LangChain: How to Combine ChatGPT and Wolfram Alfa to Get More Accurate and Detailed Answers
LangChain is an attempt to teach AI how to work with Wolfram Alpha and ChatGPT.
The system makes an API call to either Alpha or GPT 3.5, depending on the user’s question.
LangChain can solve a bunch of problems in mathematics, physics, and knowledge of the world.
It is no longer necessary to discuss whether artificial intelligence passed the Turing test. As a result, detectors that could tell artificial intelligence-generated texts from human-written ones were created. Nowadays, it is challenging, if not impossible, for anyone to accomplish this on their own.
Not relying on the perfection of such detectors, people have already begun to accept new life rules. For example, they ban the use of generative AI language tools in exams and oblige authors of scientific papers to report any use of such tools in their writing.
From this year onwards, we no longer doubt that ChatGPT can write a speech, essay, sermon, or obituary at the professional level. Now, we make completely different claims about such AI systems.
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For example, what is real AI?
- AI cannot create a great song (because, as the famous Australian rock musician, poet, and writer Nick Cave writes about lyrics written by ChatGPT in the style of Nick Cave, “songs arise from suffering and difficult internal struggle for creation, and algorithms do not feel, and the data does not suffer”);
- AI is more advanced than ever, but it frequently makes mistakes when answering questions that require:
- An understanding of units of measurement (for example, how far is it between Chicago and Tokyo?);
- Complex calculations (for example, what is 3 to the power of 73?);
- Current knowledge of recent events (for example, what happened around the globe in February 2022).
But everything in the field of AI is developing at a crazy speed. And if there are still problems with point 1 (creating “great songs”), then there have already been significant advances with point 2.
A month ago, Stephen Wolfram suggested that such a solution is GPT pooling and Wolfram Alpha. It can provide a powerful combination of linguistic intelligence, understanding, and processing information in terms of natural language, from GPT, and computational intelligence, understanding and processing information in computational terms, from Wolfram Alpha. This combination allows you to get more accurate and detailed answers by combining two kinds of thinking: linguistic and computational.
Such a combination could enable an ideal AI assistant easily switch between human text generation and non-human computational tasks with a natural language command. Ideally, ChatGPT should be taught to speak the Wolfram Language, a language in which humans and computers can “think computationally.”
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The first simplified attempt at this was by James Weaver at IBM. He created his own version of the integral approach, ChatGPT-LangChain. This is not exactly what Wolfram had in mind, but rather an imitation of his approach. Instead of teaching ChatGPT how to work with Alpha, LangChain simply combines Alpha and GPT 3.5, the technology that ChatGPT is built on. The system makes an API call to either Alpha or GPT 3.5, depending on the user’s question. If the question is more suited to a computational model requiring hard facts or calculations, it will make an API call to Alpha. But if the question requires less precision and more creativity, it will make an API call to GPT 3.5.
LangChain can solve a bunch of problems in mathematics, physics, and knowledge of the world. At the end of the conversation, LangChain “guessed” that AI would be able to reach the human intelligence level by 2035.
We encourage you to chat with LangChain yourself. Everything is free; you just need an OpenAI API key.
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