Viewing a single comment thread. View all comments

VirtualHat t1_j45dklv wrote

I think Russell and Norvig is a good place to start if you want to read more. The AI defintion is a taken from their textbook which is one of the most cited references I've ever seen. I do agree however that the first defintion has a problem. Namely with what 'intellegently' means.

The second defintion is just the textbook defintion of ML. Hard to argue with that one. It's taken from Tom Mitchell. Formally “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” (Machine Learning, Tom Mitchell, McGraw Hill, 1997).

I'd be curious to know what your thoughts on a good defintion for AI would be? This is an actively debated topic, and so far no one really has a great defintion (that I know of).


tell-me-the-truth- t1_j45e4gv wrote

yeah I can see the point behind ML definition.. i guess i was trying to say you don’t always get better with more data. the performance might saturate at some point or the new data you add could be garbage.. so i found it a bit odd to tie definition of ML to the quantity of data.. the definition you linked talks about experience.. i’m not sure how it’s defined.


VirtualHat t1_j45em2b wrote

Yes true! Most models will eventually saturate and perhaps and even become worse. I guess it's our job then to just make the algorithms better :). A great example of this is the new Large Langauge Models (LLM), which are trained on billions if not trillions of tokens, and still keep getting better :)