![]() Add on Wolfram’s comprehensive knowledge-base and you have a search killer on your hands. If combined with the Wolfram language’s computational prowess, the problem of not understanding mathematical problems can be solved. Both these shortcomings can be addressed through Wolfram Alpha. In addition to a flawed dataset, the bot also faces difficulty in mathematical calculations, as it is not trained to understand them but simply try to ‘solve’ them using natural language. The dataset for GPT 3.5 consists of information scraped from the Internet, leading to many discrepancies when it comes to specific information like the distance between cities, population statistics, and many more. There are two reasons for this, first being ChatGPT’s dataset. ![]() One place where this system falls apart is when ChatGPT is asked objectively factual questions, where it confidently spews misinformed answers. Owing to this, the bot rejects queries that it believes it does not have information to answer along with blocking answers about sensitive topics, like hate speech and self harm. While the bug of information hallucination is yet to be solved even for ChatGPT and the underlying GPT LLM, OpenAI has conducted research on reducing the amount of misinformation that the bot gives out. Unfortunately, it had a propensity to hallucinate information, even though it was trained on close to 50 million scientific papers, leading to it being shut down in a matter of two days. This short-lived LLM was launched with the grand goal of organising all scientific knowledge and making it accessible through a chatbot. The scientific community has largely ignored chatbots derived from large language models, as seen by their negative response to Meta’s ‘Galactica’ model. This union might even make it the accurate chatbot that the scientific community doesn’t know they need. ChatGPT is unbeatable at parsing natural language and making it computer-readable while Wolfram is excellent at solving complex mathematical problems by breaking it down into the Wolfram language’s symbolic expressions. When looking at the approach that the creators of Wolfram and ChatGPT have taken, the benefits of bringing them together are obvious. This language was made expressly to solve complex algebraic problems, with its latest iteration being able to take on higher-level calculus tasks like differential equations and matrix manipulation. On the other hand, Wolfram Alpha is built on the Wolfram language, a symbolic programming language that is focused on expressing complex ideas in a computational form. 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. 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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