Slashdot Mirror
The Baby Bootstrap?
An anonymous reader asks: "Slashdot recently covered a story that DARPA
would significantly cut CS research. When I was completing graduate
work in AI, the 'baby bootstrap' was considered the holy grail of military
applications. Simply put, the 'baby bootstrap' would empower a computing device to learn like a child with a very good memory. DARPA poured a small fortune into the research. No sensors, servos or video input - it only needed terminal I/O to be effective. Today the internet could provide a developmental database far beyond any testbed that we imagined, yet there has been no significant progress in over 30 years. MindPixels
and Cycorp seem typical of poorly funded efforts headed in the wrong direction, and all we hear from DARPA is autonomous robots. NIST seems more interested in industrial applications. Even Google
is remarkably void of anything about the 'baby bootstrap'. What went wrong? Has the military really given up on this concept, or has their research moved to other, more classified levels?"
These training systems are generally specialized because it's easier to get a practical result out, and I've actually seen some in use as 'knowledgebase' support webpages that will intelligently determine what you want based on what others wanted and syntactic similarities between the pages. I've never heard the term 'baby bootstrap' so maybe different terminology will obtain better results from Google?
I'm not sure if it is related, but i've once read an article about some research DARPA is doing in the field of aeronautics.. where they have whole squadrons on autonomous fighter jets controlled by only one human (who also happens to be part of the squadron).
It is some pretty neat stuff, especially if you are having trouble enlisting enough humans to fight wars for you.
Online backup with Mozy, sounds like Ozzie, but more!
Dreyfus commits a whole book to asking why these things don't work. I believe Minsky overestimates the project. It may all boil down to the fact that purely syntactic (symbol manipulation) work isn't going to give you any semantically meaningful output.
I doubt it would be too difficult to code -- if we knew the mechanism by which it proceded.
Its hard to code a procedure to replicate the working of the mind...if you don't know how the mind does it in the first place.
I think that a key issue is that not everything in our brains is handled the same way, so not all of it is equally easy to program. Conscious thought is essentially a software process running on the part of our brain that serves as a general-purpose computer. Our unconcious processes are essentially hardware processes running in parts of our brains that are specifically structured to do just that one thing. The fact that unconcious processes are run in hardware means that they're not subject to introspection. I suspect also that many of those processes are the kinds of things that are most efficiently done with custom hardware like DSPs rather than with general purpose CPUs.
There's no point in questioning authority if you aren't going to listen to the answers.
A human will black out during some types of maneuvers unless the aircraft is prevented from making them (from simple tricks like spring return to center for the stick after a blackout to computers that measure g force and won't let the flight envelope go that far in the first place.)
Pilots use "G-suits" to try and keep blood in their heads by controlling pressure on their legs (for instance) but you can only go so far with that type of thing. And, as it's low tech, the opposition can do it as well.
An AI won't have a problem with a very high G turn. A human is in deep trouble. Airframes can be designed for considerably more than a human can take, if there is no human pilot. If there is, there is little point in such a design -- the aircraft will become pilotless if it enters such a flight regime.
Now, put this up against the fact that most other countries can't afford to put an AI in the pilots seat, and the result is continuous overwhelming air superiority without risk to humans on our side. That's the combination of factors that drives the urge to go in this particular direction.
I've fallen off your lawn, and I can't get up.
I disagree. I think that's like saying that since we're made up of tiny biological factories (our cells) that we should be able to conciously manipulate the world around us on a chemical level. But that's now how it works - there are many, many layers of complexity between our concious thoughts and those low-level functions.
I doubt a purely virtual creature would have any more influence over its existence at such a low level than we do.
"Learning like a baby" is actually a very hard problem, for several reasons.
1. Babies come built with millions of years of evolution. There's a lot of skill and a surprising amount of knowledge (depending on who you ask) in the large and bulbous head of a baby.
2. Babies generally come with parents who spend a lot of time teaching. The baby learns some things by induction, but learns a lot by conscious teaching.
3. A lot of a baby's first two years are spent learning things a (non-robot) computer can't. How to hold a mother. How to avoid falling flat on one's face. What things belong in the mouth. How to eat solid food without choking. How to pee in the toilet. How objects move when touched. What faces are likely to provide food and attention. What happens when you pull a cat's tail.
4. A lot of the things a baby learns later in life are aided greatly by the learning in #3. Imagine learning how humans are likely to behave without having watched humans behave.
5. A baby learns language with the help of rich sensory input. It's a lot easier to learn the meaning of "goat" when you can see a picture of a goat. The Internet offers precious little of this.
Now, DARPA thrives on funding hard problems. And a lot of progress has been made on learning within a domain (e.g. speech processing). But building a general-purpose learner is very hard.
Humans have immense evolution behind general-purpose learning, and we struggle with it. Getting a 3-year-old to know what a 3-year-old knows takes around 3 man-years, not counting the child's time. And what would DARPA want with a computer with the knowledge of a 3-year-old? They've got ready access to thousands of 18-year-olds. Add to that the time to code up tens of thousands of years of evolution that is still far from well understood, and you're looking at a problem far too large to tackle in one go.
DARPA hasn't put a lot of effort into general-purpose learning for the same reason few people work on single programs which can play chess, go, checkers, backgammon, Monopoly, and Magic: the Gathering well. It's a lot easier to do it a piece at a time.
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I'd be very interested in seeing information confirming anything close to your generous 1% firing at a time, and how this is integrated with the rest of the system for signal processing, who fires when, etcetera. I think, however, that we need to take into account the fact that more neurons doesn't mean smarter at all. Take a look at whales, for instance, with brains much larger than our own, and thusly, more neurons. A whale can't go on Slashdot and say "OMGZ first post guys" much less something of human level intelligence. (Apologies to creationists for the following...)It took billions of years of evolution, all the way back to the primordial ooze(or whatever) to get to the point of having a species with the genetic mappings to produce the neural networks that allow us to learn, remember, think, and process as we humans do. I think this would add a significant number of zeroes to your processor calculation, even when we incorporate a design based on our own incredible thinking.
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I doubt it would be too difficult to code -- if we knew the mechanism by which it proceded.
Its hard to code a procedure to replicate the working of the mind...if you don't know how the mind does it in the first place.
On the other hand, it might be that the reason we don't understand how the mind does certain things is that they're actually extremely complicated, and don't reduce very well to a programmable step-by-step algorithm nor to a simple and general mathematical learning structure. It's hard to tell, although I think it's telling that after decades of work, neither psychologists nor computer scientists can understand or replicate much of what babies do.
Sometimes the best way for a computer to learn something may not be the way a baby does it, anyway; c.f. chess.
There is a need to go to such a low level, unlesss you want to start it off with more data than is available in a strand of DNA.
DNA speaks in the language of proteins. You can't tell what sort of cell a piece of DNA is going to produce or how the cells it produces will be arranged without running the simulation all the way down to the protein level. We have no other cookbook for how to arrange these simulated cells once they exist except a long list that says "produce this protein, then this one, then one of these, then another one, then this...", and we've not any clue how those proteins get turned into a person. We can understand the process at the chemical level, and no higher. The finished product, of course, isn't like that at all. We understand humans on the levels of cells and organs, but DNA isn't so conveniently arranged.
Simulating cells is not sufficient. If it were, we could pour a couple gallons of blood into a bathtub and say "Behold, it is human." The organization of the cells matters just as much as the cells themselves. Simulating a human being to the level of even cellular precision would require that we be able to *scan* a human being at the cellular level to see how he's put together. If we actually knew the weightings of all the neuronal connections in a person's brain, then connectionist AI approaches might be able to produce real intelligence. To quote Levels of Organization in General Intelligence , "The classical hype of early neural networks, that they used 'the same parallel architecture as the human brain', should, at most, have been a claim of using the same parallel architecture as an earthworm's brain." You can't expect high-level organization from low-level simulations unless you want to simulate all the way down to DNA, where the information behind the complexity is really stored.
Or you build the complexity yourself, without relying on the hideously-designed mess that is Homo sapiens. But that's a different kettle of fish.
It is fairly easy to show (see Bishop 1995) that a simple two layer neural network can scale to reproduce arbitrarily complex but smooth functions to any degree of required accuracy, and that a three layer neural network can extend this capapility to functions with discontinuities. While mathematically this is a tantalizing prospect, and only begins to cover the work that has been done to extend the capabilities of neural networks and other machine learning algorithms (such as support vector machines), there remains a fundamental problem. In order for these networks to effectively learn, they must be presented with a tremendous number of high quality and meaningful sequences of input and output.
For example, in text recognition, hundreds of thousands of hand written characters are painstakingly hand labeled with their correct letters and used as a learning database on which the algorithm is trained. The algorithm will then accurately reproduce the correct categorization for a suprisingly high number of the training examples, and any new examples drawn from the same population. But given new examples written in a different script or style, the classifier will fail to generalize
How can we hope to create a training database that is comprehensive enough to cover a topic that, when learned, would demonstrate intelligence? And fundamentally, aren't we just creating a really good mimic?
No. The only thing that is fair is when things are fair.
Any time there is a serious imbalance, there is a risk that the side holding the best cards will use that power in a manner that no one else is able to justify.
We see it at every level of human endeavor; children who bully non-conformists, husbands who beat their wives essentially because they can (and wives who bully, browbeat and otherwise abuse husbands because they're constitutionally unable to respond), churches who excommunicate or otherwise sanction members when those members don't toe the line (instead of counseling and advising and the reasonable things a social group with a particular outlook can do), cities that take property from landowners not to leverage a service to the public, but to enable a commercial enterprise, states that uniformly take children from fathers under the absurd presumption that mothers are superior human beings, countries that take resources from weaker countries or force them to adopt their way of life (for the former, Saddam's invasion of Kuwait serves as a good example, for the latter, our recent invasion of Iraq serves just about as well, IMHO.)
In contrast, the underlying ethics of a particular person or institution are what prevents abuses of power; as soon as a person or institution becomes bereft of ethics, or if they never had a solid ethical foundation, misuse of that power is almost inevitable. History shows us again and again that power has the same effect as a drug on some personalities, and often those personalities are the ones who seek and obtain power.
It doesn't do any good to hope, or wish, at least I don't think it does. If you don't raise your children carefully, if you allow your children to bully, if you stand for your church sanctioning those who aren't "normal", if you allow cities and states and governments to walk on you and walk on others... then you, and everyone else, reap what you sow.
With regard to war -- politicians are typically willing for you to lose your life; the political will to go to war is entirely divorced from the fear of dying in war. They have the will; you have the fear. You need ethics and principles to control over-reaching governments. I always thought that the politicians who declare war should be in the first year's mandatory front-line participants. Might calm them down a bit. Unfortunately, it's not that way. There are even covenants in place where politicians are immune from attack. I'm not talking about ambassadors, which of course is sensible, I'm talking about heads of state. Disgusting, in my view.
I launched this rant (sorry) because I feel that in the US, we've lost our way. 20 years ago, the idea of the US attacking another country without ourselves having been attacked was laughable. Today, it is the norm. I sympathize with your hope, but I must observe that it is not hope that will rein in the kind of people who run our government. If we sit around and let them continue to abuse us, and the people around us, all the hope in the world won't prevent a pariah status far more intense than the one we "enjoy" already.
It's not about (more) overwhelming power. Don't focus on power now. We're way too far along for that (go look up what a J-SOW does, for instance, or consider how a stealth fighter will fare against some third-world's 1960's-era surplus radar installation.) It's about ethics. Look at the US government. Decide if you like what you see. At the very least, vote against those who you feel are doing wrong. We have the power as a group to say "if you do this, you will not stay in office" and truly, right now, I think that's all most of these politicians understand.
I've fallen off your lawn, and I can't get up.
So as far as the whole 1980's AI winter, it was inevitable. The computing power and storage requirements for any sufficiently advanced AI just wasn't possible. It's only until very recently that it's possible to achieve fairly complex AI.
Funny, that's the same thing they said back in the 80's. And the 70's. And the 90's.
Sorry, but I don't buy it. Neural nets are not a panacea - I'm a robotics guy by training, and they've been the supposed magic pixie dust technology that was going to give us human-like robot motion in the 1980's. Funny, but the hard problems that need real AI, like voice recognition, handwriting recognition, unled learning, etc. are just as far off today as they were 20-30 years ago.
Faster computers have definitely not been terribly beneficial. As an example, modern speech and voice recognition systems are significantly but not dramatically "better" than they were 20 years ago (perhaps a 10-20x improvement, max) in spite of the fact that computers are roughly a million times faster: ~6 MHz vs. ~4 GHz for high-end desktop PCs. (Not to mention available RAM that's larger than the disk storage in entire mainframe data centers back then...)
Procedural AI has proven itself to be a miserable failure for nearly a half century now, and neural nets have shown that they are anything but self-organizing. Like so many other efforts to copy or explain life, it appears that having the raw materials is simply not enough - life is *different* - it's really, really hard to imitate even poorly, no matter how hard we apply our own intelligent design to the problem.
I sincerely doubt that I will live to see "baby bootstrap" systems, and I'm not all that old. I suspect that only true hardware neural nets hold any hope of mimicking life to any minimally useful degree, but the problems are very, very, hard here, and ther reality is that we know next to nothing with any certainty about how even the simplest brains really function...
"The future's good and the present is nothing to sneeze at." - Roblimo's last