I'm not saying that silicon/mechanical intelligence isn't possible. I'm unaware of any physical law that precludes it. But what we currently call "AI" is just the pathetic fallacy run wild.
All that said, multidimensional data-driven linear recognizers are pretty impressive.
One of the first few lines on Wikipedia about AI:
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, "AI is whatever hasn't been done yet."
I think the key concept here is Moravec’s Paradox. People believing in the "AI is whatever hasn't been done yet" quip are moving the goalpost in the definition.
Consider speech recognition. Obviously an AI problem solved, right? Except, the AI problem and what the current solutions are solving are two different things. The AI problem is understanding and appropriately reacting to spoken words, as if a human was on the other end. Current implementations are glorified pattern matchers run over clever hashes of sound recordings. There is no understanding happening, there are no concepts forming within the machine (and the understanding is not back-fed into pattern matcher to correct the sensory input on the fly). The difficult parts, the ones that make speech an AI problem, have been entirely sidestepped with mathematical tricks. It sort of works, but its scope is nowhere near the original AI problem.
Similar analysis can be made for anything that is mentioned with the "no longer AI" quip. Can a DNN recognize a hot dog? Sort of, for some definition of "hot dog", only if input images are clear and similar enough to the training set. It's a cute trick, and you can make a business out of it if you control enough of the environment around the system, but it's still nowhere near what we mean when thinking about AI recognizing objects.
If you can't replicate what a human do, it's not AI. The fact that we can only beat humans for very, very narrow applications/games and that we don't have a generalized model for learning is a clear failure of the AI hype.
We do have a generalised model for learning- it's called PAC learning [1] and it's part of Computational Learning Theory, which studies learning in computers.
Note that machine learning may be popular today, thanks to the recent successes of deep learning but learning is not the only thing that makes humans intelligence. For instance, there is reasoning about what you know, and possibly other stuff like motivation, etc.
Valid definition, why not. My point is that talking about AI without defining what you mean doesn't make sense.
Honestly, that's also the problem with most articles about AI. Articles about AI are either praising the great mystical AI for recognizing cats or blaming AI for not achieving X, but humans can.
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).
Yeah 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.
Your 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.
A dog can't replicate what a human can do, therefore it's not intelligent, right?
Intelligence is a spectrum, some of the things we have created are intelligent by the definition of the word. They are not human level intelligence, yet.
I’m not sure about the quality of my linked article but it seems like that humans becoming good at chess does not noticeably improve their other skills. They will simply be better at chess.
So our expectations might bee too high against AI getting vastly better only by doing better transfer learning.
Learning chess will make you better at other board games, even if it doesn't improve, say, your maths or language skills. For instance, chess may teach you lessons about sacrificing assets to earn a reward later on, or lessons about material advantage, tempo, controlling the board and so on, that can be applied to many different games. That counts as transfer learning and it is something that statistical machine learning cannot do at all, basically (i.e. a new model has to be trained from scratch for each different game).
As an aside- you might argue that there are common elements of board games' design that make it easier to reuse knowledge. However, statistical machine learning systems can still not transfer knowledge of one game to others, even given the common design elements that should make it easier.
I believe that intelligence is more than quantifiable. Von Neumann machines, no matter how complex, will never achieve intelligence. Again, to be completely clear, I’m not claiming machine intelligence is impossible, just that what we see presently isn’t it in any honest sense.
What do you even mean by "Von Neumann machines, no matter how complex, will never achieve intelligence."? If you mean machines that self-replicate, then there're plenty counterexamples around already (https://www.xkcd.com/387/). If you mean computers using the von Neumann architecture, then that's weirdly specific, since modern computers are at best vaguely inspired by that model, and GPUs in particular work very differently.
You're using Von Neumann machine in the sense of a self-replicating machine. I think the commenter you're replying to meant Von Neumann architecture, which is the basic layout of a computer, and has nothing to do with self-replication.
I can't help repeat this quote from a previous thread. It's from an interview of a lecturer at the London Business School who appeared on a BBC TV programme about technology:
"...Expert systems are really slightly dumb systems that exploit the speed and cheapness of computer chips...There are many expert systems in the literature which are nothing more than a series of fast if-then-else rules...you do that a couple of hundred thousand times it can look remarkably intelligent"
The interview above was broadcast in...1984. There have been advances since then of course, but at the same time this 30-year-old quote still has a certain ring of truth to it.
The surprising thing might be that humans and other "thinking" animals are nothing more than a series of fast if-then-else rules too.
Not sure if this one has been debunked yet, but I remember learning in my psych class about the guy who fell asleep, got out of bed, drove a car a bunch of miles, and at his destination murdered his in-laws, all while apparently asleep.
It was used as an example for conscious/unconscious behavior.
You must remember that "Expert System" is a term used in machine learning which specifically means a system in which the input is (logically, not literally) processed by humans at every step until the output is provided, rather than a term to describe "the best" AI systems.
As mentioned in the quote, this is usually done though if/else "if this is the case, then the next step is that".
The reason they are called Expert Systems is that they provably provide the correct output for any input 100% of the time.
The idea that ai will be a revolutionary leap is probably nonsense. What we have seen so far is slow incremental evolutionary development of the tech over decades. Everyone raising money is claiming that the quantum leap is just around the corner but there is very little evidence to support that.
What you actually see is that improvements in perceived machine intelligence show diminishing returns to increasing compute capacity which is a good sign that people are on the wrong track to achieve general ai and that future improvements in perceived intelligence will grow at a slower rate, not exponentially increase.
Unless you mean General AI. We don't have that yet.
AI is huge. Machine learning definitively is changing how people work and will work. It is not there yet but it'll get there. Some people just want to monetize on the hype.
I'm not saying that silicon/mechanical intelligence isn't possible. I'm unaware of any physical law that precludes it. But what we currently call "AI" is just the pathetic fallacy run wild.
All that said, multidimensional data-driven linear recognizers are pretty impressive.