I think you should add Clarifying some key hypotheses in AI alignment.
If you can predict the result of the data ahead of time, that seems very important for making decisions (eg. predicting stock market moves).
The accuracy of similar tests for influenza is generally 50–70%.
I don’t think these tests are similar. See here,
All of the coronavirus tests being used by public health agencies and private labs around the world start with a technique called polymerase chain reaction, or PCR, which can detect tiny amounts of a virus’s genetic material. SARS-CoV-2, the virus that causes COVID-19, has RNA as its genetic material. That RNA must first be copied into DNA. “That’s a lengthy part of the process, too,” says Satterfield, adding 15 to 30 minutes to the test.
Many doctors’ offices can do a rapid influenza test. But those flu tests don’t use PCR, Satterfield says. Instead, they detect proteins on the surface of the influenza virus. While the test is quick and cheap, it’s also not nearly as sensitive as PCR in picking up infections, especially early on before the virus has a chance to replicate, he says. By the CDC’s estimates, rapid influenza tests may miss 50 percent to 70 percent of cases that PCR can detect. The low sensitivity can lead to many false negative test results.
This paper was written by an international team of highly cited disease modellers who know about the Diamond Princess and have put their reputation on the line to make the case that this the hypothesis of high infections rate and low infection fatality might be true.
Yes, but when you actually read the paper (I read some parts), it says that their model is based on an assumption of low IFR, and in itself did not argue for low IFR (feel free to prove me wrong here).
This concept apparently goes back at least as far as Robert Ettinger, the originator of cryonics. From his seminal book introducing cryonics,
We normally think of information about the body as being preserved in the body—but this is not the only possibility. It is conceivable that ordinary written records, photographs, tapes, etc. may give future technicians enough clues to fill in missing or damaged areas in the brain of the frozen.
The time will certainly come when the brain’s method of coding memories is thoroughly understood, and messages can be “read” directly from nervous tissue, and also “read” into it. It is not likely that the relation will be a simple one, nor will it necessarily even be exactly the same for every brain; nevertheless, by knowing that the frozen had a certain item of information, it may be possible to infer helpful conclusions about the character of certain regions in his brain and its cells and molecules.
Similarly, a mass of detailed information about what he did may allow advanced physiological psychologists to deduce important conclusions about what he was, once more providing opportunity to fill in gaps in brain structure.
It follows that we should all make reasonable efforts to obtain and preserve a substantial body of data concerning what we have seen, heard, felt, thought, said, written, and done in the course of our lives. These should probably include a battery of psychological tests. Encephalograms might also be useful.
What are those implications?
Without heliocentrism (and its extension to other stars), it seems that the entire idea of going to space and colonizing the stars would not be on the table, because we wouldn’t fundamentally even understand what stuff was out there. Since colonizing space is arguably the number one long-term priority for utilitarians, heliocentrism is therefore a groundbreaking theory of immense ethical importance. Without it, we would not have any desire to expand beyond the Earth.
I tend to prefer dealing with applications, not implications
Colonizing the universe is indeed an application.
“But many worlds implies...” No, it doesn’t.
It seems implausible that a physical theory of the universe, especially one so fundamental to our understanding of matter, would have literally no practical implications. The geocentric and heliocentric model of the solar system give you the same predictions about where the stars will be in the sky, but the heliocentric model gives some important implications for the ethics of space travel. Other scientific revolutions have similarly had enormous effects on our interpretation of the world.
Can you point to why this physical dispute is different?
Both could be relevant. It could be that a subgroup that makes up the majority of the military gets benefits, so the median is higher productivity. But due to a small subgroup, the mean is lower. Any result seems interesting here.
[ETA: Don’t you think something like, “People in the Army have lower productivity but people in the Air Force have higher” would be interesting? I just am looking for something that’s relevant to the central question of the post: can training have long-term benefits on self-discipline?]
I wouldn’t consider mid-30s to be old, and my guess is that those laws are protecting people at least 40 years old
To be clear, that was exactly my point. The laws themselves just specify that you can’t discriminate based on age. It is possible that many veterans receive a benefit to self-discipline during their service, but the laws still exist because other veterans do not have that benefit—similar to how some older people are actually more hirable even if there’s another group who isn’t.
I’m not sure that follows. For many jobs, we know that people in their mid 30s are generally more productive than people who are in early career, for example. But there are still anti-discrimination laws against not hiring old people. Point being that while some of X might be good, too much of X could be bad. This could tie into Ryan’s point above that while there could be some average productivity benefits, for exceptional cases,
I expect that the veterans who fail to re-adapt to civilian life suffer an almost complete collapse of productivity.
[ETA: Also, wouldn’t you expect there to be charities for some interest group even if they were better off on average? Especially if they held a revered role within society.]
is wrong as a consequence, because you can never train yourself like you are in the Army. That fundamentally needs a group, entirely separate from the question of social incentives and environment.
How is a group separate from the question of social incentives and environment? Having a group of people to motivate you seems like intrinsically a question of social incentives and environments.
I took my friend’s suggestion to be less that we can actually gather the resources to train ourselves like we are in the military, and more that if we were to do so, it would improve our discipline in the long-run. Hence the popular wisdom (or misconception) that military “straightens people out.”
Oops yes. That’s the weaker claim, that I agree with. The stronger claim is that because we can’t understand something “all at once” then mechanistic transparency is too hard and so we shouldn’t take Daniel’s approach. But the way we understand laptops is also in a mechanistic sense. No one argues that because laptops are too hard to understand all at once, then we should’t try to understand them mechanistically.
This seems to be assuming that we have to be able to take any complex trained AGI-as-a-neural-net and determine whether or not it is dangerous. Under that assumption, I agree that the problem is itself very hard, and mechanistic transparency is not uniquely bad relative to other possibilities.
I didn’t assume that. I objected to the specific example of a laptop as an instance of mechanistic transparency being too hard. Laptops are normally understood well because understanding can be broken into components and built up from abstractions. But each our understanding of each component and abstraction is pretty mechanistic—and this understanding is useful.
Furthermore, because laptops did not fall out of the sky one day, but instead slowly built over successive years of research and development, it seems like a great example of how Daniel’s mechanistic transparency approach does not rely on us having to understand arbitrary systems. Just as we built up an understanding of laptops, presumably we could do the same with neural networks. This was my interpretation of why he is using Zoom In as an example.
All of the other stories for preventing catastrophe that I mentioned in the grandparent are tackling a hopefully easier problem than “detect whether an arbitrary neural net is dangerous”.
Indeed, but I don’t think this was the crux of my objection.
I’d be shocked if there was anyone to whom it was mechanistically transparent how a laptop loads a website, down to the gates in the laptop.
Could you clarify why this is an important counterpoint. It seems obviously useful to understand mechanistic details of a laptop in order to debug it. You seem to be arguing the [ETA: weaker] claim that nobody understands the an entire laptop “all at once”, as in, they can understand all the details in their head simultaneously. But such an understanding is almost never possible for any complex system, and yet we still try to approach it. So this objection could show that mechanistic transparency is hard in the limit, but it doesn’t show that mechanistic transparency is uniquely bad in any sense. Perhaps you disagree?
I liked it.
(Warning: Long comment)
Two weeks ago Wei Dai released his financial statement on his bet that the coronavirus would negatively impact the stock market. Since then (at the time of writing) the S&P has dropped another 9%. This move has been considered by many to be definitive evidence against the efficient market hypothesis, given that the epistemic situation with respect to the coronavirus has apparently not changed much in weeks (at least to a first approximation).
One hypothesis for why the stock market reacted as it did seems to be that people are failing to take exponential growth of the virus into account, and thus make overly optimistic predictions. This parallels Ray Kurzweil’s observations of how people view technological progress,
When people think of a future period, they intuitively assume that the current rate of progress will continue for future periods. However, careful consideration of the pace of technology shows that the rate of progress is not constant, but it is human nature to adapt to the changing pace, so the intuitive view is that the pace will continue at the current rate. [...] From the mathematician’s perspective, a primary reason for this is that an exponential curve approximates a straight line when viewed for a brief duration.
The idea that smart investors don’t understand exponential curves is absurd on its face, so another hypothesis is that people were afraid to “ring the alarm bell” about the virus, since no one else was ringing it at the time.
Determining which of the above hypotheses is true is important for determining whether you expect the market to continue declining. To see why, consider that if the “alarm bell” hypothesis was true, you might expect that now that the alarm bell has been set off, you now have no epistemic advantage over the market. The efficient market is thus reset. Nonetheless, the alarm bell might be a gradient, and therefore it could be that more people have yet to ring it. And of course both hypotheses might have some grain of truth.
Now that the market has dropped another 9%, the question on every investor’s mind is, will it drop further? Yet, if the efficient market has really been debunked, then answering this question should be doable—and I minimally attempt to do so here.
The approach I take in this post is to analyze the working assumptions of the most recent economic forecasts I could find, ie. try to determine what conditions they expect, which lead to their predictions. If I find these working assumptions to underestimate the virus’ impact based on my best estimates, then I conclude, very tentatively, that the forecast is still too sunny. Otherwise, I conclude that the alarm bell has been rung. Overall, there are no fast and easy conclusions here.
The main issue is that this crisis has unfolded far too quickly for many up-to-date forecasts to come out. Still, I find a few that may help in my inquiry.
Disclaimer: I am in no position to offer specific financial advice to anyone, and I write this post for informational purposes only. I have no expertise in finance, and I am not creating this post to be authoritative. Please do not cite this post as proof that everyone should do some action X.
I offer the following predictions about particular parameters of the virus. I admit that many of my parameters are probably wrong. But at the same time, I make a stronger claim that no one else really has a much better idea of what they are talking about. Of course, I gladly welcome people to critique my estimates here.
I expect that the coronavirus will infect at least a few hundred million people by 1/1/2022. However, I think that as the virus progresses, people will take it very seriously, which implies that the reproduction constant probably won’t be high enough for 70 − 80% of the population to be infected. I doubt that countries like the United States will be able to replicate the success at containment found in China, though I’m open to changing my mind here.
I expect the infection fatality rate (a nonstandard term that means dividing the estimated number of people infected by the number of deaths caused by the virus) to be around 0.7 to 1 percent, with significant uncertainty in both directions. (That said, a paper that was released in the Lancet yesterday says the true figure is probably closer to 5.6% and could be as high as 20%. The sheer insanity of such a prediction should give you an idea of how uncertain this whole thing still is.)
I expect the virus to temporarily peak in late April or May, but probably return in the winter and do a lot more relative damage given the cold weather.
I expect hospitals in every major country to be overwhelmed at some point. This will cause the number of deaths to rise, making the 1 percent an underestimate of the true risk. My current (wildly speculative) guess is the true number is 2 percent in untreated populations.
I expect that a vaccine will not be widespread by 1/1/2021, though I do expect one by 10/1/2021.
I expect that some sort of anti-viral will be available by this winter, somewhat dampening the impact of the virus when it hits full force. Though it has yet to be seen whether anti-virals will be effective.
I expect pretty much every country to implement measures like Italy is right now at some point, with the exception of countries with poor infrastructure that cannot manage such a quarantine.
I welcome people to view the estimates from Metaculus, which are more optimistic on some of these parameters than I am. So obviously, take the following analyses with a grain of salt.
Note: throughout this article I use the terms infection fatality rate, case fatality rate, and mortality rate somewhat interchangeably, and at times I do not know whether the author means something different by them. Some people often make careful distinctions between these terms, but it appears most people don’t. Therefore, it’s really difficult to understand what these analyses are actually saying at times.
In the last 24 hours, JP Morgan announced that
The US economy could shrink by 2% in the first quarter and 3% in the second, JPMorgan projected, while the eurozone economy could contract by 1.8% and 3.3% in the same periods.
Their prediction is based on their research concerning the coronavirus, compiled here. In many ways, their estimates are quite similar to mine, and they share my sense that this virus will be long-lasting and painful. But in other ways they seem too optimistic. Here are some points,
At one point they criticize the UK Government’s apparent estimate of 100,000 predicted deaths, by saying “To arrive at such an outcome, we had to assume that 38% of the entire UK population is infected (i.e., similar to the 1918 Spanish flu), and that 40% of infected people get sick and then experience 1% mortality; or we had to assume that only 10% of infected people get sick but then experience 4.4% mortality that’s equal to the epicenter of the virus outbreak in Wuhan. Even after accounting for Chinese infection/death underreporting and the difficulty Western countries might have replicating what China has done (the largest lockdown/ quarantine in the history of the world, accomplished via AI, big data and different privacy rules8 ), both of our modeled UK outcomes would be magnitudes worse than what’s occurring in China and South Korea.”
They concur with my vaccine and anti-viral timelines, “While the fastest timeline for vaccines to reach patients is generally 12-18 months, (i.e., Massachusettsbased Moderna’s mRNA vaccine), COVID-19 treatments could possibly become available later this year”
They cite the fall of H1N1′s mortality rate estimate (seemingly) as reason to think that this coronavirus will follow the same pattern, “Early estimates in the fall of 2009 from the WHO3 pegged the H1N1 mortality rate at 1.0%-1.3%, since they were dividing (d) by (c). Four years later, a study from the WHO and the Imperial College of London4 estimated H1N1 mortality as a function of total infections, including both the asymptomatic and the sick. Their revised H1N1 mortality rate using (b) as a denominator: just 0.02%.”
Whoever wrote this report has done a ton of research, and makes some very intelligent points. It think it would be unfair to say that intelligent investors from JP Morgan “don’t understand exponential growth.”
That said, I differ significantly in my estimate of whether the UK Government’s estimate is valid, and whether the mortality rate will fall just as H1N1 did. The author seemed to be saying that the mortality rate can safely be only be calculated as a fraction of those who got sick with severe symptoms, rather than the total infected population. This fact makes me think that they are underestimating the infection fatality rate.
On March 4th Moody’s Analytics released a forecast of economic growth conditioned on the coronavirus becoming a pandemic, which at the time they considered to have only a 35% chance of occurring. Even though this report is somewhat old now, I still include it because this was their ‘worst case’ report. Their conclusion was that,
Under the pandemic scenario, the global economy suffers a recession during the first three quarters of 2020. Real GDP decreases by almost 2 percentage points peak to trough and declines for 2020 [...] The U.S. economy contracts in all four quarters of 2020 in the pandemic scenario, with real GDP falling by approximately 1.5 percentage points peak to trough and the unemployment rate rising by 175 basis points. The struggling manufacturing, transportation, agriculture and energy industries are hit hard, but so too are the travel and tourism industries and the construction trades. However, there are significant layoffs across nearly all industries, with healthcare and government being the notable exceptions.
The modeling assumption was that “millions” would be infected, and that it would peak by March or April.
Under our alternative Global Pandemic scenario, we expect that there are ultimately millions of infections across the globe, including in Europe and the U.S. COVID-19’s mortality rate is assumed to be 2%-3%, consistent with the experience so far, and a similar percentage of those infected become so sick they need some form of hospitalization. The peak of the pandemic is assumed to occur in March and April, winding down quickly by this summer, with a vaccine in place before next winter.
While I find their estimate of the mortality rate to be rather high, this consideration is swamped by the fact that they only think it will infect “millions” of people (which I take to be perhaps 5 − 10 million) worldwide, and the fact that they think we will have a vaccine by next winter. I think Moody’s Analytics are seriously low-balling this virus.
This report is probably the best evidence that investors still aren’t taking the virus seriously. However, given that this report is about 9 days old though, I think that conclusions from this report should be interpreted with caution.
A report from Capital Economics came out in the last few days, however, I’ve been unable to find the exact report. Instead, I can quote media article such as this one, and this one. They report,
Capital Economics also cut its estimate for gross domestic product in 2020, saying the economy would expand just 0.6% instead of 1.8% as previously forecast. [...] Many economists have downgraded their growth forecasts for the second quarter and beyond, but the Capital Economics call is the most pessimistic one yet.
So apparently they expect positive growth for the year, and yet this is one of the most pessimistic predictions from economists? That is striking on its own.
Capital Economics predicts a rebound in 2021 on the assumption that strict social distancing works to contain the coronavirus epidemic.
“If such measures helped to stem the spread of the virus … they may reduce the risk of a worse-case scenario, in which one-third of the population become infected resulting in a prolonged recession,” Hunter said.
“We think this is going to have a very significant impact on activity over the next few months,” said Andrew Hunter senior U.S. economist at Capital Economics.
However, Hunter expressed hope that if the number of coronavirus cases in the U.S. peaked in the tens of thousands, then the U.S. economy could “start to recover reasonably quickly.”
It’s not clear whether their “tens of thousands” in the US is a best case or median case scenario.
It’s hard to get a real sense of what Capital Economics expects, but the article itself gives the impression that we can still contain the effects, and things will wrap up in a few months. But given that they also mention that billions of people could be infected, it’s hard to tell whether they are over or underestimating. I don’t have a strong opinion here.
On March 11, the United Kingdom released a (long) report on their economic forecast, taking into account the expected impact from the coronavirus. Unfortunately their report did not include the latest figures from the coronavirus, and therefore it’s hard to tell whether they are underestimating things.
As set out below, we agreed to close the pre-measures forecasts for the economy and public finances on 18 and 25 February respectively, to provide a stable base against which to assess the impact of the large Budget package. This was before the spread of the coronavirus was expected to have a significant effect on economic activity outside China. As discussed in the document, the outlook is therefore likely to be significantly less favourable than this central forecast suggests – especially in the short term – but to a degree that remains highly uncertain even now.
A firm called RaboResearch released a forecast on March 12th. They are relatively optimistic,
The coronavirus outbreak has led us to reduce our growth projection for the global economy to 1.6% y/y in 2020
However, their assumptions appear to diverge substantially from mine
Whether we will see a similar spread in other Eurozone member states as we have seen in Italy still remains in doubt – and for now we are not yet assuming that as a base scenario.
In their “ugly” scenario, which they consider unlikely,
would see the virus continue to rage in China, spread to ASEAN, Australia and New Zealand, and the cluster of cases in the US and Europe snowball at an exponential growth rate from their currently low base. In other words, developed economies would also be hit.
Unfortunately, they don’t include any actual numbers, so it’s hard to tell how bad their ugly scenario actually is. Their absolute worst case scenario, which they call “the unthinkable” also contains no facts or figures,
This scenario is very short. The virus spreads globally and also mutates, with its transmissibility increasing and its lethality increasing too. The numbers infected would skyrocket, as would casualties. We could be looking at a global pandemic, and at scenarios more akin to dystopian Hollywood films than the realms of economic analysis. Let’s all pray it does not come to pass and just remains a very fat tail risk.
Note that I did not bold global pandemic. That was their emphasis.
Given that their “unthinkable” scenario describes a global pandemic, which the WHO has already declared, I find it hard to believe that this firm has a clear idea of the economic effects of the coronavirus. Their vagueness makes me think that they are not using solid models of the virus, but instead unsubstantiated intuition, and that they are probably underestimating the impact.
According to this investopedia article, the top three stock market news websites are MarketWatch, Bloomberg, and Reuters. Due to the paywall on Bloomberg I only accessed MarketWatch and Reuters. Therefore, I have taken the time to open each of these websites, read the first article that I can see that seems to include both an economic forecast and some type of prediction about a parameter of the coronavirus. To be honest, I wasn’t able to find anything really specific. Nonetheless, here are some quotes I found,
The vast majority of economists predict the U.S. will start to rebound later in the year, though they are split over how soon and how fast. Some like Donabedian see a rapid recovery starting in the summer. Others predict a short recession that extends through the fall.
The more optimistic view is based on the assumption that the U.S. approach to containing the coronavirus more closely mirrors that of South Korea or Hong Kong than Italy or Iran.
“We think we will see a nice bounce back in the third quarter,” Guatieri said.
Still, even relative optimists such as Guatieri say there’s still too much uncertainty to feel confident. He and Wells Fargo’s Bullard say their firms have been changing their forecasts almost daily in the past week as the situation deteriorated. What’s made matters worse is simply not knowing the scope of the problem
“We’re not getting the insight into where we are or where we are going,” Bullard said. “So we’re all just speculating.”
Like many of the forecasts above, the articles are very vague about what they expect, and it’s hard to see what values are being plugged into these economic models, or whether their prediction is intuition alone.
I have not seen strong evidence that economic forecasters are now predicting doom. However, I have seen some weak evidence that suggests that many are misinformed about the scope of the virus, and its potential future impacts. Some forecasters, like JP Morgan, have clearly done a lot of research. Other firms are barely even using mathematical models of the virus. My own interpretation is that the places I surveyed are probably fairly overoptimistic, though it’s really hard to tell without more evidence and concrete numbers.
For my part, I think you summarized my position fairly well. However, after thinking about this argument for another few days, I have more points to add.
Disease seems especially likely to cause coordination failures since it’s an internal threat rather than an external threat (which unlike internal threats, tend to unite empires). We can compare the effects of the smallpox epidemic in the Aztec and Inca empires alongside other historical diseases during wartime, such as the Plauge of Athens which arguably is what caused Athens to lose the Peloponnesian War.
Along these same lines, the Aztec/Inca didn’t have any germ theory of disease, and therefore didn’t understand what was going on. They may have thought that the gods were punishing them for some reason, and therefore they probably spent a lot of time blaming random groups for the catastrophe. We can contrast these circumstances to eg. the Paraguayan War which killed up to 90% of the male population, but people probably had a much better idea what was going on and who was to blame, so I expect that the surviving population had an easier time coordinating.
A large chunk of the remaining population likely had some sort of disability. Think of what would happen if you got measles and smallpox in the same two year window: even if you survived it probably wouldn’t look good. This means that the pure death rate is an underestimate of the impact of a disease. The Aztecs, for whom “only” 40 percent died of disease, were still greatly affected
It killed many of its victims outright, particularly infants and young children. Many other adults were incapacitated by the disease – because they were either sick themselves, caring for sick relatives and neighbors, or simply lost the will to resist the Spaniards as they saw disease ravage those around them. Finally, people could no longer tend to their crops, leading to widespread famine, further weakening the immune systems of survivors of the epidemic. [...] a third of those afflicted with the disease typically develop blindness.
After today’s crash, what are you at now?
Either we’ll have a positive singularity, and material abundance ensues, or we’ll have a negative singularity, and paperclips ensue. That’s why my retirement portfolio is geared towards business-as-usual scenarios.
My objection to this argument is just, more generally, before the singularity there should be some period in which we have powerful AI, but the economy still looks somewhat familiar. The operationalization for this is Paul’s slow takeoff, where economic growth rates should start to pick up a little before picking up by a lot.