Sentiment analysis is at 96% and increasing rapidly. http://nlpprogress.com/english/sentiment_analysis.html
I think continual Turing testing is the only way of concluding whether an agent exhibits intelligence or not. Consider the philosophical problem of the existence of other minds. We believe other humans are intelligent because they consistently show intelligent behavior. Things that people claim to be examples of AI right now lack this consistency (possibly excluding a few very specific examples such as AlphaZero). It is quite annoying to see all these senior researchers along with graduate students spend so much time pushing numbers on those datasets without paying enough attention to the fact that pushing numbers is all they are doing.
[1]: As a concrete example, consider the textual entailment (TE) task. In the deep learning era of TE there are two commonly used datasets on which the current state-of-the-art has been claimed to be near or exceeding human performance. What these models are performing seemingly exceptionally well is not the general task of TE, it is the task of TE evaluated on these fixed datasets. A recent paper by McCoy, Pavlick, and Linzen (https://arxiv.org/abs/1902.01007) shows how brittle these systems are that at this point the only sensible response to those insistent on claiming we are nearing human performance in AI is to laugh.
So you think it's impossible to ever determine that a chimpanzee, or even a feral child, exhibits intelligence? This seems rather defeatist.
Let me elaborate on my previous point with an example. If you look at the recent works in machine translation, you can see that the commonly used evaluation metric of BLEU is being improved upon at least every few months. What I argue is that it's stupid to look at this trend and conclude that soon we will reach human performance in machine translation. Even when comparing against the translation quality of humans (judged again by BLEU on a fixed evaluation set) and showing that we can achieve higher BLEU than humans is not enough evidence. Because you also have Google Translate (let's say it represents the state-of-the-art), and you can easily get it to make mistakes that humans would never do. I consider our prolonged interaction with Google Translate to be a narrow Turing test that we continually apply to it. A major issue in research is that, at least in supervised learning, we're evaluating on datasets that are not different enough from the training sets.
Another subtle point is that we have strong priors about the intelligence of biological beings. I don't feel the need to Turing test every single human I meet to determine whether they are intelligent, it's a safe bet at this point to just assume that they are. The output of a machine learning algorithm, on the other hand, is wildly unstable with respect to its input, and we have no solid evidence to assume that it exhibits consistent intelligent behavior and often it is easy to show that it doesn't.
I don't believe that research in AI is worthless, but I think it's not wise to keep digging in the same direction that we've been moving in for the past few years. With deep learning, while accuracies and metrics are pushed further than before, I don't think we're significantly closer to general, human-like AI. In fact, I personally consider only AlphaZero to be an unambiguous win for this era of AI research, and it's not even clear whether it should be called AI or not.
If you gave 100 chimps of the highest calibre 100 attempts each, not a single one would pass a single Turing test. Ask a feral child to translate even the most basic children's book, and their mistakes will be so systematic that Google Translate will look like professional discourse. ‘Humanlike mistakes’ and stability with respect to input in the sense you mean here are harder problems than intelligence, because a chimp is intelligent and functionally incapable of juggling more than the most primitive syntaxes in a restricted set of forms.
I agree it is foolish to just draw a trend line through a single weak measure and extrapolate to infinity, but the idea that no collation of weak measures has any bearing on fact rules out ever measuring weak or untrained intelligence. That is what I called defeatist.
Wikipedia defines Turing test as "a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human". If we want to consider chimps intelligent, then in that context the definition of the Turing test should be adjusted accordingly. My point still stands: if we want to determine whether a chimp exhibits intelligence comparable to a human, we do the original Turing test. If we want to determine whether a chimp exhibits chimplike intelligence, we test not for, say, natural language but for whatever we want our definition of intelligence to include. If we want to determine whether an artificial agent has chimplike intelligence, we do the second Turing test. Unless the agent can display as consistent an intelligence as chimps, we shouldn't conclude that it's intelligent.
Regarding your point on weak measures: If I can find an endless stream of cases of failure with respect to a measure that we care about improving, then whatever collation of weak measures we had should be null. Wouldn't you agree? I'm not against using weak measures to detect intelligence, but only as long as it's not trivial to generate failures. If a chimp displays an ability for abstract reasoning when I'm observing it in a cage but suddenly loses this ability once set free in a forest, it's not intelligent.
If AI researchers were growing neurons in vats and those neurons were displaying abilities on par with chimpanzees I'd want those researchers to be able to say ‘hold up, we might be getting close to par-human intelligence, let's make sure we do this right.’ And I want them to be able to do that even though their brains in vats can't pass a Turing test or write bad poetry or play basic Atari games and the naysayers around them continue to mock them for worrying when their brains in vats can't even pass a Turing test or write bad poetry or play basic Atari games.
Like, I don't particularly care that AI can't solve or even approach solving the Turing test now, because I already know it isn't human-par intelligent, and more data pointing that out tells me nothing about where we are and what's out of reach. All we really know is that we've been doing the real empirical work with fast computers for 20ish years now and gone from no results to many incredible results, and in the next 30 years our models are going to get vastly more sophisticated and probably four orders of magnitude larger.
Where does this end up? I don't know, but dismissing our measures of progress and improved generality with ‘nowhere near as robust as [...] humans’ is certainly not the way figure it out.
> If I can find an endless stream of cases of failure with respect to a measure that we care about improving, then whatever collation of weak measures we had should be null. Wouldn't you agree?
No? Isn't this obviously false? People can't multiply thousand-digit numbers in their heads; why should that in any way invalidate their other measures of intelligence?
What exactly is incredible (relatively) about the current state of things? I don't know how up-to-date you are on research, but how can you be claiming that we had no results previously? This is the kind of ignorance of previous work that we should be avoiding. We had the same kind of results previously, only with lower numbers. I keep trying to explain that increasing the numbers is not going to get us there because the numbers are measuring the wrong thing. There are other things that we should also focus on improving.
>dismissing our measures of progress and improved generality with ‘nowhere near as robust as [...] humans’ is certainly not the way figure it out.
It is the way to save this field from wasting so much money and time on coming up with the next small tweak to get that 0.001 improvement in whatever number you're trying to increase. It is not a naive or spiteful dismissal of the measures, it is a critique of the measures since they should not be the primary goal. The majority of this community is mindlessly tweaking architectures in pursuit of publications. Standards of publication should be higher to discourage this kind of behavior. With this much money and manpower, it should be exploding in orthogonal directions instead. But that requires taste and vision, which are unfortunately rare.
>People can't multiply thousand-digit numbers in their heads; why should that in any way invalidate their other measures of intelligence?
Is rote multiplication a task that we're interested in achieving with AI? You say that you aren't interested categorizing for the sake of categorizing, but this is a counterexample for the sake of giving a counterexample. Avoiding this kind of an example is precisely why I said "a measure that we care about improving".
Compared to 1999?
Watch https://www.youtube.com/watch?v=kSLJriaOumA
Hear https://audio-samples.github.io/#section-4
Read https://grover.allenai.org/
These are not just ‘increasing numbers’. These are fucking witchcraft, and if we didn't live in a world with 5 inch blocks of magical silicon that talk to us and giant tubes of aluminium that fly in the sky the average person would still have the sense to recognize it.
> It is the way to save this field from [...]
For us to have a productive conversation here you need to either respond to my criticisms of this line of argument or accept that it's wrong. Being disingenuous because you like what the argument would encourage if it were true doesn't help when your argument isn't true.
> Is rote multiplication a task that we're interested in achieving with AI?
It's a measure for which improvement would have meaningful positive impact on our ability to reason, so it's a measure we should wish to improve all else equal. Yes, it's marginal, yes, it's silly, that's the point: failure in one corner does not equate to failure in them all.
What about generative models is really AI, other than the fact that they rely on some similar ideas from machine learning that are found in actual AI applications? Yes, maybe to an average person these are witchcraft, but any advanced technology can appear that way---Deep Blue beating Kasparov probably was witchcraft to the uninitiated. This is curve fitting, and the same approaches in 1999 were also trying to fit curves, it's just that we can fit them way better than before right now. Even the exact methods that are used to produce your examples are not fundamentally new, they are just the same old ideas with the same old weaknesses. What we have right now is a huge hammer, and a hammer is surely useful, but not the only thing needed to build AI. Calling these witchcraft is a marketing move that we definitely don't need, creates unnecessary hype, and hides the simplicity and the naivete of the methods used in producing them. If anybody else reads this, these are just increasing numbers, not witchcraft. But as the numbers increase it requires a little more effort and knowledge to debunk them.
I'm not dismissing things for the fun of it, but it pains me to see this community waste so many resources in pursuit of a local minimum due to lack of a better sense of direction. I feel like not much more is to be gained from this conversation, although it was fun, and thank you for responding.
I'm not evaluating these models on whether they are AGI, I am evaluating them on what they tell us about AGI in the future. They show that even tiny models, some 10000x to 1000000x times smaller than what I think are the comparable measures in the human brain, trained with incredibly simple single-pass methods, manage to extract semirobust and semantically meaningful structure from raw data, are able to operate on this data in semisophisticated ways, and do so vastly better than their size-comparable biological controls. I'm not looking for the human, I'm looking for small scale proofs of concepts of the principles we have good reasons to expect are required for AGI.
The curve fitting meme[1] has gotten popular recently, but it's no more accurate than calling Firefox ‘just symbols on the head of a tape’. Yes, at some level these systems reduce to hugely-dimensional mathematical curves, but the intuitions this brings are pretty much all wrong. I believe this meme has gained popularity due to adversarial examples, but those are typically misinterpreted[2]. If you can take a system trained to predict English text, prime it (not train it) with translations, and get nontrivial quality French-English translations, dismissing it as ‘just’ curve fitting is ‘just’ the noncentral fallacy.
Fundamental to this risk evaluation is the ‘simplicity and the naivete of the methods used in producing them’. That simple systems, at tiny scales, with only inexact analogies to the brain, based on research younger than the people working on it, is solving major blockers in what good heuristics predict AGI needs is a major indicator about the non-implausibility of AGI. AGI skeptics have their own heuristics instead, with reasons those heuristics should be hard, but when you calibrate with the only existence proof we have of AGI development—human evolution—, those heuristics are clearly and overtly bad heuristics that would have failed to trigger. Thus we should ignore them.
[1] Similar comments on ‘the same approaches in 1999’, another meme only true at the barest of surface levels. Scale up 1999 models and you get poor results.
[2] See http://gradientscience.org/adv/. I don't agree with everything they say, since I think the issue relates more to the NN's structure encoding the wrong priors, but that's an aside.
Actual sentiment analysis is a completely different kind of ML problem than supervised classification 'sentiment analysis' that's popular today but mostly useless for real world applications.
To get useful value out of automated sentiment analysis, that's the cost to build and maintain domain specific models. Pre-canned sentiment analysis models like the parent post linked are more often than not worthless for general purpose use. I won't say there are 0 scenarios where those models are useful, but the number is not high.
Claiming that sentiment analysis is 90something percent accurate, or even close to being solved, is extremely misleading.