Python development as it also has some drawbacks. This makes it slower and also leads to applications consuming a large amount of memory. For most programmers, Python is the best programming language for AI. Other top contenders include Java, C++, and JavaScript — but Python is likely the best all-around option for AI development.</p>
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<p><p>Plus, R can work with other programming languages and tools, making it even more useful and versatile. Julia is another high-end product that just hasn’t achieved the status or community support it deserves. This programming language is useful for general tasks but works best with numbers and data analysis. Libraries like Weka, Deeplearning4j, <a href="https://play.google.com/store/apps/datasafety?id=pl.edu.pg.chatpg&amp;hl=cs&amp;gl=US">Chat GPT</a> and MOA (Massive Online Analysis) aid in developing AI solutions in Java. However, Java may be overkill for small-scale projects and it doesn’t boast as many AI-specific libraries as Python or R. For instance, when dealing with ML algorithms, you might prioritize languages that offer excellent libraries and frameworks for statistical analysis.</p>
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<ul>
<li>Lisp&#8217;s fundamental building blocks are symbols, symbolic expressions, and computing with them.</li>
<li>Natural language processing breakthroughs are even enabling more intelligent chatbots and search engines.</li>
<li>Julia integrates nicely with databases through JDBC, ODBC, and  Spark drivers.</li>
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<p><p>For example, developers utilize C++ to create neural networks from the ground up and translate user programming into machine-readable codes. C++ has libraries for many AI tasks, including machine learning, neural networks, and language processing. <a href="https://www.metadialog.com/blog/best-programming-languages-to-choose-for-ai/">best programming languages for ai</a> Tools like Shark and mlpack make it easy to put together advanced AI algorithms. R supports many data formats and databases, making it easy to import and export data. This is vital for AI projects that use diverse and large data sources.</p>
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<p><p>Still others you only need to know about if you’re interested in historical deep learning architectures and applications. Rust provides performance, speed, security, and concurrency to software development. With expanded use in industry and massive systems, Rust has become one of most popular programming languages for AI.</p>
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<p><p>Once you are sure that AI techniques are a good fit, the next most important factor is the development team’s experience. Find out what is the preferred programming language among the developers in your team. Haskell’s learning curve, however,&nbsp; is fairly steep when compared to other languages commonly used for AI development. Haskell is a lazy language, which means that values are evaluated only when needed. Haskell has strong abstraction capabilities and built-in memory management. This language is favored by many developers because it increases productivity.</p>
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<p><p>Developers must carefully consider languages such as Python, Java, JavaScript, or R, renowned for their suitability in AI and machine learning applications. By aligning with the right programming language, developers can effectively harness the power of AI, unlocking innovative solutions and maintaining competitiveness in this rapidly evolving landscape. Julia also has a wealth of libraries and frameworks for AI and machine learning. Plus, Julia can work with other languages like Python and C, letting you use existing resources and libraries, which enhances its usefulness in AI development. Python, R, Java, C++, Julia, MATLAB, Swift, and many other languages are powerful AI development tools in the hands of AI developers. The choice of language depends on your specific project requirements and your familiarity with the language.</p>
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<p><p>It offers excellent production value and smooth integration with all key analytic frameworks. Although the execution isn&#8217;t flawless, AI-assisted coding eliminates human-generated syntax errors like missed commas and brackets. Porter believes that the future of coding will be a combination of AI and human interaction, as AI will allow humans to focus on the high-level coding skills needed for successful AI programming.</p>
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<p><p>Prolog is valued in the AI community for its impressive track record of solving tough AI problems. It is capable of effectively expressing problem relationships in AI through its inherent inference mechanisms. Prolog also has a rich set of extensions that accelerate the development process. Prolog is primarily a declarative programming language meaning that program logic is expressed through relations between facts and rules. A computation in Prolog is carried out by running a query over the implemented relations.</p>
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<p><p>It also features relatively advanced machine learning toolboxes that streamline AI development. It is a very suitable choice for numerical analysis and computational scientific analysis. Despite its high-level nature, it offers a lot of low-level programming features as well. Julia has been used by big business corporations mostly for time-series analysis, risk analysis, and even space mission planning. It’s one of the languages that was developed primarily for the statistical analysis and graphical representation of data. R is especially useful for AI development that involves time series analysis, statistical tests, linear and nonlinear modeling, and clustering.</p>
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<p><p>The programming language Haskell is becoming more and more well-liked in the AI community due to its capacity to manage massive development tasks. Haskell is a great option for creating sophisticated AI algorithms because of its type system and support for parallelism. Haskell&#8217;s laziness can also aid to simplify code and boost efficiency. Haskell is a robust, statically typing programming language that supports embedded domain-specific languages necessary for AI research. The best programming language for artificial intelligence is commonly thought to be Python.  It is widely used by AI engineers because of its straightforward syntax and adaptability.</p>
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<p><p>It also makes it simple to abstract and declare reusable AI components. Performing advanced statistical modeling, hypothesis testing, and regression analysis. With its integration with web technologies and the ability to run in web browsers, JavaScript is a valuable language for creating accessible AI-powered applications. Deepen your knowledge of AI/ML &amp; Cloud technologies and learn from tech leaders to supercharge your career growth. This prevalence has created a fantastic playing ground for companies looking to develop more AI solutions.</p>
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<p><p>Lisp’s syntax is unusual compared to modern computer languages, making it harder to interpret. Relevant libraries are also limited, not to mention programmers to advise you. Scala took the Java Virtual Machine (JVM) environment and developed a better solution for programming intelligent software.</p>
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<p><p>Machine learning libraries implemented natively in Haskell are scarce which makes its usage in AI somewhat limited. Quite a few companies, however, have use Haskell to power their business processes. The biggest disadvantages of Scala are lack of community support, low adoption, limitations on backward compatibility, and a steep learning curve. Programs written in Scala have much less boilerplate code compared to those written in Java and this adds to its usability and simplicity.</p>
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<p><p>As AI continues to advance, these languages will continue to adapt and thrive, shaping the future of technology and our world. Python is often the first language that comes to mind when talking about AI. Its simplicity and readability make it a favorite among beginners and experts alike. Python provides an array of libraries like TensorFlow, Keras, and PyTorch that are instrumental for AI development, especially in areas such as machine learning and deep learning. While Python is not the fastest language, its efficiency lies in its simplicity which often leads to faster development time. However, for scenarios where processing speed is critical, Python may not be the best choice.</p>
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<p><p>Even when it comes to natural language processing which is so integral to user experience today, Python has an edge thanks to tools like SpaCy and NTLK. Of course, Python, C++, Java, JavaScript, Swift, and R aren’t the only languages available for AI programming. Here are two more programming languages you might find interesting or helpful, though I wouldn’t count them as top priorities for learning. Not really, but it may indeed point the way to the next generation of deep learning development, so you should definitely investigate what’s going on with Swift. It will also examine the differences between traditional coding and coding for AI and how AI is changing programming.</p>
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<p><h2>Java and other JVM languages</h2>
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<p><p>Popular in education research, Haskell is useful for Lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism. In addition, because of its versatility and capacity to manage failures, Haskell is considered a safe programming language for AI. Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow are a few of the specialist libraries available in Python, making it an excellent choice for AI projects. The Python community is lively and supportive, with many developers and experts ready to help those working on AI. The strong Python community offers knowledge, support, and inspiration to AI developers.</p>
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<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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" width="300px" alt="best programming languages for ai"/></p>
<p><p>JavaScript is widely used in the development of chatbots and natural language processing (NLP) applications. With libraries like TensorFlow.js and Natural, developers can implement machine learning models and NLP algorithms directly in the browser. JavaScript’s versatility and ability to handle user interactions make it an excellent choice for creating conversational AI experiences. A number of AI applications involve big data and crunching of big numbers.</p>
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<p><p>The syntaxes belonging to Python are very simple and can be easily learned. Python takes a short development time in comparison to other languages like Java, C++, or Ruby. Python supports object-oriented, functional as well as procedure-oriented styles of programming. There are plenty of libraries in Python, which make our tasks easier. So those are the 8 most popular AI programming languages developers can learn and work with in 2022.</p>
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<p><p>Its AI capabilities mainly involve interactivity that works smoothly with other source codes, like CSS and HTML. It can manage front and backend functions, from buttons and multimedia to data storage. As a programmer, you should get to know the best languages for developing AI. Below are 10 options to consider and how they can benefit your smart projects. Programming languages are notoriously versatile, each capable of great feats in the right hands. AI (artificial intelligence) technology also relies on them to function properly when monitoring a system, triggering commands, displaying content, and so on.</p>
</p>
<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width="302px" alt="best programming languages for ai"/></p>
<p><p>It also makes parallel programming and using many cores naturally fast. It works well whether using multiple threads on one machine or distributing across many machines. R is also used for risk modeling techniques, from generalized linear models to survival analysis. It is valued for bioinformatics applications, such as sequencing analysis and statistical genomics.</p>
</p>
<p><p>This provides a high-performance foundation for various AI algorithms, including statistical models and neural networks. Find out how their features along with use cases and compare them with our guide. R stands out for its ability to handle complex statistical analysis tasks with ease. It provides a vast ecosystem of libraries and packages tailored specifically for statistical modeling, hypothesis testing, regression analysis, and data exploration. These capabilities enable AI professionals to extract meaningful insights from large datasets, identify patterns, and make accurate predictions.</p>
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<p><p>It also has a wide range of libraries and tools for AI and machine learning, such as Weka and Deeplearning4j. Furthermore, Java’s platform independence means that AI applications developed in Java can run on any device that supports the Java runtime environment. That being said, Python is generally considered to be one of the best AI programming languages, thanks to its ease of use, vast libraries, and active community.</p>
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<h3>TIOBE Index for August 2024: Top 10 Most Popular Programming Languages &#8211; TechRepublic</h3>
<p>TIOBE Index for August 2024: Top 10 Most Popular Programming Languages.</p>
<p>Posted: Mon, 05 Aug 2024 07:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMidkFVX3lxTE5DMkpqZl9JM1h2SU9rc3lxLUxjOU1wZGpBd1VVLUVSSjcxbm9LSDRXNnJ6X0ZzUEswUkZabWJUOVdCQW9aeFFtN1FrcERfOVlFUU1yOEhqZG1zc3lrbldfSGNRQW42S3pXZFBzQkpfT2Q3R2cxSGc?oc=5' rel="nofollow">source</a>]</p>
</div>
<p><p>Python, the most popular and fastest-growing programming language, is an adaptable, versatile, and flexible language with readable syntax and a vast community. Some of the most prominent advantages of MATLAB are ease of use and a rich library of ready-to-use functions that can automate the development of many common computing applications. It offers versatile tools for plotting algorithms and function outputs, and platform independence. Expressiveness, concise syntax, and concurrency principles make Scala an easy-to-use and efficient programming language. It allows developers to use both object-oriented and functional programming concepts. Scala inherits Java’s security and can interoperate with it seamlessly.</p></p>
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