This means that the chatbot has learned the pattern of human-like speech along with the capability of translating a query in a human language to one in a machine-understandable language.
ChatGPT is trained on GPT 3.5, a large language model that has a dataset containing 175 billion parameters, using which it has learned to respond to prompts in natural language with coherent responses. To understand why these two vastly-different applications can work so well together, we must first delve into the approach that they each take to solving a problem. Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions. He also showed off the capabilities of Wolfram Alpha and how it can be used to ‘inject’ data points into ChatGPT, which the bot then accepted as the correct response. Wolfram demonstrated the tendency of ChatGPT to give factually incorrect answers which sounded like they might be accurate, highlighting the feature of the chatbot to correct itself when prompted. In a blog post published recently, Stephen Wolfram, the founder and CEO of Wolfram Research, explored the idea of combining the capabilities of Wolfram Alpha and ChatGPT. Bringing together Wolfram Alpha and ChatGPT With modern advances in natural language processing and chatbots, this forerunner of modern AI might change the landscape for NLP-powered problem solving. For all its computational intelligence, the website sometimes struggles with identifying queries in natural language. These questions can range from high-level calculus to the amount of calories in a given dish, and Wolfram Alpha will provide an answer while showing the steps to the solution. Wolfram Alpha, an answering machine built on this language, uses natural language processing, the world’s largest repository of computable knowledge, and a custom-made symbolic programming language to provide answers to mathematical questions. Ever since its genesis in 1988, the Wolfram language has been the go-to language to solve complex scientific problems.
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