You are unlikely to see me posting here again, after today. There is a saying here that politics is the mind-killer. My heretical realization lately is that philosophy, as generally practiced, can also be mind-killing.
As many of you know I am, or was running a twice-monthly Rationality: AI to Zombies reading group. One of the bits I desired to include in each reading group post was a collection of contrasting views. To research such views I've found myself listening during my commute to talks given by other thinkers in the field, e.g. Nick Bostrom, Anders Sandberg, and Ray Kurzweil, and people I feel are doing “ideologically aligned” work, like Aubrey de Grey, Christine Peterson, and Robert Freitas. Some of these were talks I had seen before, or generally views I had been exposed to in the past. But looking through the lens of learning and applying rationality, I came to a surprising (to me) conclusion: it was philosophical thinkers that demonstrated the largest and most costly mistakes. On the other hand, de Grey and others who are primarily working on the scientific and/or engineering challenges of singularity and transhumanist technologies were far less likely to subject themselves to epistematic mistakes of significant consequences.
Philosophy as the anti-science...
What sort of mistakes? Most often reasoning by analogy. To cite a specific example, one of the core underlying assumption of singularity interpretation of super-intelligence is that just as a chimpanzee would be unable to predict what a human intelligence would do or how we would make decisions (aside: how would we know? Were any chimps consulted?), we would be equally inept in the face of a super-intelligence. This argument is, however, nonsense. The human capacity for abstract reasoning over mathematical models is in principle a fully general intelligent behaviour, as the scientific revolution has shown: there is no aspect of the natural world which has remained beyond the reach of human understanding, once a sufficient amount of evidence is available. The wave-particle duality of quantum physics, or the 11-dimensional space of string theory may defy human intuition, i.e. our built-in intelligence. But we have proven ourselves perfectly capable of understanding the logical implications of models which employ them. We may not be able to build intuition for how a super-intelligence thinks. Maybe—that's not proven either. But even if that is so, we will be able to reason about its intelligent behaviour in advance, just like string theorists are able to reason about 11-dimensional space-time without using their evolutionarily derived intuitions at all.
This post is not about the singularity nature of super-intelligence—that was merely my choice of an illustrative example of a category of mistakes that are too often made by those with a philosophical background rather than the empirical sciences: the reasoning by analogy instead of the building and analyzing of predictive models. The fundamental mistake here is that reasoning by analogy is not in itself a sufficient explanation for a natural phenomenon, because it says nothing about the context sensitivity or insensitivity of the original example and under what conditions it may or may not hold true in a different situation.
A successful physicist or biologist or computer engineer would have approached the problem differently. A core part of being successful in these areas is knowing when it is that you have insufficient information to draw conclusions. If you don't know what you don't know, then you can't know when you might be wrong. To be an effective rationalist, it is often not important to answer “what is the calculated probability of that outcome?” The better first question is “what is the uncertainty in my calculated probability of that outcome?” If the uncertainty is too high, then the data supports no conclusions. And the way you reduce uncertainty is that you build models for the domain in question and empirically test them.
The lens that sees its own flaws...
Coming back to LessWrong and the sequences. In the preface to Rationality, Eliezer Yudkowsky says his biggest regret is that he did not make the material in the sequences more practical. The problem is in fact deeper than that. The art of rationality is the art of truth seeking, and empiricism is part and parcel essential to truth seeking. There's lip service done to empiricism throughout, but in all the “applied” sequences relating to quantum physics and artificial intelligence it appears to be forgotten. We get instead definitive conclusions drawn from thought experiments only. It is perhaps not surprising that these sequences seem the most controversial.
I have for a long time been concerned that those sequences in particular promote some ungrounded conclusions. I had thought that while annoying this was perhaps a one-off mistake that was fixable. Recently I have realized that the underlying cause runs much deeper: what is taught by the sequences is a form of flawed truth-seeking (thought experiments favored over real world experiments) which inevitably results in errors, and the errors I take issue with in the sequences are merely examples of this phenomenon.
And these errors have consequences. Every single day, 100,000 people die of preventable causes, and every day we continue to risk extinction of the human race at unacceptably high odds. There is work that could be done now to alleviate both of these issues. But within the LessWrong community there is actually outright hostility to work that has a reasonable chance of alleviating suffering (e.g. artificial general intelligence applied to molecular manufacturing and life-science research) due to concerns arrived at by flawed reasoning.
I now regard the sequences as a memetic hazard, one which may at the end of the day be doing more harm than good. One should work to develop one's own rationality, but I now fear that the approach taken by the LessWrong community as a continuation of the sequences may result in more harm than good. The anti-humanitarian behaviors I observe in this community are not the result of initial conditions but the process itself.
What next?
How do we fix this? I don't know. On a personal level, I am no longer sure engagement with such a community is a net benefit. I expect this to be my last post to LessWrong. It may happen that I check back in from time to time, but for the most part I intend to try not to. I wish you all the best.
A note about effective altruism…
One shining light of goodness in this community is the focus on effective altruism—doing the most good to the most people as measured by some objective means. This is a noble goal, and the correct goal for a rationalist who wants to contribute to charity. Unfortunately it too has been poisoned by incorrect modes of thought.
Existential risk reduction, the argument goes, trumps all forms of charitable work because reducing the chance of extinction by even a small amount has far more expected utility than would accomplishing all other charitable works combined. The problem lies in the likelihood of extinction, and the actions selected in reducing existential risk. There is so much uncertainty regarding what we know, and so much uncertainty regarding what we don't know that it is impossible to determine with any accuracy the expected risk of, say, unfriendly artificial intelligence creating perpetual suboptimal outcomes, or what effect charitable work in the area (e.g. MIRI) is have to reduce that risk, if any.
This is best explored by an example of existential risk done right. Asteroid and cometary impacts is perhaps the category of external (not-human-caused) existential risk which we know the most about, and have done the most to mitigate. When it was recognized that impactors were a risk to be taken seriously, we recognized what we did not know about the phenomenon: what were the orbits and masses of Earth-crossing asteroids? We built telescopes to find out. What is the material composition of these objects? We built space probes and collected meteorite samples to find out. How damaging an impact would there be for various material properties, speeds, and incidence angles? We built high-speed projectile test ranges to find out. What could be done to change the course of an asteroid found to be on collision course? We have executed at least one impact probe and will monitor the effect that had on the comet's orbit, and have on the drawing board probes that will use gravitational mechanisms to move their target. In short, we identified what it is that we don't know and sought to resolve those uncertainties.
How then might one approach an existential risk like unfriendly artificial intelligence? By identifying what it is we don't know about the phenomenon, and seeking to experimentally resolve that uncertainty. What relevant facts do we not know about (unfriendly) artificial intelligence? Well, much of our uncertainty about the actions of an unfriendly AI could be resolved if we were to know more about how such agents construct their thought models, and relatedly what language were used to construct their goal systems. We could also stand to benefit from knowing more practical information (experimental data) about in what ways AI boxing works and in what ways it does not, and how much that is dependent on the structure of the AI itself. Thankfully there is an institution that is doing that kind of work: the Future of Life institute (not MIRI).
Where should I send my charitable donations?
Aubrey de Grey's SENS Research Foundation.
100% of my charitable donations are going to SENS. Why they do not get more play in the effective altruism community is beyond me.
If you feel you want to spread your money around, here are some non-profits which have I have vetted for doing reliable, evidence-based work on singularity technologies and existential risk:
- Robert Freitas and Ralph Merkle's Institute for Molecular Manufacturing does research on molecular nanotechnology. They are the only group that work on the long-term Drexlarian vision of molecular machines, and publish their research online.
- Future of Life Institute is the only existential-risk AI organization which is actually doing meaningful evidence-based research into artificial intelligence.
- B612 Foundation is a non-profit seeking to launch a spacecraft with the capability to detect, to the extent possible, ALL Earth-crossing asteroids.
I wish I could recommend a skepticism, empiricism, and rationality promoting institute. Unfortunately I am not aware of an organization which does not suffer from the flaws I identified above.
Addendum regarding unfinished business
I will no longer be running the Rationality: From AI to Zombies reading group as I am no longer in good conscience able or willing to host it, or participate in this site, even from my typically contrarian point of view. Nevertheless, I am enough of a libertarian that I feel it is not my role to put up roadblocks to others who wish to delve into the material as it is presented. So if someone wants to take over the role of organizing these reading groups, I would be happy to hand over the reigns to that person. If you think that person should be you, please leave a reply in another thread, not here.
EDIT: Obviously I'll stick around long enough to answer questions below :)
There very much is a difference.
Probability is a mathematical construct. Specifically, it's a special kind of measure p on a measure space M such that p(M) = 1 and p obeys a set of axioms that we refer to as the axioms of probability (where an "event" from the Wikipedia page is to be taken as any measurable subset of M).
This is a bit like highlighting that Euclidean geometry is a mathematical construct based on following thus-and-such axioms for relating thus-and-such undefined terms. Of course, in normal ways of thinking we point at lines and dots and so on, pretend those are the things that the undefined terms refer to, and proceed to show pictures of what the axioms imply. Formally, mathematicians refer to this as building a model of an axiomatic system. (Another example of this is elliptic geometry, which is a type of non-Euclidean geometry, which you can model as doing geometry on a sphere.)
The Frequentist and Bayesian models of probability theory are relevantly different. They both think of M as the space of possible results (usually called the "sample space" but not always) and a measurable subset E ≤ M as an "event". But they use different models of p:
Now let's suppose that M is a hypothesis space, including some sector for hypotheses that haven't yet been considered. When we say that a given hypothesis H is "likely", we're working within a partial model, but we haven't yet said what "likely" means. The formalism is easy: we require that H ≤ M is measurable, and the statement that "it's likely" means that p(H) is larger than most other measurable subsets of M (and often we mean something stronger, like p(H) > 0.5). But we haven't yet specified in our model what p(H) means. This is where the difference between Frequentism and Bayesianism matters. A Frequentist would say that the probability is a property of the hypothesis space, and noticing H doesn't change that. (I'm honestly not sure how a Frequentist thinks about iterating over a hypothesis space to suggest that H in fact would occur at a frequency of p(H) in the limit - maybe by considering the frequency in counterfactual worlds?) A Bayesian, by contrast, will say that p(H) is their current confidence that H is the right hypothesis.
What I'm suggesting, in essence, is that figuring out which hypothesis H ≤ M is worth testing is equivalent to moving from p to p' in the space of probability measures on M in a way that causes p'(H) > p(H). This is coming from using a Bayesian model of what p is.
Of course, if you're using a Frequentist model of p, then "most likely hypothesis" actually refers to a property of the hypothesis space - though I'm not sure how you would find out the frequency at which hypotheses turn out to be true the way you figure out the frequency at which a coin comes up heads. But that could just be my not being as familiar thinking in terms of the Frequentist model.
I'll briefly note that although I find the Bayesian model more coherent with my sense of how the world works on a day-by-day basis, I think the Frequentist model makes more sense when thinking about quantum physics. The type of randomness we find there isn't just about confidence, but is in fact a property of the quantum phenomena in question. In this case a well-calibrated Bayesian has to give a lot of probability mass to the hypothesis that there is a "true probability" in some quantum phenomena, which makes sense if we switch the model of p to be Frequentist.
But in short:
Yes, there's a difference.
And things like "probability" and "belief" and "evidence" mean different things depending on what model you use.
Yep, we disagree.
I think the disagreement is on two fronts. One is based on using different models of probability, which is basically not an interesting disagreement. (Arguing over which definition to use isn't going to make either of us smarter.) But I think the other is substantive. I'll focus on that.
In short, I think you underestimate the power of noticing implications of known facts. I think that if you look at a few common or well-known examples of incomplete deduction, it becomes pretty clear that figuring out how to finish thinking would be intensely powerful:
I could keep going. Hopefully you could too.
But my point is this:
Please note that there's a baby in that bathwater you're condemning as dirty.
Those are not different models. They are different interpretations of the utility of probability in different classes of applications.
You do it exactly the same as in your Bayesian example.
I'm sorry, but this Bayesian vs Frequentist conflict is for the most part non-existent. If you use probability to... (read more)