Why are brains so much better at learning/improvising than computers?
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Brains have the remarkable ability to learn and improvise because they can adapt, make connections, and understand complex patterns in real-time, which computers still struggle to replicate accurately.
Instead of thinking of the brain as a single computer, remember that it is made up of almost 200 billion cells, which are themselves fairly competent; individual cells can hunt, flee predators, find mates, & even learn. Single celled animals are some of the most successful organisms on Earth. So imagine the brain is actually a composite of almost 200 BILLION individual computers, a single neuron can have over 15,000 connections with other cells. Some people estimate there may be over 600 trillion connections in the human brain.
We’ve really only just begun to learn about the brain, our understanding of it is exceptionally limited, and grossly over exaggerated.
It’s all about specificity vs. generalisation. Computers process all information as numbers and therefore base their memory and “thinking” on very rigid mathematical algorithms. This means that they are able to process a lot of information very quickly, and recall that information accurately, but it isn’t good at making connections because it is so precise that either two things are the same or their not the same.
The human brain is nothing like a computer. It processes a different information from your different senses and instead of thinking in maths it is based on concepts. This is a much less precise, but much more flexible system. A computer doesn’t know what a cat is in general. It can know that these specific things are cats and will recall exactly how that specific cat looked. Your brain, however, focuses on the broad strokes so it can take the characteristics it has seen in the different things called cat and tell whether something new it sees is a cat or not, but it will never be able to perfectly recall how a specific cat looked.
The latest advances in AI, neural networks, is actually trying to mimic how the human brain works.
Brains are sentient. A computer is not a brain. It follows commands in program. It can process information really fast but it cannot adapt or make adjustments that are outside of the commands or limitations of the software.
People *want* things, and have the capability of figuring out through experience how to get what they want.
Computers don’t want anything, they just execute instructions. If you make a very clever set of instructions, you can sort of simulate a system that “wants” to accomplish specific things, like “recognizing crosswalks” or “picking the most likely next word in this sentence,” but these are very narrow compared to human desires as we interact with our environment.
Computers are called that way because they can compute *anything*. They are universally programmable for any task. The trouble is that it’s not easy to figure out how to program them to do really complicated things.
Now, when I say “complicated”, you might think about things like math or chess. But those are actually simple from the point of view of a computer, because they follow very clearly defined rules. You can write all the rules of chess on a small piece of paper, and it’s not that hard to explain them. But how do you explain, completely and precisely, how to identify a face in an image, based on its pixel values? This is something that we find so easy that we don’t even think about it, but turns out to be really difficult to express in a computer program.
What we have come to realize more and more in recent years is that this is not the way to go. It’s much better to write a program that allows the computer to learn on its own. Then you give it a bunch of images, and you say “these ones contain faces, these ones don’t – please learn a way to decide which is which”. And you leave it to the algorithm to come up with a good way to do it.
These algorithms work with *artificial neural networks*. These are simulations of computational units that are simplified versions of the neurons in our brains. Neurons connect to other neurons, and what they compute depends on which neurons they are connected to and how strongly. This means that you can change what’s being computed by altering these connection strengths, which turns out to be very helpful for learning things, as you can keep making small changes to the connections and see whether things improve or not (and in fact, you can use maths to figure out directions in which to change the connections that are likely to lead to improvement, so that you don’t just have to try at random).
The brain itself is also a neural network, only a much more complicated one. And the brain has its own learning algorithm(s) that allow it to adapt the connections between neurons to learn new things. We don’t know exactly how these learning algorithms work – this is a hot topic of research.
So in short, the brain comes pre-wired as a learning machine. Computers do not, but they can be programmed to learn, and when you do, they can start do to things similar to what brains can do.
When it comes to improvisation, that’s more a question of having enough world knowledge to be able to adapt to different scenario’s you haven’t seen before or haven’t specifically been trained for. Most AIs struggle with this as they are trained on rather specific tasks, so they can do those tasks very well but not anything new that you give them. Recently of course this is starting to change to with systems like ChatGPT that can answer questions about many different topics (but still can’t, for instance, ride a bike, or recognize images, as that’s not what these *large language models* are trained for). The more general world knowledge an AI has learned, the more it will be able to improvise.
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