Pattern matching can solve everything, if given enough storage and training data. Memorizing trillions of sentences is basically what makes GPT3 amazing.
You're absolutely correct that patten matching AIs won't ever be truly intelligent. But then again, many humans also never exceed what can be simulated with good pattern matching. And an AGI household robot only needs to be as smart as the maid that it's replacing.
I'm optimistic that pure pattern matching will get us to usable AGI AI.
Pattern matching can solve everything, if given enough storage and training data.
There's never going to be training data for "how things are going to be next year". A lot of large scale systems involve emergence [1], patterns which previously were not visible suddenly appearing. I think even today's AI can do things that a bit beyond pattern matching (learning to learn, etc). But pure matching as such is inherently limited.
I would be surprised if AI predictions for "how things are going to be next year" would be worse than expert human predictions at the 90% percentile. I mean most trends for next year will already be around this year, they'll just be too weak to notice.
I believe pattern matching is an important part, but intelligence comes from how you organize these patterns and relate them to one another. E.g. you can learn pictures of a dog by pattern matching but you can’t learn if a dog can beat up a bear, if there’s a bear outside a human just knows not to let the dog out.
What we need is a pattern detector + a the ability to create basically infinite ANNs (or be able to multitask on them) + an event loop that takes input feeds (from cameras, microphones etc,) does some kind of reasoning and then pushes to its output feeds (wheels, etc.)
I think you use pattern matching to extract unique objects, store these objects as a node with its own simple neural net + long term storage where it only stores pictures of this object plus a dataset about it e.g, how often you see it. You then you organize them into an object hierarchy. Each new object is compared against all other objects we’ve stored using their pattern marchers. The higher the output the more weight we give their “connection.” Each object is made up of of sub objects so they are the top of their own tree as well, so you can run this pattern finder on the dataset of individual objects itself and if you find new objects the tree recurses. You can then check these objects against existing ones etc.
A general intelligence does this constantly, in real time. Then it’s a quick algorithm.
1. Have I seen this object before
2. No, but it shares characteristics with animals (an object that groups together all things that look like animals.)
3. It’s much larger than my dog, and I’ve seen large animals attack small ones more often than not.
4. My dog is also a dick, and attacks other animals more often than not
5. It’s probably a threat
Just scaling modern compute won’t get you there unless you’re willing to dedicate a few orders of magnitude more energy than a human being to do so. You need a completely different, distributed, architecture if you are going to be able to compare billions of objects against billions of objects every time you see something new and in real time.
Machine Learning is great but it’s only the learning part. Intelligence is reasoning about multiple things in relation to one another not detecting a pattern. You might trick yourself into thinking you’re getting there because pattern matching is powerful but it’ll get you to the intelligence of microbe at best. Even then you need something that’s driving the actions.
You're absolutely correct that patten matching AIs won't ever be truly intelligent. But then again, many humans also never exceed what can be simulated with good pattern matching. And an AGI household robot only needs to be as smart as the maid that it's replacing.
I'm optimistic that pure pattern matching will get us to usable AGI AI.