The next generation of AI assistance could be a fusion of ChatGPT and Wolfram Alpha.
Imagine a platform having the ability to generate human-like responses as ChatGPT, but also having access to the “computational superpower” of Wolfram Alpha, allowing you to perform precise calculations beyond human capabilities.
ChatGPT has been the talk of the town for a while thanks to its ability to generate seemingly correct answers, from essays to job interview simulations, to blog content, etc. But as we use it more, we begin to see its limitations. That’s where Wolfram Alpha comes in.
As Stephen Wolfram, the founder and CEO of Wolfram Alpha, emphasizes, not every “useful” task is actually “human-like”. In fact, the primary reason computers were built in the first place was to perform computations that are beyond humans. And that’s where Alpha excels.
Wolfram Alpha is designed differently from ChatGPT, but they share a common interface: natural language. This presents an exciting opportunity to connect both models to create the ultimate AI assistant. This system will be able to seamlessly switch between human-like text generation and beyond-human computational tasks with just a simple natural language command.
Why settle for just “good enough” when we can upgrade?
How Wolfram Alpha Can Massively Enhance ChatGPT
In case you haven’t realized, both AI models are far from perfect. While ChatGPT might fool some grandmas into thinking it is human, it often falls short when it comes to computations. And it “guesses” a lot, which is unsuitable for hardcore research purposes.
You can’t ask it about current events (no access to the Internet), or hard facts, and it will likely struggle with basic Math homework.
For this reason, you can’t rely on ChatGPT for precise answers.
Despite its exceptional capabilities in performing computations, Wolfram Alpha may struggle in understanding the nuances of a user’s inquiry and the intent behind it, unlike ChatGPT. Let’s explore what makes them different.
There are two approaches to building AI systems: statistical and symbolic.
ChatGPT uses a statistical approach, as it is trained on a large dataset of text, and learns the patterns and relationships between words and phrases, allowing it to generate human-like answers.
Wolfram Alpha, on the other hand, uses a symbolic approach. It is a knowledge-based system that uses a set of rules, logic, and representations of knowledge to answer questions and perform computations.
Unlike ChatGPT, Wolfram has its own computation language that can represent as many variables in the real world as possible in formal symbolic ways. You can ask it to compute any fact-based query, ranging from mathematical computations to data analysis, as well as provide factual information on weather, geography, and finance.
But this benefit goes beyond humans, as Alpha could massively augment other AI models as well.
If we combine ChatGPT with Wolfram Alpha, the two models can complement each other, creating a more reliable and complete system. ChatGPT can generate human-like text while Wolfram Alpha can use its knowledge to provide precise, symbolic computational language. This allows the user to ask questions in natural language and get accurate answers based on real data.
Additionally, ChatGPT can be used to provide natural language explanations of the results generated by Wolfram Alpha.
Who is Building a ChatGPT and Wolfram Integration?
Stephen Wolfram recently stirred up interest by suggesting the merging of ChatGPT and Wolfram Alpha. While he didn’t give away any specifics, his blog post left us wondering if the team at Wolfram is secretly working on something revolutionary. Furthermore, he’s actively encouraging developers to come up with their own ideas for blending both language models.
Meanwhile, IBM’s Quantum Computing Advocate, James Weaver, took matters into his own hands and gathered a team of developers to create his own version of this merger. He calls it ChatGPT-LangChain, and while it’s not exactly what Stephen had in mind, it’s a close simulation of it.
Instead of training ChatGPT to work with Alpha, LangChain creates a system that blends both 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 something that’s better suited for a computational model (requires 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.
Think of it as allowing different parts of your brain to perform different tasks that are tailored for it. While Weaver’s idea is simple and practical, a more refined version is likely to be released in the next few months as people begin to see the value in a more complete AI chatbot.
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