Slashdot Mirror
'Modern AI is Good at a Few Things But Bad at Everything Else' (wired.com)
Jason Pontin, writing for Wired: Sundar Pichai, the chief executive of Google, has said that AI "is more profound than ... electricity or fire." Andrew Ng, who founded Google Brain and now invests in AI startups, wrote that "If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future." Their enthusiasm is pardonable.
[...] But there are many things that people can do quickly that smart machines cannot. Natural language is beyond deep learning; new situations baffle artificial intelligences, like cows brought up short at a cattle grid. None of these shortcomings is likely to be solved soon. Once you've seen you've seen it, you can't un-see it: deep learning, now the dominant technique in artificial intelligence, will not lead to an AI that abstractly reasons and generalizes about the world. By itself, it is unlikely to automate ordinary human activities.
To see why modern AI is good at a few things but bad at everything else, it helps to understand how deep learning works. Deep learning is math: a statistical method where computers learn to classify patterns using neural networks. [...] Deep learning's advances are the product of pattern recognition: neural networks memorize classes of things and more-or-less reliably know when they encounter them again. But almost all the interesting problems in cognition aren't classification problems at all.
[...] But there are many things that people can do quickly that smart machines cannot. Natural language is beyond deep learning; new situations baffle artificial intelligences, like cows brought up short at a cattle grid. None of these shortcomings is likely to be solved soon. Once you've seen you've seen it, you can't un-see it: deep learning, now the dominant technique in artificial intelligence, will not lead to an AI that abstractly reasons and generalizes about the world. By itself, it is unlikely to automate ordinary human activities.
To see why modern AI is good at a few things but bad at everything else, it helps to understand how deep learning works. Deep learning is math: a statistical method where computers learn to classify patterns using neural networks. [...] Deep learning's advances are the product of pattern recognition: neural networks memorize classes of things and more-or-less reliably know when they encounter them again. But almost all the interesting problems in cognition aren't classification problems at all.
...before a bunch of angry old coots post telling us that none of this is AI.
Nothing can be good at everything. That is why there are differnt things. Even something as basic as a hammer has multiple versions. Even if you go and look at just nailing, there are several types that are good for one, but bad for another.
Ut zould be news if it where NOT thee case. This "news" is "dog bites man" please come back when it is "man bites dog".
Don't fight for your country, if your country does not fight for you.
AI getting into the trough (https://en.wikipedia.org/wiki/Hype_cycle) again (https://en.wikipedia.org/wiki/AI_winter)?
Prominent people seem to fear AI (http://time.com/3614349/artificial-intelligence-singularity-stephen-hawking-elon-musk/), but isn't this just Fear of the Unknown? I mean, Elon and Stephen are really smart people, but do they know that most NN:s come down to linear algegra and spiced with non-linearities in the end, just simulating neurons? I mean neurons are common-place on the planet already, equipped with malice and stuff...
We don't have AI, in any form, in the modern world. We have code which solves program similar to a neural network and we have code which can mutate within very strict limits with genetic algorithms. We have nothing even approaching "artificial intelligence," which at the very minimum of the bar would be the level of an "intelligent" Human. If it's not better than a Human with an IQ of no less than 135 at literally everything it's not AI. We have nothing remotely close to equal to an actually retarded Human with an IQ of 70.
"If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future."
Perhaps AI is already generating the majority of Slashdot posts these days.
Not a lot of people are interested in automating "ordinary human activities" but they are very interested in automating very specialized activities. These activities include assembling objects, inspecting objects, moving a vehicle loaded with goods to a destination and estimating risk in the stock market. These aren't ordinary human activities but they'll put half the country out of work.
Anons need not reply. Questions end with a question mark.
And we will see more and more things humans do replaced by AI/machine learning/automation. Especially tasks with well-defined rule sets. Low skill labor is still going to be at risk of being automated away, especially as sensors and robotics continue to improve as well.
This. It makes sense that google will tout its neural networks, they own them. And yes, the reality is that many tasks and displays of "intelligence" will be difficult of those specific algorithms to handle efficiently or correctly. But the field is in its infancy. Computers haven't been around for even a century. I think though that they have in very specific terms been intelligent all along. The fact that they can do math such as understand 2+2=4 is in and of itself AMAZING.
Why it doesn't impress us is because we know what's going on inside and can dispell the magic. We know how it works. If I showed you a machine and I said "it can treat you like a therapist and cure your depression with greater success rate than the worlds renound phychiatrists", or some other seemingly "beyond computers" task; you would say that's artificial intelligence. But once I show you the secret sauce, the algorithm, the data points, the learning attributes it takes in and the process it uses, it's no longer intelligent, it's just a dumb machine using someone it was given. That's because we don't know why we are intelligent. We can use natural language, and we can do facial recognition, and we can determine creatively how to fix something we haven't seen before. We don't understand the process we take as toddlers to gain those skills. If we did, we would replicate it simply.
True AI will never become a reality because we have to understand it to build it, and by understanding it, we remove the magic and dispell that which was created as "true AI". We just keep moving the goal posts in search of something that is seemingly human. We will get there though. There is nothing in our heads that the universe and all of physics has barred us from creating. There is no law like gravity that states lIntelligence shall not exist but for within the head of a human being". Computers are better than us at chess, go, poker, and so many other tasks. Surely that is intelligence already.
Yea, but for the most part one of them wins out. While we can fight semantics, Hollywood will always win.
The Anti-Hero is a Hacker, not a cracker. Because it can cover so many types of people who do so many different things. From just being good at a computer to breaking into a high security area.
The same thing with AI and Machine Learning. The AI gives the computer a personality that we could learn to love or fear.
If something is so important that you feel the need to post it on the internet... It probably isn't that important.
Exactly.
Plus, "we" often fall into the flawed thinking that AI/robots/automated systems/whatever-you-want-to-call-it has to figure out how to do activities as humans do them. Much more likely, I see a world where we adapt the way work is done to better meet the strengths of these automated systems. You see this, for example, in food production. Instead of inventing processing machines that can deal with all the variation in "natural" vegetables, processors started demanding that farmers grow vegetables that are relatively uniform in shape and size and don't buy the vegetables that don't fit the tolerances of their machines. In many cases it's much easier to control the structure of work inputs than to develop a "work machine" that can deal with all sorts of edge cases.
We don't have AI, in any form, in the modern world.
Not true at all unless you are narrowing the definition of AI to such a narrow degree as to make it effectively meaningless.
We have nothing even approaching "artificial intelligence," which at the very minimum of the bar would be the level of an "intelligent" Human
Nonsense. Dogs do not as a general proposition approach human level intelligence. Yet do have real and measurable intelligence. A computer with the intelligence of a dog could very fairly be described as intelligent. AI does not have to pass human intellect be classified as intelligence or to be useful.
The argument that AI isn't even close, or isn't here, is just plain stupid. It won't take "perfect" or "true" AI to replace an imperfect prone-to-error human in a job. We're being blinded by the need for perfection when it will only take "good-enough" AI to start replacing human workers.
Even worrying about the problem of AI is rather stupid when the problem of automation is the more immediate issue staring the economy in the face. We're working quickly to replace cashiers, warehouse and assembly line workers, and soon we will be replacing drivers. Just targeting these jobs will make millions of people unemployable. And don't try and regurgitate that age-old mantra of go-get-an-education either. Not every human is capable of being re-trained for a more advanced skill, and we have a hell of a lot more humans on the planet to employ with this next evolution of job decimation. And when you start thinking about the types of jobs you held in order to get an education, you quickly realize that automation is looking to remove the bottom half of the ladder of success. Rather hard to climb that proverbial ladder when the first rung is 12 fucking feet in the air, and you're competing with a few million people.
Our economy is going to feel this pain well before we start having to worry about any shitty form of AI.
We saw roughly how heavier-than-air flight would work, but we didn't have the pieces to put it together. We understood the airborne part enough to carry humans dating back at *least* to the sixth century (earliest recorded 'paragliding'). We couldn't make a practical aircraft, but we could see how the pieces would play a role in such a marvel if we solved other pieces.
Here, the current 'AI' craze doesn't even in theory extrapolate to higher-order displays of intelligence. It is a highly practical field to advance and is certainly useful, but *if* we want to go to more 'intelligent' systems, it's going to be based on a different methodology, or at least no one who understands the field can see a hypothetical extrapolation of this approach that leads to those results.
The problem people have is that a useful, albeit narrow discipline is conflated with the entirety of human intelligence. I have seen many in the field understandably trying to discourage the phrase 'AI' to head off very annoying irrelevant conversations and concerns.
XML is like violence. If it doesn't solve the problem, use more.
Not a lot of people are interested in automating "ordinary human activities" but they are very interested in automating very specialized activities.
People are VERY interested in automating "ordinary human activities" but automation != AI outside of very specialized niches. A dishwashing machine is automation of an ordinary human activity but it is decidedly not AI. It's not clear what you actually mean by "ordinary human activities" but humans have been automating those since there were humans.
These activities include assembling objects, inspecting objects, moving a vehicle loaded with goods to a destination and estimating risk in the stock market. These aren't ordinary human activities but they'll put half the country out of work.
No it would decidedly not put half the country out of work. First off actually assembling objects does not require the device to be intelligent in the sense of AI. Automation != AI and the two concepts are orthogonal for purposes of most assembly work. Second, the percentage of people doing assembly work in manufacturing in the US is no where close to half the work force. 10-20% tops. I am GM for a company that does this sort of work. Third, automation is EXPENSIVE and you have to do a relatively high volume of work to justify the expense of automation over people. That's not going to change any time soon.
AI isn't going to put transport workers out of jobs anytime soon either. When goods get delivered exactly how do you think they are going to get loaded and unloaded from the truck? Even if the worker isn't driving they aren't going to send goods to their destination without someone to watch over them and facilitate the transaction any time soon. It's not as if UPS is going to be able to magically make packages appear on your door step by teleporting them from the truck.
Estimating risk in the stock market? No that won't take humans out of the loop any time soon either.
+1. This is algorithms and infant ML.
I can take my kid and train him to swim and then train him to drive a car and get rudimentary skill in a week in both.
You can only do this after about six years of full-time learning in how to navigate in the real world and how to operate his body. This is the hard part, the part that humans learn in their first six years and AIs don't: dealing with the external world.
Learning to swim and learning to drive a car are easy; machines can do that. Learning to make a peanut-butter-and-jelly sandwich out of what is in the refrigerator: now that's hard.
Low skill labor is still going to be at risk of being automated away, especially as sensors and robotics continue to improve as well.
Probably not to the degree you imply. The reason is simple economics. Automation is in most cases expensive and if you actually do the financial analysis (which I do for a living FYI) you'll find that it's nearly impossible to automate most jobs to such a degree that low skill labor becomes unnecessary. Automation is used in high volume or high content value or high risk jobs. While automation has gotten and will continue to get cheaper, it's unlikely to reach such a low price point that it pushes people out of the work force entirely within the lifetime of anyone reading this. To do that you would have to have near human level intelligence AI that you can sell for less money per unit than a human costs. That is a FAR more difficult goal to reach than most people realize. People are flexible and for low production volumes or ill defined tasks rather inexpensive.
That describes me. Perhaps I am an AI.
I'm a good cook. I'm a fantastic eater. - Steven Brust
...before a bunch of angry old coots post telling us that none of this is AI.
Let's put some of that into context.
A 5-year old can recognize a dog in an image in about 1/2 a second. A neuron takes about 0.05 seconds to activate and fire, so on average the entire recognition process takes about 10 steps.
Those steps include reading the image (sensing and converting the image data to internal form), and activating the physical response: saying "dog" or clicking the right button or whatever.
So let me ask this: what AI algorithm takes ten *steps* to recognize something as complicated as a dog?
Note that this works with dogs partially obscured (half masked by a tree, for instance), any size, rotated, from any angle (top down, face on, from the side), any breed (dalmations and chihuahuas), toys made to look like dogs, and cartoon dogs.
The algorithm does this at a very high level of accuracy, and can tell dogs apart from other animals with similar features: cats, opossums, and so on.
And the algorithm does this without a zillion training examples. A typical 5-year old has seen far fewer dogs than the Tensor Flow algorithm training set.
So tell me again: in what measure is our current level of AI anywhere close to being "real" AI?
I think part of the problem is a lot of folks don't really understand how current AI technology which hasn't changed in decades works compared to how our minds work things out. Recall that there was a recent AI project to find the meaning of the Internet and the answer it came up with was "Cats" because they seem to appear far more often then any other topic on the Internet. That is a mathematical mean or average, the optimal answer but ask any normal person and cats won't be the answer that they give you.
And there in lies one of the biggest problems with our current AI, it's only able to do things that we ask it and they need a clear solution. You can't exactly ask an AI, "do you think this person lived a happy life?". What makes this question bizarre is we're not entirely sure what the answer to that is ourselves but in most cases we can usually tell.
30 years ago when I was taking AI in college my professor summed this up perfectly. He said "AI is like artificial milk. Artificial milk doesn't have to be as good as real milk, it's just quite handy if you haven't got any real milk."
Interesting point. Touting the magic two letters 'AI' has a more significant impact than saying machine learning to the populace and the money kings. Correctness be damned. On a side note, I get more nervous with our reliance on these technologies when I hear things like "we don't know what happens inside those things, but it looks like it's doing what we want it to - so don't worry about it." True story.
Computers don't understand 2+2. They perform the operation by moving electrons from one place to another, ending in a pattern that humans interpret as 4.
What does this even mean? I can't figure it out, but I wonder if NLP can...
Learning to make a peanut-butter-and-jelly sandwich out of what is in the refrigerator: now that's hard.
It's also sexual harassment if you ask a woman to do that. http://domainincite.com/20201-...
The shepherds did so well protecting the flock that the sheep no longer believed that wolves existed.
Are you familiar with the Chinese Room argument?
Use any definition other than "pattern recognition" and we don't have it.
So me any form of intelligence that isn't some form of pattern recognition. Hell the entire field of physics and every other science is simply the act of observing patterns and building a model to describe them that has predictive value. At it's most basic form that is just sophisticated pattern recognition.
However, I don't think our learning exactly how consciousness arises and how to duplicate it will deprive it entirely of its magic and wonder.
I need a wheelchair van for my son. Help me get the word out. https://www.gofundme.com/wheelchair-van-for-jj
Our brains have many different parts each with thier own function(s). It's pretty apparent humans have many simple algorithms running simultaneously, in addition to whatever else happens. So the obvious conclusion that will surprise no one is that a true AI like a human would just have deep learning (or a number of deep learning modules) as a single component among thousands that would be required to get the emergent behavior that is strong AI. The technique is simple, easy to implement, and accomplishes certain simple tasks with ease so it's usefulness as a tool when designing strong AI won't ever go away. There is no magic in our molecules, there is nothing magic about strong AI, it should be obvious a single algorithm won't ever make a strong AI, we and other animals prove it can be done it's just difficult. People need to get over it and realize that when millions of people work together, contributing our results and pooling our knowledge, we can make much of science fiction a reality (hopefully just the better parts).
What you are saying reminds me of the old discussion of the Artistic Method vs the Scientific Method.
The artistic method attempts to solve a problem by coming up with a solution, determining if it provides a solution, and if it does not rejecting it and starting over.
The scientific method attempts to refine its initial solution over many iterations, in order to eventually come up with a solution that works. Only when they hit a point that does not offer any more paths for refinement will they reject the original path.
Intelligence requires a mix of both methods. Algorithms are stuck in the scientific method without a reset button.
deep learning, now the dominant technique in artificial intelligence, will not lead to an AI that abstractly reasons and generalizes about the world. By itself, it is unlikely to automate ordinary human activities.
Exactly, precisely this. They can't 'think', and never will. The approach being used is completely wrong, or at least incomplete, because we don't even have a clue how we are capable of 'thinking'.
Bearing in mind that, ever from the 60s, the AI community has come up, time and again, with exuberant forecasts that never came to pass, it is interesting that some keep issuing equally exuberant forecasts. A human brain emulation by 2020? The Singularity by 2030? Chances are the AI for the foreseeable will be more of the same: more and more systems that a excel at very, very narrow fields.
Computers don't understand 2+2. They perform the operation by moving electrons from one place to another, ending in a pattern that humans interpret as 4.
Is there "understanding" if the computer arrives to the conclusion by simulating brains at a physical level using same functions organic brains do?
I mean human brains are machines, after all and not supernatural by themselves in any way. Just couple magnitudes higher in complexity than what we can currently build
Yes, they understand 4. That's why they can emit 4 beeps, use 4 in the operation of a subsequent transaction, and know what is greater than and less than. Your understanding is no better.
I'd love to hear you differentiate the two.
Humans don't understand 2+2. They perform the operation by sending electro-chemical impulses from axons to dendrites, ending in a pattern that others interpret as 4.
HSJ$$*&#^!#+++ATH0
NO CARRIER
I don't need it to be a new artificial Einstein.
I just want it to do the dishes and the laundry and clean the house.
The rest I can do myself.
The current AI algorithms seem quite good at automatically solving some well defined problem, given enough input and learning cycles.
Is it really such a stretch of the imagination that we will come up with a more automated way, a sort of meta AI, which will detect if a given problem hasn't been defined yet, and how it may be defined, in order to afterwards pass it on to the learning-through-simulation subsystem?
One DeepMind guy said that AlphaGo would have utterly failed, if they would have modified the board by adding an extra column, whereas a human would still probably do okay-ish. So I am guessing that some meta-algorithm will be developed to detect such a modification, and then adjust the underlying subsystems accordingly.
Same with all the stated situations that self-driving cars seem to suck at currently (snow, construction sites, Tesla crashes). Right now, it seems that researches have to explicitly add the programming for these unforseen situations. But I bet this part will be automated, too: when the Tesla crashed into that trailer it didn't 'see', it should automatically detect that it messed up (crash is bad), and re-evaluate all previous sensor data to come up with a solution that would not have ended with a crash, and adjust its NN weights accordingly. This doesn't seem too much of a stretch in light of AlphaZero, and might very well lead to exponential AI 'cleverness'.
AI isn't really intelligence
No true scotsman.
Its knowledge based on what is in its data base.
Yes, just like yours. And just like you, they can add to their knowledge.
Hardly any of it is considered really deep self learning other then some programmed learning abilities.
Yes, those parts are what we call AI.
But then again, people get excited about Space X launching a rocket that was done back in the 60's and 70's? Will AI hit a brick wall as well?
The DC-X was flown in the 90's. It ran out of funding after it's last launch caused damage. And just like the gap with VTVL, AI research had a couple of winters and the hype train is once again going strong.
But the cycle of hype and the resulting disillusionment doesn't mean that it doesn't exist. The field has progressed and the capabilities of AI have expanded.
bloody cowards.
Computers do not have any ability to understand. any more than a basket caring 2 cans with another 2 cans added understands that it has 4 cans. The computer and the basket have no consciousness or ability to understand. Being able to state 4 is the answer is different from understanding 4. Some interesting facts about 4 can be stated by a computer it may have more information about the number 4 than a person but still it has zero understanding.
You can't handle the truth! - Because I don't post left all my comments get modded down, bye bye Karma.
The key to deep learning neural networks is being able to simulate the performance, because it doesn't have a causal model that we would use to predict an outcome or weed out spurious correlations. That is to say it can't hold a million simulated conversations with a human figuring out what works and what doesn't. But it can do that with physics and other STEM branches, like it can play a driving simulator or the kerbal space program. And it can come up with new concepts within those constraints. I watched a documentary on AlphaGo not that long ago, and if you watch the expert reaction to move 37 in game 2 it's like WTF!? and unlike say Deep Blue it's not just the computer thinking even deeper than humans. It's basically going its own way and making a move no human would make. It's a double standard where for a human it would be novel and creative, for an AI it's just the result of an algorithm.
That has a lot of potential for say Rube Goldberg machines, like you give the computer a set of tools and basically says you figure out how these could be combined to achieve some sort of goal. It's a bit like giving the computer a toolbox and saying build me a house and it'll figure out what tools to use in which order to reach the end goal. The problem has been in building simulations that are sufficiently accurate that they can be applied in the real world and actually work like in the simulation. A lot of people here commented on how the Falcon Heavy launch performed pretty much exactly like in the CGI simulation. And Musk commented on it too in the press conference, it was a validation of their ability to design rockets through computer simulations. If it can be simulated, you can throw a DNN at it and ask it to optimize "the game". And that game has pretty serious real world implications.
Live today, because you never know what tomorrow brings
Well, for one, thing, in their 'minds' they see 10 + 10 = 100.
Computers have no cognition.
That is nonsense.
Every most stupid neural network "program" is trained to "recognize".
And the programmer did _nothing_
Every NN is basically an off the shelf empty brain, working the same way as any other empty brain, in other words: nothing at all. Only after training it does what it does: recognize stuff. Aka: performing "cognition".
Cost free eBook I read (by iBook/Kobo/Amazon/ObookO/Gutenberg etc.): "The Green Odyssey" by Philip Jose Farmer.
A lot of mathematicians would say if you haven't studied ZF set theory, you don't understand 2+2 either. And if you have studied it, you know it describes addition as a process of algorithmic symbol manipulation. Exactly the sort of thing computers are great at.
Maybe the main difference is that humans delude themselves into thinking they "understand" things when really they're just following rules. Computers don't have that problem. So which is more intelligent?
"I'm too busy to research this and form an educated opinion, but I do have time to tell everyone my uninformed opinion."
Oh for crying out loud, stop using the true scotsman fallacy in this case. The fallacy requires a person to make a subjective statement that nothing applies to. If you don't like the definition of 'intelligence' being used then say so, but this isn't no true scotsman. As far as I know, intelligence means ability to gather knowledge through external experience, which is a hard and fast definition that can either apply to 'AI' or not. For an 'AI' to truly be intelligent at playing Go, it needs to start with a seed and be shown instructions to Go and learn how to play from that. If someone programmed the rules for Go into it, then it has not learned to play through intelligence. The same 'seed' should also similarly be able to learn to play chess, do s crossword, or identify animals. Intelligence implies a certain amount of general purpose ability, since there are no bounds in the definition for the number of things you use knowledge for in order to be intelligent. This is why a human savant that can't talk or understand anything about life but playing Go or Chess is not called 'intelligent'.
Laws are rules for the court, but merely a bottom bar to hit for life. Think beyond laws in your actions always.
Here, the current 'AI' craze doesn't even in theory extrapolate to higher-order displays of intelligence.
I'd love to see the theory that claim is based on. I've never heard of any theory of "higher-order" intelligence (whatever that means) that tells us the current approach won't scale. If you're going to make claims about what is or isn't possible "in theory", those claims need to be based on an actual theory. Otherwise, it's empty rhetoric.
Many AI researchers believe the current approach can and probably will ultimately lead to human like intelligence. They reason like this. Humans have human like intelligence. We don't understand the details of how it works, but we're learning more all the time. Current approaches to AI are inspired by how the brain works. We're making really fast progress at it. And as we learn more about the brain, we can apply those new insights to AI too.
This gives us a pretty clear road that, if followed to the end, has a good chance of producing human like intelligence. We can't see to the end of the road yet. We can just see to the next bend. But that's ok, we don't need to. We'll just follow it one bend at a time and it'll get us there eventually.
"I'm too busy to research this and form an educated opinion, but I do have time to tell everyone my uninformed opinion."
I don't like YOUR definition of intelligence. Not enough haggis.
As far as I know, intelligence means ability to gather knowledge through external experience
What, so a webcrawler is intelligent? It "gathers knowledge".
I'd typically just boil it down to "learning".
For an 'AI' to truly be intelligent at playing Go, it needs to start with a seed and be shown instructions to Go and learn how to play from that.
Keep up with the times grandpa. That's exactly what they did. No learning sets needed.
if someone programmed the rules for Go into it, then it has not learned to play through intelligence.
You need to better differentiation what you mean by "Shown instructions" and "programmed the rules".
The same 'seed' should also similarly be able to learn to play chess, do s crossword, or identify animals.
You have no idea what a "seed" is do you? It's just some nebulous term you've got rumbling around in your head that's pseudo-magical.
Anyway, the closest thing would be their method of machine learning. Which, for the link above which talks about DeepMind Alpha, it uses neural network and reinforcement learning. YEEEEESSSSSSS it self-taught go, chess and shogi. "The seed" is what DeepMind Alpha is. It's the tool that can learn things. That's the intelligence part. THE SAME TECHNIQUE is also used for object recognition. Hell, I dunno, maybe they could get deepMind to recognize pictures of cats.
Intelligence implies a certain amount of general purpose ability,
No, but general intelligence would be a lot cooler than specific, niche, narrow intelligence.
This is why a human savant that can't talk or understand anything about life but playing Go or Chess is not called 'intelligent'.
Wow, fuck you too dude. Most geeks are just some fraction of autistic savant. I know I'm better at math than English. If you're going to define my intelligence by my weakest link, then I'm going to call you an idiot for your ignorance on the current state of AI.
Regardless of the limitations of backprop, the traditional logic-based approach will never fully recover from the blow of discovering distributed representation.
https://youtu.be/wuhbqcMzOaw
Mobile goalpost. Exactly.
When we know how to do it with software, it quits being AI, and starts being called by the name of the specific method of doing that thing.
Usually.
Some years back somebody designed an antenna with a certain characteristic, using a genetic algorithm. (I don't recall the characteristics sought. Omnidirectionality. Effectiveness over a wide range of frequencies. Whatever.) The genetic algorithm generated a bunch of semi-random designs, simulated the performance of each, culled out the worst, then generated a new set of semi-random designs from parts of the survivors to start the cycle over again. When the best design in a generation was good enough, it stopped.
The electrical engineers built an actual antenna according to the best design. It worked very well. And when they studied at the design -- which, if I recall correctly, looked like a randomly twisted paperclip -- they didn't really understand how it worked. (If I don't recall it correctly, the target may have been some sort of electrical circuit -- amplifier, low-pass filter, etc -- and the screwball designs were agglomerations of resistors, capacitors, etc., semi-randomly picked and connected.)
Was that genetic algorithm "AI"? If it could design other things, reliably, that'd be cool. That I haven't heard anything more about it, it might be a "one hit wonder", barely more successful than the Rolamite or the Rovac. https://duckduckgo.com/?q=rola... https://duckduckgo.com/?q=rova...
Evolution produced ball-and-socket joints, which work in an obvious way. It also produced wrist and ankle joints, which just look like a jumble of odd-shaped rocks that more or less fit together. But somehow, they work.
There's no time like the present. Well, the past used to be.