[VorpalWay]

It is not just AGI that is poorly defined. Plain AI is moving goalposts too. When the A* search algorithm was introduced in the late 60s, that was considered AI, when SVM (support vector machines) and KNN (K nearest neighbor) were new, they were AI. And so on.

These days it is neural networks and transformer models for language in particular that people mean when they say unqualified AI.

It is very hard to have a meaningful discussion when different parties mean different things with the same words.

[dataflow]

I think the Turing test ought to be fine, but we need to be less generous to the AI when executing it. If there exists any human that can consistently tell your AI apart from humans without without insider knowledge, then I don't think you can claim to have AGI. Even if 99.9% of humans can't tell you apart.

So I'm very curious if any AI we have today would pass the Turing test under all circumstances, for example if: the examiner was allowed to continue as long as they wanted (even days/weeks), the examiner could be anybody (not just random selections of humans), observations other than the text itself were fair game (say, typing/response speed, exhaustion, time of day, the examiner themselves taking a break and asking to continue later), both subjects were allowed and expected to search on the internet, etc.

[hattmall]

>So I'm very curious if any AI we have today would pass the Turing test under all circumstances

Are you actually curious about this? Does any model at all come even remotely close to this?

[Wowfunhappy]

I really wish I could wave a magic wand and make everyone stop using the term "AI". It means everything and nothing. Say "machine learning" if that's what you mean.

[catlifeonmars]

To be pedantic, “machine learning” is even underspecified. It’s marginally better in that it sheds _most_ of the marketing baggage, but it still refers to a general concept that can mean may different things across different fields of study.

[VorpalWay]

Machine learning: that definitely includes SVM and regression models. Oh and decision trees. Probably a few other things I'm not thinking of right now. Many people will unfortunately be thinking of just neural networks though.

(By the way, if something like a regression model or decision tree can solve your problem, you should prefer those. Much cheaper to train and to run inference with those than with neural networks. Much cheaper than deep neural networks especially.)

[Wowfunhappy]

Wait, a decision tree is machine learning?

[krackers]

Expert systems are basically decision trees which are "gofai" (good old fashioned ai) as opposed to deep learning. I've never really seen a good definition for what counts as "gofai" (is all statistical learning/regression gofai? What about regression done via gradient descent?). There's some talk in [1]

[1] https://www.beren.io/2023-04-10-Why-GOFAI-failed/

[VorpalWay]

Yes: you fit a decision tree to your dataset in an automated fashion, that fits the definition of machine learning. Just as you would use backpropagation to fit a neural network to your data.

This is what I learnt at university some decades ago, and it matches what wikipedia says today: https://en.wikipedia.org/wiki/Decision_tree_learning

[Wowfunhappy]

Oh, if the tree is made by the computer based on training data, that feels to me like what most people would agree is “artificial intelligence” in 2026 (which is why I think people should actually say “machine learning”).

[VorpalWay]

That is how decision trees are usually made in my experience. Though I guess you could make one by hand. You could also make a (small) neural network by hand.

In which case you could argue that neither DTs nor NNs are ML. Only the training itself is ML/AI. An interesting perspective, but this will probably just confuse the discussion even further.

[marcus_holmes]

Agree. I talk about LLMs when discussing them, and avoid the term "AI" unless I'm talking about the entire industry as a whole. I find it really helps to be specific in this case.