What are the plausible scientific limits of molecular nanotechnology?

Richard Jones, author of Soft Machines has written an interesting critique of the room-temperature molecular nanomachinery propounded by Drexler:

Rupturing The Nanotech Rapture

If biology can produce a sophisticated nanotechnology based on soft materials like proteins and lipids, singularitarian thinking goes, then how much more powerful our synthetic nanotechnology would be if we could use strong, stiff materials, like diamond. And if biology can produce working motors and assemblers using just the random selections of Darwinian evolution, how much more powerful the devices could be if they were rationally designed using all the insights we've learned from macroscopic engineering.

But that reasoning fails to take into account the physical environment in which cell biology takes place, which has nothing in common with the macroscopic world of bridges, engines, and transmissions. In the domain of the cell, water behaves like thick molasses, not the free-flowing liquid that we are familiar with. This is a world dominated by the fluctuations of constant Brownian motion, in which components are ceaselessly bombarded by fast-moving water molecules and flex and stretch randomly. The van der Waals force, which attracts molecules to one another, dominates, causing things in close proximity to stick together. Clingiest of all are protein molecules, whose stickiness underlies a number of undesirable phenomena, such as the rejection of medical implants. What's to protect a nanobot assailed by particles glomming onto its surface and clogging up its gears?

The watery nanoscale environment of cell biology seems so hostile to engineering that the fact that biology works at all is almost hard to believe. But biology does work--and very well at that. The lack of rigidity, excessive stickiness, and constant random motion may seem like huge obstacles to be worked around, but biology is aided by its own design principles, which have evolved over billions of years to exploit those characteristics. That brutal combination of strong surface forces and random Brownian motion in fact propels the self-assembly of sophisticated structures, such as the sculpting of intricately folded protein molecules. The cellular environment that at first seems annoying--filled with squishy objects and the chaotic banging around of particles--is essential in the operation of molecular motors, where a change in a protein molecule's shape provides the power stroke to convert chemical energy to mechanical energy.

In the end, rather than ratifying the ”hard” nanomachine paradigm, cellular biology casts doubt on it. But even if that mechanical-engineering approach were to work in the body, there are several issues that, in my view, have been seriously underestimated by its proponents.

...

Put all these complications together and what they suggest, to me, is that the range of environments in which rigid nanomachines could operate, if they operate at all, would be quite limited. If, for example, such devices can function only at low temperatures and in a vacuum, their impact and economic importance would be virtually nil.

The entire article is definitely worth a read. Jones advocates more attention to "soft" nanotech, which is nanomachinery with similar design principles to biology -- the biomimetic approach -- as the most plausible means of making progress in nanotech.

As far as near-term room-temperature innovations, he seems to make a compelling case. However the claim that "If ... such devices can function only at low temperatures and in a vacuum, their impact and economic importance would be virtually nil" strikes me as questionable. It seems to me that atomic-precision nanotech could be used to create hard vacuums and more perfectly reflective surfaces, and hence bring the costs of cryogenics down considerably. Desktop factories using these conditions could still be feasible.

Furthermore, it bears mentioning that cryonics patients could still benefit from molecular machinery subject to such limitations, even if the machinery is not functional at anything remotely close to human body temperature. The necessity of a complete cellular-level rebuild is not a good excuse not to cryopreserve. As long as this kind of rebuild technology is physically plausible, there arguably remains an ethical imperative to cryopreserve patients facing the imminent prospect of decay.

In fact, this proposed limitation could hint at an alternative use for cryosuspension that is entirely separate from its present role as an ambulance to the future. It could perhaps turn out that there are forms of cellular surgery and repair which are only feasible at those temperatures, which are nonetheless necessary to combat aging and its complications. The people of the future might actually need to undergo routine periods of cryogenic nanosurgery in order to achieve robust rejuvenation. This would be a more pleasant prospect than cryonics in that it would be a proven technology at that point; and most likely the vitrification process could be improved sufficiently via soft nanotech to reduce the damage from cooling itself significantly.

I present an algorithm I designed to predict which position a person would report for an issue on TakeOnIt, through Bayesian updates on the evidence of other people's positions on that issue. Additionally, I will point out some potential areas of improvement, in the hopes of inspiring others here to expand on this method.

For those not familiar with TakeOnIt, the basic idea is that there are issues, represented by yes/no questions, on which people can take the positions Agree (A), Mostly Agree (MA), Neutral (N), Mostly Disagree (MD), or Disagree (D). (There are two types of people tracked by TakeOnIt: users who register their own opinions, and Experts/Influencers whose opinions are derived from public quotations.)

The goal is to predict what issue a person S would take on a position, based on the positions registered by other people on that question. To do this, we will use Bayes' Theorem to update the probability that person S takes the position X on issue I, given that person T has taken position Y on issue I:

Really, we will be updating on several people T_j taking positions T_y on I:

I will bet 500 karma that a funny picture thread will appear on Less Wrong within one year. If anyone is interested in the bet, we can better define terms.

Right now the LW software doesn't support karma transfers. Until it does and we can develop a more robust prediction market, let's just record the karma transfers on the wiki page that already exists for this purpose.

I will also give 100 karma to anyone that donates $10 to the SIAI before the current fundraising campaign is over.

10,000 karma for the first person with a karma transfer source code patch?

I stumbled upon an article called The New Nostradamus, reporting of a game-theoretic model which predicts political outcomes with startling effectiveness. The results are very impressive. However, the site hosting the article is unfamiliar to me, so I'm not certain of the article's verity, but a quick Google seems to support the claims, at least on a superficial skimming. Here's his TED talk. The model seems almost too good to be true, though. Anybody know more?

Some choice bits from the article:

The claim:

In fact, the professor says that a computer model he built and has perfected over the last 25 years can predict the outcome of virtually any international conflict, provided the basic input is accurate. What’s more, his predictions are alarmingly specific. His fans include at least one current presidential hopeful, a gaggle of Fortune 500 companies, the CIA, and the Department of Defense.

The results:

The criticism rankles him, because, to his mind, the proof is right there on the page. “I’ve published a lot of forecasting papers over the years,” he says. “Papers that are about things that had not yet happened when the paper was published but would happen within some reasonable amount of time. There’s a track record that I can point to.” And indeed there is. Bueno de Mesquita has made a slew of uncannily accurate predictions—more than 2,000, on subjects ranging from the terrorist threat to America to the peace process in Northern Ireland—that would seem to prove him right.

[...]

To verify the accuracy of his model, the CIA set up a kind of forecasting face-off that pit predictions from his model against those of Langley’s more traditional in-house intelligence analysts and area specialists. “We tested Bueno de Mesquita’s model on scores of issues that were conducted in real time—that is, the forecasts were made before the events actually happened,” says Stanley Feder, a former high-level CIA analyst. “We found the model to be accurate 90 percent of the time,” he wrote. Another study evaluating Bueno de Mesquita’s real-time forecasts of 21 policy decisions in the European community concluded that “the probability that the predicted outcome was what indeed occurred was an astounding 97 percent.” What’s more, Bueno de Mesquita’s forecasts were much more detailed than those of the more traditional analysts. “The real issue is the specificity of the accuracy,” says Feder. “We found that DI (Directorate of National Intelligence) analyses, even when they were right, were vague compared to the model’s forecasts. To use an archery metaphor, if you hit the target, that’s great. But if you hit the bull’s eye—that’s amazing."

Related to: Beautiful Probability, Probability is in the Mind

Arnold Kling ponders probability:

How one thinks about probability affects how one thinks about economics. Consider the use of the word "probability" in each of the following sentences:

1. What is the probability that when a fair coin is flipped it will come up heads?
2. What is the probability that exactly two number-one seeds will make it to the final four in the March Madness basketball tournament?
3. What is the probability that New York City will rank higher relative to other cities five years from now in terms of college graduates?

We would answer the first question by saying that the probability is 50 percent, based on the very definition of a fair coin. This is an axiomatic interpretation of probability. The axiomatic view treats probability as a matter of pure logic, with statements that do not require any empirical testing.

We would answer the second question by looking up historical records for the NCAA basketball tournament. This is the frequentist account of probability, which treats probability as counting outcomes from repeated trials. A frequentist would claim that the only way we can know that a coin has a 50 percent probability of coming up heads is by actually flipping a coin enough times to verify this empirically.

The third question cannot be answered on the basis of axioms or observed frequencies. The probability estimate is purely subjective. The subjective account of probability is that it reflects an individual belief that cannot be proven either logically or empirically.

In the tradition of Reddit, and a little inspired by Robin, this is a simple link to an interesting page somewhere else - I leave comment and discussion to the very awesome Less Wrong community.

Edit: Eliezer has in the past been uncomplimentary of the "accursèd frequentists". In at least Beautiful Probability and Probability is in the Mind, he has characterized (for at least some problems) the "frequentist" approach as being wrong, and the "Bayesian" approach as being right. Kling suggests different problems for which different approaches are approrpriate.