Joho the Blog » 2003

See you at Pop!Tech

It was a hard choice, but I’ve decided to go to Pop!Tech again this year. It was hard precisely because Pop!Tech is such a good conference, but it conflicts with DigitalID World, which was terrific last year and looks like it’ll be at least as good this year. I really want to go to both, but physics is making that impossible. Damn physics.

I finally decided on Pop!Tech because its territories are more unfamiliar to me. But I will sorely miss the friends and ideas at DigID World. I wholeheartedly recommend both events.

Categories: misc Date: June 29th, 2003

June 28, 2003

[NKS] Starting point

If you’re just visiting this blog for the first time in the past couple of days and are faced with the endless scroll of live-bloggage of the Wolfram conference, here’s a place to start. The actual blog coverage begins in the entry prior to that one.

Categories: uncat Date: June 28th, 2003

[NKS] Jason Cawley: Philosophical Implications

[Jason was a researcher on historical and philosophical topics for the NKS book.]

He starts by talking about what NKS says about Free Will. Wolfram isn’t claiming that free will is impossible or natural. It’s a much more limited claim than that, directed against the spontaneity position and the behaviorist position. Spontaneity: Will is uncaused. Behaviorism: There’s a simple scheme for representing what wills do. Both too easily conflate being free and being unpredictable. Wolfram’s found a wedge between determinism and predictability. Wills are more unpredictable than behaviorists think. But will’s unpredictability doesn’t mean that it’s undetermined. Unpredictability is built into the system because of the system’s complexity. One should read that section of NKS as arguing against those two positions more than as establishing a positive doctrine of free will.

As a person, Wolfram is committed to determinism. But he doesn’t think that he’s proven it or that it follows from NKS. He thinks NKS makes determinism more plausible but doesn’t prove it.

Q: [Me] If we’re computationally equivalent to Rule 110 and all other such systems, then what distinguishes intellligent systems?

A: Not their cleverness, but there are other factors.

Q: Then how can you talk about FW without talking about the factors specific to systems that have will?

A: Wolfram is making a more limited claim. He’s talking about one piece of evidence — unpredictability — that’s been used by various FW theories.

Q: Shouldn’t he be talking about subjective vs. objective?

A: Complexity is independent of whether you’re inside or outside the system.

Q: In a note, he mentions Augustine. Where’s God in this?

A: [I suspect that Jason wrote the note] Religions are heterogeneous when it comes to the FW question. He talks about Augustine because Augustine tried to put together FW and predestination. Augustine distinguishes between what’s knowable to us and to God. This lines up with what’s subject to computation; you get the same sort of seems-free-to-us vs. seems-not-free-to-God viewpoint.

Just as Wolfram has presented open problems in NKS, Jason has open problems in philosophy for us, particularly for philosophy of science.

What it means to stick your neck out, Popper-ianly, when dealing with computability. You’re making falsifiable predictions about something that was logically derived. With NKS, you’re experimenting on what used to be the model. E.g., The Principal of Computation Equivalence claims that Class III rules are universal but it could be wrong; its falsifiable.
The definition of randomness. It’d be good to have a definition that captures the ordinary meaning but also works in the sciences.
Distinguish prior determinism, epistemology and …
When you look at where a system is migrating to (an attractor or a constraint) it’s different than looking at how it evolves. Discuss amongst yourselves.
Distinguish rules, models and theories. A rule isn’t a model because it’s abstract. Models have to correspond to something in reality. When can you make predictions? When can you just see what happens? Can you have a theory that doesn’t make predictions?

Q: [Me] What about the scope of NKS? Is it an ontology? Doesn’t it seem tied to the happenstanace that we have computers and thus is simply a way of seeing the world, especially since Wolfram rightly says that a model always leaves something out and is something of a political decision?

A: He certainly likes to make heroic generalizations. Theoretical physicists like to stick their necks out and let others show them wrong. He has an ontology that says you can get everything as an emergent property of space. That’s his intuition because he gets so much from what’s simple. He has a philosophy of pure form. How can he know? He doesn’t much care.

Q: [Me] But doesn’t this prove that McLuhan was right and we see our world through our technology?

A: Sure, but when new tech comes along, we don’t throw out the previous insights. We still incorporate what we see through telescopes. [Yeah, but revolutions do occur that re-do the fundaments.]

Q: Does he avoid using the term “emergence” because thinks simplicity and complexity are the same? Because he thinks emergence implies the properties aren’t there at the beginning?

A: He thinks “emergence” is a buzz word. And it’s present in the system from the beginning of its complexity.

Q: When you think about philosophers, which one strikes you as being Wolframian?

A: Hmm. Plato, because of his focus on forms. But Plato thought the forms had to be less detailed and specific than their instances, whereas Wolfram is all about seeing the complexity of forms. [I think he’s more like Hegel in the Logic, deriving everything from the simplest of starting points.

I left the conference after this session because I have some family stuff and because the rest is almost all too technical for me.

Categories: uncat Date: June 28th, 2003

[NKS] Wolfram: Computational Equivalence

[Continued live-blogging of the Wolfram conference in Waltham, MA.]

When looking at Rule 110, he wonde3red what happened if you consider everything that happens as a computation. He shows a Cellular Automaton [CA] that generates primes and another that does powers of 2. (The white stripes fall on the primes or the powers.)

He discovered the principle of Computational Equivalence (PCE) by looking at lots of CAs. The principle: If you look at a process, the process will correspond to a computation of equivalent sophistication. The computer revolution has taught us that it’s possible build a single, universal machine that will do any computation. He’s taken that idea seriously and applied it to the natural sciences.

The PCE says that except in cases where the system is doing something really simple, it’s most likely the case that the system is doing a computation of equivalent sophistication. That principle has many implications and predictions. E.g., it predicts that a system with simple rules should be capable of computations equivalent in sophistication to any other computer of equal sophistication. I.e., Rule 110 should be a universal computer. Rule 110 is really really random looking, but it has some “local structures,” i.e., there are identifiable structures (lines, triangles) in the swirling mist. These might rerpresent useful information. You might have thought that to do universal computation you need very complex systems with very complex rules (e.g., lots of logic gates), but instead you can do it with Rule 110 which arises from extremely simple rules.

This has implications. For natural science it means that among systems in nature one expects to see many systems capable of universal computation.

The PCE wraps together several things. First, it means there’s an upper limmit on the computations that can be done by a system. You can’t keep adding complex rules to get more complex computations; once you get past the threshold at which a system is a universal computer, you can’t get any further. Adding registers, for example, won’t increase the sophistication of the computation although it obviously might speed up the actual calculations. (This is like Church’s thesis, he says.) But the PCE gets its teeth by saying that not only is there an upper limit, but this limit is achieved by many systems.

To do a computation, perhaps you have to feed Rule 110 complex inputs. But the PCE says that that’s not necessary. Rule 110, even when the initial conditions are simple yields computations of enormous complexity. [I've always been fuzzy on this point. Still am. Where in Rule 110 is the computing of Pi or the lighting effects for Doom III?]

PCE is in one sense a law of nature [In the book he leaves out the "in one sense."] In another it’s a law of computation. One must ask “How could this principle be wrong?” As a law of nature, it could disagree with reality. As an abstract fact, it might yield false deductions. He thinks that it will come to seem as obvious as the Second Law of Thermodynamics. (He pauses to suggest that in fact the Second Law isn’t obvious.)

So how might it be wrong? Systems might have too much or too little computational sophistication. Models in the past haven’t noticed or cared about this. If constraint-based systems operated as well in nature as initial value problems, then the PCE couldn’t be right. [I lost his point.] Or, in physics, if quantities are continuous, then you could do…[and I lost the point again...he's talking very fast and I am skating on very thin ice]. But Wolfram believes the universe is discrete, not continuous, so that objection doesn’t hold.

Human thinking is supposedly more sophisticated than what computers can do. Wolfram disagrees.

But the PCE could fail at the low end, i.e., maybe Rule 30 isn’t a universal computer. Maybe there’s some regularity in Rule 110. We usually think of repetition and nesting as regularity but perhaps there’s another form of regularity. And maybe selects for rules that are complex but have that unexpected form of regularity.

Wolfram thinks the principle is true but his point is that there are ways it could fail. [And thus the PCE is a scientific statement. He doesn't use the word "falsifiable" but he's clearly thinking it.]

Systems like 110 seem complex because they’re doing computations as complex as we are when we’re trying to make sense of it.

Computational Irreducibility (CI) argues against predictability. If a system is complex, we can’t predict where it will be in a thousand steps except by running the thousand steps. That is, we can’t figure out the outcome with less effort than the system itself expends. [This is one of the ideas that first attracted me to Wolfram's thought.] Computational reducibility has been at the heart of many of the sciences: we can tell exactly where the moons of Jupiter will be in the year 3005 without having to wait until 3005. But you can’t predict will step 3005 of Rule 110 will be without going through all 3005 steps.

The PCI and PCE means that it simply won’t be possible to find exact solutions (mathematical functions) for some systems.

(In response to a question): The Scott Aronson review was disappointing because Wolfram spent time with him trying to correct his misconstructions but it got published anyway.

Take a question like whether there’s extraterrestial life. We recognize earth life because it shares ancestry. But is there an abstract definition of life? He can’t find one. The same is probable even more true of definitions of intellligence. There’s a threshold of computational sophistication, but beyond that there isn’t an essence of intelligence. Lots of systems have crossed the threshold to universal computation. There’s lots of fun things to talk about distinguishing intelligent systems from merely universally computational ones, involving concepts such as meaning and intention, but unfortunately we’re out of time. [!]

Categories: uncat Date: June 28th, 2003

4 Comments »

June 27, 2003

[NKS] Biological systems

It’s a panel on how NKS applies to medicine.

First: “Challenges to Conceptualizing Biological Systems: Wobble, redundancy and the unpredictable,” by Elaine Bearer, Brown U. We have conceptual problems incorporating wobble, redundancy and the unpredictable.

The evolutionary hypothesis says that biological systems raise through the process of selection which ignores details. It operates on the results, so there may be multiple ways to get to the same outcome. There’s a ton of evidence that supports the idea that it’s the outcome that counts, including wobble in DNA-protein interactions. Also, many functions are redundant. Wobble means that more than one triplet can spcify the same amino acide; the third nucleotide can vary. E.g., methionine is coded for by ATG but also by ATT and ATC. There’s no 1:1 relationship between the nucleotide code and the protein. Also, transcription factor-DNA interactions have no code. [No idea what that last sentence means.] There are many combinations that will work.

There’s also redundancy: more than one protein or copy of a gene can have the same outome. E.g., the ability to change cell positions is crucial and there are over 50 proteins that take its cytoskeleton structure apart. She shows an amazing video of a platelet taking apart its cytoskeleton and puting it together again like an earthworks mound around the center of the cell in order to form a clot. She’s discsovered which proteins enable this. She used Mathematica to simulate how the protein spreads. [This isn't a CA thing but more of an example of the power of Mathematica.]

She points to differences in conceptualization that get in the way of a fruitful conversation among biologists and mathematicians. For example, for her randomness is easy: it’s death. Life is randomness harnessed into regularity and repeatability. [I think she's getting at the difference between randomness and complexity that occasionally confused me in NKS.]

Ilan “Lanny” Kirsch, chief of the genetics branch of the Center for Cancer Researcfh at the National Cancer Institute in Bethesda. He’s going to talk more generally.

Cancer is a genetic disease caused by genetic instability. The genome of a cancer is the not the same as the genome of the normal cell from which it arose; the DNA in a tumor is different that of the cell that gave rise to it. The change in DNA that is cancer is caused by interitance, encironment and randomness. So how does NKS modeling work? We’ll look at three general examples.

1. Pathways and systems. Define the initial conditions annd the rules that descrfibe the pathway. [He doesn't explain what pathways he's talking about.]

2. Sequential steps in carcinogenesis. Usually it’s not a single gene that goes but the alteration of sequential genes that causes cancer. NKS can define the sequential rule/condition changes lead to the coutme of malignant transformation.

3. Undersanding and modeling instability. This one is more problematic. It moeans modeling instability itself, studying the basis of change. Wolfram’s example of mutation (p. 321? 391?) is a very good starting point. In the example, there is a mutation of a rule (i.e., if the left and right block is black, the middle block turns white instead of black, or whatever). The sort of randomness he sees in genes look very much like the randomness Wolfram shows and seem to be capable of being modeled by CA. Perhaps we’re seeing the collision of CA. Are mutations the result of the intersection of programs each with its own rule and initial conditions?

Wolfram: What sort of questions should pure NKS investigators concentrae on that would help biologists? In morphological studies, what are the appropriate rules? What are the primitives? Network based systems? Mobile automata? And what’s the appropriate level for trying to do modeling? What sort of questions would you like to be able to answer about these questions?

Kirsch: [Didn't undestand a word. Too much medical jargon for me.]

Bearer: Computational biological models have been held up by the belief that you need to have everything in place. But NKS may help us figure out what the missing factors are. When we were modelling, the Mathematica guy said that there has to be an inhibitor at a particular position because otherwise the model says that the branching would be other than it is. [Impressive.]

Categories: misc Date: June 27th, 2003

[NKS] Wolfram: Applying to the physical world

When you try to model a natural phenomenon, you inevitably drop out some of the phenomenon as irrelevant; that’s the nature of modeling. You can’t then complain that the model doesn’t capture something that it wasn’t intended to capture.

Lt’s take snow flakes as an example. When snowflakes form, they start from a seed. When a crystal bond is formed, some heat is released, inhibiting neighboring molecules from attaching. So, make a rule that says a cell will only be filled in if ___, and you get a snowflake form. You can make predictions from this such as: Big snowflakes will have holes in them from where arms collide; that turns out to be true. The model works. But it won’t answer a question like how far an arm will grow at a particular temperature. [I don't know why.]

So, how does one assess models? The best models are ones where you put a little in and get a lot out. A bad model gets more complex because you have to keep adding new considerations until you’re putting in more than you get out.

Models used to be mechanistic: you push A and B moves; there’s a small chain of inference. In another form of modeling, about 300 years old, we use equations to model systems. That’s a much more abstract form of modeling. But some of the equations can be very hard. E.g., you can explain snowflake generation via partial differential equations, but they’re very hard to solve. But NKS adds a new type of model: a simple set of rules producing complex phenomena.

It’s not proper to object that snowflakes aren’t made of CA cells because CA is a model of snowflakes. We don’t think that the earth is solving a differential equation when it moves through space. Differential equations are an abstraction. Similarly, a CA model of a snowflake is an abstract representation of how snowflakes work.

Randomness in models

We see examples of randomness in the natural world, e.g., fluid motion. Where does the randomness come from? There are three possible origins:

1. Classically, randomness comes from external perturbation, e.g., a boat being kicked around by the randomness of the ocean’s surface. Randomness of this sort: Brownian motion and some electronic noise. The randomness you get out isn’t part of the system you’re studying.

2. Chaos theory points to systems in which the initial conditions are random, e.g., a toin coss or the spin of a wheel. The three-body problem in gravity was one of the first cases of this studied: a change in a billionth of a degree results in hugely different results. There’s some effect from the outside that causes the initial conditions to be random. The randomness doesn’t come from the system we’re modeling.

3. You can get randomness without going outside the system. E.g., Rule 110 is intrinsically random.

Constraints

In traditional mathematical models, one can have an equation that is a constraint on the system that solves the equation. Typical example is a boundary problem. [Suddenly over my head. Prepare for vagueness. ] Constraint-based models don’t tell you how to fill in the constraints to solve the problem. His example of a constraint-based model is the question of what the closest packing of circles is. This is very hard to solve if the circles are different sizes. [I've lost the point. Damn not-knkowing-math-iness!]

Biology wants to know where the complexity of organisms come from. Initially, Wolfram assumed it was a different class of phenomenon because the biological systems adapt and change over time. But he’s concluded that adaption and evolution isn’t the issue. What forms of explanation should we give for biological systems? Will simple rules do or do we need much more complicate rules? Maybe biology is in fact sampling really simple programs. So, does the complexity come as a response to the visual system of predators [he seems to be thinking about patterns of fur] or does it come from simple programs? We think it has to have a complex explanation (evolution) because it’s complex. Nah, says Wolfram. [Evolutionists don't necessarily think that pigmentation patterns have been "carefully tuned" by natural selection. The question is whether we can get past pigmentation and get to flight or sight or kidneys.] Natural selection is good at progressively shortening or lengthening bones, but it’s not good at creating complicated things. Natural selection actually simplifies things, not makes them more complicated. We see this in technology where a form of natural selection makes stuff simpler, e.g., Fedex bills have gotten simpler. Natural selection operates well where you can make small changes and not have them be disastrous, just as engineering does.

His model explains how sea shells are formed. If you exclude ridiculous shapes — e.g., ones that leave no room for the animal — all of the ones his model draws are found in nature. So, you don’t need natural selection to explain them. Likewise for the shapes of leaves.

How do you find a model? If you think it’s a CA sort of thing, you can just match ‘em up. But it can be really hard to go from a natural phenomenon to its model. It is an unsolvable computational problem in the general case. So what do you do in practice? First, you can use Wolfram’s Atlas of Simple Programs and see if you recognize one. [The mug shot approach.]

The other thing you can do is search through all possible models of some particular kind. This seems crazy because if you were to search all possible equations, you’d never find it. But because you’re looking for simple rules, it can work.

Categories: misc Date: June 27th, 2003

[NKS] Analyzing Simple Programs

I’m in the beginners section on how to do cellular automata. The instructor (I came in 10 seconds late and missed his name) is explaining how to use a particular piece of software.

Q: You suggest we look for “interesting stuff” when playing around with CA. But what do you mean by “interesting”?

A: Everyone has a different viewpoint. But there are basic things like classifying them [according to Wolfram's 4 classes of CA, the 4th being complex/random]. Or you might notice that in Rule 30 big white triangles come at particular intervals. You can ask about the distribution of these triangles and plot according to the size of the triangle and where it shows up. You might understand more about how Rule 30 works. These localized structures are incredibly interesting.

A: Isn’t there a problem with relying on perception to notice randomness?

Q: Yes, perception isn’t reliable. That’s why this is non-trivial.

Categories: uncat Date: June 27th, 2003

[NKS] Wolfram: How it works

Wolfram is explaining the argument of his book. My blogging this is not going to be more helpful than reading more considered expositions, including Wolfram’s own. So, I plan on only jotting down some stray notes and thoughts.

Trivia: A cellular automaton (CA) gets its number by converting the pattern of bits that express the rule of the CA into a decimal number.

Wolfram shows how simple rules can lead to great complexity in simple CA. He asks how typical this is in the computational world. Is it only CA that generate complexity from simple rules? CA seem special (everything updates at the same time, there are local rules, etc.) so is this generalizable? So Wolfram systematically removes each of the CA’s special features. E.g., what happens if you don’t update all the cells at the same time? So he looks at some variations (substitution systems, replacement systems) and finds that they too can generate complexity from simple rules.

Tag systems: Look at the first element of a string, chop it off, and add different strings at the end based on the color of the first element. If you chop off one element, you get nested patterns. If you chop off two or more, you get much more complex behavior. In cyclic tag systems at alternating steps you add cells or not depending on the step number. [Aha! In the book tag systems turn out to be crucial in explaining the universality of CA 110, and I didn't understand them until now.]

[It's 11:05 and he's lost me. Too much math.]

He’s finding complex patterns in multidimensional systems and networked systems evolving through time, all part of his argument that simple-makes-complex isn’t an artifact of CA.

Nor is it dependent on the complexity of initial conditions. [This I believe is part of Wolfram's radicalization of formalism: initial conditions are a type of contingency and having to rely on initial conditions would mean that the system isn't entirely formal and mathematical.]

[He's talking about constraints. I'm lost again. But he bounces up a level and says the point is that you can force constraints to yield complexity. This apparently has application to the nature of crystals.]

He’s finding the same simplicity-yields-complexity in arithematic and math. Part of his point is that it’s a property not merely of an artifical construct like a CA. But I’m not sure if he’s re-pounding the same nail or whether he’s finding important insights within each area he’s discussing.

Now he’s talking about CA in which cells aren’t only black or white but could be any shade of gray. Guess what? Simple rules yield complexity. And it’s true of differential equations also.

He’s summarizing: Each of these types of systems comes up over and over again so it’s worth understanding something about how they work [A plea for a science of computation]. We’ve seen over and over again that simple rules can bring about great complexity. As soon you pass a very low threshhold of complexity of the initial rules you get all sorts of wild results.

Next topic: Analyzing what simple programs do. What can you do to analyze a hugely complex CA-generated pattern. The end of the story is that there isn’t a way to crack something like that. But what does “crack” mean? Can we go from the sequence back to the rule and initial condition that generated it? Nope. When we say something has regularities, we mean we can summarize it more briefly than by just repeating the sequence. [E.g., "It's a checkerboard" is a lot shorter than listing all 64 squares.] Can we compress some of the complex CA? Nah. Run-length encoding doesn’ work. Block-based compresssion doesn’t work. Dictionary-based? Nope.

We say something is random if we can’t summarize it. When we call something complex, not only can’t we find a unique summary but we can’t find a summary of the properties we actually care about.

Wolfram is endorsing simply looking at patterns. With our eyes. Our visual systems are quite good at discerning patterns. How does our visual system do that? What kind of things will our visual system be able to disentangle? It’s very good at noticing repetition. It’s ok at noticing nesting where there are big blocks of repeating color.

Application to cryptography.

Summary of this part of the talk: One’s assumption when confronted with something like Rule 30 is to say that there’s got to be some pattern of regularity hidden there. But he’s tried lots of ways of “cracking” it and none work.

Summary of the talk overall: These are the sorts of issues that pure NKS talks about.

After lunch he’s going to talk about more technical approaches. Bad news for the likes of me.

Categories: uncat Date: June 27th, 2003

Block that metaphor

From RottenTomatoes‘ aggregation of movie reviews:

“Watching Charlie’s Angels: Full Throttle is like being trapped inside a pinball machine operated by a 6-year-old having a sugar rush.”
— Kirk Honeycutt, HOLLYWOOD REPORTER

“Watching Full Throttle is like being pummeled for two hours with a feather duster. It leaves no scars, but you do feel the pain.”
— Peter Travers, ROLLING STONE

“Charlie’s Angels: Full Throttle is like eating a bowl of Honeycomb drenched in Red Bull — a dizzying mouthful of unabashed silliness that leads to an equally precipitous crash once the buzz wears off after the film’s first hour.”
— Elvis Mitchell, NEW YORK TIMES

["Block that metaphor" either is a blatant violation of the New Yorker's copyright or is a loving homage. We'll leave it to the courts to decide.]

Categories: misc Date: June 27th, 2003

1 Comment »

[NKS] Why I Care about Wolfram

I am not qualified to have an opinion about Wolfram’s A New Kind of Science. I’ve read it 1.75 times just to confirm this fact. I can’t evaluate the claims about how much of what he says is new, but I also sort of don’t care, except in a gossip-y sort of way. So why am I interested?

First, since I was in high school I’ve been bothered by the notion of laws. It’s a metaphor that scientists immediately reject: there’s no governing body, there’s no jail time for miscreants. So, then it’s just regularities and correlations. But that’s not much of an explanation. Wolfram tries to explain phenomena by asking what’s the simplest computer program that could have generated it. I don’t know that that is any more of an explanation, but it’s at least a radically different type of explanation. One indication of its radicalness: some phenomena cannot be predicted by solving an equation but only by running the program.

I like the fact that his approach holds hope (but I can’t evaluate how much) for understanding complex phenomena. Traditional science often punks out there. I asked a physicist friend of mine about this, a guy who was in grad school with Wolfram btw, and he said that the structure of heavy atoms is too complex to be managed — so far — by our equations. So, here’s exactly the sort of problem that Kuhn pointed to, a limit against which the current paradigm bumps. And it’s not in some marginal area. Complexity is clearly hugely important, from snowflakes to brains to galaxies. Wolfram may have (I can’t tell) made progress in understanding how complexity can be generated from very simple rules.

I’m also interested in watching the scientific community’s reaction to it. Will it embrace or reject (or take a third path) this? Likewise, will there be sufficient application of Wolfram’s ideas to recalcitrant existing problems to establish it as a workable paradigm? Fascinating.

And I’m very interested in his metaphysics because it seems to be the apotheosis of a modern trend: the triumph of formalism. The discussion in the comments section of my blog recently about Kurzweil and Searle typifies the deep division in our thought. Many of us find it obvious that the brain is hardware running software, and thus we will be able to move the software into another medium and run it losslessly, just like we can move our copy Sim City and our saved games from one computer to another. But this seems to me to be so fundamentally wrong, for reasons I won’t discuss again here. Wolfram takes the brain-as-software idea to its ultimate extension: the universe is software. His books attempts to derive space, time and the fundamental particles of physics from purely formal considerations. Wow.

He’s also an excellent writer and a truly interesting character. I’ve had the opportunity to spend a little time with him, and I like him.

Although I’m frustrated by my inability to follow his argument past page 500 or so, I also think there’s a certain benefit to being forced into agnosticism about his content, for the questions that circle “Is he right?” are fascinating on their own.

Categories: misc Date: June 27th, 2003

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[NKS] Wolfram: The NKS Enterprise

He’s going to talk about three components of the New Kind of Science (NKS) enterprise: Pure NKS, applied NKS and the NKS way of thinking. [I'm live-blogging and don't have time for quote marks.]

The intellectual core of pure NKS means asking the abstract question: What sort of simple programs are there and what do they do? It’s an independent area of intellectual inquiry that one day will be viewed as a discipline like physics and mathematics. It’s topic: the computational world.

The pure science might not have any applications but what’s been driving it is the hope that it does. Applied NKS takes what we learn from pure NKS and use it to model elements of nature, and even to human organizations.

The NKS way of thinking extends this to philosophy and art. NKS may not provide a model for human organization but it may provide a way of thinking about human organization. Also education.

“In order for NKS to realized its potential, it’s absolutely crucial that the pure NKS be properly developed.” A danger is that pure cores get left behind because of the excitement about applications.

The core has a simple story: One day it should be like physics and math with its own questions and methods and thhat is recognized as its own thing that gets to define its own boundaries.

It’s also important that NKS be embedded in other sciences. That’s the only way applications will happen. So, while Pure NKS should be its own field, the applications should not spring from a separate and distinct NKS.

You’d think that sciences are defined by their subject matter, e.g., biology is defined as the study of all living things. In fact, they’re defined by their methodologies. The questions they ask are the ones answerable by their methodologies. [Very Kuhn-ian. But it also fits with Wolfram's attempt to explain the universe purely through formal terms; subject matter doesn't count any more than a mathematician cares whether you're adding apples or oranges.] NKS needs to be implanted through real practitioners, although the questions NKS asks are different. NKS enables some tough questions in these fields to be answered. [Pure Kuhn: new paradigms grow in part in response to anomalies in existing paradigms. Wolfram knows his Kuhn.] In this way NKS is similar to mathematics.

Is there a general applications layer between the pure NKS and applied NKS? Yes, sort of. But it’d be a mistake to focus on that layer instead of focusing on particular application areas. Unlike philosophy, when NKS goes into an application area, it has lots of clear things to say, whereas philosophy has trouble moving from the pure to the applied with any clarity.

How do you confirm the rightness of NKS? Pure NKS simply wants to explore the computational world and see what’s out there. Whether that’s worth doing can’t be judged by the applications. The principle of computational equivalence, however, is an exception within pure NKS because there are predictions that can be made and it is falsifiable, but Wolfram’s not going to talk about this tomorrow. [Damn! I'm only here for the one day!] But in general, pure NKS is like math in terms of its justification. With applied NKS, it works like physics and other sciences: you see if you’re explaining stuff. By the way, when NKS is applied it generally works on more complex problems than the traditional sciences can handle. [He means "complex" in the complexity theory way, e.g., turbulence.]

Are there technologies that arise from applied NKS? Sure, particularly and obviously around computer technology.

History of NKS So Far

It was satisfying that the initial print run of 50,000 of A New Kind of Science sold out in one day. He’s planted the ideas by writing the book, lecturing, etc. But beyond that, how do ideas get introduced into the world? Let’s look at previous paradigm shifts.

There are typical responses: It’s wrong, it’s been done before, and it doesn’t make any sense.

But it’s hard to quickly say that NKS is wrong because there’s a lot in it. And it’s been done before is a denial based on the need to connect what one is doing with what one’s done before. And it seems like it doesn’t make sense because it’s different from what’s gone before. [Wolfram's reply to the "It's been done before" objection was weak, I thought. Better to say that it builds on what's been done but is new in important ways.]

The reaction against NKS shows that it’s being taken seriously. I had viewed science as a less emotionally-involved enterprise … [audience laughs knowingly]

He’s been flooded with emails, etc. His site’s guestbook shows that the visitors come first from the physical sciences and then from mathematics. The most frequent request is for software that will let people do their own experiments. NKS Explorer enables this. [Does it require Mathematica?]

About 50 papers have come out that refer to NKS. They almost uniformly cover the chapters of the book.

He’s handed out a booklet of “Open Problems and Projects.” Most have to do with pure NKS issues, but he’s continuing to work on this and will post on his site open questions in the applications area as well.

He’s been building an “atlas” of what’s out there in the computational world. It’s available on his website. The “Wolfram Atlas” is repository of information. It enables people interested in pure NKS to meet, sort of an experiment in Open Source science. There will probably soon be an online forum for discussing NKS. They haven’t done that yet because they wanted to wait for the furor to die down so that a reasonable discussion can be held.

Application areas he’s particularly interested in:

He wants there to be survey articles in various fields to show the activity around NKS. He’s very interested in how the idea NKS can be used in schools. He’d like somehow to incubate 50 pure NKS professors in order to seed academia; he’s running a summer school as a start. He’s found the best reaction among those at the beginning of their careers and those who are near the end; those in the middle have too much invested in what they’re currently doing. He believes that there will be some dramatic applications that will help people understand what NKS is about.

Categories: misc Date: June 27th, 2003

Why I’m epigrammatic today

I’m running out to an all-day conference about and by Wolfram. I don’t expect there to be wifi so I’m off-line all day…

LATER: Ok, I was wrong. I’m here with about 250 others and there’s wifi. Wolfram is just welcoming us…

Categories: uncat Date: June 27th, 2003

Why matters matters: Short version

Brains aren’t hardware running software any more than pool tables are.

Categories: misc Date: June 27th, 2003

June 26, 2003

3 Justices

So three justices of the United States Supreme Court think that it’s ok for the government to tell us what type of consensual sex we can have with whom.

Scary.

Categories: politics Date: June 26th, 2003

9 Comments »

Paynter on Saltire

Frank Paynter subjects Steve MacLaughlin - so funny he has a laugh in the middle of his name - to his long form interview. I haven’t had time to read it yet because I’m running out to a lunch thing, but Frank’s got a way with the Q&A.

Categories: web Date: June 26th, 2003

Were you pitched today?

I received an email today from a “boutique” PR firm asking me if I’d be interested in writing about one of their clients, a company that provides enterprise IM. The email msg was personalized to the extent that it had my name and my weblog’s name at the top of it.

The product seems not particularly interesting and the pitch is rather naive - I’m not really going to get excited because people can IM with other team members when they’re on a conference call with a client - but I am curious about whether we’re being spammed. Did you get this pitch today, too?

If so, maybe we can do something to help educate the PR firm; I don’t want to be at the receiving end of their pitches and press releases.

I heard back from the PR guy who sent it to me. They went through MediaMap and sent the pitch to about 25 bloggers and 100 media outlets. “To my delight, I have received several positive responses which should result in some great coverage for our client. Yes, the idea of IM is nothing new, but in the context of a real live application story and how a real business uses it, I thought it would be of interest.”

I wonder why.

Categories: web Date: June 26th, 2003

6 Comments »

Breakage

We had one of those lunches last week where right after the first glass fell and broke, a bowl cracked. And for breakfast, I had dropped one of the mugs we’d gotten from a penniless friend for our wedding 24 years ago.

The glass that broke at lunch had been one of our “good” ones, a cheap blue tumbler that we use pretty much only on the sabbath. My wife picked up a couple of replacements today. They’re blue and tumbler-shaped, but no one is going to confuse them with the originals.

I found this depressing. I am not sentimental about glasses. I didn’t even care hardly at all about the wedding gift breaking. It’s just stuff. (Yeah, well, try saying that about my computer.) What bothers me about the replacements is that they’re plastic. And it bothers me not for environmental reasons.

I’m 52. We’re still re-using cheap plastic cups our children got at kiddie events over 15 years ago, the sort of cup movie theaters use for their $4.00 small size soda. Occasionally one of them cracks. But we have a shelf of far more substantial Disney character cups brought back from Disney on Ice® and Disney on Parade® and Disney on Crack® and Disney Owns Your Freakin’ Ideas® that show fewer signs of age than I do. When my time comes, I will be handing them down to my children, along with my coffee can of miscellaneous screws and the Cuban cigar hidden in my closet that is now twelve years too old to smoke. (The Cuban cigar is legal because I only intended to burn it as an anti-Castro protest.)

So, if we get good quality plastic glasses, I am going to be drinking from them for the rest of my days. I don’t like them enough for that. I like the idea of them even less. I don’t want to be outfitted for life. Because I’m a middle class American, I like shopping, I like novelty, I like assuming that in five years — if I have another five years, if we have another five years — my stuff will be different and better. I don’t want to buy a new suit that’s so durable that as it’s being fitted I’m thinking, “Yup, this is definitely the suit I’m going to be buried in.”

I’m not saying any of this is rational or justifiable. I’m just saying it is.

Categories: misc Date: June 26th, 2003

June 25, 2003

Guess whose PC is broken?

Yup, it’s back in the shop. I never did get the !@#$-er to work and I don’t think they will either. My attempts include: Taking out the ATA card. Replacing the graphics card. Turning off hyperthreading. Turning off the onboard ethernet and putting in my old ethernet card. Running the virus checker every night. Running chkdsk several times. Spending about $20 on the Radeon support line finding out that they don’t know nuthin’. Contacting ASUS and being told that it could be lots of things. Nevertheless, all day long it’s gotten worse and worse, crashing to a re-start at closer and closer intervals. You’d almost think it was a heat thing except the motherboard monitor says it’s a cool 90F in there.

So, I’m thinking about my alternatives. I spent a chunk of money on the new mobo, RAM and case. I don’t know that the store will take it back since it seems to work when they take my drives out so it seems to have something to do with my software. (After a clean install of XP and with only Office XP added, it was still crashing.) So, I may have a major piece of metal on my hands. Suppose I were to try linux on it? (I forgot to try booting up knoppix before I dropped it off at the PC shop. Damn!)

I do need Windows for a few reasons. First, I want to know what the rest of the world is experiencing (=suffering). Second, there are some business docs that I work with that use Office features that Star Office doesn’t offer. Third, I play games. So, maybe I take my high-end graphics card out of my current machine and replace it with something simpler. I build a new new machine, not as high-end, to run games and Office and Quicken. I use the current new machine as my daily linux box, even though the new machine is way over-spec’ed for linux.

Hmm.

Categories: tech Date: June 25th, 2003

5 Comments »

QuickTopic goes Pro

Note: I have a financial relationship with Steve Yost, the creator of QuickTopic. But I have that relationship in large part because I’m such a big fan of Steve and his digital progeny. I believe I’d be saying what I’m about to say even if I didn’t know Steve.

QuickTopic, my favorite fast-and-easy discussion board, is now offering a Pro (= for pay) version. For $49/year, you get to make it look visually like a part of your site, get administrative tools, and get to use QuickThread which lets you create a QuickTopic thread out of any existing email thread. QuickTopic’s normal version is still completely free to users.

Steve created QT because he saw a need and, as a Good Citizen of the Web, he’s invested thousand of hours in making QuickTopic work like a charm. I’m happy to see him try to make a few bucks out of it. In fact, I hope he makes buckets of bucks.

Categories: web Date: June 25th, 2003

1 Comment »

Simulated Life

I’m woefully behind in this area, so I’m sure others have proposed this and then disposed of it. But here’s a question for the Moravek/Kurzweilians who think it’s obvious that if we model a brain’s 100B neurons in software, the computer is conscious: If we were to model an entire body’s molecules or atoms in software, would the computer now be alive?

By sheer coincidence, Steve Talbott’s fabulous newsletter today takes on Rodney Brooks’ new book, Flesh and Machines: How Robots Will Change Us. In particular, Talbott argues against the idea that humans are “just machines.” Talbott’s aim in this relatively brief essay is to remind us of how non-machine like we are as we look ever more closely at what’s “really” going on. And it’s not just quanta that are non-machine-like; cells themselves cannot be understood solely at the level of molecules:

Moss is one of many researchers looking at the complex chemical dynamics of the cell as a whole, and noting that there is no one-way chain of cause and effect determining the cell’s order. This order (which is passed from one generation to the next) is irreducibly manifested in the cell as a whole, with each part (including the DNA) being effect as well as cause.

I like that Talbott then broadens the question to: Why are we so willing to hear this? He proposes an answer:

In a society where the cry echoes from all sides, “You are nothing but a machine”, we can rightly ask whether what we are really hearing is “I sense that I am becoming nothing but a machine and, dammit all, I won’t tolerate anyone else being more than I am”.

I am a big fan of Talbott’s.

Rodney Brooks was staying at the same B&B as I was at a Pop!Tech conference a couple of years ago, and we had a drink together one night. I liked him a lot, and I think his approach to robotics is brilliant. He told me that night that he was thinking about investigating the nature of life. Looks like he’s headed in that direction.

Categories: web Date: June 25th, 2003

Open KM

Jerry Ash has opened up his Knowledge Management site to anyone who wants to see what’s there; previously it had been open only to members.

Isn’t it nice to see the world trending in the right direction every now and then?

Categories: web Date: June 25th, 2003

Blogging without the Chrono-Reversing

My friend Paul English doesn’t write a blog. But he does write blog-ish essays on topics which he then aggregates on his site. It’s like a blog turned sideways and sorted alphabetically by topic. See, for example, this on judging people by how they treat waiters.

Categories: web Date: June 25th, 2003