The Complexity Of Evaluating AI Thinking

By this point, we know that AI can beat sophisticated Turing tests. But how does it “think,” exactly? Perhaps a better and less vague way to phrase it would be: how does the neural net use those artificial neurons to come up with its cognitive or quasi-cognitive behaviors?

Ok, let me take another stab at that: how does AI differ from the ways that humans think with biological brains?

It has to be evident that one major difference is access to information.

We humans are not born with access to information. We have to acquire it – in analog ways that are extremely inefficient. The AI, by contrast, is able to connect to the Internet and scan the known body of information on there in the blink of an eye – which gives the LLMs a tremendous advantage.

Having said that, we come to deeper questions about how AI entities and humans use what they’ve learned. Some scientists and research teams are exploring this in some interesting ways, ways that I think most people would find it hard to follow.

Take this paper on the scaling of AI, versus the scaling of human “performance.” How do you measure cognitive performance? These researchers are game to pursue this fairly broad question, which I find difficult, given the abstraction of even measuring human cognitive performance. We’re biological brains, in physical, biological bodies, with limited data, and all kinds of errant stimuli around us all the time, in a physical environment that is chaotic and disordered, to say the least.

The Playful Life

That leads to another interesting foray into AI cognition that’s oriented toward the creative side of thinking. It plays into our ongoing analysis of AI agents that are more autonomous than what came before them, their digital “ancestors,” so to speak.

Memory and Impact

Leaving aside that thorny onion, let me talk a little bit about what I encountered in the comments.

He left this link to a paper.

If you’re not familiar with what a homeostatic memory mechanism is, you’re not alone.