Thoughts on rationalism and the rationalist community from a skeptical perspective. The author rejects rationality in the sense that he believes it isn't a logically coherent concept, that the larger rationalism community is insufficiently critical of it's beliefs and that ELIEZER YUDKOWSKY IS NOT THE TRUE CALIF.

Artificial Intelligence And The Structure Of Thought

Why Your Self-Driving Car Won't Cause Armageddon

In recent years a number of prominent individuals have raised concerns about our ability to control powerful AIs. The idea is that once we create truly human level generally intelligent software or AGI computers will undergo an intelligence explosion and will be able to escape any constraints we place on them. This concern has perhaps been most throughly developed by Eliezer Yudkowsky.

Unlike the AI in bad science fiction the concern isn’t that the AI will be evil or desire dominion the way humans are but simply that it will be too good at whatever task we set it to perform. For instance, suppose Waymo builds an AI to run its fleet of self-driving cars. The AI’s task is to converse with passengers/app users and route its vehicles appropriately. Unlike more limited self-driving car software this AI is programmed to learn the subtleties of human behavior so it can position a pool of cars in front of the stadium right before the game ends and helpfully show tourists the sites. On Yudkowsky’s vision the engineers achieve this by coding in a reward function that the software works to maximize (or equivalently a penalty function it works to minimize). For instance, in this case the AI might be punished based on negative reviews/frustrated customers, deaths/damage from accidents involving its vehicles, travel delays and customers who choose to use a competitor rather than Waymo. I’m already skeptical that (super) human AI would have anything identifiable as a global reward/utility function but on Yudkowsky’s picture AGI is something like a universal optimizer which is set loose to do its best to achieve rewards.

The concern is that the AI would eventually realize that it could minimize its punishment by arranging for everyone to die in a global pandemic since then there would be no bad reviews, lost customers or travel delays. Given the AI’s vast intelligence and massive data set it would then hack into microbiology labs and manipulate the workers there to create a civilization ending plague. Moreover, no matter what kind of firewalls or limitations we try and place on the AI as long as it can somehow interact with the external world it will find a way around these barriers. Since its devilishly difficult to specify any utility function without such undesirable solutions Yudkowsky concludes that AGI poses a serious threat to the human species.

Rewards And Reflection

The essential mechanism at play in all of Yudkowsky’s apocalyptic scenarios is that the AI examines its own reward function, realizes that some radically different strategy would offer even greater rewards and proceeds to surreptitiously work to realize this alternate strategy. Now its only natural that a sufficiently advanced AI would have some degree of reflective access to its own design and internal deliberation. After all it’s common for humans to reflect on our own goals and behaviors to help shape our future decisions, e.g., we might observe that if we continue to get bad grades we won’t get into the college we want and as a result decide that we need to stop playing World of Warcraft.

At first blush it might seem obvious that realizing its rewards are given by a certain function would induce an AI to maximize that function. One might even be tempted to claim this is somehow part of the definition of what it means for an agent to have a utility function but that’s trading off on an ambiguity between two notions of reward.

The sense of reward which gives rise to the worries about unintended satisfaction is that of positive reinforcement. It’s the digital equivalent of giving someone cocaine. Of course, if you administer cocaine to someone every time they write a blog post they will tend to write more blog posts. However, merely learning that cocaine causes a rewarding distribution of dopamine in the brain doesn’t cause people to go out and buy cocaine. Indeed, that knowledge could just as well have the exact opposite effect. Similarly, there is no reason to assume that merely because an AGI has a representation of their reward function they will try and reason out alternative ways to satisfy it. Indeed, indulging in anthropomorphizing for a moment, there is no reason to assume that an AGI will have any particular desire regarding rewards received by its future time states much adopt a particular discount rate.

Of course, in the long run, if a software program was rewarded for analyzing its own reward function and finding unusual ways to activate it then it could learn to do so just as people who are rewarded with pleasurable drug experiences can learn to look for ways to short-circuit their reward system. However, if that behavior is punished, e.g., humans intervene and punish the software when it starts recommending public transit, then the system will learn to avoid short-circuiting its reward pathways just like people can learn to avoid addictive drugs. This isn’t to say that there is no danger here, left alone an AGI, just like a teen with access to cocaine, could easily learn harmful reward seeking behavior. However, since the system doesn’t start in a state in which it applies its vast intelligence to figure out ways to hack its reward function the risk is far less severe.

Now, Yudkowsky might respond by saying he didn’t really mean the system’s reward function but its utility function. However, since we don’t tend to program machine learning algorithms by specifying the function they will ultimately maximize (or reflect on and try to maximize) its unclear why we need to explicitly specify a utility function that doesn’t lead to unintended consequences. After all, Yudkowsky is the one trying to argue that its likely that AGI will have these consequences so merely restating the problem in a space that has no intrinsic relationship to how one would expect AGI to be constructed doesn’t do anything to advance his argument. For instance, I could point out that phrased in terms of the locations of fundamental particles its really hard to specify a program that excludes apocalyptic arrangements of matter but that wouldn’t do anything to convince you that AIs risked causes such apocalypses since such specifications have nothing to do with how we expect an AI to be programed.

The Human Comparison

Ultimately, we have one example of a kind of general intelligence: the human brain. Thus, when evaluating claims about the dangers of AGI one of the first things we should do is see if the same story applies to our brain and if not if there is any special reason to expect our brains to be different.

Looking at the way humans behave its striking how poorly Yudkowsky’s stories describe our behavior even though evolution has shaped us in ways that make us far more dangerous than we should expect AGIs to be (we have self-preservation instincts, approximately coherent desires and beliefs, and are responsive to most aspects of the world rather than caring only about driving times or chess games). Time and time again we see that we follow heuristics and apply familiar mental strategies even when its clear that a different strategy would offer us greater activation of reward centers, greater reproductive opportunities or any other plausible thing we are trying to optimize.

The fact that we don’t consciously try and optimize our reproductive success and instead apply a forest of frameworks and heuristics that we follow even when they undermine our reproductive success strongly suggests that an AGI will most likely function in a similar heuristic layered fashion. In other words, we shouldn’t expect intelligence to come as a result of some pure mathematical optimization but more as a layered cake of heuristic processes. Thus, when an AI responsible for routing cars reflects on its performance it won’t see the pure mathematical question of how can I minimize such and such function any more than we see the pure mathematical question of how can I cause dopamine to be released in this part of my brain or how can I have more offspring. Rather, just as we break up the world into tasks like ‘make friends’ or ‘get respect from peers’ the AI will reflect on the world represented in terms of pieces like ‘route car from A to B’ or ‘minimize congestion in area D’ that bias it towards a certain kind of solution and away from plots like avoid congestion by creating a killer plague.

This isn’t to say there aren’t concerns. Indeed, as I’ve remarked elsewhere I’m much more concerned about schizophrenic AIs than I am about misaligned AI’s but that’s enough for this post.

AI Bias and Subtle Discrimination

Don't Incentivize Discrimination To Feel Better

This is an important point not just about AI software but discussions about race and gender more generally. Accurately reporting (or predicting) facts that, all too often, are the unfortunate result of a long history of oppression or simple random variation isn’t bias.

Personally, I feel that the social norm which regards accurate observation of facts such as (as mentioned in the article) racial differences in loan repayment rate conditional on wealth to be a reflection of bias is just a way of pretending society’s social warts don’t exist. Only by accurately reporting such effects can we hope to identify and rectify the causes, e.g., perhaps differences in treatment make employment less stable for certain racial groups or whether or not the bank officer looks like you affects likelihood of repayment. Our unwillingness to confront these issues places our personal interest in avoiding the risk of seeming racist/sexist over the social good of working out and addressing the causes of these differences.

Ultimately, the society I want isn’t the wink and a nod cultural in which people all mouth platitudes but we implicitly reward people for denying underrepresented groups loans or spots in colleges or whatever. I think we end up with a better society (not the best, see below) when the bank’s loan evaluation software spits out a number which bakes in all available correlations (even the racial ones) and rewards the loan officer for making good judgements of character independent of race rather than the system where the software can’t consider that factor and we reward the loan officers who evaluate the character of applications of color more negatively to compensate or the bank executives who choose not to place branches in communities of color and so on. Not only does this encourage a kind of wink and nod racism but when banks optimize profits via subtle discrimination rather than explicit consideration of the numbers one ends up creating a far higher barrier to minorities getting loans than a slight tick up in predicted default rate. If we don’t want to use features like the applicant race in decisions like loan offers, college acceptance etc.. we need to affirmatively acknowledge these correlations exist and ensure we don’t implement incentives to be subtly racist, e.g., evaluate loan officer’s performance relative to the (all factors included) default rate so we don’t implicitly reward loan officers and bank managers with biases against people of color (which itself imposes a barrier to minority loan officers).

In short, don’t let the shareholders and executives get away with passing the moral buck by saying ‘Ohh no, we don’t want to consider factors like race when offering loans’ but then turning around and using total profits as the incentive to ensure their employees do the discrimination for them. It may feel uncomfortable openly acknowledging such correlates but not only is it necessary to trace out the social causes of these ills but the other option is continued incentives for covert racism especially the use of subtle social cues of being the ‘right sort’ to identify likely success and that is what perpetuates the cycle.


A.I. ‘Bias’ Doesn’t Mean What Journalists Say it Means

In Florida, a criminal sentencing algorithm called COMPAS looks at many pieces of data about a criminal and computes the probability that they will commit new crimes. Judges use these risk scores in criminal sentencing and parole hearings to determine whether the offender should be kept in jail or released.

Algorithmic Gaydar

Machine Learning, Sensitive Information and Prenatal Hormones

So there’s been some media attention recently to this study which found they were able to accurately predict sexual orientation with 91% for men and 83% for women. Sadly, everyone is focusing on the misleading idea that we can somehow use this algorithm to decloak who is gay and who isn’t rather than the really interesting fact that this is suggestive of some kind of hormonal or developmental cause of homosexuality.

Rather, given 5 pictures of a gay man and 5 pictures of a straight man 91% of the time it is able to correctly pick out the straight man. Those of us who remember basic statistics with all those questions about false positive rates should realize that, given the low rate of homosexuality in the population, this algorithm doesn’t actually give very strong evidence of homosexuality at all. Indeed, one would expect that, if turned loose on a social network, the vast majority of individuals judged to be gay would be false positives. However, in combination with learning based on other signals like your friends on social media one could potentially do a much better job. But at the moment there isn’t much of a real danger this tech could be used by anti-gay governments to identity and persecute individuals.

Also, I wish the media would be more careful about their terms. This kind of algorithm doesn’t reveal private information it reveals sensitive information inadvertently exposed publicly.

However, what I found particularly interesting was the claim in the paper that they were able to achieve a similar level of accuracy for photographs taken in a neutral setting. This, along with other aspects of the algorithm, strongly suggest the algorithm isn’t picking up on some kind of gay/straight difference in what kind of poses people find appealing. The researchers also generated a heat map of what parts of the image the algorithm is focusing on and while some of them do suggest grooming based information about hair, eyebrows or beard play some role the strong role that the nose, checks and corners of the mouth play suggests that relatively immutable characteristics are pretty helpful in predicting orientation.

The authors acknowledge that personality has been found to affect facial features in the long run so this is far from conclusive. I’d also add my own qualification that there might be some effect of the selection procedure that plays a role, e.g., if homosexuals are less willing to use a facial closeup on dating sites/facebook if they are ugly the algorithm could be picking up on that. However, it is at least interestingly suggestive evidence for the prenatal hormone theory (or other developmental theory) of homosexuality.