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I thought cryonics was unlikely to work because a bunch of information might be lost even at the temperatures that bodies are usually preserved in. I now think this effect is most likely not serious and cryonics can work in principle at the temperatures we use, but present-day cryonics is still unlikely to work because of how much tissue damage the initial process of freezing can do.
As I said, I think it’s not just that the language is poetic. There is an implicit inference that goes like
People who would not voluntarily undergo surgery without long-term adverse effects on their health to improve the life of a stranger are evil.
Most researchers who would be in a position to know the state of the evidence on the long-term adverse health effects of kidney donation don’t personally donate one of their kidneys.
Most researchers are unlikely to be evil.
So it’s unlikely that most researchers believe kidney donation has no long-term adverse health effects.
I’m saying that there is no definition of the word “evil” that makes statements (1) and (3) simultaneously true. Either you adopt a narrow definition, in which case (3) is true but (1) is false; or you adopt a broad definition, in which case (1) is true but (3) is false.
This is not a point about stylistic choices, it’s undermining one of the key arguments the post offers for its position. The post is significantly stronger if it can persuade us that even established experts in the field agree with the author and the hypothesis being advanced is in some sense “mainstream”, even if it’s implicitly held.
I don’t think it’s a matter of poetic license. You’re making an empirical claim that if specialists actually believed kidney donation had no long-term side effects, they would be lining up to donate their kidneys and we would see a much higher rate of kidney donations in the US. I think this claim is wrong because the inconvenience of surgery is substantial enough to block people from donating their kidneys even in the absence of long-term side effects.
The use of the word “evil” sneaks in an assumption that most people would be happy to make this tradeoff to improve a stranger’s life at the cost of some inconvenience to themselves, but I think this claim is actually false. So the fact that this doesn’t happen gives very little evidence that specialists don’t take claims about the small long-term health effects of kidney donation seriously.
Have most of the researchers looking at kidney donation donated a kidney? Have most nephrology researchers donated a kidney? Most surgeons doing kidney transplants? Obviously not, otherwise we’d have more than 200 donations to strangers each year in the US. There are 10,000 board-certified nephrologists, and a few more hundred are added each year, if they took this data seriously they’d all donate.
Heck, on top of those you can add nephrology researchers, the medical statisticians who happen to focus on kidney disease, transplant surgeons, and all well-informed nurses in the nephrology units… thousands of these specialists are created each year. If most of them believed donation to be essentially safe the shortage of kidneys would be half-sovled.
Maybe they are all evil people? They will not take even a marginal risk to save a life.
We usually don’t call people “evil” for not inconveniencing themselves by going through surgery, so you seem to be using this word in a fairly non-standard way here.
Just to elaborate: if I had a condition that could be cured either by having an operation equivalent to a kidney donation or paying $3k, I would almost certainly pay $3k. However, I could likely save a statistical life by donating this $3k to an effective charity. So my not donating my kidney to a stranger provides no more evidence of my evil nature than my not donating $3k to save the life of a random stranger, because I price the inconvenience of the surgery at more than $3k even if the surgery has no long-run consequences for my health.
There is more data, and better data, e.g. data gathered in double-blinded RCTs, that shows things like:
Homeopathy works very well for a variety of conditions, sometimes better than real drugs used to treat them.
Increasing the healthcare budget and the amount of healthcare people receive. Both in rich countries (e.g. USA) and poor ones (India). Having no effect on mortality.
I can make both of these claims based on many individual RCTs, as well as based on the aggregation of all existing RCTs.
I’m not saying that these claims make sense, they don’t, there are critical lenses through which we analyze research. But if you claim to “just follow the data”, and ignore the issue of data quality, selection bias, and fraud… without applying a critical lens, you are lost.
It seems to me like claim (2) could easily make sense if you interpret it more charitably as “the mortality effects are too small for the studies to detect”. I don’t have a particularly strong prior that marginal healthcare spending is all that useful for increasing life expectancy—diminishing returns can mean that the average dollar spent on healthcare does much more than the marginal dollar.
Can you justify your claim that (2) does not make sense?
I don’t think those ratings are comparable. On the other hand, my estimate of 3d was apparently lowballing it based on some older policy networks, and newer ones are perhaps as strong as 4d to 6d, which on the upper end is still weaker than professional players but not by much.
However, there is a big gap between weak professional players and “grandmaster level”, and I don’t think the raw policy network of AlphaGo could play competitively against a grandmaster level Go player.
This is not quite true. Raw policy networks of AlphaGo-like models are often at a level around 3 dan in amateur rankings, which would qualify as a good amateur player but nowhere near the equivalent of grandmaster level. If you match percentiles in the rating distributions, 3d in Go is perhaps about as strong as an 1800 elo player in chess, while “master level” is at least 2200 elo and “grandmaster level” starts at 2500 elo.
Edit: Seems like policy networks have improved since I last checked these rankings, and the biggest networks currently available for public use can achieve a strength of possibly as high as 6d without MCTS. That would be somewhat weaker than a professional player, but not by much. Still far off from “grandmaster level” though.
I think you’re ignoring the qualifier “literally portrayed” in Matthew’s sentence, and neglecting the prior context that he’s talking about AI development being something mainly driven forward by hobbyists with no outsized impacts.
He’s talking about more than just the time in which AI goes from e.g. doubling the AI software R&D output of humans to some kind of singularity. The specific details Eliezer has given about this scenario have not been borne out: for example, in his 2010 debate with Robin Hanson, he emphasized a scenario in which a few people working in a basement and keeping all of their insights secret hit upon some key software innovation that enables their piece of consumer hardware to outcompete the rest of the world.
It’s worth noting that Robin Hanson also said that “takeoff” is most likely to take months. He just said it for ems, and in his world, that rate of growth was being driven by the entire world economy working as a whole rather than one local part of the world having such better software that it could outcompete everyone else with vastly less material resources. I find you saying this is a “mild win” for Eliezer’s prediction incomprehensible given that we live in a world where individual AI labs are being valued at ~ $100B and raising tens of billions of dollars in capital.
I assume John was referring to Unitary Evolution Recurrent Neural Networks which is cited in the “Orthogonal Deep Neural Nets” paper.
It might be right, I don’t know. I’m just making a local counterargument without commenting on whether the 2.5 PB figure is right or not, hence the lack of endorsement. I don’t think we know enough about the brain to endorse any specific figure, though 2.5 PB could perhaps fall within some plausible range.
While I wouldn’t endorse the 2.5 PB figure itself, I would caution against this line of argument. It’s possible for your brain to contain plenty of information that is not accessible to your memory. Indeed, we know of plenty of such cognitive systems in the brain whose algorithms are both sophisticated and inaccessible to any kind of introspection: locomotion and vision are two obvious examples.
I downvoted this comment for its overconfidence.
First of all, the population numbers are complete garbage. This is completely circular. You are just reading out the beliefs about history used to fabricate them. The numbers are generated by people caring about the fall of Rome. The fall of Rome didn’t cause of decline in China. Westerners caring about the fall of Rome caused the apparent decline in China.
I will freely admit that I don’t know how population numbers are estimated in every case, but your analysis of the issue is highly simplistic. Estimates for population decline do not just depend on vague impressions of the significance of grand historical events such as the fall of Rome. Archaeological evidence, estimates of crop yields with contemporary technology on available farmland, surviving records from the time, etc. are all used in forming population estimates.
It’s far from being reliable, but what we know seems clear enough that I would give something like 80% to 90% chance that the first millennium indeed had slower population growth than the first millennium BC. You can’t be certain with such things, but I also don’t agree that the numbers are “complete garbage” and contain no useful information.
Second, there was a tremendous scientific and technological regress in Rome. Not caused by the fall of Rome, but the rise of Rome. There was a continual regress in the Mediterranean from 150BC to at least 600AD. Just look at a list of scientists: it has a stark gap 150BC-50AD.
I think you’re conflating a lack of progress with regression here. I remark in the post that the slowdown in population growth seems to have begun around 200 BC, which is consistent with what you’re saying here if you take it as a statement about growth rates and not about levels. If the pace of new discoveries slows down, that would appear to us as fewer notable scientists as well as slower growth in population, sizes of urban centers, etc.
Aside from that, there are also many alternative explanations of a gap in a list of scientists, e.g. that Rome was comparatively less interested in funding fundamental research compared to the Hellenistic kingdoms. Progress in fundamental sciences doesn’t always correlate so well with economic performance; e.g. the USSR was much better at fundamental science than their economic performance would suggest.
It is more controversial to say that the renaissance 50AD-150AD is a pale shadow of the Hellenistic period, but it is. In 145BC Rome fomented a civil war in Egypt, destroying Alexandria, the greatest center of learning. In 133BC, the king of Pergamon tried to avoid this fate by donating the second center of learning. It was peaceful, but science did not survive.
I don’t know what you’re referring to by “Rome fomented a civil war in Egypt in 145 BC”. 145 BC is when Ptolemy VI died; but as far as I know, there was no single “civil war” following his death, Alexandria was not destroyed, and Rome was not involved directly in Egyptian politics for a long time to come. Alexandria remained one of the major urban centers of the Mediterranean until the 3rd century AD—perhaps even the largest one.
Well, that’s true, but at some level, what else could it possibly be? What other cause could be behind the long-run expansion in the first place, so many millennia after humans spanned every continent but Antarctica?
Technological progress being responsible for the long-run trend doesn’t mean you can attribute local reversals to humans hitting limits to technological progress. Just as a silly example, the emergence of a new strain of plague could have led to the depopulation of urban centers, which lowers R&D efficiency because you lose concentrations of people working together, and thus lowers the rate of technological progress. I’m not saying this is what actually happened, but it seems like a possible story to me.
I’m very skeptical about explanations involving wars and plagues, except insofar as those impact technological development and infrastructure, because a handful of generations is plenty to get back to the Malthusian limit even if a majority of the population dies in some major event (especially regional events where you can then also get migration or invasion from less affected regions).
I agree, but why would you assume wars and plagues can’t impact technological development and infrastructure?
McEvedy and Jones actually discuss a regional breakdown in the final section of the book, but they speculate too much for the discussion to be useful, I think. They attribute any substantial slowdown in growth rates to population running up against technological limits, which seems like a just-so story that could explain anything.
They note that the 3rd century AD appears to have been a critical time, as it’s when population growth trends reversed in both Europe and China at around the same time: in Europe with the Crisis of the Third Century, and in China with the fall of the reconstituted Han dynasty and the beginning of the Three Kingdoms period. They attribute this to technological constraints, which seems like an unsupported assertion to me.
The other important population center is India, where we have very few records compared to Europe and China. Datasets generally report naively extrapolated smooth curves for the Indian population before the modern period, and that’s because there really isn’t much else to do due to the scarcity of useful information. This doesn’t mean that we actually expect population growth in India to have been smooth, just that in the absence of more information our best guess for each date should probably be a smoothly increasing function of the date. As McEvedy and Jones put it, “happy is the graph that has no history”.
I agree that locations isolated from Eurasia would most likely not show the same population trends, but Eurasia was ~ 75% of the world’s population in the first millennium and so events in Eurasia dominate what happens to the global population.
I’ve actually written about this subject before, and I agree that the first plague pandemic could have been significant: perhaps killing around 8% of the global population in the four years from 541 to 544. However, it’s also worth noting that our evidence for this decline is rather scant; we know that the death toll was very high in Constantinople but not much about what happened outside the capital, mostly because nobody was there to write it down. So it’s also entirely conceivable that the death toll was much lower than this. The controversy about this continues to this day in the literature, as far as I know.
The hypothesis that the bubonic plague was responsible is interesting, but by itself doesn’t explain the more granular data which suggests the slowdown starts around 200 BC and we already see close to no growth in global population from e.g. 200 AD to 500 AD. HYDE doesn’t have this, but the McEvedy and Jones dataset does.
It’s possible, and perhaps even likely, that the explanation is not monocasual. In this case, the first plague pandemic could have been one of the many factors that dragged population growth down throughout the first millennium.
Yes, this summary seems accurate.