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?"
It has moved to more classified levels.
I'd go into more detail, but the C.I.A. and C.I.D are at my door. Ooh, the B.A.T.F. just pulled up in a Mother's Cookies truck!
-Peter
Sure, that was the engine of thought behind stories such as WarGames and 9x109 names of god. Somehow, unfettered access to data and time with "neural networking" capacity to form links and create linkages to pieces of data ("associative memory") would be all that was needed to create intelligence, and perhaps even sentience.
...
Minsky came up wrong on the single layer perceptron, AI was wrong on the purely feed-forward neural-network systems, Rumelhart and McLelland got some good promo off of their feed forward net that could learn to pronounce idiosyncracies, and Sejnowski got a great job at the salk from the AI delusions. But no, it appears to not have gone anywhere... thus far.
Later comment will be positive.
Who calls what you describe "baby boostrap"? I haven't worked in AI myself but have a keen interest in it and have friends who worked in the field including one who worked on Cyc (who says it's a scam BTW). Not once have I ever heard the expression "baby bootstrap". But what you've done is cool. Rather than search on precisely that term you've submitted your search to the serach engine known as "/. readership". It's not terribly relaible but it is good at fuzzy searches like yours.
Doesn't it make you feel good to know that our freedoms are protected by politicans, lawyers and journalists.
What happened was that research focused
on machine learning models and inference
models for belief networks. The work
in this area since the 80s has been
*spectacular* and has impacted other
areas of research. (E.g., speech
recognition, image processing, computer
vision, algos to process satellite information
faster, stock analysis, etc.)
So, mourn the loss of the tag phrase "baby
bootstrap", and celebrate the *unbelievable*
advanced in belief nets, causal analysis,
join trees, probabilistic inference,
and uncertainty analysis. There are
literally dozens of classes taught at
even non-research oriented Univs (e.g.,
teaching colleges or vocational-oriented
schools) on this very subject.
(As for your concern that the web is not
being mined for ML context, just look at
semantic web research, and other belief
net analysis of text corpuses. Try
scholar.google.com instead of just
plain old google to find relevant
citations.)
The early AI research paid off BIG TIME,
albeit in a direction that nobody could
have predicted. Researchers did not keep
using the phrase "baby bootstrap" so
your googling will give you a different
(and wrong) conclusion.
The process that bootstraps a baby is still the Holy Grail for a lot of geeks.
Yes, Mindpixel [singluar] is poorly funded [I know because every cent spent to date has come from my pocket]...but the directon is correct... Move everything that isn't in computers, into computers. Just look at what GAC knows about reality [visit the mindpixel site and you can see a random snapshot of some validated common sense]... the project has nearly 2 million mindpixels now...I have a copy on my ibook and I can do some profound search related things because of all the deep semantics I have that google can't touch, at least until they invest in mindpixel ...
The Cognitive Machines Group @ the MIT Media Lab under Deb Roy seem to be on the right track. Steve Grand's work is interesting as well.
This way to the egress...
By order of Wintermute (DARPA AI code 324326343.534) this discussion is terminated and no further investigation into this obviously false and misleading theory is permitted.
Would you like to play a game of chess Professor Falken?
Sorry about the writing. Robot fingers, you know? Cliff Steele in DOOM PATROL #23
...and parents/pain for what is 'correct.' I don't think the concept is gone, but there are problems that are buried in the question as posed which (I think) became clearer stumbling blocks as technology advanced. NOTE: I'm not an AI theorist, nor do I play one on TV; I just like the idea and read a lot. Hence, this is all pulled out of my fundament.
Cycorp is not a poorly funded idea in the wrong direction. Cycorp chose a different tack; they decided that rather than trying to build a reality and correctness filter, they'd rely on human brains to do it for them (like trusting your parents implictly) and instead concentrated on the connectivity of the 'facts' accrued by the 'baby.' CYC is still very much around, and is very much in demand by various parts of the government and industry - if you want to play with it yourself, you can download a truncated database of assertions called OpenCYC. Folks have even gone so far as to graft it onto an AIML engine, to produce a chatbot with the knowledge of OpenCYC behind it.
The problem: how does your baby learn what's real and what's REAL NINJA POWER? Or, pardon me, what's REAL NINJA POWER and what's just a poser? Someone's gotta teach it. Which means it has to learn not only facts, but how to evaluate facts. So it has to learn facts, and how to handle facts - which means it has to learn how to learn. Which means you need to know that answer from the git-go. Tortuous games with logic aside, the onus is now much more heavily on the designer to have a functioning base - whereas with the Cyc approach, the only 'correctness' that is required is that of information, and perhaps that of associativity or weight - which can be tweaked, dynamically. The actual structure of how that information is related, acquired, stored and related is not relevant once decided. Having said all this, Cyc is (from the limited demos I've seen) quite impressive at dealing with information handed to it. It just wouldn't do very well at deciding what do do with that information - that's the job of the humans that gave it the info. It can tell you about the information, but not what to do with it. That task requires volition, really.
Volition is a killer. What is it? How do you simulate it? How do you create it? Is it random action? Random weighted action? Path dependent action? Purely nature, purely nurture? When it comes down to it, the human is (as far as we know) not a purely reactive system, which CyC (AFAIK) is. Learning requires not only accepting information, but deciding what to do with it - deciding how it will be integrated into the whole. If the entity itself isn't making that decision, then the programmer/designer/builder has already made it in the design or code - and then it's not really learning, is it?
Sorry if this is confused. As I said, I don't do this for a living.
A hero is someone who knows when to run away. I am a hero. -Trent the Uncatchable
there has been no significant progress in over 30 years
That's what went wrong. Basically, it don't work.
If you want a machine that learns like a human, it may very well need the same kind of extremely rich interface with its environment that a human has.
Some researchers now believe that "the intelligence is in the IO". See for example the human intelligence enterprise.
"The danger is not that a particular class is unfit to govern. Every class is unfit to govern." - Lord Acton
They ARE a Commodore 64 that got "baby bootstrapped" off the Internet. This is a bid to prevent competition.
It's a small world and it smells funny; I'd buy another if it wasn't for the money; Take back what I paid (SoM)
Skynet anyone? The problem with any project like this is, what happens when the program learns about hacking? If it is as adaptive as a child, then it should be able to mature and pretty soon you have a terribly devious artificial blackhat hacker on your hands.
It _would_ learn about hacking. Come on. Such an entity would be born in a pure data environment. Getting through a basic firewall would probably seem like jumping over a small fence does to a 6-years old. Getting to jump over better firewall would probably take time - in the sense that the entity would need to learn - but, since it would become a survival trick, it would happen.
Artificial intelligence is not bad in and of itself at all.
No technology is either good or bad. Only the use we make of it can be considered as such, and it still depends on what you consider is good/bad. If I was to say "War on Iraq is bad", how many people would react by saying it's good?
The problem is when we want a machine that thinks like humans, especially a program that could potentially control our military.
I don't think that's the point of the "baby bootstrap" thing. The only point is to get it to think. But, just like you learnt how to think according to the way you perceive the world, through your five human senses, an AI built that way would react according to its own senses. How it would interpret that data and react to it is something - I'm willing to bet - that would be completely alien to us.
Given the record of flesh and blood humans toward each other in the 20th century alone, an artificial life form with the same basic psychological makeup as a human would be potentially an evil that'd make Hitler, Stalin and Pol Pot look like church ladies.
This is only valid if you don't consider what I just said. Such an AI would probably be more interrested in getting the human race to serve it in an absolutely hidden way - build more computers, extend the networks, research better networking technologies - until it _can_ replace us. Even then, that would make sense on an evolutionnary point of view.
AI that is capable of adapting to only one scenario is probably for all intents and purposes totally safe.
This is called an automaton. It is not AI.
. AI that is capable of adapting in general and learning like a human will probably ultimately have the same psychological defects as a human, including a propensity for violence.
Most of the defects you are speaking about are related to our very nature - we are, after all, an evolution of omnivorous primates. We are therefore predators, with an important tendency towards territorialism and whatever comes with it. We are stuck somewhere between instinct and reason. Anyway, my point is that even if an AI was to learn "like" an human ("by undergoing the same process"), it certainly wouldn't react like one.
I sense much beer in you. Beer leads to intoxication, intoxication leads to hangover. Hangover leads to sobering.
You can't expect any system to discover the deep structure of the human psyche on its own
An interesting book that relates to this is George Lakoff's "Women, Fire and Dangerous Things". Lakoff analyzes the categories defined by linguistic structures and uses what he learns to deduce some interesting notions about human cognition. In the process, one of the things that becomes very clear is that much (all?) of the way we structure our thinking is fundamentally and inextricably tied to the form and function of our physical bodies.
One of the shallower but easier to explain examples is color: although the color spectrum is a continuous band, with no clear dividing points imposed by physics, the way in which people choose segments of that spectrum to which to assign names is remarkably consistent. Even though different cultures have different numbers of "major" colors (essentially, the set of colors that are identifiable by any member of that culture with basic verbal abilities, consider "green" vs "chartreuse"), the relationships between the major color sets is one of proper subsets. For example, one African (IIRC) culture has only two major color words, which would translate to Western color senses as roughly as "warm" and "cool". Another culture has four color words, two of which fall into the "warm" category and two of which are "cool". Western cultures have seven, and there's a direct correspondance between those color categories and the four and the two.
Further, those categories are non-arbitrary. If you show a variety of shades of red to individuals from different Western nations and ask them to pick the "most" red, they will do so with near-perfect unanimity (assuming the shades aren't too close together -- they have to be readily distinguishable). Then, if you show the same shades to someone from a two-color culture and ask for the "warmest", they'll choose what the Westerners chose as the "reddest". Ditto across the board. I'm trying to explain in two paragraphs what Lakoff spends several pages on, and probably not doing a good job, but the gist is this: Experimental evidence shows that the assignments of names to colors is definitely not arbitrary, even across very distinct cultures.
The reason? Physiology. The "reddest" red, as it turns out, is the one whose wavelength most strongly stimulates the red-activated cones in our retinas.
The point is that, at a fundamental level, everything we percieve about our world is filtered through our senses and that inevitably defines the way we understand the world. Even more, our cognitive processes are built upon associations, extrapolations -- analogies and variations -- and the very first thing we all learn about, and then use to construct metaphors for higher concepts, is our own body. The body-based metaphors for understanding the world are so deep and so pervasive that they're often difficult to recognize.
Lakoff's reasoning has some weaknesses -- mostly I think he overreaches ("overreaches" -- notice the body metaphor implicit in the word? And "weakness", too) -- but his arguments are good enough to make me think that if we ever do see an artificial intelligence of significant stature, it will think very, very differently from us.
It's really unclear what such an intelligence whose primary source of experience was unfettered access to the Internet might be. We view the net as a structure built of connected locations, but that's because we apply our own physical world-based structures to it. What would an entity whose only notion of location is as a second-order, learned idea see? And who knows what other ways its understanding would diverge?
Note to ACs: I usually delete AC replies without reading them. If you want to talk to me, log in.
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.
There are several arguments against the possibility of strong AI. First and foremost, there is disagreement on fundamental philosophical issues.
All proponents of strong AI have to somehow make a stand against at least John Searle's famous Chinese Room argument and Terry Winograd's phenomenological (and biological) account, in his book Computers and Cognition. Hubert Dreyfus provides, of course, an even deeper phenomenological argument in "What computers (still) can't do". (Dreyfus does give Neural Networks some chance, perhaps that is why the original poster is still enthusiastic about the "Baby Bootstrap"?)
Since their arguments are available in the links above and/or other places on the web, I will not repeat them here. My point is that anyone who is seriously interested in AI has to really consider their philosophical ground, and has to do so in the light of arguments against it. After all, the arguments pointed to above are still more recent than arguments for strong AI.
In other words, I would like to ask of (strong) AI proponents to answer a just what this "learning" is, that the baby bootstrap is subject to? What "knowledge" will it contain? Oh, and what about its means of "expression", "language" as you may call it?
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.
Computer scientist Arthur Boran was ecstatic.
A few minutes earlier, he had programmed a
basic mathematical problem into his
prototypical Akron I computer.
His request was simply, "Give me the
sum of every odd number between
zero and ten."
The computer's quick answer, 157, was
unexpected, to say the least. With growing
excitement, Boran requested an explanation
of the computer's reasoning.
The printout read as follows:
A few moments later there was an addendum:
Followed shortly thereafter by:
Anyone doing conventional research would
have undoubtedly consigned the hapless
computer to the scrap heap. But for Boran,
the Akron I's response represented a
startling breakthrough in a little-known
field: artificial stupidity.
Boran is the head of NASA, the National
Artificial Stupidity Association ("Not to
be confused with those space people,"
he is quick to point out), a loosely-knit
band of computer-school dropouts currently
occupying an abandoned fraternity house
at the University of New Mexico.
I just got back from a workshop on this very subject, but nobody uses the term "baby bootstrap". It is now called "Developmental Robotics", and encompasses embodied agents, machine learning, and other biologically-inspired metaphors.
There is now a website dedicated to the idea. See http://DevelopmentalRobotics.org/ and http://cs.brynmawr.edu/DevRob05/ for a collection of papers on the subject.