That is the second time in recent months that the AI world has got all enthusiastic about math. The rumor mill went into overdrive last November, when there have been reports that the boardroom drama at OpenAI, which saw CEO Sam Altman temporarily ousted, was brought on by a recent powerful AI breakthrough. It was reported that the AI system in query was called Q* and will solve complex math calculations. (The corporate has not commented on Q*, and we still don’t know if there was any link to the Altman ouster or not.) I unpacked the drama and hype on this story.

You don’t must be really into math to see why these things is potentially very exciting. Math is de facto, really hard for AI models. Complex math, resembling geometry, requires sophisticated reasoning skills, and plenty of AI researchers imagine that the flexibility to crack it could herald more powerful and intelligent systems. Innovations like AlphaGeometry show that we’re edging closer to machines with more human-like reasoning skills. This might allow us to construct more powerful AI tools that might be used to assist mathematicians solve equations and maybe give you higher tutoring tools. 

Work like this may help us use computers to succeed in higher decisions and be more logical, says Conrad Wolfram of Wolfram Research. The corporate is behind WolframAlpha, a solution engine that may handle complex math questions. I caught up with him last week in Athens at EmTech Europe. (We’re hosting one other edition in London in April! Join us? I’ll be there!) 

But there’s a catch. To ensure that us to reap the advantages of AI, humans must adapt too, he says. We’d like to have a greater understanding of how the technology works so we will approach problems in a way that computers can solve. 

“As computers improve, humans need to regulate to this and know more, get more experience about whether that works, where it doesn’t work, where we will trust it, or we will’t trust it,” Wolfram says. 

Wolfram argues that as we enter the AI age with more powerful computers, humans must adopt “computational pondering,” which involves defining and understanding an issue and after which breaking it down into pieces in order that a pc can calculate the reply. 

He compares this moment to the rise of mass literacy within the late 18th century, which put an end to the era when just the elite could read and write.  

“The countries that did that first massively benefited for his or her industrial revolution … Now we want a mass computational literacy, which is the equivalent of that.”