trentmkelly/LessWrong-43k huggingface dataset:
The Weak Inside View
by Eliezer Yudkowsky — 2008-11-18T18:37:33.000Z

Followup to: The Outside View's Domain

When I met Robin in Oxford for a recent conference, we had a preliminary discussion on the Singularity - this is where Robin suggested using production functions. And at one point Robin said something like, "Well, let's see whether your theory's predictions fit previously observed growth rate curves," which surprised me, because I'd never thought of that at all.

It had never occurred to me that my view of optimization ought to produce quantitative predictions. It seemed like something only an economist would try to do, as 'twere. (In case it's not clear, sentence 1 is self-deprecating and sentence 2 is a compliment to Robin. --EY)

Looking back, it's not that I made a choice to deal only in qualitative predictions, but that it didn't really occur to me to do it any other way.

Perhaps I'm prejudiced against the Kurzweilian crowd, and their Laws of Accelerating Change and the like. Way back in the distant beginning that feels like a different person, I went around talking about Moore's Law and the extrapolated arrival time of "human-equivalent hardware" a la Moravec. But at some point I figured out that if you weren't exactly reproducing the brain's algorithms, porting cognition to fast serial hardware and to human design instead of evolved adaptation would toss the numbers out the window - and that how much hardware you needed depended on how smart you were - and that sort of thing.

Betrayed, I decided that the whole Moore's Law thing was silly and a corruption of futurism, and I restrained myself to qualitative predictions (and retrodictions) thenceforth.

Though this is to some extent an argument produced after the conclusion, I would explain my reluctance to venture into quantitative futurism, via the following trichotomy:

On problems whose pieces are individually precisely predictable, you can use the Strong Inside View to calculate a final outcome that has never been seen before - plot the trajectory of the first moon rocket before it is ever launched, or verify a computer chip before it is ever manufactured.

On problems that are drawn from a barrel of causally similar problems, where human optimism runs rampant and unforeseen troubles are common, the Outside View beats the Inside View. Trying to visualize the course of history piece by piece, will

...
Failure By Affective Analogy
by Eliezer Yudkowsky — 2008-11-18T07:14:55.000Z

Previously in series: Failure By Analogy

Alchemy is a way of thinking that humans do not instinctively spot as stupid. Otherwise alchemy would never have been popular, even in medieval days. Turning lead into gold by mixing it with things that seemed similar to gold, sounded every bit as reasonable, back in the day, as trying to build a flying machine with flapping wings. (And yes, it was worth trying once, but you should notice if Reality keeps saying "So what?")

And the final and most dangerous form of failure by analogy is to say a lot of nice things about X, which is similar to Y, so we should expect nice things of Y. You may also say horrible things about Z, which is the polar opposite of Y, so if Z is bad, Y should be good.

Call this "failure by affective analogy".

Failure by affective analogy is when you don't just say, "This lemon glazing is yellow, gold is yellow, QED." But rather say:

"And now we shall add delicious lemon glazing to the formula for the Philosopher's Stone the root of all wisdom, since lemon glazing is beautifully yellow, like gold is beautifully yellow, and also lemon glazing is delightful on the tongue, indicating that it is possessed of a superior potency that delights the senses, just as the beauty of gold delights the senses..."

That's why you find people saying things like, "Neural networks are decentralized, just like democracies" or "Neural networks are emergent, just like capitalism".

A summary of the Standard Prepackaged Revolutionary New AI Paradigm might look like the following - and when reading, ask yourself how many of these ideas are affectively laden:

The Dark Side is Top-Down. The Light Side is Bottom-Up.

The Dark Side is Centralized. The Light Side is Distributed.

The Dark Side is Logical. The Light Side is Fuzzy.

The Dark Side is Serial. The Light Side is Parallel.

The Dark Side is Rational. The Light Side is Intuitive.

The Dark Side is Deterministic. The Light Side is Stochastic.

The Dark Side tries to Prove things. The Light Side tries to Test them.

The Dark Side is Hierarchical. The Light Side is Heterarchical.

The Dark Side is Clean. The Light Side is Noisy.

The Dark Side operates in Closed Worlds. The Light Side operates in Open Worlds.

The Dark Side is Rigid. The Light Side is Flexible.

The Dark Side is Sequ

...
Failure By Analogy
by Eliezer Yudkowsky — 2008-11-18T02:54:06.000Z

Previously in series: Logical or Connectionist AI?
Followup to: Surface Analogies and Deep Causes

"One of [the Middle Ages'] characteristics was that
'reasoning by analogy' was rampant; another characteristic was almost
total intellectual stagnation, and we now see why the two go together.
A reason for mentioning this is to point out that, by developing a keen
ear for unwarranted analogies, one can detect a lot of medieval
thinking today."
-- Edsger W. Dijkstra

<geoff> neural nets are over-rated
<starglider> Their potential is overrated.
<geoff> their potential is us

-- #sl4

Wasn't it in some sense reasonable to have high hopes of neural networks? After all, they're just like the human brain, which is also massively parallel, distributed, asynchronous, and -

Hold on. Why not analogize to an earthworm's brain, instead of a human's?

A backprop network with sigmoid units... actually doesn't much resemble biology at all. Around as much as a voodoo doll resembles its victim. The surface
shape may look vaguely similar in extremely superficial aspects at a
first glance. But the interiors and behaviors, and basically the whole
thing apart from the surface, are nothing at all alike. All that biological neurons have in common with gradient-optimization ANNs is... the spiderwebby look.

And who says that the spiderwebby look is the important fact about biology? Maybe the performance of biological brains has nothing to do with being made out of neurons, and everything to do with the cumulative selection pressure
put into the design. Just
like how the performance of biological brains has little to do with
proteins being held together by van der Waals forces, instead of
the much stronger covalent bonds that hold together silicon. Sometimes
evolution gets stuck with poor
building material, and it can't refactor because it can't execute
simultaneous changes to migrate the design. If biology does some neat
tricks with chemistry, it's because of the greater design pressures
exerted by natural selection, not because the building materials are so
wonderful.

Maybe neurons are just what brains happen to be made out of, because the blind idiot god is too stupid to sit down and invent transistors. All the modules ge...
Whither OB?
by Eliezer Yudkowsky — 2008-11-17T19:38:20.000Z

Robin plans to cut back posting shortly, after he and I have our long-awaited Disagreement about AI self-improvement. As for myself - I'm not finished, but I'm way over schedule and need to move on soon. I'm not going to stop posting entirely (I doubt I could if I tried) but I'm not going to be posting daily.

There are three directions that Overcoming Bias could go from here:

First, we could find enough good authors to keep going at a post per day. Say, seven people who can and will write one post per week. We can't compromise on quality, though.

Second, we could try to shift to a more community-based format. Our most popular post ever, still getting hits to this day, was not written by Robin or myself or any of the recurring editors. It's "My Favorite Liar" by Kai Chang, about the professor who inserted one false statement into each lecture. If one-tenth of our readers contributed a single story as good as this... but neither Robin nor myself have time to vet them all. So one approach would be to have a community forum where anyone could post, readers voted the posts up and down, and a front page to which the editors promoted posts deemed worthy. I understand that Scoop has software like this, but I would like to know if our readers can recommend better community software (see below).

Third, we could close OB to new submissions and keep the archives online eternally, saying, "It had a good run." As Nick put it, we shouldn't keep going if it means a slow degeneration.

My own perspective: Overcoming Bias presently gets over a quarter-million monthly pageviews. We've built something that seems like it should be important. It feels premature, but I would like to try to launch an online rationalist community.

At this point, I'm advocating a hybrid approach: Keep OB open with fewer posts of the same gravitas, hang out a sign for new authors, and also try starting up a community-based site with user submissions and more frequent shorter posts. I've got plenty of light stuff to post, links and the like.

But: What software should we use to support a rationalist community?

The Oxford Future of Humanity Institute and the Singularity Institute have volunteered to provide funding if necessary, so we aren't limited to free software.

And obviously we're not looking f...
Logical or Connectionist AI?
by Eliezer Yudkowsky — 2008-11-17T08:03:12.000Z

Previously in series: The Nature of Logic

People who don't work in AI, who hear that I work in AI, often ask me: "Do you build neural networks or expert systems?" This is said in much the same tones as "Are you a good witch or a bad witch?"

Now that's what I call successful marketing.

Yesterday I covered what I see when I look at "logic" as an AI technique. I see something with a particular shape, a particular power, and a well-defined domain of useful application where cognition is concerned. Logic is good for leaping from crisp real-world events to compact general laws, and then verifying that a given manipulation of the laws preserves truth. It isn't even remotely close to the whole, or the center, of a mathematical outlook on cognition.

But for a long time, years and years, there was a tremendous focus in Artificial Intelligence on what I call "suggestively named LISP tokens" - a misuse of logic to try to handle cases like "Socrates is human, all humans are mortal, therefore Socrates is mortal". For many researchers, this one small element of math was indeed their universe.

And then along came the amazing revolution, the new AI, namely connectionism.

In the beginning (1957) was Rosenblatt's Perceptron. It was, I believe, billed as being inspired by the brain's biological neurons. The Perceptron had exactly two layers, a set of input units, and a single binary output unit. You multiplied the inputs by the weightings on those units, added up the results, and took the sign: that was the classification. To learn from the training data, you checked the current classification on an input, and if it was wrong, you dropped a delta on all the weights to nudge the classification in the right direction.

The Perceptron could only learn to deal with training data that was linearly separable - points in a hyperspace that could be cleanly separated by a hyperplane.

And that was all that this amazing algorithm, "inspired by the brain", could do.

In 1969, Marvin Minsky and Seymour Papert pointed out that Perceptrons couldn't learn the XOR function because it wasn't linearly separable. This killed off research in neural networks for the next ten years.

Now, you might think to yourself: "Hey, what if you had more than two layers in a neural network? Maybe then it could learn the XOR function?"

Well, b...
Boston-area Meetup: 11/18/08 9pm MIT/Cambridge
by Eliezer Yudkowsky — 2008-11-16T03:19:09.000Z

There will be an OB
meetup this Tuesday in Cambridge MA, hosted by Michael Vassar, Owain Evans
(grad student at MIT), and Dario Amodei (grad student at Princeton).
The event will take place on the MIT campus, in a spacious seminar room in MIT's Stata Center. Refreshments will be provided. Details and directions below the fold.

Please let us know in the comments if you plan to attend.

(Posted on behalf of Owain Evans.)

Time/date: 9pm, Tuesday 18 November.

Place: Room d461 in MIT's Stata Center.

Address:
The Stata Center: 32 Vassar Street, Cambridge.

Directions:
The nearest T stop is Kendall/MIT on the Red Line.

Enter Stata via the entrance facing Main Street (with a big metal
"MIT" sign outside it) from 8.45pm and one of the hosts will guide you
to d461. Alternatively, here are
directions
to d461 once you reach Stata.

Email:
owain (at) mit edu

Phone:
If you can't find the room or if you arrive late and are unable to
enter the building, call 610-608-3345.
The Nature of Logic
by Eliezer Yudkowsky — 2008-11-15T06:20:32.000Z

Previously in series: Selling Nonapples
Followup to: The Parable of Hemlock

Decades ago, there was a tremendous amount of effort invested in certain kinds of systems that centered around first-order logic - systems where you entered "Socrates is a human" and "all humans are mortal" into a database, and it would spit out "Socrates is mortal".

The fact that these systems failed to produce "general intelligence" is sometimes taken as evidence of the inadequacy, not only of logic, but of Reason Itself. You meet people who (presumably springboarding off the Spock meme) think that what we really need are emotional AIs, since logic hasn't worked.

What's really going on here is so completely different from the popular narrative that I'm not even sure where to start. So I'm going to try to explain what I see when I look at a "logical AI". It's not a grand manifestation of the ultimate power of Reason (which then fails).

Let's start with the logical database containing the statements:

|- Human(Socrates)
|- \x.(Human(x) -> Mortal(x))

which then produces the output

|- Mortal(Socrates)

where |- is how we say that our logic asserts something, and \x. means "all x" or "in what follows, we can substitute anything we want for x".

Thus the above is how we'd write the classic syllogism, "Socrates is human, all humans are mortal, therefore Socrates is mortal".

Now a few months back, I went through the sequence on words, which included The Parable of Hemlock, which runs thusly:

Socrates raised the glass of hemlock to his
lips...
"Do you
suppose," asked one of the onlookers, "that even hemlock will not be
enough to kill so wise and good a man?"
"No," replied another bystander, a student of
philosophy; "all men are
mortal, and Socrates is a man; and if a mortal drink hemlock, surely he
dies."

"Well," said the onlooker, "what if it happens that
Socrates isn't
mortal?"

"Nonsense," replied the student, a little sharply;
"all men are mortal
by definition; it is part of what we mean by the word 'man'.
All men are mortal, Socrates is a man, therefore Socrates is
mortal. It is not merely a guess, but a logical certainty."

...
Selling Nonapples
by Eliezer Yudkowsky — 2008-11-13T20:10:58.000Z

Previously in series: Worse Than Random

A tale of two architectures...

Once upon a time there was a man named Rodney Brooks, who could justly be called the King of Scruffy Robotics. (Sample paper titles: "Fast, Cheap, and Out of Control", "Intelligence Without Reason"). Brooks invented the "subsumption architecture" - robotics based on many small modules, communicating asynchronously and without a central world-model or central planning, acting by reflex, responding to interrupts. The archetypal example is the insect-inspired robot that lifts its leg higher when the leg encounters an obstacle - it doesn't model the obstacle, or plan how to go around it; it just lifts its leg higher.

In Brooks's paradigm - which he labeled nouvelle AI - intelligence emerges from "situatedness". One speaks not of an intelligent system, but rather the intelligence that emerges from the interaction of the system and the environment.

And Brooks wrote a programming language, the behavior language, to help roboticists build systems in his paradigmatic subsumption architecture - a language that includes facilities for asynchronous communication in networks of reflexive components, and programming finite state machines.

My understanding is that, while there are still people in the world who speak with reverence of Brooks's subsumption architecture, it's not used much in commercial systems on account of being nearly impossible to program.

Once you start stacking all these modules together, it becomes more and more difficult for the programmer to decide that, yes, an asynchronous local module which raises the robotic leg higher when it detects a block, and meanwhile sends asynchronous signal X to module Y, will indeed produce effective behavior as the outcome of the whole intertwined system whereby intelligence emerges from interaction with the environment...

Asynchronous parallel decentralized programs are harder to write. And it's not that they're a better, higher form of sorcery that only a few exceptional magi can use. It's more like the difference between the two business plans, "sell apples" and "sell nonapples".

One noteworthy critic of Brooks's paradigm in general, and subsumption architecture in particular, is a fellow by the name of Sebastian Thrun.

You may recall the 2005 DARPA Grand Challenge for the driverless cars. How many way...
Bay Area Meetup: 11/17 8PM Menlo Park
by Eliezer Yudkowsky — 2008-11-13T05:32:25.000Z

Robin Gane-McCalla plans to organize regular OB meetups in the Bay Area. The next one is 8PM, November 17th, 2008 (Monday night) in Menlo Park at TechShop. (Note that this is a room with seating, not a restaurant, so we hopefully get a chance to actually talk to each other - though I'll try to stay in the background myself.)

RSVP at Meetup.com.
The Weighted Majority Algorithm
by Eliezer Yudkowsky — 2008-11-12T23:19:58.000Z

Followup to: Worse Than Random, Trust In Bayes

In the wider field of Artificial Intelligence, it is not universally agreed and acknowledged that noise hath no power. Indeed, the conventional view in machine learning is that randomized algorithms sometimes perform better than unrandomized counterparts and there is nothing peculiar about this. Thus, reading an ordinary paper in the AI literature, you may suddenly run across a remark: "There is also an improved version of this algorithm, which takes advantage of randomization..."

Now for myself I will be instantly suspicious; I shall inspect the math for reasons why the unrandomized algorithm is being somewhere stupid, or why the randomized algorithm has a hidden disadvantage. I will look for something peculiar enough to explain the peculiar circumstance of a randomized algorithm somehow doing better.

I am not completely alone in this view. E. T. Jaynes, I found, was of the same mind: "It appears to be a quite general principle that, whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought." Apparently there's now a small cottage industry in derandomizing algorithms. But so far as I know, it is not yet the majority, mainstream view that "we can improve this algorithm by randomizing it" is an extremely suspicious thing to say.

Let us now consider a specific example - a mainstream AI algorithm where there is, apparently, a mathematical proof that the randomized version performs better. By showing how subtle the gotcha can be, I hope to convince you that, even if you run across a case where the randomized algorithm is widely believed to perform better, and you can't find the gotcha yourself, you should nonetheless trust that there's a gotcha to be found.

For our particular example we shall examine the weighted majority algorithm, first introduced by Littlestone and Warmuth, but as a substantially more readable online reference I will use section 2 of Blum's On-Line Algorithms in Machine Learning. (Blum has an easier-to-read, more concrete version of the proofs; and the unrandomized and randomized algorithms are presented side-by-side rather than far part.)

The weighted majority algorithm is an ensemble method - a way to combine the advice from several other algorith...
Worse Than Random
by Eliezer Yudkowsky — 2008-11-11T19:01:31.000Z

Previously in series: Lawful Uncertainty

You may have noticed a certain trend in recent posts: I've been arguing that randomness hath no power, that there is no beauty in entropy, nor yet strength from noise.

If one were to formalize the argument, it would probably run something like this: that if you define optimization as previously suggested, then sheer randomness will generate something that seems to have 12 bits of optimization, only by trying 4096 times; or 100 bits of optimization, only by trying 1030 times.

This may not sound like a profound insight, since it is true by definition. But consider - how many comic books talk about "mutation" as if it were a source of power? Mutation is random. It's the selection part, not the mutation part, that explains the trends of evolution.

Or you may have heard people talking about "emergence" as if it could explain complex, functional orders. People will say that the function of an ant colony emerges - as if, starting from ants that had been selected only to function as solitary individuals, the ants got together in a group for the first time and the ant colony popped right out. But ant colonies have been selected on as colonies by evolution. Optimization didn't just magically happen when the ants came together.

And you may have heard that certain algorithms in Artificial Intelligence work better when we inject randomness into them.

Is that even possible? How can you extract useful work from entropy?

But it is possible in theory, since you can have things that are anti-optimized. Say, the average state has utility -10, but the current state has an unusually low utility of -100. So in this case, a random jump has an expected benefit. If you happen to be standing in the middle of a lava pit, running around at random is better than staying in the same place. (Not best, but better.)

A given AI algorithm can do better when randomness is injected, provided that some step of the unrandomized algorithm is doing worse than random.

Imagine that we're trying to solve a pushbutton combination lock with 20 numbers and four steps - 160,000 possible combinations. And we try the following algorithm for opening it:

Enter 0-0-0-0 into the lock.

If the lock opens, return with SUCCESS.

If the lock remains closed, go to step 1.

Obviously we can improve thi...