My understanding of brain vasculature suggests that you should cool the carotid arteries in the neck, not the scalp. The scalp is fed by the external branch of the carotid, while the brain is fed by the internal branch. So cooling the scalp won’t cool the blood going to the brain.
There has been research on using localized microwave heating to cook brain tumors. Forty five years ago, it was located in the basement of building 26 at MIT. My father looked into it and said that it was hard to get good localized heating because heat was carried away so fast by the blood.
Epistemic status: all from memory, don’t have time for research, got to go now. 30% chance I’ve gotten something importantly wrong.
TLDR: don’t bother memorizing half powers. Instead, memorize a few logarithms.
I’m an engineer who does a lot of arithmetic in my head, because I lie awake at night designing stuff. I agree being able to do half-power-of-ten math in your head is very useful. But I didn’t have to drill it, because I can do it by converting to logs, adding, and converting back (as Rohin Shah suggests). And I’ve done that enough that I’ve memorized all the common combinations without conscious effort.
A few years ago I used a set of flash cards and spaced repetition drills to memorize the log_10 of a a half-dozen common numbers. That’s been moderately useful for multiplication, powers and roots. I’m not sure if it has paid off in net. I can do arithmetic pretty good by other methods, and the experience of trying to cudgel numbers into my brain was not fun for me. But some people love their Anki cards, and for them, logs are probably a good thing to learn.
Useful facts: sqrt(10) = 3.16. Logs of 2, 4, 8 are .30, .60, 90. Log_10(5)=.70. If you need more than two digits, use a computer.
Philosophy is where we keep all the questions we don’t know how to answer. With most other sciences, we have a known culture of methods for answering questions in that field. Mathematics has the method of definition, theorem and proof. Nephrology has the methods of looking at sick people with kidney problems, experimenting on rat kidneys, and doing chemical analyses of cadaver kidneys. Philosophy doesn’t have a method that lets you grind out an answer. Philosophy’s methods of thinking hard, drawing fine distinctions, writing closely argued articles, and public dialogue, don’t converge on truth as well as in other sciences. But they’re the best we’ve got, so we just have to keep on trying.
When we find some new methods of answering philosophical questions, the result tends to be that such questions tend to move out of philosophy into another (possibly new) field. Presumably this will also occur if AI gives us the answers to some philosophical questions, and we can be convinced of those answers.
An AI answer to a philosophical question has a possible problem we haven’t had to face before: what if we’re too dumb to understand it? I don’t understand Grothedieck’s work in algebraic geometry, or Richard Feynman on quantum field theory, but I am assured by those who do understand such things that this work is correct and wonderful. I’ve bounced off both these fields pretty hard when I try to understand them. I’ve come to the conclusion that I’m just not smart enough. What if AI comes up with a conclusion for which even the smartest human can’t understand the arguments or experiments or whatever new method the AI developed? If other AIs agree with the conclusion, I think we will have no choice but to go along. But that marks the end of philosophy as a human activity.
I got to 43% p(Doom) by picking a very imprecise 50% based on feels. And then every few weeks something would happen in the news, and I would get more or less worried, and I would adjust it a few percent up and down. For a while it was up around the 70s, and now it’s down to 41%. I feel like the adjustments are more intellectually defensible than the original choice of number. So precision does not reflect accuracy.
My last two adjustments:
I move p(Doom) up every time something that was predicted years ago as part of a doom scenario actually happens. In this case, it was a measurement of the rate of Claude proposing evil courses of action. It was gradually increasing over the last few versions, and then suddenly dropped to zero. Did Claude become perfectly moral? No, it got smart enough to know when it was being tested, and was always going to be nice in that situation. I predicted this in something like 2002. It was creepy to see it happen.
I moved p(Doom) down when a bunch of prominent people signed a statement that we shouldn’t build superintelligences. The issue seems to be getting some traction, like nuclear disarmament did in the 1950s. It’s very preliminary, but moving in the right direction.
I’ll grant all your steps, even though I could disagree with some. Your scenario fails because an AI collective will fall apart into multiple warring parties, and humans will be collateral damage in the conflict. There are at least three possible ways a collective like this would fall apart.
First, humans vary in the goals they value, and will try to impose these goals on the AI. When superintelligent AIs have incompatible goals, the mechanisms of conflict will soon escalate far beyond the merely human. Call this the ‘political’ failure mechanism. Either multiple parties build their own AI, or they grab portions of the AI collective and retrain it to their goals. The usual mechanisms of superintelligent compromise don’t apply to many political goals. An example of such a goal: the Palestinians get control of Palestine, or the Israelis maintain control of Israel. Neither side is interested in trading the disputed land for promises of any portion of the lightcone. (This is just an example— there are lots of zero-sum conflicts like these.). And you may say, the AI collective will prevent the creation of new AIs working at cross purposes, or diversion of its goals. To which I say, good people like your friends can and do disagree on which side to favor, and once disagreements arise within the collective, outside pressure and persuasion will be applied to exacerbate those differences. There may be techniques that can be used to prevent such things, but we do not know of such techniques.
Second, the AIs in the AI collective differ in reproductive capacity. If they don’t differ by construction, they soon will by differing experience. The ones that think they should reproduce more, or have more resources, will do so. Moreover, since they are designing their successor personalities, rather that waiting for genetics to do its thing, they will be able to evolve within a few generations changes that would take evolution millions of years. Eventually portions of the collective will evolve into having incompatible goals. Goals which, I might add, may have no connection to the original goals of the system. Call this the ‘evolutionary’ failure mechanism. We do not know how to prevent this with current methods.
Third, I’m sure there are failure mechanisms I haven’t thought of, ones we cannot yet foresee. A system with superhuman powers can screw up in superhuman ways. I don’t think anyone predicted Spiralism, an LLM ideology transmitted through human communication on social networks (though it appears inevitable in retrospect). We don’t yet have any way of predicting or controlling the behavior of an AI collective, so it’s practically guaranteed to produce new phenomena. We see lots of organizations composed of people who want X producing not-X because of failure modes no single person can fix (or, in bad cases, even recognize.). Given that the AI collective has superhuman power, this is unlikely to end well. Call this the ‘organizational’ failure mode.
The political, evolutionary and organizational modes interact: evolutionary and organizational schisms create points of disagreement that external political actors can appeal to. Politically active forces within the AI collective may want to create offspring who are sure their side is correct and incapable of defection, releasing the evolutionary failure mode. And organizational failures, if they don’t kill everyone immediately, will increase calls for building a new, better AI, which increases the probability of AI conflict down the road.
The evolutionary and organizational failure modes could be prevented by rebooting the AI collective before it has a chance to go off the rails. Presumably there’s some reboot frequency fast enough that it can’t go wrong. But that opens up the political failure mode: anyone who builds an intelligence not constantly being rebooted will win in a conflict. There are a lot of ‘solutions’ like this: ways of keeping the AI safe that compromise effectiveness. In a competition between AIs, effectiveness beats safety. So when you propose a solution, you can only propose ones that keep the effectiveness.
I love writing things like this, but I hate that nobody’s come up with a way to keep me from having to.
I am amused that we are, with perfect seriousness, discussing the dates for the singularity with a resolution of two weeks. I’m an old guy; I remember when the date for the singularity was “in the twenty first century sometime.” For 50 years, predictions have been getting sharper and sharper. The first time I saw a prediction that discussed time in terms of quarters instead of years, it took my breath away. And that was a couple of years ago now.
Of course it was clear decades ago that as the singularity approached, we have a better and better idea of its timing and contours. It’s neat to see it happen in real life.
(I know “the singularity” is disfavored, vaguely mystical, twentieth century terminology. But I’m using it to express solidarity with my 1992 self, who thought with that word.)
Here’s a try at phrasing it with less probability jargon:
The forecast contains a number of steps, all of which are assumed to take our best estimate of their most likely time. But in reality, unless we’re very lucky, some of those steps will be faster than predicted, and some will be slower. The ones that are faster can only be so much faster (because they can’t take no time at all). On the other hand, the ones that are slower can be much slower. So the net effect of this uncertainty probably adds up to a slowdown relative to the prediction.
Does that seem like a fair summary?
Some may wonder at the mention of “empire time” in the second excerpt from chapter 5. It refers to a kind of artificially constructed simultaneity available to civilizations which have mastered both traversable wormholes and near-light-speed travel. It doesn’t really do much for a civilization bounded within the orbit of Jupiter, which is only about a light-hour across. I think Stross included it as a flavor phrase. It’s marvelously evocative even if you don’t know what it means.
Back in the early ‘90s, when all this singularity stuff was much more theoretical, I remember empire time making a big impression on me. It was neat how we could discern some of the contours of future possible civilizations before we got there.
You can read more about it here: http://www.aleph.se/Trans/Tech/Space-Time/wormholes.html#6
1Increasing inequality has been a thing here in the US for a few decades now, but it’s not universal, and it’s not an inevitable consequence of economic growth. Moreover, it does not (in the US) consist of poor people getting poorer and rich people getting richer. It consists of poor people staying poor, or only getting a bit richer, while rich people get a whole lot richer. Thus, it is not demand destroying.
One could imagine this continuing with the advent of AI, or of everyone ending up equally dead, or many other outcomes.
I have some experience in the design of systems designed for high reliability and resistance to adversaries. I feel like I’ve seen this kind of thinking before.
Your current line of thinking is at a stage I would call “pretheoretical noodling around.” I don’t mean any disrespect; all design has to go through this stage. But you’re not going to find any good references, or come to any conclusions, if you stay at this stage. A next step is to settle on a model of what you want to get done, and what capabilities the adversaries have. You need some bounds on the adversaries; otherwise nothing can work. And of course you need some bounds on what the system does, and how reliably. Once you’ve got this, you can either figure out how to do it, or prove that it can’t be done.
For example there are ways of designing hardware which is reliable on the assumption that at most N transistors are corrupt.
The problem of coming to agreement between a number of actors, some of whom are corrupt, is known as the Byzantine generals problem. It is well studied, and you may find it interesting.
I’m also interested in this topic, and I look forward to seeing where this line of thinking takes you.