To Understand the Future of AI, Study Its Past
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Not much going on in this area. Which is why robot manipulation in unstructured situations still sucks, after 50 years.
https://www.theverge.com/2018/1/10/16865506/laundroid-laundr...
So not quite solved just yet. And I'll give you long odds against them working out the kinks in the next five years.
2. Human operators are doing the heavy lifting (quite literally) by loading the pieces one by one in a standardized initial state.
I'm talking about a machine that will fold men's shirts that are initially sitting in a pile just as they came out of the dryer.
Think about all the time going to waste. Yes, there are tasks which some actually want to keep manual (maybe you love cooking) and there are (subpar) online service, which try to replace that labor, but there is no automatic option otherwise. And outsourcing labor is not what I mean. I want a washing machine at home, which is a lot simpler to handle than outsourcing it to a washing business.
I sometimes wonder, if the reason is really a technical one or if there are other factors, social or incentive problems, which hold progress back in those areas...
Try folding a shirt with mittens on some time to get some idea of what a robot with a standard manipulator would have to deal with.
Maybe the solution is lower standards?
Reminds me of some story about a person doing something and another person saying "that looks painful, what's the trick", with the answer being "not minding that it hurts".
I suspect this sort of algorithm will be applied to robot manipulation in the next couple of years, and would be extremely surprised if DeepMind et al weren't already working on it.
Remember there is a very fine line between creativity and illusion. Technically there is not much of a difference because both are a distortion of reality. Even we humans have a hard time distinguishing this sometimes as many new inventions used to be deemed crazy at first. It looks like when it comes to concepts that we hadn't seen, we were also susceptible to the same flaw just like those machines.
I don't really know much about symbolic AI but can it really solve this problem? If it can then the human game may really be over!
At this point a display of great competence no longer means that a human did it. Sure; all the parts haven't been completely connected to make self driving cars, general game playing AIs or conversation bots. But on any discrete task it is no longer reasonable to say "computers can't possibly do that" the way I could about a Go game between two 9 dan players in 2010.
I can still say "not commercially viable" and I can point to specific attempts that don't work, but computers are now on the same threshold as humans - [data + time = results]. It may be more data and more time than a human, but that is a big change from [logically modelled domain = results] which is where we were before in AI.
> ...AI systems whose actions cannot be closely scrutinized and explained...
Author is one of the large group of people who are in for a shock when they try to scrutinize and explain a humans crazy actions. I can't even explain why I get the wrong result sometimes when I add numbers in my head.
A human brain can learn to recognize cat pictures given a relatively small amount of input versus a convolutional neural net.
A human brain hits a limit somewhere in terms of attention span, though - I used this to understand AlphaGo's accomplishment. AlphaGo is not inherently better at solving complex problems compared to a human brain, but it is demonstrably better at focusing on one complex problem until it has amassed the equivalent of thousands of years of training.
I'd guess that the human inability to concentrate on one problem that long is actually a clue. Humans come up with new go and chess insights continually. Maybe it's because we're bad at evaluating the new ideas objectively for such complex problems, but it may also be a clue that the human brain has a more efficient method to develop new ways of analyzing complex problems.
The best machine-based deep learning approaches still need to crunch through an enormous data set. Especially so for unsupervised learning.
To give the ANN a fair go, I think you should pretrain on another dataset while evolving the architecture. Let that chug for, say, 500 million years. Then refine its performance on your cat dataset and see how long it takes for that step.
A human brain that's already learned to see and recognize objects can learn to recognize cat pictures from a small amount of input data, and a neural network that's trained to see and recognize objects (which explicitly didn't include cats) can also learn to recognize cat pictures from the same amount of input data. One-shot learning or few-shot learning is a thing and gets reasonable results.
And a human brain that's learning to see "from scratch" (i.e. infants) can't learn from a few pictures, it needs huge amounts (many months) of rich image data - i.e. not passive but interactive/experimental, which is known to be neccessary for visual system development in mammals (I recall a bunch of cat experiments) and is known to have much, much better sample efficiency for artificial systems.
So as far as I know there's no good reason to assume that human brain can learn cat pictures from less input than neural networks. What we're seeing and misattributing to the "learning capabilities" here is actually the effectiveness of transfer learning, transferring knowledge and skills everyone's learned as a kid to new (but actually very related) problems.
>> ...AI systems whose actions cannot be closely scrutinized and explained...
> Author is one of the large group of people who are in for a shock
> when they try to scrutinize and explain a humans crazy actions. I
> can't even explain why I get the wrong result sometimes when I add
> numbers in my head.
"The cotton industry engages experienced buyers who purchase cotton. Their procedure is to pull a tuft of cotton out of a random bale of a lot. They will look at that tuft and feel it, tease it out, listen to the crackling, perhaps, as they do so, touch it with their tongue, and through this procedure they will determine the class of cotton the bales represent. There are about a dozen such classes. As a result of their decisions purchases are made at certain prices, blends are made in certain proportions. Now these buyers can not yet be replaced by the machine.
"Why not? Surely the data is not too complicated for it?
"Probably not, but what data is this you refer to? No textile chemist knows exactly what it is that the buyer tests when he feels a tuft of cotton. Presumably there's the average length of a thread, their feel, the extent and nature of their slickness, the way they hang together and so on. Several dozen items, subconsciously weighed out of years of experience. But the quantitative nature of these tests is not known. Maybe even the very nature of some of them is not known. So we have nothing to feed the machine. Nor can the buyers explain their own judgment. They can only say, 'Well, look at it. Can't you tell it's class such and such?'"
Asimov wrote this in the 1940's and in this passage he tried to illustrate how unquantifiable and impenetrable human judgment was compared to artificially intelligent robots, which he saw as ultimately rational calculating machines. Ironically, today AI techniques such as neural networks are criticized for making much the same sort of impenetrable judgments.
It hasn't aged that badly.
"USDA’s classing methodology is based on both grade and instrument standards used hand-in-hand with state-of-the-art methods and equipment. The system is rapidly moving from reliance on the human senses to the use of high-volume, precision instruments that perform quality measurements in a matter of seconds."
[1] https://www.cottoninc.com/cotton-production/quality/classifi...
We don't need people to be able to tell you how and why they made that decision, we can treat the human decision-makers as a 'black box' and learn to effectively copy the behavior of that black box without necessarily needing to understand how and why it makes these decisions. We don't need textile chemists to know exactly what it is that the buyer tests when he feels a tuft of cotton if we can replicate the process of "subconsciously weighed out of years of experience" by having a machine look at enough expert decisions that amount to literal years of experience.
For example, Transformer architectures may be able to generate seemingly realistic text, but after a couple of paragraphs a human can pick out the incoherence.
Furthermore ML still struggles with out of distribution (ood) samples which humans are able to navigate easily.
> ... but computers are now on the same threshold as humans
This parity is still extremely limited and far from general. Computers were developed in the first place to handle fast computation with ease. What we are seeing with ML is an extension of that computation by encoding complex information (such as images) extremely well. This is still in the same computation plane as calculators (performing operations on floats and integers), but forgive my pun, only deeper.
Human cognition is much more than computation. What we have today with machine learning is still very much computation.
Are we sure? And don’t say “computers can’t appreciate a sunset” or any begged questions like that.
And there's no begged question about appreciated sunsets. The link between emotional and aesthetic perceptions is one of the key problem areas.
Because aesthetic perception includes immediate sense data, but also links it with cultural contexts, personal memories, and sometimes a degree of artistic improvisation to communicate all of the related experiences, memories, and associations.
It's precisely the difference between recognising a sunset with an image classifier and experiencing it with (say) another human that is far beyond current systems.
There is so much to human cognition that researchers haven’t or have been unable to address in AI.
We can tell from our memories that when we experience something, it is not simply raw perception. In fact our System 1 minds are filtering out most of our sensory input so that even if two people are watching the same sunset, they’ll notice it differently.
And on top of that, our memories are infused with so many other details that are not sensory input but artefacts from our lived experiences, personalities and cultural heritage. So that now even if two people are noticing similar things, they’ll still experience the sunset differently.
And next time you are looking at a sunset, try to gather of all your thoughts and explain precisely how you are experiencing it. That is hard.
So yes even a simple begged question like that shows the differences between human and computer cognition enough.
So? That was true for Chess at one time, and Checkers or tic tac toe back in the 1900s. It's just a more computationally advanced version of a game with simple rules, not a general intelligence...
> Sure; all the parts haven't been completely connected to make self driving cars, general game playing AIs or conversation bots.
This implies that connecting these systems is just a detail to be worked out. But the systems have nothing in common ontologically---they share no inputs, outputs, or intermediate concepts. The fact that they both use neurons doesn't mean much. You may as well say we can connect snails and dogs.
Only in the most tautological sense - explanation has to be made in a form of narrative in order to communicate it. As for the correctness aspect, I recall reading on HN recently that the study which claimed to demonstrate that such explanations are wrong had some bad issues with their data science.
I believe that since the population size of intelligent agents will grow exponentially fast and pretty soon, one should consider one of the following options
i) Do pretty much the same as before.
ii) Join a FAANG company.
iii) Do PhD+ level research in ML/CS/AI.
iv) Make money as fast as you can.
v) Some option I didn't think about.
If you pick (i), it seems likely that your kids (if any) will have no money or/and power to remain relevant. Pick (ii)+(iv) or (iii)+(iv). The latter seems preferable but it is harder.
I would go further and say that once machines will be able to lie better than humans, we are done.
>> produce high powered tools rather than true autonomous agents
including tools which will help to tell a compelling story and/or lie to people. We can see precursors of these tools appearing during the last few years.
Always a good way to ensure you've covered everything
Should come up with something smart and make money. Let's call "be truly smart" (what a smarter AI would do) and not just "academic smart" or "coder smart".
Otherwise it's pure research and effectively a cost center. There is no standalone AI/ML etc... capability that makes money on it's own. It's all where it's integrated with existing consumer or enterprise products.
Understanding that, whomever builds and scales product adoption, are the ones who are really controlling the direction of AI. If you look at who are building and scaling consumer and enterprise products it's really only a handful of players.
So if you're a successful startup with a good product that uses AI driven features, you're either gonna get acquired or a major (FAANG etc...) will just copy and scale your product way faster than you can. So again, all roads lead to ii).
You can do trading, no need to have clients in that case.
And the symbolists make the same mistake they did the first time. Imbuing programs with nicely-named symbols and hard coded logic does not possess them with understanding, it arrests away their ability to learn it.
Simple programs do not understand themselves, they have no more awareness of the logic they are running than a mouse has awareness of its neurons. Symbols and their programs represent understanding only as much as they map to concepts in the programmer's mind, not their own. Understanding must be something that partakes in computation, not the definition of the program.
Classical chess AI, built with perfect chess simulators, idealized search and expert heuristics, despite flowing through interpretable and semantically sensibile programs that humans have built, are entirely isolated from the semantics of their programs—for the names and the layout of the data structures are not properties that the program itself has any access to.
Despite the limitations of machine learning, this cannot unreservedly be said of neural networks, which are demonstrably extracting semantically meaningful latent spaces from highly complex inputs as part of their computation. ML is still not self-reflective in any useful sense (it cannot hear itself think; it does not see itself learn), but at least it is handling the first level of the task on top of which we might conceivably build understanding. To MuZero, a game of chess is an aspect of its network that it could perceive, at least within a given branch of its search. And we know these networks must be building something at least knowledge-analogous, since how else could a network like GPT-2 answer questions (however imperfectly) across a range of out-of-domain tasks, like knowledge retrieval and translation?
And this is why Gary Marcus' position (besides his repeated telling of false claims) misses the point. Yes, we should embed programs with priors and reasoning beyond brute connectionism—and to that, most people agree;—but this understanding cannot live in the symbols, it must by necessity live in the structure of the computation, and this structure must itself be accessible to the AI. It is this latter thing that the ML community is already doing, in likely the majority of ML papers, of which the convolutions Gary likes so much are just the tip of the mountain. It is this latter thing that explains, much against the grain of Gary's claims, why MuZero is a better network than AlphaZero.
>"Yes, we should embed programs with priors and reasoning beyond brute connectionism—and to that, most people agree;—but this understanding cannot live in the symbols, it must by necessity live in the structure of the computation."
I'd argue it needs to live in the computation and live in the symbols and be able transparently go between these. But how to make that work is still unknown.
To me, AI = self consciousness, anything less is just fancy ML.
If you ask the question "are we going to have self-conscious machines in 30 years" I would bet against it. If anyone has any reason to bet _for_ it, I'd like to hear those reasons :)
Sounds like you are not from the field.
It's like you haven't seen the advancements in CV or NLP (not involving translations) made capable entirely due to fancy neural network arcitectures
For example...
DNNs are one of the best tools we have for bringing uncertain, hugely complex real world audio, text, and visual data into a form where we can manipulate it symbolically or mathematically with traditional programming. In other words, they bring more domains into the scope of automation. (Observe that none of what I just said sounded like "AI" as popularly imagined).
https://twitter.com/tabithagold/status/1070736319901519876 https://twitter.com/tabithagold/status/1071189769499996161
"....The old-fashioned car manufacturers said 'We believe in electric motors too, and we can derive electric motors by injecting petrol into the engine."
There is a persistent lure to symbolic AI, and it is the lure of anthropomorphization, of thinking that AI must be smart in the way humans are smart, by manipulating symbols.
But natural language is not so much a central expression of machine intelligence as it is of human intelligence. Humans confuse linguistic aptitude with intelligence. Symbolic manipulation will be at best an API by which machines can relate to humans as the machines get smarter and smarter. Symbolic tools that conform to the bandwidth limitations of humans, which are constraints that our current machines don't face. (Historically they did, and symbolic AI made more sense then, since neural-net training was unfeasible.)
The lure of symbolic AI puts us in the bondage of old ideas, as Keynes would say. Symbolic AI is the equivalent of replacing our economy's fiat currency with an arbitrary supply of a yellow mineral.
I could go on, but I'll just link to this page, for those who are interested: https://pathmind.com/wiki/symbolic-reasoning