Or apparent ceiling. Sometimes ideas haven't really "ceilinged out" so to speak, but they've run into an artificial barrier due to available compute resources at a specific point in time, etc.
Somebody (Geoffrey Hinton, Andrew Ng, I don't remember exactly) made the point that neural networks were in a pretty moribund place, until the confluence of three things came along: more compute power, more data, and algorithmic improvements. Given that, one wonders what other older ideas are waiting to be resurrected by a similar confluence.
That's not really an artificial barrier. The program which encodes General AI can already be discovered by brute force, given sufficient compute resources.
There's a difference between ideas for which there is not enough compute right now and ideas that are computationally intractable according to our knowledge of the physical universe.
Wolfram says there are roughly 2E76 ergs of mass-energy in the universe. [2]
So even if all matter and energy in the universe were put together to power a computer that did nothing but count upwards from zero, there's only enough matter and energy in our universe for the computer to count to about 10^91, or roughly 2^304 [3]. That's not even counting trying to use each resulting set of bits for anything.
So I don't think we'd be able to design too many useful computers by flipping bits until a useful algorithm comes out (at least not in this universe.)
[1] https://www.schneier.com/blog/archives/2009/09/the_doghouse_...
[2] http://www.wolframalpha.com/input/?i=mass-energy+in+the+know...
[3] http://www.wolframalpha.com/input/?i=log2(mass-energy+of+the...)
Due to evolution, the coordination of individual entities has happened repeatedly. Individual molecules became living cells, some suggest living cells merged into cells with mitochondria, viruses and cells merged, single cells merged into multicellular organisms, insects organized into social colonies, and now primates, i.e. humans have also organized into social colonies.
This doesn't prove evolution always creates social multicellular life, but it means (IMO) there is a clear pattern that provides a reason to assume it does short of specific reasoning or evidence about a barrier - why should the bacterial level be unique, and which bacteria would be the highest level?.
*I don't think you can separate intelligence from a social context. What we think of intelligence is closely related to things like the willingness to give up current rewards for long term ones. But it's not "smart" to do that unless you have justified faith that you live in a stable society where people are trusted to follow social rules.
Even if that's true (and I don't think anybody can prove that point one way or the other just yet), who's to say that we have "sufficient compute resources" right now? That's what I'm getting at... an idea might be useful / valid in principle, but just not completely usable today because we don't have enough compute cycles available. The analogy is to ANN's which were limited by the computers available in the 80's and 90's, but became markedly more useful with modern CPU's and - even more so - with the advent of modern GPU's.
Right. And let me add this: what I was getting at is that the ceiling is not an innate part of the approach itself. Perhaps I should have said "external" instead of "artificial". Anyway, yes, the point is about current computing resources vs computing resources (and other factors) at some indeterminate point in the future.
Of course, given that it seems obvious that any program which encodes a general artificial intelligence will be complex, I expect it would take longer than the expected life of the universe to generate it with a random number generator using all of the compute resources available on the planet. So it's theoretically possible but practically impossible, which makes the OP's statement essentially pedantic.
while(passes_turing_test(general_ai) == false) {
general_ai = generate_random_code;
}The fundamental problem, as I understand it, is that none of us really understand what consciousness is - we all "know it when we see it" - which is a test that is not so amenable to automation. Perhaps if we had enough examples of different kinds of consciousness, we could set up a ML process :)
Watts suspects from his readings that sentience makes up the majority of our day-to-day physical and sensorium activities and sapience (consciousness) is a very thin self-aware layer on top of that. If his sources that he's reading are correct, then we can go a long way implementing practical and profitable applications with "just" ML, but with less focus on trying to reach AGI. There are more radical suggestions within the decidedly philosophical side of the cognitive science community that seem to wonder if consciousness isn't simply a very sophisticated, highly complex ML'ish pattern-matching illusion, and we're all reductively representable as deterministic entities somehow.
All this postulating is too far away from the testable engineering/tinkering/science'ing I prefer to inform myself about, so I'm not quite sure what to make of it, other than to observe that ML hasn't made much of a dent in the sentience side, as we're nowhere near a general-purpose robot that cleans floors, does the laundry, and simultaneously avoids squishing the house cat, none of which requires sapience.
I don't think that's as solid an assumption as your comment makes it sound. We all know magic when we see it, yet it doesn't exist. Many of us recognise when something "feels pain" even if it's a cuddly toy, or recognise "evil presence in the room" when it's gas poisoning or sleep paralysis or biped-shaped-shadows and low frequency sound.
Many of us think we see a level of intelligence where there is none, in perception of God or Evolution "designing" creatures or "coming up with clever solutions", and many refuse to recognise some level of intelligence in humans in cases where they use good reasoning but get the wrong answer, or do something dumb from using the wrong reasoning, and we have ethical/medical arguments about whether people in comas and similar locked-in syndromes are conscious and aware or not, and whether any plants are.
We have continual newspaper headlines along the lines of "scientists recognise {Dolphins, Elephants, $animal} is more intelligent than previously thought" - if we simply "know it when we see it", it should be pretty clear what creatures are conscious and which aren't.
Certainly we can recognise people-like-us as conscious, but to get "enough examples of different kinds of consciousness" we'd have to be able to know it when we see it, and I'm not sure we do.
That's fair, and in some ways I agree. But I think a certain amount of that is implicit in "I know it when I see it." The phrase implies that there may be significant divergence of opinion.
An Approximation of the Universal Intelligence Measure
https://link.springer.com/chapter/10.1007/978-3-642-44958-1_...
Measuring universal intelligence: Towards an anytime intelligence test
https://www.sciencedirect.com/science/article/pii/S000437021...
Given sufficient computing resources, this should yield strong AI in just a few minutes.
but- even when you pass that test, you will have a nearly infinite number of marcov-chain generators and similar programs to sort through.
My point is that if you want to solve a program by generating random code, you need machine-executable fitness test.
We don't have a machine-executable test for consciousness. The Turing test relies on the intuition of humans, and we have a very finite supply of that.
Not really relevant to your point, but Markov chain generators aren't able to generate syntactically correct English.
(Or, they are able to generate it, but they aren't able to stop themselves from generating syntactically invalid English, and almost all of their output will in fact be invalid.)
Generating macbeth is a prompt most people here couldn’t pass, myself included...
Input is a Macbeth outline/play summary (e.g. https://www.cliffsnotes.com/literature/m/macbeth/play-summar... ) Contraints are "in the style of : KingLear and Othello"
and compare the produced text to Macbeth
Andrew Ng says something like that on his deep learning Coursera course.
Also, Animats mentions perceptrons. A neural network is a generalized (combination of) perceptron(s) on steroids. That ceiling was basically removed again with the advent of the above-mentioned improvements. Similarly, the gradient descent used to optimize a neural network resembles hill climbing, as well.
Agreed. Train a billion-parameter model on million-feature sensor inputs from "normal" conditions, and it will drive really well until its massive over-fitting runs into slightly unusual circumstances, like a tumbleweed rolling across the road. Then it will do something completely unpredictable, and people will probably die. ML plus pervasive surveillance can automate a lot of routine work, but it has a serious problem with outliers.
<sarcasm> Seems a small price to pay for progress. We lost 37,000 people to auto accidents in 2016. I wouldn't be surprised if one or more of those deaths involved a mis-identified tumbleweed.</sarcasm>
If you just take the visualization part we have a massive amount of work to do. Understanding how these networks solve problem in sophisticated ways will motivate new advances to mitigate shortcomings. It is a software problem that can have huge benefits. For example, see the paper Visualizing Loss Functions For Neural Networks as an example of the incredible insight to be gained with better visualization.
The intuition of loss functions hasn't changed much in the last several decades. If you have a local optimizer it needs to be able to find its way down further than it already is. Anything you can do to remove junk configurations from even being considered during optimization will help. But knowing when that is happening has been really hard because it's just so computationally intensive to generate plots like in that paper.
It really bothers me that he (who is clearly an excellent entrepreneur and human being in general) makes such strong statement on a topic he is clearly not an expert in and is so vocal about it too. Given the amount of influence and number of online followers he has, I find this irresponsible.
Many people have made estimates for human level AI, without relying on specific technologies. e.g. Vernor Vinge, Ray Kurzweil, Bill Joy. For example, the lower part of Vinge's range included the 2020's - for his prediction made a few decades ago.
Is Musk accurate? How can any of these be very accuracte, with so much unknown, as you note. Is he optimistic (er, predicting too early)? I guess so.
I'll note that even for Musk's own companies, upon which he is the expert, and upon which he wields more influence than anyone, he is notably optimisitic in his estimates...
Anyway, the answer to my question is: no, he didn't speak of those specific technologies, just AI.
BTW some more in my other comment in this thread: https://news.ycombinator.com/item?id=16350798
What's your evidence for saying he does not say so? A bit mean of you to downvote without asking for evidence.
And anyways, downvoting is not a big deal and they have the absolute right to do so. It's just that stating why you disagreed or downvoted would be helpful.
In other words, this would be a huge noise generator.
Asking me for evidence someone didn't say something is a bit absurd.
As someone who as lived through most of the last four decades, this seems like a very reasonable expectation. I mean, this maybe wasn't obvious to people who weren't born into the IT industry, but to me? the '90s seemed to have a 'holy shit, this changes everything!" moment every few years. (Of course, some things stay the same. UNIX is older than I am, as is c, and both are recognizable and ubiquitous today.) - and I think as the aughts rolled around, those 'holy shit, this changes everything!' moments became, ah, more evenly distributed. More of this new tech touched the lives of ordinary people in obvious ways.
> on the basis of current developments in this area.
This part... I think that you have to understand that the way we talk about technological change is functionally science fiction. Yes, yes, it's very likely that the high rate of change maintained over the last few decades will continue and even continue to accelerate.[1] but I personally treat claims of where tech will be in 10 years as functional science fiction. A lot like how I treat predictions of what sectors of the stock market will be where in ten years. Yes, the market as a whole will probably go up... but to say you know the winners and losers ten years out? that's... a strong claim.
So yeah, saying "there will be revolutionary changes in the next decade!" is fine, and probably right. there were revolutionary changes last decade. And, hell, you could say that ML and similar have already given us revolutionary changes this decade. Arguing that ML will give us revolutionary changes next decade? well, maybe... but it looks more like Delphi than C to me, if you know what I mean. (Of course, I'm IT; I operate, manage and maintain infrastructure; I'm no programming expert. so take that how you will.)
[1]the counterpoint here is moore's law. Your compute per dollar is not getting massively better over time the way it was in the 80s through the 00s. I mean, it's still trending towards better, but not nearly as fast as it was. The difference between the compute power of available compute resources in 1980 and 2000 is gong to be dramatically greater than the difference between the compute power of available compute resources from 2000 to 2020. This means that if you want to make predictions, you can't rely as much on computers being more powerful in the future.
Most of these imminant AI predictions are founded on computer complexity soon approaching that of the human brain. Even some fundamental discovery, like RNA playing an information processing role within/between neurons, that pushes brain complexity up by many orders of magnitude, would not push back exponential growth reaching it by all that many years.
The whole thing hinges on Moore's Law, which is (currently) looking rickety.
But I think that within not so many years we'll be able to build an NN-based AI (an NNAI) that incrementally learns to become as smart as a little animal (...) once we have animal-level AI, a few years or decades later we may have human-level AI, with truly limitless applications (...)
Will AI Surpass Human Intelligence? Interview with Prof. Jürgen Schmidhuber on Deep Learning
https://www.infoq.com/articles/interview-schmidhuber-deep-le...
2) Here's a quote from DeepMind claiming their DQN system is a stepping stone towards Artificial General Intelligence:
This work represents the first demonstration of a general-purpose agent that is able to continually adapt its behavior without any human intervention, a major technical step forward in the quest for general AI.
Human-level control through Deep Reinforcement Learning
https://deepmind.com/research/dqn/
3) Here's another example from MIT Press:
“With appropriate uses of the deep learning technologies, we could be a further step closer to the true human intelligence.”
Deep Learning Machine Beats Humans in IQ Test
https://www.technologyreview.com/s/538431/deep-learning-mach...
4) Here's Elon Musk telling NIPS that auto-cars will be significantly better than humans in 2020:
Now at the conference on Neural Information Processing Systems (NIPS) yesterday, Musk said that they could achieve some level of full self-driving within two years, but that the more important timeline would be 3 years, at which point self-driving capabilities would be significantly better than human drivers.
The quote doesn't mention deep learning, but he was at NIPS talking about deep learning.
Elon Musk updates timeline for a self-driving car, but how does Tesla play into it?
https://electrek.co/2017/12/08/elon-musk-tesla-self-driving-...
I believe, Eliezer Yudkowski et al Singularitarians have redoubled their efforts to convince everyone that the Singularity is Coming bolstered by the recent successes of Deep Learning- although youwon't find Yudkowski at least trying to make any specific predictions about AGI, or about anything.
> But almost all the interesting problems in cognition aren’t classification problems at all.
That's BS. Knowing how to name or interpret event, to apply a symbol to the noise of sensory data, is a crucial part of cognition and it happens to be the foundation of the symbolic reasoning Gary Marcus and company tout so often.
The whole "emperor has no clothes" tone of these critiques is in itself false, because the people at the center of deep learning are well aware of its limits and trying hard to work around them.
I'll try to address the epithets one by one:
* Greedy - Yes, deep learning is data hungry. No one denies it. That is a great boon for storage companies and GPU makers, so we may briefly ponder the incentives of neighboring industries to push DL. However, there's a lot of work being done to diminish the number of examples an algorithm must train on before it can make an accurate prediction. You can Google "one-shot learning" and "zero-shot" learning for more info.
* Brittle - On the one hand, deep neural networks learn what they train on; on the other, they are pretty strictly judged by their ability to generalize to data they haven't seen before. In fact, it is this ability that has one them notoriety. For work on making them less brittle, see Hinton's recent work on "capsule networks" among other research. (Capsule networks are also deep learning.)
* Opaque - interpretability is a problem, but it's not as simple as calling DNNs a black box. (Yes, the billions of parameters are not really human readable, but they are machine-readable and we can apply functions to them to obtain insight into the model.) In fact, there are many approaches to interpretability. It's some of the most exciting research in AI and it's coming out of really strong labs.
Here's something recent from DeepMind: Learning explanatory rules from noisy data https://deepmind.com/blog/learning-explanatory-rules-noisy-d...
* Shallow - This is the hard one. Gary Marcus and others suggest that we can augment learning algorithms by pre-programming them with rules or knowledge of the world. Fine. But it would seem to me much more impressive that these algorithms arrive at knowledge without human intervention of the rules-based kind, which is precisely what Hinton and LeCun and Ng have advocated doing with unsupervised learning, which is cough also deep.
One of the weird things about the role that Gary has cast for himself as the gadfly of deep learning is that he's very critical of this one narrow branch of AI. In fact, the top labs using deep learning are already combining it with other types of machine learning algorithms. DeepMind's AlphaGo is DL + reinforcement learning + Markov decision process.[0] Pedro Domingo is very clear that combining algorithms across machine-learning and AI sub-disciplines is the most promising path.
I'm not sure I know of any "deep neural networkists" who espouse a dogma that would actually justify Marcus's strange obsession. Most people, including the godfathers of deep learning, are agnostic. They use algorithms that work and will combine them with other algorithms that work when those come along. It seems like a semantic quibble. The real work continues.
Opacity comes from what you get after training - a big matrix of weights. Now what? "Deep Dream" was an attempt to visualize what a neural net used for image classification was doing, by generating high-scoring images from the net. That helped some. Not enough.
The ceiling for machine learning may be in sight, though. Each generation of AI goes through this. There's a new big idea, it works on some problems, enthusiasts are saying "strong AI real soon now", and then it hits a ceiling. We've been through that with search, the General Problem Solver, perceptrons, hill-climbing, and expert systems. Each hit a ceiling after a few years. (I went through Stanford just as the expert system boom hit its rather low ceiling. Not a happy time there.)
The difference this time is that machine learning works well enough to power major industries. So AI now has products, people, and money. The previous generations of AI never got beyond a few small research groups in and around major universities. With more people working on it, the time to the next big idea should be shorter.
[1] https://blog.openai.com/adversarial-example-research/