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It's totally valid to define AI this way. But just keep in mind, when most people talk about AI, they're knowingly talking of something much more limited. So y
by edanm 8y ago
It's totally valid to define AI this way.
But just keep in mind, when most people talk about AI, they're knowingly talking of something much more limited. So you're going to constantly have communication failures with people who are defining AI differently than you.
(Many people nowadays use the term AGI [Artificial General Intelligence] to mean what you think of as AI, btw).
- ekianjo 8y agoYeah I know about AGI but I dont like that term because it implies that the classifiers we have nowadays are good enough to be called "intelligence". They are just statistical models with great number of layers, nothing else.
- davnn 8y agoMaybe you want to read: https://en.m.wikipedia.org/wiki/Model-dependent_realism https://en.m.wikipedia.org/wiki/Model-dependent_realism
- YeGoblynQueenne 8y agoYour description ("statistical models with great number of layers") tells me you're talking about neural networks. However, we have "nowadays" many classifiers that are not neural networks and therefore have no layers of any sort, like SVMs, KNN or logistic regression and are not even statistical, like decision trees/forests. I should also point out that literally all the classifiers "we have nowadays" as per your comment, have been known for at least 20 years (including deep neural networks). I'm pointing all this out because your comment suggests to me that your knowledge of AI and machine learning in particular is very recent and goes as far as perhaps the last five or six years, when deep nets popularised the field. If that is so- please consider reading up on the history of AI. It is an interesting field that goes back several decades and has had many impressive successes (and some resounding failures) that predate deep learning by many years. I recommend the classic AI textbook "AI- A modern Approach" by Stuart Russel and Peter Norvig. You'll notice there that, even in recent versions, machine learning is a tiny part of the material covered. Because there is so much more to AI than just deep neural networks, or statistical classifiers. If I'm wrong, on the other hand, and you already have a broad knowledge of the field, then I apologise for assuming too much.
- User23 8y agoPeter Norvig's Paradigms of AI Programming: Case Studies in Common Lisp is a good read too.
- srtjstjsj 8y agoCan you prove that human intelligence is not statistical models with great number of layers, nothing else (besides some hard-coded instincts)?