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Comment by mnd999 | Hacker News Reader

Anyone else getting a bit bored with all these AI does some super specialised task better than humans after enormous amounts of training. It’s not very interesting anymore.

Sure, it can do crosswords well but the average human that does crosswords well can also do a zillion other things and this type of AI is not getting us any closer to that.

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If you skim the paper, you’ll realize what’s most interesting are the new techniques they developed to accomplish this, advancing the field of machine learning in the process.

Constraint propagation and search are hardly new techniques. In fact they're both from or from before the previous "AI winter." It's a shame they've been left out of so many of the deep learning generation's wheelhouses. They were considered fundamentals by the field a very short time ago.

Also, I'm not knocking this paper at all. I think it's a great applied paper! Stitching together techniques to actually do something is a Herculean task. Hell, it's mostly what I do too.

Do you have any idea just how specialized the human brain is?

I can just imagine if evolution was a side spectator event, people commenting: "Broca's area just regulates breathing. And that Wernicke's area is just pattern recognition in sounds. Those aren't going to get us to anything important."

Point me to actual large generalized models in nature that aren't composed of smaller specialized functions and you might have a leg to stand on.

(Oh wait, no, those legs things are pretty specialized too, and each have their own specialized parts. Bad analogy.)

Well, good luck with your identifying an example of complex generalization without subspecialties!

But every specialized model like this is getting us closer to "doing a zillion other things." By logic it is exactly one step closer. The general AI agent will be composed of many such models.

> The general AI agent will be composed of many such models.

Not necessarily, there is good evidence that a single model can work for many tasks - for example, the recent Gato system https://www.deepmind.com/publications/a-generalist-agent is a good example. It's just that we usually don't do that because for most practical purposes we want an agent for a specific purpose, and for most research experiments we want a simpler experiment to isolate some factor, so we usually train single-task models and don't try to make general systems.

Gato is a model of models, if you read the paper they trained it on multiple datasets, and is actually what I was referring to in my comment.

Gato is not a model of models - of course they trained it on multiple datasets, but the result is a single model that shares weights across the tasks (the key difference between single model and model-of-models architectures) so it is able to transfer knowledge from one dataset to the other datasets and also generalizes to unseen tasks on which Gato was not trained at all.

> this type of AI is not getting us any closer to that.

That is not obvious at all.

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