Why we should suspect that the brain has a limited ability to recurse, but prefers to daisy-chain instead:

The house the malt the rat the cat the dog the cow with the crumpled horn the maiden all forlorn the man all tattered and torn the priest all shaven and shorn the cock that crowed in the morn the farmer sowing his corn kept waked married kissed milked tossed worried killed ate lay in was built by Jack.

… to boldly go where no man has gone before!

This final phrase of the classic Star Trek opening spiel had two problems with it, one as seen by people after the fact, and the other as seen by those who had gone before.

As seen by earlier generations, the phrase “to boldly go” is a split infinitive.  If E.E. Smith had written Star Trek in the ’20s, he would have written “boldly to go.”  Avoidance of split infinitives, like many elements of grammatical style, was a cognitively expensive signalling behavior that advertised, essentially, that the speaker or writer was in the educated classes in an era where being educated meant you knew Latin.  Infinitives in Latin are single words formed by inflection, rather than with a keyword such as “to” in English, so you can’t put an adverb in the middle of one.  Avoidance of split infinitives lasted in “proper” English at least until mid-20th century, but had begun to fade (to slowly fade ) away thereafter.

But there’s no real reason in English not to split infinitives.  They are completely understandable, and often less ambiguous than alternative constructions. Apart from being a cognitively expensive signalling behavior, they had no value, indeed a cost in cumbersome and ambiguous sentences.  Like many rules which were tacked onto the language by well-meaning grammarians, they were overly simplistic formalizations of a system, English grammar, which was and remains much deeper and more complex than anyone thought it was.

The lack of the ability of “hand-coded” grammar to handle real language is most clearly displayed in the early attempts at machine translation, which were an abject failure.  Only after 50 years of trying in natural language understanding, using statistically inferred models not formalized by humans, has serious progress been made (and there’s lots more progress needed before basic competence is achieved).

The other, retrospective, problem with the Star Trek blurb, that the phrasing was considered sexist, was corrected in later incarnations to the more politically correct “where no one has gone before.”  This, as it turns out, is another example where the unthinking application of a simple, formalized rule to “fix” something actually makes it worse.

In the ’60s, the use of “man” in such a context was standard and unambiguous.  It meant a human being (in fact, in my Websters from that era, “human being” is the first primary meaning, specialization to adult males being secondary).  Star Trek, if you remember or have studied these things at all, was in its time one of the most progressive, liberal science fiction shows ever.  It depicted a crew and implied a culture where barriers based on race and sex had been significantly lowered compared to the contemporary norm.  So in the original, “man” meant “human.”

Of course, the Enterprise went all over the galaxy seeking new life, new civilizations.  The citizens of these civilizations had been there before.  The distinction between “man” and these people is a fine one but one which can be reasonably be made consistent with the storyline.

But what happens if you switch from “man,” meaning human, to “one,” meaning, well, anyone. The term is intentionally more inclusive: it pushes the boundary from that between humans and non-humans, to that between someone and something.  But wait: doesn’t that mean that all the denizens of the strange new worlds, who have gone there before, are now not someones, but somethings?  Isn’t the new phrasing, taken out of the context of American academia and applied to Star Trek without thought or understanding, actually worse than before?  In classic Trek, the aliens are non-human people.  In PC Trek, they’re non-persons.

Just as was Victorian proper grammar, politically correct speech patterns are primarily a cognitively expensive signalling behavior.  They have exactly the same import: the speaker is educated, intelligent, and ambitious enough to pay the cognitive price to consciously modify the vernacular.  But as we have seen, PC speech is often yet another case of simplistic human-formalized rules, applied in a context-free way.  They fail on their own terms — implying just the wrong thing, as above — when context shifts.

In other words, simple human-formalized rules applied blindly to something as complex as grammar are brittle, a property they share with bureaucratic rules and AI programs.

Human ethics are similar to human language in their depth and complexity.  They are famously just as difficult to capture in simplistic formalism.  Indeed, given the examples of PC speech, it’s quite arguable that grammar and speech are a proper subset of ethics.  You can’t even reason about whether the Star Trek example is right or wrong without understanding language at a probably better than state of the art level.  And it’s certain that all the subtleties of ontology and epistemology are part of ethics, just as they are of language.

AI is just, in the past decade or so, beginning to get traction in the natural language field beyond the simple human-written formal rules stage.  As for ethics, we’ve just barely gotten into the simple human-written formal rules stage. But if you want a preview of what machine ethics will ultimately look like, study modern natural language processing.

Foresight Institute Feynman Prize winner Dr. Ralph Merkle, perhaps better known to Nanodot readers for his nanotech work, has just won the IEEE’s Hamming Medal along with Martin Hellman and Whitfield Diffie:

Thirty-five years ago, Martin Hellman, Whitfield Diffie and Ralph Merkle developed an easy method for sending secure messages over insecure channels. With the advent of the Internet, their technology, called public key cryptography, is now used continuously everywhere in the world.

“When a lock icon appears at the bottom of your browser, it’s using public key cryptography. Your computer and the merchant’s computer can talk back and forth across an insecure channel and exchange credit card information in a way that someone listening in cannot get it,” said Hellman, Stanford professor emeritus of electrical engineering.

The Institute of Electrical and Electronics Engineers (IEEE) has named Hellman, Diffie and Merkle the 2010 Richard W. Hamming Medalists. Hellman said he was especially happy that the award recognizes the contribution of Merkle, whose early work on public key encryption didn’t get the acknowledgement it deserved.

“Ralph really deserves equal credit with us. I am really glad to see him being recognized on this award,” Hellman said.

Read the whole article for the interesting details and politics behind the work, and a great photo of all three back in 1975 (lots of hair).  Congratulations!  —Chris Peterson

Over the past ten to fifteen years, research in computational linguistics has undergone a dramatic “paradigm shift.” Statistical learning methods that automatically acquire knowledge for language processing from empirical data have largely supplanted systems based on human knowledge engineering. The original success of statistical methods in speech recognition has been particularly influential in motivating the application of similar methods to almost all areas of natural language processing. Statistical methods are now the dominant approaches in syntactic analysis, word sense disambiguation, information extraction, and machine translation.
Nevertheless, there is precious little research in computational linguistics on learning for “deeper” semantic analysis. …

–Raymond Mooney, in a paper at the AAAI 2004 Spring Symposium on Language Learning.

Although we have talked mostly about robotics in terms of how AI has been advancing, it’s instructive to look at developments in the other subfields as well. Natural Language Processing is among the oldest. Turing’s classic paper from 1950 laid out the ability to converse in ordinary, unstructured text as an unequivocal test of the point a machine could be said to think.

Although much can and has been written on the validity of the Turing Test as specified, it is clearly true that a computer with the ability to converse fluently in written and spoken English would be enormously more useful than the computers we have today.  I think it’s also reasonably clear that most people would assume that there was “somebody home,” i.e. begin to impute intelligence and a self to such a computer.

The paradigm shift in NLP has been a result of two things: the increasing willingness (and ability) of AI researchers to use statistics and other numerical methods from the scientist’s toolkit, and the increasing size and avalibility of databases and corpuses and the processing power to subject them to intense analysis.  The amateur AIer today can go on the web and obtain for free enough data and programs to cobble together an NLP system better than anything that existed in 1990.  The processing power in a high-end workstation is good enough for a reasonable amount of research, but a couple more orders of magnitude will help immensely.

What statistical methods have done, in essence, is replace the hand-written grammars that characterized classic-era AI NLP.  These are probabilistic, trained on huge corpuses, and are considerably more robust in use than the old ones.  On the other hand, they don’t reach up into the heights of semantics as well. I’d claim that there’s not a NLP system today that understands its entire vocabulary as well as SHRDLU did its.  The reason is that for its tiny vocabulary, SHRDLU could have a hand-written piece of code for each concept, and thus have a real understanding, in some sense, of the concept.  However, this will change over time.  To begin with, people will simply write code for the most important concepts.  People will come up with schemes to form new code for new concepts from fragments of old code and/or search methods like genetic programming.

Current leading-edge NLP systems (most of them proprietary, AFAIK) are surprisingly good at talking about whatever it is they actually know about, i.e. have a deep semantic model of, as long as you’re literal and prosaic (and expect them to be the same).  I think it’s a toss-up whether automatic programming of semantics makes it to hypohuman border this decade — but AI with hand-coded semantics such as Siri seems likely to be ubiquitous, and competent, by 2020.

It’s pure palladium, 8 nm wide, made at the University of Birmingham’s Nanoscale Physics Research Laboratory.

h/t Nanowerk

In E.E. Smith’s famous Lensman series, the galaxy is the battleground between two races of superintelligent beings, the (good) Arisians and the (evil) Eddorians.  When I listen to people who worry that we are about to create a superintelligence which will take over the world, I get the impression they’ve come from reading “Galactic Patrol” and think that we are on the verge of disastrously creating an Eddorian unless we buckle down quick and figure out how to build a friendly Arisian instead.

In the books, the superintellects had lots of ESP powers but we can dismiss those.  The actual intellectual capability they were imputed to have was the ability to predict.  Prediction is of course the sine qua non of intelligence, but the Arisians were able to predict, e.g., five years ahead of time, that a certain man would be sitting in a barber’s chair and a kitten would jump onto his lap, jostling the barber’s arm and giving him a scratch.  All from the laws of physics and the knowledge of initial conditions.

There are many reasons why this is simply, completely, totally, always forever and truly impossible.

First of all the laws of physics are quantum and have a built-in probabalistic uncertainty. By the same token, it is impossible to know the initial conditions of any substantial portion of the universe to any very high precision: measuring a particle necessarily changes its state in a way do not completely know.

Second, huge parts of the phenomena of interest, a many levels of ontology, are in dynamic systems that are subject to chaotic behavior.  The Butterfly Effect reigns not only in weather, but in markets and politics and epidemiology and computers (one different bit out of a gigabyte can completely change the program’s behavior) and every human mind.

Computers are a particularly hard case of this.  Very basic theorems of computer science tell us that one cannot in general predict what a program will do without actually running it.  This is fine if your superintellect has plenty more processing power than the computer in question, and can emulate it.  But the closer the computer you’re trying to predict comes to having your own processing power, the more likely it will surprise you.

A weird special case of this is that you can’t even predict a universe if you yourself are part of it, because you are a computer with processing power equal to yourself.  (This, BTW, is where our notion of free will comes from: our world models must necessarily exempt our self-models from their general basis in determinism.) You could cheat and force yourself to act in the future according to a list of actions you prepared today, but you wouldn’t be acting all that intelligently; and you wouldn’t be acting with free will, either.

A more obvious case is simply a world with two (well-matched) superintellects, in which at least somewhere they are in competition, maybe even just a friendly game of chess.  In a game between two identical chess computers, each gets to see one ply deeper into the future than the other one did.  Neither can know enough to guess what the other one is going to do for sure.

In a world with lots of superintellects, no one will be able to predict any detail on which they compete.

The debate held at Foresight 2010 between Robin Hanson and Mencius Moldbug on the subject of futarchy is now online at Vimeo.

Watch it online or download it:

1. Get a vimeo account by registering.
2. Option-click on the download link close to the bottom right on the video’s page
3. Wait an hour It’s a gigabyte, almost.
(This is known to work in FireFox and Safari. Don’t know about other browsers. YMMV.)

Many many thanks to Monica Anderson for doing the video.

“It was a true solar-plexus blow, and completely knocked out, Perkins staggered back against the instrument-board. His outflung arm pushed the power-lever out to its last notch, throwing full current through the bar, which was pointed straight up as it had been when they made their landing.”

My current research in AI, such as it is, is an attempt to build a system that’s capable of understanding the above quote. It’s from the middle of a book, and it is much much harder to understand, fully, than you might think. What I intend to do here is to unravel the process by which someone reading the book could be said to understand it. Largely the concern is about what kind of mental structures are being built and what structures must have been built by reading the previous half of the book for the passage to do what it does in the mind of the reader.

Without further ado, let us jump into the quote, which starts at the beginning of a paragraph:

“It was a true solar-plexus blow,”

There are two sources for the comprehension of this clause. First is the preceding paragraph, where a fight is described. A scene and script have been built up, like a movie in the mind. In particular, one man is holding a girl (who is struggling to escape) and another is trying to tie her feet. She kicks the second man, and that is the blow that’s being referred to.

Unlike a cinematic movie, however, much that would be evident on the screen has been left out. The specific positions of the bodies, the clothing in some cases, and many aspects of the background have been left to the imagination. In other words, the “movie” is a sequence of abstractions.

It is in no sense simply a pile of predicates, however. When I read this, I come away with a semi-visual motion script, such as could be used to orchestrate a re-enactment by action-figure dolls, even though the text doesn’t come close to specifying the actual positions or motions I imagine.

The second source is the reader’s memories of pertinent experiences, either of watching fights or having been in them. In the multi-level abstraction structure that’s being built, by and large, at least in the hands of a skillful writer, the things that get mentioned are the things that you’d pay attention to if watching the scene. It’s well established, for example in studies of eyewitness acounts in criminology, that people confabulate what happens between such points in their memory of actual events, much less from verbal stories. So to that extent, the structure of a story reflects that of memory.

If you’ve ever taken a hard blow to the solar plexus, you’ll have a much deeper understanding of this passage than someone who hasn’t. I have, and the sensation is unique; nothing else in my experience feels the same or has the same effects. If you have, note that among the few descriptions of clothing that were provided was that the girl was wearing riding boots.

At a higher level, the scene is part of an attempted abduction of the girl by the men. On this level, the reader is on tenterhooks to discover whether the abduction will succeed, given the girl’s spirited and at least partially efficacious resistance.

“and completely knocked out,”

Syntactically, this is a bit of a garden path; we expect it to be a conjunct of the previous predicate until we see the comma. It turns out to be a participle introducing the second clause. It appears to be for the benefit of those readers who have not experienced solar plexus blows. It describes the effect well enough to follow the action sensibly, but doesn’t really capture the experience.

This points out that there can be different amounts of actual understanding going on in different readers each of whom would claim to have understood the passage: there can be ties to emotions, sensations, memories, and/or mental models in various combinations and strengths.

“Perkins staggered back against the instrument-board.”

Perkins is the second man, and his staggering back is completely predictable from the description of the action so far. In fact, it’s predictable that staggering back is part of the process of his collapsing, which isn’t, and doesn’t need to be, stated explicitly. The ability to predict is one of the key elements of understanding, so we can propose that there is a model of collapsing after being knocked out (or struck in the solar plexus) that abstracts away from any particulars about the individual (or his specific position) that allows the extrapolation of the “movie,” if need be.

It’s the instrument-board that is the new item. In order to understand this, the reader has to pull into play a much broader background structure than heretofore. The action is taking place in the control room of a spaceship, and the “instrument-board” is its control panel. One is reminded of a programming language variable being looked up in a containing context after being found unbound in the local one.

“His outflung arm pushed the power-lever out to its last notch,”

Now we see that there are multiple disconnected levels of abstraction, as well as disconnected items of physical description, that need interpolation. We hear about the arm and the lever, which is local and concrete. We can imagine an arm striking a lever and pushing it. We still don’t know anything about how big the board is, where the lever is on the board, whether the board is horizontal, vertical, or tilted. We don’t know where Perkins is with respect to the pilot’s (and co-pilot’s?) seat(s). On the other hand, we do know much higher-level things about control panels, and power levers (I, for example, call to mind typical airplane cockpits as well as the spaceship control rooms in SF movies I’ve seen.) Although various things about the spaceship have been mentioned before in the book, no good description of the control room has been given; we have to assemble this as some useful level of abstraction as we read this passage.

“throwing full current through the bar,”

This is the key development, not only of the passage, but of the entire book. Note that without the model that the reader will have built up by that point, the phrase means virtually nothing. You might well think that there is a place where drinks are served on the ship.

The book is, as the perspicacious reader will have guessed, E. E. Smith’s 1928 space opera classic Skylark of Space, and its premise is that a bar of copper plated with a (fictional) transuranic metal can convert its mass to pure kinetic energy with the application of a current. The bar is the drive motor of the spaceship. As far as I know, there’s no other context, even in science fiction, where the motor of any vehicle is referred to as a bar. It certainly doesn’t happen in reality, and will not have been the case in any reader’s experience. Many of us have seen bars of copper, and to that extent the symbol is “grounded;” but in the salient sense of the story it is not. Its meaning comes completely from the model that has been built up out of pure words. (In simple terms, no AI with a static, pre-programmed ontology will be able to understand this century-old kids SF adventure story at even the most pedestrian level.)

The really remarkable thing about this phrase is that it throws implications across virtually every level at which the book is to be understood.

At the physical level, it describes the closing of a circuit and the application of voltage to a piece of metal.

At the technological level, using the (fictional) understanding of space drives built up before, that means that the ship will be placed under very heavy acceleration.

Back at the physical bodies level in which we were understanding the fight before, it means that the parties will be thrown to the floor and unable to move.

The fight will be at least suspended. At something closer to the plot level, the parties, pinned to the floor by acceleration, will be unable to stop the ship until the fuel runs out, stranding them far away in space.

This converts them from abductors and victim, where the conflict is all interpersonal, to fellow lifeboat passengers facing a common doom. There is room for various character development as they adjust to the shift in circumstances.

In a larger context, from the outside it will seem as if the abduction succeeded. This will force the girl’s fiance to give chase in his own spaceship (already a hackneyed plot by 1928, to be sure, but thereby all the more predictable for the reader).

But the fact that the ship will fly at top acceleration until its fuel is exhausted implies that the succeeding action will be far-removed from the familiar terrestrial scenes it has taken place in so far. In fact it converts the story from one of personalities and struggle in familiar circumstances (a la the Illiad) to a true voyage of the imagination (like the Odyssey).

If you are want to understand things deeply, you typically want to call in comparable things which can illuminate them by analogy. In this case the arm on the lever is like the tornado in Wizard of Oz (or of course the storm in the Odyssey); it not only throws the protagonists into a strange new world, but motivates their subsequent adventures with the quest to get home.

There is NO knowledge representation and inference scheme in the NLP field today that has even a snowball’s chance of capturing all this. But a human reader with a good grounding in the classics can see that this sentence is the turning point and spark of the whole book on something like five different levels simultaneously.

That’s quite a kick.

“… which was pointed straight up as it had been when they made their landing.”

In something of an anticlimax, Smith is keeping the reader up to date with the physical model of the ship, just in case someone wonders why, having gone down to land, it didn’t keep going down when the juice was turned back on. It had come down backwards, hanging on its thrust. Yet another model — and level of abstraction.

Phaedon Avouris, winner of the Feynman Prize in 1999, is head of the nanoscale science and technology group At IBM, which has recently reported significant advances in synthesizing transistors from graphene using conventional lithography methods.

IBM Demonstrates Graphene Transistor Twice as Fast as Silicon

Graphene transistors promise 100GHz speeds

Graphene Transistors that Can Work at Blistering Speeds

Big Blue demos 100GHz chip

Nanoclast interviews Avouris

and the Science paper,

100-GHz Transistors from Wafer-Scale Epitaxial Graphene

What does this all mean?  Basically, they have overcome a couple of substantial hurdles on the way to a carbon-based electronics, namely the bandgap issue and the ability to fab at wafer scale.  They still have a way to go: they need to bring gate length down by a factor of 10 or so to be in the range of silicon, and probably a few more hurdles and a lot of just plain legwork as well.  But if the research goes through to development, and the development goes through to manufacturing, we’ll have chips that are about two-and-a-half times as fast as the corresponding ones in silicon.

The bottom line, for my money, is that Moore’s Law is safe (in the sense that it will continue to hold true) for another decade at least.  I don’t see this as being a huge spike ahead of Moore’s Law, since graphene has a lot of catch-up to play, but in the long run it probably has more upside potential in speed and size, especially if/when they can get those nanoribbons atomically precise.

The first AI blog was written by a major, highly respected figure in the field. It consisted, as a blog should, of a series of short essays on various subjects relating to the central topic. It appeared in the mid-80s, just as the ARPAnet was transforming over into the internet.

The only little thing I forgot to mention was that it didn’t actually appear in blog form, which of course hadn’t been invented. The WWW didn’t appear until the next decade. It appeared in book form, albeit a somewhat unusual one since it was, as mentioned, a series of short essays, one to a page. It was, of course, Marvin Minsky’s Society of Mind.

Of course, you’re reading a blog about AI right now. The difference is that that was Minsky, and this is merely me. If you haven’t read SOM, put down your computer and go read it now.

Good. You’re back.  Here’s why SoM is relevant to our subject of whether and how soon AI is possible:

It remains a curious fact that the AI community has, for the most part, not pursued Society of Mind-like theories. It is likely that that Minsky’s framework was simply ahead of its time, in the sense that in the 1980s and 1990s, there were few AI researchers who could comfortably conceive of the full scope of issues Minsky discussed—including learning, reasoning, language, perception, action, representation, and so forth. Instead the field has shattered into dozens of subfields populated by researchers with very different goals and who speak very different technical languages. But as the field matures, the population of AI researchers with broad perspectives will surely increase, and we hope that they will choose to revisit the Society of Mind theory with a fresh eye.  (Push Singh — further quotes from the same source)

In other words, here’s a comprehensive theory of what an AI architecture ought to look like that is the summary of the lifework of one of the founders and leaders of the field, and yet no one has seriously tried to implement it.  (When I say serious, I mean put as much effort into it as has gone into, say, Grand Theft Auto.)  (There has been a serious effort to implement the theoretical approach of the CMU wing of classical AI, namely SOAR.)

Part of the reason for this is that SoM is in some sense only half a theory:

Minsky sees the mind as a vast diversity of cognitive processes each specialized to perform some type of function, such as expecting, predicting, repairing, remembering, revising, debugging, acting, comparing, generalizing, exemplifying, analogizing, simplifying, and many other such ‘ways of thinking’. There is nothing especially common or uniform about these functions; each agent can be based on a different type of process with its own distinct kinds of purposes, languages for describing things, ways of representing knowledge, methods for producing inferences, and so forth.
To get a handle on this diversity, Minsky adopts a language that is rather neutral about the internal composition of cognitive processes. He introduces the term ‘agent’ to describe any component of a cognitive process that is simple enough to understand, and the term ‘agency’ to describe societies of such agents that together performs functions more complex than any single agent could.

… but SoM doesn’t have a lot to say about what the individual functions are or how implemented, outside a few examples.  Since AI has for the past few decades concentrated on immediate results, most of the work has been on parts of the problem that could be described as stuff that would be inside a single agent, or at most an agency.

A good example of this happened a few years ago with the winning of the DARPA Grand Challenge and thus the development of the self-driving car. A few months after that happened, I was having a conversation with an AI researcher at a conference.  I maintained that the difference between the results of the first and second races — nobody got more than a mile or so, and then a couple years later several cars finished the whole 130-mile course –  represented real progress.  He pooh-poohed the idea.  All the techniques used in the cars had been previously known and published, he said.  All that had happened was that they had been integrated together into a working system.

I think this attitude goes a long way to explaining the lack of work on SoM and other overall cognitive architecture theories.  But as I reasoned previously:

The difference was, the Wright brothers knew an extra Good Trick, which was how to control the plane in the air once it was flying.
So to develop a working AI, we need the power, which we don’t think is going to be a problem. We need the lift, which is the kind of techniques found in narrow AIs and discussed above. And finally we need the control.

SoM represents a theory of how the control might work.  Where does that leave us?  Can we simply take Minsky’s books and papers and build an AI with all the existing narrow skill programs acting as agents? Hardly.  There’s a lot of work to be done, and probably several new Good Tricks left to be found.

The bottom line, though, is that we are not facing a blank wall.  We are facing a corridor with a sign reading “This way to the egress.”  Indeed we are partway down the corridor already; robotics and self-driving cars have required the development of integrated cognitive architectures along the lines that will probably lead to success.  Note that Brooks’ subsumption architecture had a lot in common with SoM.

So there is at least a case to be made that we are into the home stretch.  Of course that’s where the race really heats up and all the excitement happens…

• 40s: Cybernetics, the notion the brain did logic in circuits, feedback
• 50s: the computer, stored programs, Logic Theorist
• 60s: LISP, semantic nets, GOFAI
• 70s: SHRDLU, AM
• 80s: AI winter, expert systems, neural nets
• 90s: robots, machine learning
• 00s: DARPA grand challenge level of competence

The main point of this post is to answer any objections of the form: you’ve been working on this so long, why don’t you have it yet?  (Or perhaps, AI is the technology of the future and always will be. )

One key thing to note is that cybernetics was the original line of inquiry that was going to let us understand how the brain worked and allow us to build smart machines.  Many people assume that cybernetics failed since it more or less disappeared as a discipline.  But in fact it learned some very key and useful insights, forming the basis of control theory and neuroscience; but it fell apart due to personalities in its cadre (a veritable soap opera between Wiener and McCulloch and Pitts involving Wiener’s daughter) and political disfavor in the US involving Wiener’s antiauthoritarian stances.

So GOFAI was born with a built-in bias against some of the insights of cybernetics.  That has now been repaired; it was forced by the reintegration of control theory and the growing use of knowledge from neuroscience in the 90s, when AI robotics began to get serious.  There are reasons AI floundered in the 80s, and that’s one — another is a diversion from basic research to applications before it was really ready.

Another point that is rarely made is that AI, the small sub-discipline of CS, isn’t the real major part of the work in the 20th century that will have led to intelligent machines.  It’s the invention of the computer itself and all the work that’s been done to bring us the processing power we need to do the job, and the software to manage it and the complexity of human-comparable systems.  And nobody could reasonably claim that that effort has been standing still, or has come to nothing, or anything even vaguely similar.

An AI will be a hardware/software network and system so complex and powerful that it will make the entire ARPANET of the 70s look like a toy — and it will have to manage its own internals completely automatically.  I personally think that it will need the internal robustness that can only come from incorporating feedback and automatic resource management into the basic fabric of its computing platform.  But that’s the kind of thing that can easily be done in a decade, once someone decides to do it.  And it will be useful for a lot of other applications as well!

I’m about to start a series of posts on the topic of why I think AI is actually possible.  I realize that most of the readers here don’t probably need too much convincing on that subject, but you’d be surprised how many very smart people, many of them professors of computer science, are skeptical to some extent or another on that point.

To start off, though, I’m just soliciting comments on the subject to try and get some feel for where the readership is on the subject, and what are the issues anyone feels are important to the argument.

Start your comment off with an indication when you think we’ll have human-level AI, and go from there:

• in the next decade
• in the 20s
• 2030-2050
• 2050-2100
• thereafter
• never

Feynman anniversary event to be held at University of South Carolina. h/t Nanowerk

In February 1960, the Caltech magazine Engineering & Science published Feynman’s “Plenty of Room”, and it has been re-published ten times since then. This has become one of the best-known papers in the history of nanotechnology. The fiftieth anniversary of the initial publication of “Plenty of Room” presents us with an opportunity to reflect upon Richard Feynman’s legacy in nanotechnology. The University of South Carolina will convene a symposium to consider the talk, the man, and the field of nanotechnology during the past fifty years. The Symposium takes place at the University of South Carolina on Friday and Saturday, 12 and 13 February 2010. All full program (in PDF format) is available. Registration fee: $25; no charge for USC faculty, staff or students.

Note this USC is South Carolina, not Southern California

From Ars Technica: Nobel Intent, a thought-provoking article on what the prevalence of computational science portends for reproducibility in science:

Victoria Stodden is currently at Yale Law School, and she gave a short talk at the recent Science Online meeting in which she discussed the legal aspects of ensuring that the code behind computational tools is accessible enough for reproducibility. The obvious answer is some sort of Creative Commons or open source license, and Stodden is exploring the legal possibilities in that regard. But she makes a forceful argument that some form of code sharing will be essential.

“You need the code to see what was done,” she told Ars. “The myriad computational steps taken to achieve the results are essentially unguessable—parameter settings, function invocation sequences—so the standard for revealing it needs to be raised to that of when the science was, say, lab-based experiment.” This sort of openness is also in keeping with the scientific standards for sharing of more traditional materials and results. “It adheres to the scientific norm of transparency but also to the core practice of building on each other’s work in scientific research,” she said. But the same worries that apply to more traditional data sharing—researchers may have a competitor use that data to publish first—also apply here. In the slides from her talk, she notes that a survey she conducted of computational scientists indicates that many are concerned about attribution and the potential loss of publications in addition to legal issues. (The biggest worry is the effort involved to clean up and document existing code.)

Still, this sort of disclosure, as with other open source software, should provide a key benefit: more interested parties able to evaluate and improve the code. “Not only will we clearly publish better science, but redesigned and updated code bases will be valuable scientific contributions,” Stodden said. “Over time, we won’t stagnate forever on one set of published code.”

via Ars Technica: Nobel Intent: Keeping computers from ending science’s reproducibility.

Roadmaps to Nanotech and AGI

slides are here

Josh

[note -- we know about the permission problem, trying to get it fixed][should be fixed now]

Glenn Reynolds, a past Foresight Director, writes some analysis of the recent special election in Mass.:

Of course, what the GOP apparat does is less important nowadays than it was. As I noted before, there’s a whole lot of disintermediation going on here — Scott Brown got money and volunteers via the Internet and the Tea Party movement, to a much greater degree than he got them from the RNC. Smart candidates will realize that, too.

And lies don’t work as well as they used to. Obama promised transparency and pragmatic good government, but delivered closed-door meetings and outrageous special-interest payoffs. This made people angry. If Republicans promise honesty and less-intrusive government, but go back to their old ways, the likelihood that the Tea Party will become a full-fledged third party is much greater. …

via Instapundit » Blog Archive » SO, BROWN WON. This is big news; while the White House is still in the healthcare bunker, things li….

We don’t deal with politics here but we are concerned with technological developments that improve social decision-making and governance.  The internet has clearly been such a technology.  As one very tiny part of the generation of computer scientists that built it, I will happily accept the plaudits of a grateful world in their behalf …

Of course, the Internet could be improved as a fact-finding device, and ought to, as Eric Drexler notes:

We could benefit immensely from a medium that is as good at representing factual controversies as Wikipedia is at representing factual consensus.

What I mean by this is a social software system and community much like Wikipedia — perhaps an organic offshoot — that would operate to draw forth and present what is, roughly speaking, the best evidence on each side of a factual controversy. To function well would require a core community that shares many of the Wikipedia norms, but would invite advocates to present a far-from-neutral point of view. In an effective system of this sort, competitive pressures would drive competent advocates to participate, and incentives and constraints inherent in the dynamics and structure of the medium would drive advocates to pit their best arguments head-to-head and point-by-point against the other side’s best arguments. Ignoring or caricaturing opposing arguments simply wouldn’t work, and unsupported arguments would become more recognizable.

Success in such an innovation would provide a single place to look for the best arguments that support a point in a debate, and with these, the best counter-arguments — a single place where the absence of a good argument would be good reason to think that none exists.

Today is the last day of the free webcast of the 2010 Foresight Conference being held now in Palo Alto.

The bandwidth coming out of the Sheraton is marginal, so the video may be low-res, but we will be posting high-res videos later, funds permitting (feel free to assist with this goal!).

You can also follow the conference on Twitter at #Foresight2010, and send in your questions in real time to the speakers that way.

Wish you all could be here with us today!  —Chris Peterson

There’s still time to register, but if you just can’t participate in person this year, check out the free webcast of the Foresight Conference being held this weekend in Palo Alto.

The bandwidth coming out of the Sheraton is marginal, so the video will be low-res, but we will be posting high-res videos later, funds permitting (feel free to assist with this goal!).

Unfortunately the Senior Associate Reception debate between Robin Hanson and Mencius Moldbug on futarchy will not be webcast.

You can also follow the conference on Twitter at #Foresight2010, and try sending in your questions in real time to the speakers that way.

Wish you all could be here with us this weekend!  —Chris Peterson

Two nanoparticles connected by a polymer will tend to be drawn together at finite temperatures (though not at absolute zero) because as the polymer chain explores the states available to it, there are many more tangled and balled up ones than stretched-out straight ones — even though there is no overt force pulling the chain to any particular tangled state.  Such a situation is called an entropic spring, and behaviors like this are some of the more interesting aspects of physics at the microscale.

An arXiv paper by physicist Erik P. Verlinde purports to show that gravitational effects have the same mathematical logic behind them (in a very broad analogical sense), arising from the holographic universe description of physics (a far-out variant of string theory).  Now I don’t come close to having the physics to evaluate the theory, but Verlinde appears to be a respectable physicist.  Czech physicist Luboš Motl blogged about it:

So I remain undecided whether or not there is a sharp insight waiting along the lines of Verlinde’s paper.

and then allowed Verlinde to guest-post a long explanatory comment.

The derivation of the Einstein equations (and of Newton’s law in the earlier sections) follows very similar reasonings that exist in the literature, in particular Jacobson’s. The connection with entropy and thermodynamics is made also there. But in those previous works it is not clear WHY gravity has anything to do with entropy. No explanation for this apparent connection between gravity and entropy has been given anywhere in the literature. I mean not the precise details, even the reason why there should be such a connection in the first place was not understood.

My paper is the first that gives a reason why. Inertia, and hence motion, is due to an entropic force when space is emergent. This is new, and the essential point. This means one HAS TO keep track of the amount of information. Differences in this amount of information is precisely what makes one frame an inertial frame, and another a non-inertial frame. Information causes motion.
This can be derived without assuming Newtonian mechanics.

“Space is emergent”???  Yep, in the holographic theory, 3D space is an emergent phenomenon of a 2D information pattern (see the link above).  Weird stuff, but no weirder than other forms of string theory.

As mentioned, I don’t claim to follow this at the technical level, but given how important the math of entropy is at the microscale, it’s fun to speculate about its being important at the most macro of macroscales as well.

A round-up of commentary about the state of nanotech research, given the 50th anniversary of Feynman’s talk:

Adam Keiper in the WSJ

If this dispute over nano-nomenclature only involved some sniping scientists and a few historians watching over a tiny corner of Feynman’s legacy, it would be of little consequence. But hundreds of companies and universities are teeming with nanotech researchers, and the U.S. government has been pouring billions of dollars into its multiagency National Nanotechnology Initiative.
So far, none of that federal R&D funding has gone toward the kind of nanotechnology that Drexler proposed, not even toward the basic exploratory experiments that the National Research Council called for in 2006. If Drexler’s revolutionary vision of nanotechnology is feasible, we should pursue it for its potential for good, while mindful of the dangers it may pose to human nature and society. And if Drexler’s ideas are fundamentally flawed, we should find out—and establish just how much room there is at the bottom after all.

Eric Drexler on Keiper and on the NRC report

The evaluation of the feasibility of molecular manufacturing and recommendations for research form the concluding section of the body of the NRC’s Triennial Review of the National Nanotechnology Initiative. In the three years since the publication of the NRC report, I have seen no document from a Federal-level source that acknowledges these conclusions, and, of course, none that offers a substantive response.
This is of concern, because the NRC report calls for a sharp break with past thinking. To put it bluntly, much of the opinion in general circulation about molecular manufacturing (both pro and con) is rubbish because it is based on mythology, and not on the scientific literature. The NRC report can be criticized on several points, but it isn’t rubbish.

Dexter Johnson on Keiper and Drexler

I am nonplussed. Are we to believe that Prof. Moriarty is one of only a handful of scientists capable of securing funding for his experiments into molecular nanotechnology?

Richard Jones on the same

Of the ideas dealt with in “Plenty of Room”, some have already come to pass and have indeed proved economically and societally transformative. These include the idea of writing on very small scales, which underlies modern IT, and the idea of making layered materials with precisely controlled layer thicknesses on the atomic scale, which was realised in techniques like molecular beam epitaxy and CVD, whose results you see every time you use a white light emitting diode or a solid state laser of the kind your DVD contains. I think there were two ideas in the lecture that did contribute to the vision popularized by Drexler – the idea of “a billion tiny factories, models of each other, which are manufacturing simultaneously, drilling holes, stamping parts, and so on”, and, linked to this, the idea of doing chemical synthesis by physical processes.

Jones ends with a observation about the course of nanotech development:

Perhaps for the first time in recent years a major new technology is largely being developed outside the USA, in Europe to some extent, but with an unprecedented leading role being taken in places like China, Korea and Japan. In these places the “nanotech schism” that seems so important in the USA simply isn’t relevant; people are just pressing on to where the technology leads them.

This is a key observation.  Jones slants it as if to say that therefore, the “schism” wasn’t really important after all.  But to come away with that impression would be to miss a very important point:  The USA is blowing its opportunity to be a leader in one of the most important technologies of the 21st century because of the political shenanigans.