Jaden Schaefer

Let me explain it.

In the most simple terms, 100 times less energy and nearly triple the accuracy is what they've been able to achieve.

So when they're training these systems, when they're using these AI, they're able to

get outputs for 100 times less energy, and the accuracy is tripled.

Basically, the approach they took is merging some traditional neural networks with symbolic rule-based reasoning.

Instead of kind of throwing these massive compute at pattern matching, the system is kind of breaking the problem into a bunch of smaller logical steps and categories.

And then they're going to, I mean, what's interesting, though, is this is basically a lot closer, I think, how humans actually think through a problem, right?

When you have a big problem, you think, okay, what are the steps to, like, achieve that?

You kind of break it down in your head.

I mean, that's literally where the term break it down came from.

And they're just teaching the AI model to do this in a more human way.

And surprise, surprise, it saves energy.

Obviously, our brains are designed to not, you know, burn too much energy when we're trying to figure stuff out.

I think this matters because U.S.

data centers and also AI workloads now consume more than 10%.

of the entire country's total electrical output.

Like this is so much electricity.

And I think that number is projected to double by 2030.

So if neuro symbolic approaches can deliver this kind of efficiency gain across more domains, I think it's going to make a really big impact.

Not just, you know, some people are like, oh, it's awesome for the environment.