This can prevent you from being able to deduct the interest as investment interest expense on your taxes due to interest tracing rules (you have to show the loan was not commingled with non-investment funds in an audit), and create a recordkeeping nightmare at tax time.
Re hedging, a common technique is having multiple fairly different citizenships and foreign-held assets, i.e. such that if your country become dangerously oppressive you or your assets wouldn’t be handed back to it. E.g. many Chinese elites pick up a Western citizenship for them or their children, and wealthy people fearing change in the US sometimes pick up New Zealand or Singapore homes and citizenship.
There are many countries with schemes to sell citizenship, although often you need to live in them for some years after you make your investment. Then emigrate if things are starting to look too scary before emigration is restricted.
My sense, however, is that the current risk of needing this is very low in the US, and the most likely reason for someone with the means to buy citizenship to leave would just be increases in wealth/investment taxes through the ordinary political process, with extremely low chance of a surprise cultural revolution (with large swathes of the population imprisoned, expropriated or killed for claimed ideological offenses) or ban on emigration. If you take enough precautions to deal with changes in tax law I think you’ll be taking more than you need to deal with the much less likely cultural revolution story.
April was the stock market’s best month in 30 years, which is not really what you expect during a global pandemic.
Historically the biggest short-term gains have been disproportionately amidst or immediately following bear markets, when volatility is highest.
Sure, it’s part of how they earn money, but competition between them limits what’s left, since they’re bidding against each other to take the other side from the retail investor, who buys from or sells to the hedge fund offering the best deal at the time (made somewhat worse by deadweight losses from investing in speed).
It doesn’t suggest that. Factually, we know that a majority of investors underperform indexes.
Absolutely, I mean that when you break out the causes of the underperformance, you can see how much is from spending time out of the market, from paying high fees, from excessive trading to pay spreads and capital gains taxes repeatedly, from retail investors not starting with all their future earnings invested (e.g. often a huge factor in the Dalbar studies commonly cited to sell high fee mutual funds to retail investors), and how much from unwittingly identifying overpriced securities and buying them. And the last chunk is small relative to the rest.
When there’s an event that will cause retail investors to predictively make bad investments some hedge fund will do high frequency trades as soon the event becomes known to be able to trade the opposite site of the trade.
I agree, active investors correcting retail investors can earn normal profits on the EMH, and certainly market makers get spreads. But competition is strong, and spreads have been shrinking, so that’s much less damaging than identifying seriously overpriced stocks and buying them.
Thank you, I enjoyed this post.
One thing I would add is that the EMH also suggests one can make deviations that don’t have very high EMH-predicted costs. Small investors do underperform indexes a lot by paying extra fees, churning with losses to spreads and capital gains taxes, spending time out of the market, and taking too much or too little over risk (and especially too much uncompensated risk from under diversification). But given the EMH they also can’t actively pick equities with large expected underperformance. Otherwise, a hedge fund could make huge profits by just doing the opposite (they compete the mispricing down to a level where they earn normal profits). Reversed stupidity is not intelligence. [Edited paragraph to be clear that typical retail investors do severely underperform, just mainly for reasons other than uncanny ability to find overpriced securities and buy them).]
That consideration makes it more attractive, if one is uncertain about an edge, to consider investments that the EMH would predict should be have very modest underperformance, but some unusual information would suggest would outperform a lot. I was persuaded to deviate from indexing after seeing high returns across several ‘would-have-invested in’ (or did invest a little in, registered predictions on, etc) cases of the sort Wei Dai discusses. So far doing so has been kind to my IRR vs benchmarks, but because I’ve only seen results across a handful of deviations (one was coronavirus-inspired market puts, inspired in part by Wei Dai and held until late March based on a prior plan of letting clear community transmission in the US become visible), and my understanding from colleagues in the pandemic space), the likelihood ratio is weak between the bottom two quadrants of your figure. I might fill in ‘deluded lucky fool’ in your poll. Yet I don’t demand a very high credence in the good quadrant to outweigh the underdiversification costs of using these deviations as a stock-picking random number generator. That said, the bar for even that much credence in a purported edge is still very demanding.
I’d also flag that going all-in on EMH and modern financial theory still leads to fairly unusual investing behavior for a retail investor, moreso than I had thought before delving into it. E.g. taking human capital into account in portfolio design, or really understanding the utility functions and beliefs required to justify standard asset allocation advice (vs something like maximizing expected growth rate/log utility of income/Kelly criterion, without a 0 leverage constraint), or just figuring out all the tax optimization (and investment choice interactions with tax law), like the Mega Backdoor Roth, donating appreciated stock, tax loss harvesting, or personal defined benefit pension plans. So there’s a lot more to doing EMH investing right than just buying a Vanguard target date fund, and I would want to encourage people to do that work regardless.
I agree human maturation time is enough on its own to rule out a human reproductive biotech ‘fast takeoff,’ but also:
In any given year the number of new births is very small relative to the existing workforce, of billions of humans, including many people with extraordinary abilities
Most of those births are unplanned or to parents without access to technologies like IVF
New reproductive technologies are adopted gradually by risk-averse parents
Any radical enhancement would carry serious risks of negative surprise side effects, further reducing the user base of new tech
IVF is only used for a few percent of births in rich countries, and existing fancy versions are used even less frequently
All of those factors would smooth out any such application to spread out expected impacts over a number of decades, on top of the minimum from maturation times.
MIRI researchers contributed to the following research led by other organisations
MacAskill & Demski’s A Critique of Functional Decision Theory
This seems like a pretty weird description of Demski replying to MacAskill’s draft.
The interesting content kept me reading, but it would help the reader to have lines between paragraphs in the post.
I have launch codes and don’t think this is good. Specifically, I think it’s bad.
A mouse brain has ~75 million neurons, a human brain ~85 billion neurons. The standard deviation of human brain size is ~10%. If we think of that as a proportional increase rather than an absolute increase in the # of neurons, that’s ~74 standard deviations of difference. The correlation between # of neurons and IQ in humans is ~0.3, but that’s still a massive difference. Total neurons/computational capacity does show a pattern somewhat like that in the figure. Chimps’ brains are a factor of ~3x smaller than humans, ~12 standard deviations.
Selection can cumulatively produce gaps that are large relative to intraspecific variation (one can see the same relationships even more blatantly considering total body mass). Mice do show substantial variation in maze performance, etc.
And the cumulative cognitive work that has gone into optimizing the language, technical toolkit, norms, and other factors involved in human culture and training into are immensely beyond those of mice (and note that human training of animals can greatly expand the set of tasks they can perform, especially with some breeding to adjust their personalities to be more enthusiastic about training). Humans with their language abilities can properly interface with that culture, dwarfing the capabilities both of small animals and people in smaller earlier human cultures with less accumulated technology or economies of scale.
Hominid culture took off enabled by human capabilities [so we are not incredibly far from the minimum need for strongly accumulating culture, the selection effect you reference in the post], and kept rising over hundreds of thousands and millions of years, at accelerating pace as the population grew with new tech, expediting further technical advance. Different regions advanced at different rates (generally larger connected regions grew faster, with more innovators to accumulate innovations), but all but the smallest advanced. So if humans overall had lower cognitive abilities there would be slack for technological advance to have happened anyway, just at slower rates (perhaps manyfold), accumulating more by trial and error.
Human individual differences are also amplified by individual control over environments, e.g. people who find studying more congenial or fruitful study more and learn more.
Survey and other data indicate that in these fields most people were doing p-hacking/QRPs (running tests selected ex post, optional stopping, reporting and publication bias, etc), but a substantial minority weren’t, with individual, subfield, and field variation. Some people produced ~100% bogus work while others were ~0%. So it was possible to have a career without the bad practices Yarkoni criticizes, aggregating across many practices to look at overall reproducibility of research.
And he is now talking about people who have been informed about the severe effects of the QRPs (that they result in largely bogus research at large cost to science compared to reproducible alternatives that many of their colleagues are now using and working to reward) but choose to continue the bad practices. That group is also disproportionately tenured, so it’s not a question of not getting a place in academia now, but of giving up on false claims they built their reputation around and reduced grants and speaking fees.
I think the core issue is that even though the QRPs that lead to mostly bogus research in fields such as social psych and neuroimaging often started off without intentional bad conduct, their bad effects have now become public knowledge, and Yarkoni is right to call out those people on continuing them and defending continuing them.
There is a literature on firm productivity showing large firm variation in productivity and average productivity growth by expansion of productive firms relative less productive firms. E.g. this , this , this , and this.
OK, thanks for the clarification!
My own sense is that the intermediate scenarios are unstable: if we have fairly aligned AI we immediately use it to make more aligned AI and collectively largely reverse things like Facebook click-maximization manipulation. If we have lost the power to reverse things then they go all the way to near-total loss of control over the future. So i would tend to think we wind up in the extremes.
I could imagine a scenario where there is a close balance among multiple centers of AI+human power, and some but not all of those centers have local AI takeovers before the remainder solve AI alignment, and then you get a world that is a patchwork of human-controlled and autonomous states, both types automated. E.g. the United States and China are taken over by their AI systems (inlcuding robot armies), but the Japanese AI assistants and robot army remain under human control and the future geopolitical system keeps both types of states intact thereafter.
Failure would presumably occur before we get to the stage of “robot army can defeat unified humanity”—failure should happen soon after it becomes possible, and there are easier ways to fail than to win a clean war. Emphasizing this may give people the wrong idea, since it makes unity and stability seem like a solution rather than a stopgap. But emphasizing the robot army seems to have a similar problem—it doesn’t really matter whether there is a literal robot army, you are in trouble anyway.
I agree other powerful tools can achieve the same outcome, and since in practice humanity isn’t unified rogue AI could act earlier, but either way you get to AI controlling the means of coercive force, which helps people to understand the end-state reached.
It’s good to both understand the events by which one is shifted into the bad trajectory, and to be clear on what the trajectory is. It sounds like your focus on the former may have interfered with the latter.
I think we can probably build systems that really do avoid killing people, e.g. by using straightforward versions of “do things that are predicted to lead to videos that people rate as acceptable,” and that at the point when things have gone off the rails those videos still look fine (and to understand that there is a deep problem at that point you need to engage with complicated facts about the situation that are beyond human comprehension, not things like “are the robots killing people?”). I’m not visualizing the case where no one does anything to try to make their AI safe, I’m imagining the most probable cases where people fail.
Haven’t you yourself written about the failure modes of ‘do things predicted to lead to videos that people rate as acceptable’ where the attack involves surreptitiously reprogramming the camera to get optimal videos (including weird engineered videos designed to optimize on infelicities in the learned objective? E.g. in your discussion of an unaligned benchmark here:
We train an autoregressive generative model, to maximize the log probability assigned to the actual sequence of actions and observations produced by the AI (with each observation conditioned on the past actions). This isn’t actually a good way to train the generative model, but it’s not really central to the discussion.
We train the reward function by showing humans sequences of actions and predicted observations, asking them to assign scores, then predicting those scores with supervised learning. We show humans the sequences of actions that look most promising to the system...
I am quite confident that “world which looks appealing on screen” is not the same as “world which is good;” the gap is already large today, and it will grow much larger over time.
Maintaining a fiction. Ultimately the goal of this AI is to make the sensors look like good things are happening in the world. This can be done either by making the world good or by carefully controlling what gets fed to the sensors. In general, there are many fewer constraints on “what gets fed to the sensors” than “what the world is actually like,” and so it’s going to be possible to produce much more desirable-looking outcomes, much more easily, by controlling the sensors.
If the humans understand what the AI is doing in detail then they can observe the sequence of actions to notice any actions that might corrupt the sensors. But as soon as the AI is engaging in complicated plans this appears to be totally intractable. Even if the AI was human-level, there are many extremely hard-to-detect plans: for example, the AI could collaborate with an external attacker, who will surreptitiously corrupt the sensor. Or the AI could create a copy of itself on an external server who then helped orchestrate a hard-to-detect corruption.
Are you now very confident that no AI company would implement something with this vulnerability? Or does the paragraph below just mean that the part where the AI gets control of the camera has to happen before the robot armies (or even robot armies identified as traceable to the AI in question), which then happen?
Part I has this focus because (i) that’s where I think the action is—by the time you have robot armies killing everyone the ship is so sailed, I think a reasonable common-sense viewpoint would acknowledge this by reacting with incredulity to the “robots kill everyone” scenario, and would correctly place the “blame” on the point where everything got completely out of control even though there weren’t actually robot armies yet (ii) the alternative visualization leads people to seriously underestimate the difficulty of the alignment problem, (iii) I was trying to describe the part of the picture which is reasonably accurate regardless of my views on the singularity.
Because it definitely seems that Vox got the impression from it that there is never a robot army takeover in the scenario, not that it’s slightly preceded by camera hacking.
Is the idea that the AI systems develops goals over the external world (rather than the sense inputs/video pixels) so that they are really pursuing the appearance of prosperity, or corporate profits, and so don’t just wirehead their sense inputs as in your benchmark post?
I think the kind of phrasing you use in this post and others like it systematically misleads readers into thinking that in your scenarios there are no robot armies seizing control of the world (or rather, that all armies worth anything at that point are robotic, and so AIs in conflict with humanity means military force that humanity cannot overcome). I.e. AI systems pursuing badly aligned proxy goals or influence-seeking tendencies wind up controlling or creating that military power and expropriating humanity (which eventually couldn’t fight back thereafter even if unified).
E.g. Dylan Matthews’ Vox writeup of the OP seems to think that your scenarios don’t involve robot armies taking control of the means of production and using the universe for their ends against human objections or killing off existing humans (perhaps destructively scanning their brains for information but not giving good living conditions to the scanned data):
Even so, Christiano’s first scenario doesn’t precisely envision human extinction. It envisions human irrelevance, as we become agents of machines we created.
Human reliance on these systems, combined with the systems failing, leads to a massive societal breakdown. And in the wake of the breakdown, there are still machines that are great at persuading and influencing people to do what they want, machines that got everyone into this catastrophe and yet are still giving advice that some of us will listen to.
The Vox article also mistakes the source of influence-seeking patterns to be about social influence rather than systems that try to increase in power and numbers tend to do so, so are selected for if we accidentally or intentionally produce them and don’t effectively weed them out; this is why living things are adapted to survive and expand; such desires motivate conflict with humans when power and reproduction can be obtained by conflict with humans, which can look like robot armies taking control.takes the point about influence-seeking patterns to be about. That seems to me just a mistake about the meaning of influence you had in mind here:
Often, he notes, the best way to achieve a given goal is to obtain influence over other people who can help you achieve that goal. If you are trying to launch a startup, you need to influence investors to give you money and engineers to come work for you. If you’re trying to pass a law, you need to influence advocacy groups and members of Congress.
That means that machine-learning algorithms will probably, over time, produce programs that are extremely good at influencing people. And it’s dangerous to have machines that are extremely good at influencing people.
There’s an enormous difference between having millions of dollars of operating expenditures in an LLC (so that an org is legally allowed to do things like investigate non-deductible activities like investment or politics), and giving up the ability to make billions of dollars of tax-deductible donations. Open Philanthropy being an LLC (so that its own expenses aren’t tax-deductible, but it has LLC freedom) doesn’t stop Good Ventures from making all relevant donations tax-deductible, and indeed the overwhelming majority of grants on its grants page are deductible.
I think this is under-discussed, but also that I have seen many discussions in this area. E.g. I have seen it come up and brought it up in the context of Paul’s research agenda, where success relies on humans being able to play their part safely in the amplification system. Many people say they are more worried about misuse than accident on the basis of the corruption issues (and much discussion about CEV and idealization, superstimuli, etc addresses the kind of path-dependence and adversarial search you mention).
However, those varied problems mostly aren’t formulated as ‘ML safety problems in humans’ (I have seen robustness and distributional shift discussion for Paul’s amplification, and daemons/wireheading/safe-self-modification for humans and human organizations), and that seems like a productive framing for systematic exploration, going through the known inventories and trying to see how they cross-apply.
No superintelligent AI computers, because they lack hypercomputation.