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Comment by Zenst | Hacker News Reader

Me personaly, I define AI as a system that can be fed non-biased input and learn from that input to derive conclusions that we as humans can relate too.

So feeding a system lots of cat pictures (ie biased-input) to teach it what a cat is for me is not AI. But a system which you feed in lots of random pictures and it learns by itself what a cat is, that would be AI, least for me.

What will be really interesting is a system which you can feed in all the childrens section and see what comes out at the end, would be most insightful into how we teach children and what we teach them. So a completely different area of AI use from that alone - learning how to learn better.

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You're describing supervised vs unsupervised learning. We have both systems, one isn't really defined more as AI than the other.

Good to know. Though thinking it thru, any unsupervised learning would have to have some reference points, be them hard codeded into the core system to ina form of supervised learnings. Otherwise the AI could recognise a cat but would know it by a completely different name and unless it had been given a good description of a cat to associate with such image forms or a picture with a label. Then it would never know what a cat was in the way we know them. Which may be a good or a bad thing. But if the AI referers to cats as 478912's then we would not know what it was on about and whilst it may be intellegent, it would be in a way that we would be unable to understand and relate too. Ironicly i suspect if you had a top end AI system and asked it what defined AI it may very well come back with the answer 42, which many would even understand, though no comprehend.

Fun feild of work still, I bet.

Well, you can show it a photo of a cat and ask it "what's this?", and it'll say "this is a 478912". That's how clustering works. Obviously it's not going to just speak to you, but there are ways of extracting the categories out of it (obviously, otherwise why run it if you can't read the results?)!

>>But a system which you feed in lots of random pictures and it learns by itself what a cat is, that would be AI, least for me.

A very good definition. Also adding to your point. Lets say a machine is fed with a billion pictures. Can the machine automatically categorize them by reading through them? Doesn't matter if it refers to a cat as some alphanumeric name 'ab12er' or a dog as 'p09iuy'.

But it should be able to categorize them. Then it should be able to read some encyclopedia or some source of information and study the behaviors of 'ab12er' and 'p09iuy'. Or the opposite, see 'ab12er' and 'p09iuy' and recognize them and describe them what they are.

Seems to me you are defining AI as a generalized categorization algorithm. This sounds very narrow as it doesn't take into account any actual intelligent behavior. For example, I believe constructing tools in order to accomplish a specific goal (eg, constructing a net in order to catch fish) to be intelligent behavior. The categorization AI you describe would be incapable of this. Though it does sound like it would be a necessary prerequisite.

Thing is though by giving a encyclopedia you are feeding in a bias thru a form of base reference. Thinking it thru a system could learn by itelf what a cat was, but it would not know it by that name. With that the creation of AI could be truly artifical and intellegent, but in a form we do not comprehend or even understand, yet it could be in its own right intellegent.

That's called unsupervised learning and it already exists. Supervised learning is more practical though. I'm not sure why you distinguish them because they are both AI and sometimes even the same algorithms.

This is called Deep Learning. It can automatically form clusters from unlabelled training data and that is why there is a lot of excitement around it right now.

For that to happen the AI needs to be pointed to what is a cat.

Children know cats by mimicking adults who point out the objects to them.

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