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'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.
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...
f it's not better than a Human with an IQ of no less than 135 at literally everything it's not AI.
Well it looks like you just made up your own definition of AI. I've never seen that anywhere.
It's Artificial Intelligence, not Artificial Higher-than-average-human Intelligence.
If they made a robot dog that behaves exactly like a real dog, with all the doglike mental powers, I would definitely call that real AI. Unfortunately they're still nowhere near making dog-level AI.
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.
If it's not better than a Human with an IQ of no less than 135 at literally everything it's not AI.
Why? We recognize and can measure intelligence in animals, so there is a wide range of non-human, natural intelligence that has been identified. Why would artificial intelligence have to start above all that?
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.
+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.
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.