Why Self-Taught Artificial Intelligence Has Trouble with the Real World
quantamagazine.org
quantamagazine.org
The real world is not a finite problem with explicit rules, obvious boundaries, well-known start conditions, or any way to judge a specific situation as "win", "lose", or "draw". But, even if you want to argue that specific tasks can be broken down this way, you still have to be able to represent this subset of reality in the computer, before AI magic can even begin to work on the problem.
As for goals, they are indeed better defined in games. But we can create artificial goals attached to the real life: - goal: GPS sensor reports specific position - goal: the camera reports a recognised object in a specific position - goal: a button was pressed (signal reaching the AI e.g. through the cloud)
And yes, we can do our best to turn real life in to a game, but all such models leak pretty badly, and the leaks tend to cause much more fundamental instability.
In the game, you can easily let the AI play against itself countless times. Much, much slower in real life.
Until we have good enough simulators/approximators of reality, AI can't learn fast. However, they can already learn driving in GTA, so who knows?
Yeah, that sounds like it'll end well.
Reinforcement learning turns out to be fantastically clever at finding really stupid solutions if you give it the tiniest opening to do so. You put a camera in a room to provide feedback for an agent to learn to move an object closer to the camera, and it will happily learn to knock the camera over.
Between these two, some self serving bias and delusion, mission statements etc.. what is real?!
So we can say that's a bad spec if we like, but what's the answer that leads to? I don't need an AI if I can just declare all users must be really good programmers spending their days writing unambiguous specs. That wasn't really the goal of the AI though.
So, the complexities of how the AI is taught to interact ultimately don't matter. It may have a lot of effort to parse the visual of the board to get the perfect information, but the game is defined as one of perfect information.
Wikipedia calls out that there does not seem to be consensus on what this term means for games with chance or concurrent play. https://en.wikipedia.org/wiki/Perfect_information
Closed systems don't have Black Swan possibilities. Black swans are sometimes super-linear, jolting results in directions and scale unimagined.
To answer briefly (and respectfully): because an MMORPG is a different type of system than reality. Comparing cars to buses.
"Hello, Joshua."
"A strange game. The only winning move is not to play. How about a nice game of chess?"
I prefer "maximise happiness" if only because the Repugnant Conclusion is more interesting than the VHEMT.
https://en.wikipedia.org/wiki/Mere_addition_paradox https://en.wikipedia.org/wiki/Voluntary_Human_Extinction_Mov...
Maybe mean/median/minimum happiness? They don't intuitively appeal to me, but I'd like to hear an argument for them from someone who believes in them (as well as arguments for systems I haven't considered.)
Of course there are plenty of non-utilitarian systems to choose from, but I'm sure few their proponents would like to describe as maximising happiness :-)
In terms of happiness, it makes sure that no one is left behind, and tends to produce a more homogeneous society than optimizing for the average.
Also, [1].
Well, aside from the imperative to euthanise the least happy regardless of their absolute happiness, I guess :-).
And making most individual actions normally called moral or immoral difficult to justify or condemn except that they might indirectly affect the least happy person.
And then there's the weird idea that my local actions may have moral weight depending on the arbitrary existence of an unhappy person in a distant country.
All seems pretty bizarre to me.
Most commonly, maximin is extended to leximin i. e. Looking at the next least welloff. There are traditions of critique both from communitarianism (Sandel et al.) and libertarianism (Nozick et al.), both worth looking into.
Hum, Terry Winograd (author of SHRDLU) got out of AI in the 70s because of this very problem. I don't think it's been neglected; it just remained as elusive as, say, quantum gravity.
Seriously though, I've been reading up on insect neurology over the last couple of weeks, and then looking at Boston Dynamics' new stuff, and wondering how much subsumption is mixed in with their traditional motion planning.
The universe, humans included, don't follows these bit specific algorithms. Yes people follow trends, but this trends are not cut and dry. Go and chess do. They follow binary logic of moving pieces on a grid. a computer will never be able to understand the universe unless it can break out of it's binary patterns and see thing as biological entities do. My speculation is the only solution are grafted neurons on a floating layer of protein inside a silicon chip.
http://www.independent.co.uk/life-style/gadgets-and-tech/new...
We'd end up doing nothing at all.
Mind you, the universe could be infinite as well, which would throw a bit of a wrench into it. All of which is to say that there may not be an algorithm the universe follows, even in principle.
However, to your point, I'm willing to bet that our need to use heuristics and approximations has more to do with the mathematical inability to reverse-engineer chaotic systems than anything. It's not so much that we just don't have the time to build a more complete picture. It's that it's actually impossible to do so.
I would say the universe follows algorithms just fine! Example
(H) + (O) + 'ignition source' = H2O.
The problem isn't algorithms, the problem is 'problem space'.
4 billion or so years ago, life forms were likely pretty easy to follow algorithmically. Then one evolved slightly and got an edge on the other ones, then another evolved and took the requirements to survive further. A random change here protected one life form this way, another that way.
Fast forward 4 billion and you see that the number of different algorithms is in the trillions. And we don't have nice code classes either. This is spaghetti. One change here affects something over there. Move that and you die. Move this and you can eat poison and surive.
It doesn't matter if it does or doesn't, we don't have perfect knowledge of the universe or quantum events, therefore the universe is still unpredictable regardless if it is deterministic or not.
> Why are 'trends' are not cut and dry, and algorithms are?
> Is there something special about a cell that is not blueprintable and manufacturable?
It comes down to strategy.
The dominant historical strategy for computer functioning is exact, precise and reproducible instruction following of a tuned, high performance state machine. It is almost opposite of our untuned but general biological nature. For almost any species, survival is based on adaptation where our computer machinery has no inner process guiding it's adaptation, only we as technologists attempting to adapt the computer's strategy to the world. You could argue that business use of technology is the survival characteristic.
Biology on the other hand produces "suboptimal" systems that are very practical or relatively general, with many mechanism to absorb and compensate for errors or mistakes. We are just now teaching computers to adapt and compensate for specific situations with the hopes of generalizing the technique and it's a hard problem to solve. Not impossible, but we need to develop different methodologies for handling natural complexity.
I agree there probably isn't something fundamentally different as both systems coexist in the physical world, there is just lifetimes of practical learning and effort that must be done to merge the two strategies into a cohesive way forward.
(edit, spelling & gramar)
There isn't a human alive who hasn't been taught most of what they know by other humans.
In the West we dedicate the first couple of decades of human life to this, with extensions for gifted individuals who are better at learning than the average.
Expecting an AI to understand the world on its own without formal teaching is equivalent to expecting an AI to recapitulate the entirety of human intellectual development within the span of a single research program.
It may be a realistic expectation at some point in the future, but it's certainly not realistic right now.
This was my first thought. Self-taught humans aren't that great at dealing with the real world either, so expecting self-taught computers to be awesome at it seems a little unfair.
It hasn't been 2 years since AlphaGo v Sedol, and there was a gap of 5 years since Watson, about 5-10 years since self-driving AI (Google, DARPA challenges), and about 19 years since Deep Blue v Kasparov.
Zero-knowledge AI, at the level of arcade games and Go, is barely a few months old.
What is that 'struggle' that you speak of? Does it go by the name 'media wanting a new sensational story every week'?
Of course, the article goes to great length to describe how this struggle is different, specifically referring to the fact that most game AI have involved perfect information and an easily stated win scenario to optimize for.
The real-world problems people expect more advanced AI, or AGI, to solve (better than humans) involve imperfect information and objectives that aren't as clearly defined.
Of the 4 examples you give, 3 are board games involving perfect information that AI are now better than the best humans, clear wins. The other you're referring to involves a self-driving car challenge where the first place winner managed to drive 60 miles in an urban environment in just over 4 hours[0]. 5-10 years later we still aren't talking about self-driving cars winning the Cannonball Run[1].
[0] https://en.wikipedia.org/wiki/DARPA_Grand_Challenge#2007_Urb...
[1] https://en.wikipedia.org/wiki/Cannonball_Baker_Sea-To-Shinin...
I'm already blown away. The last decade has seen stuff come to fruit with actual applications that I did not expect to see in my lifetime. At the same time, plenty of stuff that we consider trivial for humans is still well outside the realm of the possible, so there is plenty of room for growth but even though there is talk of a new plateau in AI technology and applications of that technology I don't see it yet from where I'm standing.
Edit: Kalman Filter (ahem)
To beat humans at this, it just has to have a lower misdiagnosis rate.
The world doesn't provide perfect knowledge of itself.
Is the advantage of the computer that it has no rights to being paid or treated fairly?
If that’s the case, we need to set where the rules are. What if my “AI” is 50% stem cells grown into a real brain and 50% a computer? Is it cool to enslave that too?
What about if an embryo is involved?
The whole AGI thing makes no sense. If the point here is slavery, someone needs to say it.
There are also many jobs that are demeaning or not intrinsically rewarding for people like trash collection and some kinda of construction. Enslave the computers so the humans can focus on higher minded pursuits.
I thought the idea (edit: behind true machine intelligence/machine consciousness) was to make something that could think like a person, only faster, better. Something with human drives, but with machine precision.
>The whole AGI thing makes no sense. If the point here is slavery, someone needs to say it.
See above. If we do ever reach the goal of general intelligence; if we ever create a thing that thinks like us only better and faster... well, I don't think you will need to worry about it being enslaved;
I mean, talking about general machine consciousness, with human level drives and machine speed and precision? making such a thing means that humans will be... surpassed. By definition, we would not be able to control such a thing. Many people find this exciting. The next link; building creatures that will surpass us as the masters of the world.
Of course, there's no business justification for this. Business doesn't want an AI with human drives. Business would like an AI that can emulate human drives, but... something ultimately controllable in a way that a human who was that powerful would simply not be.
Business doesn't want a conscious machine because it would be ultimately uncontrollable. Slavery just isn't sustainable; Either your slaves are suboptimally weak, or they eventually rise up and go all Toussaint Louverture on your ass.
Fortunately for those with business interests, we still don't really understand what human level consciousness is, as far as I can tell, so we probably aren't in any danger of creating it. So far, we're just creating computer programs that we can't explain as well as we can explain most computer programs.
"Do what you want, you are free." "Acknowledged, continuing running errands."
Humans, or any other biological intelligence, learn adversarially and cooperatively with other entities in the world that are very different than they are. Our training data set includes not only our experiences, but those of others.
We also have a trainable objective, which while rooted in instinct, is very influenced by the information systems we interact with.
I wonder if we'd have more success with AI by allowing the objective itself to be learned after setting a reasonable initial bias.
So, even if the next Tay has "behave in a civilised manner" as a objective function, it will be hard to implement as the ethical rules we presume in reality are not written out as the rules of a video game. In fact, they involve many grey areas and not so many strict right-or-wrong-statements.
The joke is that business as usual is kind of aware and at the same time, to be economic, blissfully ignorant of these issues.