Experts Experts Weigh In on How AI is Trained Weigh In on How AI is Trained Evolutionary Neural Network AI

By Aidan Gannon

Most people assume that modern AI are smarter because their “brains” got bigger, but this is usually the opposite. It’s actully because modern AI are tested with tens of thousands of population, not a hundred at most. Infact, AI’s neural networks used to be bigger.

Generally, a “Neural Network” type AI is used. These adapt and take in information, modify the “values” via hidden nodes, and then export them via output nodes. These AI are modeled after our brains, but unlike our brain with billions of neurons, these usually have at most 125. These learn slowly, but are able to perform tasks much more effectively.

These AI are used for reccomendation systems, along with the other main type of AI. Some of these have many slight varitons runned overtime, producing better AI slowly. Usually they have set amount of nodes between the input and output. The first one without this was NEAT, to model the brain more. Usually though it uses a “Genetic algorithim”, similar to natural selection.

In ChatGPT

The Neural Networks here have giant inputs and outputs. When asked about how big the ChatGPT neural network is, Kenneth couldn’t give specifics but it has massive amounts of input and output nodes, and a lot of hidden nodes. Unlike normal evolutionary networks, one “species” (a population of a generation) gets thousands of data points from users. Everytime the AI gets liked, it is more likely to repeat that process.

“And it was funny to watch the AI that created both the voice like what it sounded like, as well as the things that it said. It would kind of flip between a serious mode and sort of a joking mode. But of course, it's AI that doesn't really understand what it's doing.”

How AI... can lie unknowingly

Yes, ChatGPT can lie. It doesn’t know the difference between truth and plausability. It usually lies through a process called “Hallucinating.” This is similar to people in court cases when they don’t have all the information, and make something that seems to fit. Usually it’s because of lack of data. Sometimes this would be from overfitting (Not knowing that topic well, but knowing one topic extremely) or underfitting (not knowing anything). And if an AI doesn’t know a topic it just makes something up.

How does AI learn

“You know, I was pretty convinced that the brain, the human brain or any other natural brains don't look like these fixed, multilayer structures that people were using and that there must be some reason for that.”

The image does not show many of the smaller conections. The unused node is from the AI having a very low bias for most connections to the node of around ~0.01. This effectively makes it unused. The green is when if it is above threshold (input node) is it positive or negative, or if it is negative if it was positive. The line thickness is based on the “bias”, or multiplier, that connection recieves.

(image exported from NEAT.py after 1024 generations of a cartpole AI)

They learn slowly, and optimize over time. The image below shows a AI learning, using high population runs. High population means more is known and more combinations can be tried. As the neural networks get bigger, it takes longer to run, as 620 generations took 10.6 hours and 900 generations took 19.1 hours. Interestingly, the best Ai don’t survive; sometimes the worst do. This is demostrated that the AI peaked at ~880 fitness, but decreased down to ~810 after a few generations.

“Yeah. Which is really interesting. I mean, that whole phenomenon is not very human. It doesn't seem human at first, although maybe if we think about it, it might be kind of human, but just the capacity or the tendency to just think that something completely made up is actually a legitimate answer.”