“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.

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 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…

To make a heavier-than-air airplane fly, you need an engine.  If you have an airframe with lift-to-drag ratio r, stall speed s, and weight w, and a propellor with thrust efficiency e, you need an engine with power p=swr/e to fly. Power<p, no fly. Power>p, fly.

Both of the major American flying machine efforts understood this.  Langley spent huge effort developing light, powerful engines.  The brothers Wright built their own aeroengine from scratch in their bicycle shop.

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.

What I just said is an example of reasoning by analogy.  To an extent much greater than usually realized, most cognition and reasoning is based on analogy.  When you perform a physical skill, the specific sequence of sensory and motor signals is never exactly any of the ones that happened during practice; but they’re close enough that the mapping is straight-forward.

This is something that is well-known to the AI mainstream:

But “the big feature of human-level intelligence is not what it does when it works but what it does when it’s stuck,” Minsky said. When faced with novelty, Minsky claims, human intelligence applies “reasoning by analogy” to make the most direct tap into the cognitive glue that fuses knowledge domains.
Reasoning by analogy is a way of adapting old knowledge, which almost never perfectly matches the present situation, by following a recipe of detecting differences and tweaking parameters. It all happens so quickly that no “thinking” seems to be involved.  (EE Times)

The particular kind of reasoning by analogy that would make an associative memory machine work well can be called analogical quadrature.  This is the form of problem done most famously by Melanie Mitchell’s Copycat program: you have three things A, B, and C, and you want to find a fourth D such that A:B::C:D.  In the associative memory scheme, you need to do not the actual action you did in the memory, but the action that fits the current situation the way the remembered action fit the remembered situation.

As a simple example, if the remembered action was done by someone else, the parallel could be mapping things so that the action is done by you this time. In other words, analogical quadrature enables imitation.

If you can somehow represent your concepts as points in an n-dimensional space, analogical quadrature is falling-down easy: D=C+B-A in ordinary vector algebra. Of course, sometimes the mapping into n-space is problematical, and we are thrown back on symbolic methods such as those of the FARGitecture.

Those have their own problems, essentially the same ones as any symbolic AI: the operations and ontology in, e.g., Copycat are all idiosyncratic and hand-coded, and there’s no clear way to build a learning machine that extends them automatically.

I’ll go out on a limb and guess that the ultimate solution will involve elements of both extremes.  Search will be needed both to find new operations for symbolic formulations, and to find appropriate mappings into n-space for the subsymbolic ones.  A few key insights — new Good Tricks — will be necessary to unify the known methods and give us a solid understanding of, and engine for, analogical quadrature.  That’ll be a huge step towards general AI.

The solution, of course, is: Duh. the brain doesn’t work like a von Neumann model with an active processor and passive memory.  It has, in a simplified sense, a processor per fact, one per memory.  If I hold up an object and ask you what it is, you don’t calculate some canonicalization of it as a key into an indexed database. You compare it simultaneously to everything you’ve ever seen (and still remember).  Oh, yeah, that’s that potted aspidistra that Aunt Suzie keeps in her front hallway, with the burn mark from the time she …

The processing power necessary to to that kind of parallel matching is high, but not higher than the kind of processing power that we already know the brain has.  It’s also not higher than the processing power we expect to be able to throw at the problem by 2020 or so.  Suppose it takes a million ops to compare a sensed object to a memory.  10 MIPS to do it in a tenth of a second.  A modern workstation with 10 gigaops could handle 1000 concepts. A GPGPU with a teraops could handle 100K, which is still probably in the hypohuman range.  By 2020, a same priced GPGPU could do 10M concepts, which is right smack in the human range by my best estimate.

Associative memory gets you a lot.  You don’t have to parse an unknown object for algorithmic retrieval.  You don’t have to come with some one-size-fits-all representation and/or classification scheme.  Indeed, each object in memory can have its own representation if necessary or useful.

It gets better.  The memories aren’t all, or even mostly, objects.  They’re typically actions.  Let’s suppose the actions are represented as situation-action-resulting situation triples — something like Minsky’s trans-frames.  Then we can use the associative memory to

• recognize, as described above
• predict: search on the situation and action; the prediction is the result in the best match
• plan: match on situation and desired result; do the action from the best match
• generalize: every time a was done, b happened
• model: by chaining predictions, etc

There was an attempt to do this kind of thing in mainstream AI under the name “case-based reasoning” a couple of decades ago, but it appears to have foundered for several reasons, not least of which was the inability to do heavy-duty parallel matching on extensive memory sets.

There are a number of things that need to be added to the scheme for it to be useful and robust, like embedding it in a hierarchical, multiagent architecture, the ability to do analogical quadrature, and the ability to find useful representations.  But that’s for another post.

Bayer (the same company that makes the aspirin) is now beginning to manufacture multi-walled carbon nanotubes in industrial quantities.  The pilot plant will produce 200 tons per year, and the market is expected to grow at 25% per year.

The MWCNTs are for materials use, meaning mostly fiber-reinforced composites, e.g. airplanes, tennis racquets, arrows,

and the like.  The major advantages over conventional polymers / fibers is that the CNTs are stronger and conductive (both electrically and thermally) — producing a plastic that is more like metal in many ways, but still much lighter.  The conductivity is supposed to be comparable to copper, i.e. good enough to use as wiring in many applications.  Looking at the data for CNTs as a polymer additive, the major effect on mechanical properties was to make them less stretchy (and about 10% stronger), while having a major effect on conductivity properties.  Nobody has yet, as far as I know, managed to figure out how to make a composite that has the really high tensile strength possibilities of the raw nanotubes.  Alternatively, CNTs in light metal matrices such as aluminum or magnesium seems to have significant possibilities.  Time will tell — but there’s still a major advance to be made.

The individual CNTs in the mix are on average 8 or so walls, 15 nm diameter, and over a micron long (i.e. an aspect ratio of at least 60 and probably in the hundreds).

We can be a bit more specific about what “well-specified” means.  In general, if the task has a static ontology that can be laid out by the programmers, it’s within the scope of current practice.  A huge part of the progress of early AI was in fact simply building up (hand-made) ontologies.  An ontology includes, BTW, not just a list of concept names, but the semantics: code to recognize, predict, simulate, and perform whatever things we’d expect a person to be able to do who we would describe as “understanding” the concept.

The difference between this “static AI” and real human-level intelligence is that people learn new concepts constantly.  We will learn several words a day our entire lives (estimates range from 1 to 10 and of course this depends on individual intelligence and environment). Concepts are constantly changing and growing, splitting and merging, being half-forgotten and rediscovered.

Not only the ability to create new concepts, but the fluidity and adaptability of the ones we already have, enable the robustness of human intelligence.

There’s been a lot less research on how to build concepts than there has been involving the formalization of existing ones in static form.  There’s a bias toward the latter since you get a machine that can do something useful much quicker that way.

However, there has been research in creating new concepts and we can say something about it.  It seems to be the area where the high computational resources make a difference.  The most general approach we have is search, in various forms. Deep Blue invented startling new chess strategies on the fly.  These robots evolved a number of concepts through simulated evolution.

(ps — if you want your research paper to be picked up by the pop-sci news and blogosphere, simply include the words “robot” and “predator” in it )

Going back to Lenat’s AM, it’s been understood that search, in various forms, is capable of the kind of learning we need, but also that it tends to run out of steam sooner rather than later.  In other words, it seems likely that a properly set-up search is capable of inventing a fairly sophisticated concept, but you need another setup for the next one.  It’s generally accepted that some sort of evolutionary search is going on in the brain, but the system that controls it, sets up the search spaces, defines the fitness functions, and so forth, is definitely not well understood.

Thus the key to understanding when and whether general AI can happen lies in the high-level organization that can guide the application of focused search to produce a growing set of concepts that work coherently together.

Hot air ballooning is quite popular today; people think of balloons as being quaint and pretty and natural, or at least more natural than airplanes.

Actually, a modern hot-air balloon uses more fuel than an airplane does to fly the same payload for the same time. The reason, of course, is that hot air needs to be hot, but the balloon needs to be light, so that the material needs to be thin, which means in practice that heat is lost through the balloon, and needs to be regenerated by burning fuel.

With nanotech we could make a fabric of diamond sheets for strength, with vacuum for insulation, and thin metallic films (or graphene sheets) to reflect thermal radiation. That means that we could have a balloon that was much lighter than woven nylon, and yet enormously better insulated.

The air in a balloon may be typically heated to around 100C, making it 0.93 kg/m^3 (compared with 1.2 at 20C). Call it roughly 0.25 kg/m^3 lifting capacity.

But if we can insulate it, we could fill it with steam instead (at the same temperature). (Steam would condense on the walls of an uninsulated bag.) Steam at 100C has a density of 0.59, call it 0.6 kg lifting capacity. Since we aren’t losing heat (much), we could superheat it and get some extra lift, say a few 0.1kg/m^3, but there would likely be a tradeoff with insulation weight, energy rates to cover leakage, etc. Even without it, the balloon lifts its own weight, including the water in the steam (and probably 100 times that of just the balloon).

(Postscript: It’s occasionally assumed that diamond is strong enough to make air-buoyant vacuum-filled balloons. This doesn’t actually work. Hydrogen remains the champ, with a density of 0.09 kg/m^3, which is essentially negligible. But even so, a steam balloon would only have to have twice the volume of a hydrogen balloon with the same lift, as compared to 5 times for hot air.)

To make a donation to the Gada Prize fund, click thru to the prize page, click on the “Join Now” button to the right, go to the Donation section, and select “Gada Prize” from the project pulldown.

The Sigil of Scoteia

In case you’re unfamiliar with it, it’s the frontispiece of the novel The Cream of the Jest by James Branch Cabell.  Why does the Sigil make a good AI test?

One reason is that it requires a considerably more holonic interpretation process than just separating the text would.  It’s in a handwritten script in an extremely idiosyncratic font — you need to have a good guess what the word is to figure out what the letters are, and vice versa.  Information must flow down as well as up the interpretation stack.  It takes a few minutes to figure out the Sigil; you can read the jammed-up letters version straight off:

|IAMESBRAN
CHCABELLMADETHISB
OOKSOTHATHEWHOWILLSMAY
READTHESTORYOFMANSETERNA
LLYUNSATISFIEDHUNGERINSEAR
CHOFBEAUTY|ETTARRESTAYSINACCE
SSIBLEALWAYSANDHERLOVLINES
SISHISTOLOOKONONLYINHISDRE
AMS|ALLMENSHEMUSTEVADEAT
THELASTANDMANYARETHE
WAYSOFHEREVASION

(stumbling perhaps over the name of one of the characters near the middle).

But then, once you have the words, you’ve only gotten started.  The test isn’t “separate this into words” — it’s “what does this mean?”

You could work out the words but not be able to explain them in context. You might be able to tell what the Sigil was physically in the book but be completely clueless as to its emotional meaning.  I claim that the question “what does this mean?” has different answers at every point across the diahuman range of intelligence.  Cabell was an unexcelled master of cryptic, poetic, romantic fantasy, based on a very thorough knowledge of mythology and keen insight into human nature.  Think of him as Tolkein multiplied by James Joyce, filtered through the light touch of Wodehouse.

Thus actually to “get it” with Cabell, you have to be able to understand things on lots of different levels at once.

I often feel smug at the dumbness of people in the sense that surely AI must be a low bar — how hard could it be to beat that, whatever that might have happened to be.  But there are other times when, contemplating people like Cabell, I feel like giving up, it’s just too hard.

If you can work out the words in the Sigil, you’re at the hypo/diahuman border.  If you can write a book like Cream of the Jest, you’re at the dia/epihuman border.