bias

All posts tagged bias

One of most important assumptions in The Age of Ems is that non-em AGI will take a long time to develop.

1.

Scott Alexander at SlateStarCodex complains that Robin rejects survey data that uses validated techniques, and instead uses informal surveys whose results better fit Robin’s biases [1]. Robin clearly explains one reason why he does that: to get the outside view of experts.

Whose approach to avoiding bias is better?

  • Minimizing sampling error and carefully documenting one’s sampling technique are two of the most widely used criteria to distinguish science from wishful thinking.
  • Errors due to ignoring the outside view have been documented to be large, yet forecasters are reluctant to use the outside view.

So I rechecked advice from forecasting experts such as Philip Tetlock and Nate Silver, and the clear answer I got was … that was the wrong question.

Tetlock and Silver mostly focus on attitudes that are better captured by the advice to be a fox, not a hedgehog.

The strongest predictor of rising into the ranks of superforecasters is perpetual beta, the degree to which one is committed to belief updating and self-improvement.

Tetlock’s commandment number 3 says “Strike the right balance between inside and outside views”. Neither Tetlock or Silver offer hope that either more rigorous sampling of experts or dogmatically choosing the outside view over the inside view help us win a forecasting contest.

So instead of asking who is right, we should be glad to have two approaches to ponder, and should want more. (Robin only uses one approach for quantifying the time to non-em AGI, but is more fox-like when giving qualitative arguments against fast AGI progress).

2.

What Robin downplays is that there’s no consensus of the experts on whom he relies, not even about whether progress is steady, accelerating, or decelerating.

Robin uses the median expert estimate of progress in various AI subfields. This makes sense if AI progress depends on success in many subfields. It makes less sense if success in one subfield can make the other subfields obsolete. If “subfield” means a guess about what strategy best leads to intelligence, then I expect the median subfield to be rendered obsolete by a small number of good subfields [2]. If “subfield” refers to a subset of tasks that AI needs to solve (e.g. vision, or natural language processing), then it seems reasonable to look at the median (and I can imagine that slower subfields matter more). Robin appears to use both meanings of “subfield”, with fairly similar results for each, so it’s somewhat plausible that the median is informative.

3.

Scott also complains that Robin downplays the importance of research spending while citing only a paper dealing with government funding of agricultural research. But Robin also cites another paper (Ulku 2004), which covers total R&D expenditures in 30 countries (versus 16 countries in the paper that Scott cites) [3].

4.

Robin claims that AI progress will slow (relative to economic growth) due to slowing hardware progress and reduced dependence on innovation. Even if I accept Robin’s claims about these factors, I have trouble believing that AI progress will slow.

I expect higher em IQ will be one factor that speeds up AI progress. Garrett Jones suggests that a 40 IQ point increase in intelligence causes a 50% increase in a country’s productivity. I presume that AI researcher productivity is more sensitive to IQ than is, say, truck driver productivity. So it seems fairly plausible to imagine that increased em IQ will cause more than a factor of two increase in the rate of AI progress. (Robin downplays the effects of IQ in contexts where a factor of two wouldn’t much affect his analysis; he appears to ignore them in this context).

I expect that other advantages of ems will contribute additional speedups – maybe ems who work on AI will run relatively fast, maybe good training/testing data will be relatively cheap to create, or maybe knowledge from experimenting on ems will better guide AI research.

5.

Robin’s arguments against an intelligence explosion are weaker than they appear. I mostly agree with those arguments, but I want to discourage people from having strong confidence in them.

The most suspicious of those arguments is that gains in software algorithmic efficiency “remain surprisingly close to the rate at which hardware costs have fallen. This suggests that algorithmic gains have been enabled by hardware gains”. He cites only (Grace 2013) in support of this. That paper doesn’t comment on whether hardware changes enable software changes. The evidence seems equally consistent with that or with the hypothesis that both are independently caused by some underlying factor. I’d say there’s less than a 50% chance that Robin is correct about this claim.

Robin lists 14 other reasons for doubting there will be an intelligence explosion: two claims about AI history (no citations), eight claims about human intelligence (one citation), and four about what causes progress in research (with the two citations mentioned earlier). Most of those 14 claims are probably true, but it’s tricky to evaluate their relevance.

Conclusion

I’d say there’s maybe a 15% chance that Robin is basically right about the timing of non-em AI given his assumptions about ems. His book is still pretty valuable if an em-dominated world lasts for even one subjective decade before something stranger happens. And “something stranger happens” doesn’t necessarily mean his analysis becomes obsolete.

Footnotes

[1] – I can’t find any SlateStarCodex complaint about Bostrom doing something in Superintelligence that’s similar to what Scott accuses Robin of, when Bostrom’s survey of experts shows an expected time of decades for human-level AI to become superintelligent. Bostrom wants to focus on a much faster takeoff scenario, and disagrees with the experts, without identifying reasons for thinking his approach reduces biases.

[2] – One example is that genetic algorithms are looking fairly obsolete compared to neural nets, now that they’re being compared on bigger problems than when genetic algorithms were trendy.

Robin wants to avoid biases from recent AI fads by looking at subfields as they were defined 20 years ago. Some recent changes in AI are fads, but some are increased wisdom. I expect many subfields to be dead ends, given how immature AI was 20 years ago (and may still be today).

[3] – Scott quotes from one of three places that Robin mentions this subject (an example of redundancy that is quite rare in the book), and that’s the one place out of three where Robin neglects to cite (Ulku 2004). Age of Em is the kind of book where it’s easy to overlook something important like that if you don’t read it more carefully than you’d read a normal book.

I tried comparing (Ulku 2004) to the OECD paper that Scott cites, and failed to figure out whether they disagree. The OECD paper is probably consistent with Robin’s “less than proportionate increases” claim that Scott quotes. But Scott’s doubts are partly about Robin’s bolder prediction that AI progress will slow down, and academic papers don’t help much in evaluating that prediction.

If you’re tempted to evaluate how well the Ulku paper supports Robin’s views, beware that this quote is one of its easier to understand parts:

In addition, while our analysis lends support for endogenous growth theories in that it confirms a significant relationship between R&D stock and innovation, and between innovation and per capita GDP, it lacks the evidence for constant returns to innovation in terms of R&D stock. This implies that R&D models are not able to explain sustainable economic growth, i.e. they are not fully endogenous.

Book review: The Charisma Myth: How Anyone Can Master the Art and Science of Personal Magnetism, by Olivia Fox Cabane.

This book provides clear and well-organized instructions on how to become more charismatic.

It does not make the process sound easy. My experience with some of her suggestions (gratitude journalling and meditation) seems typical of her ideas – they took a good deal of attention, and probably caused gradual improvements in my life, but the effects were subtle enough to leave lots of uncertainty about how effective they were.

Many parts of the book talk as if more charisma is clearly better, but occasionally she talks about downsides such as being convincing even when you’re wrong. The chapter that distinguishes four types of charisma (focus, kindness, visionary, and authority) helped me clarify what I want and don’t want from charisma. Yet I still feel a good deal of conflict about how much charisma I want, due to doubts about whether I can separate the good from the bad. I’ve had some bad experiences in with feeling and sounding confident about investments in specific stocks has caused me to lose money by holding those stocks too long. I don’t think I can increase my visionary or authority charisma without repeating that kind of mistake unless I can somehow avoid talking about investments when I turn on those types of charisma.

I’ve been trying the exercises that are designed to boost self-compassion, but my doubts about the effort required for good charisma and about the desirability of being charismatic have limited the energy I’m willing to put into it.

Book review: Bonds That Make Us Free: Healing Our Relationships, Coming to Ourselves, by C. Terry Warner.

This book consists mostly of well-written anecdotes demonstrating how to recognize common kinds of self-deception and motivated cognition that cause friction in interpersonal interactions. He focuses on ordinary motives that lead to blaming others for disputes in order to avoid blaming ourselves.

He shows that a willingness to accept responsibility for negative feelings about personal relationships usually makes everyone happier, by switching from zero-sum or negative-sum competitions to cooperative relationships.

He describes many examples where my gut reaction is that person B has done something that justifies person A’s decision to get upset, and then explaining that person A should act nicer. He does this without the “don’t be judgmental” attitude that often accompanies advice to be more understanding.

Most of the book focuses on the desire to blame others when something goes wrong, but he also notes that blaming nature (or oneself) can produce similar problems and have similar solutions. That insight describes me better than the typical anecdotes do, and has been a bit of help at enabling me to stop wasting effort fighting reality.

I expect that there are a moderate number of abusive relationships where the book’s advice would be counterproductive, but that most people (even many who have apparently abusive spouses or bosses) will be better off following the book’s advice.

Book review: Value-Focused Thinking: A Path to Creative Decisionmaking, by Ralph L. Keeney.

This book argues for focusing on values (goals/objectives) when making decisions, as opposed to the more usual alternative-focused decisionmaking.

The basic idea seems good. Alternative-focused thinking draws our attention away from our values and discourages us from creatively generating new possibilities to choose from. It tends to have us frame decisions as responses to problems, which leads us to associate decisions with undesirable emotions, when we could view decisions as opportunities.

A good deal of the book describes examples of good decisionmaking, but those rarely provide insight into how to avoid common mistakes or to do unusually well.

Occasionally the book switches to some dull math, without clear explanations of what benefit the rigor provides.

The book also includes good descriptions of how to measure the things that matter, but How to Measure Anything by Douglas Hubbard does that much better.

Book review: The Motivation Hacker, by Nick Winter.

This is a productivity book that might improve some peoples’ motivation.

It provides an entertaining summary (with clear examples) of how to use tools such as precommitment to accomplish an absurd number of goals.

But it mostly fails at explaining how to feel enthusiastic about doing so.

The section on Goal Picking Exercises exemplifies the problems I have with the book. The most realistic sounding exercise had me rank a bunch of goals by how much the goal excites me times the probability of success divided by the time required. I found that the variations in the last two terms overwhelmed the excitement term, leaving me with the advice that I should focus on the least exciting goals. (Modest changes to the arbitrary scale of excitement might change that conclusion).

Which leaves me wondering whether I should focus on goals that I’m likely to achieve soon but which I have trouble caring about, or whether I should focus on longer term goals such as mind uploading (where I might spend years on subgoals which turn out to be mistaken).

The author doesn’t seem to have gotten enough out of his experience to motivate me to imitate the way he picks goals.

Book review: The Willpower Instinct: How Self-Control Works, Why It Matters, and What You Can Do To Get More of It, by Kelly McGonigal.

This book starts out seeming to belabor ideas that seem obvious to me, but before too long it offers counterintuitive approaches that I ought to try.

The approach that I find hardest to reconcile with my intuition is that self-forgiveness over giving into temptations helps increase willpower, while feeling guilt or shame about having failed reduces willpower, so what seems like an incentive to avoid temptation is likely to reduce our ability to resist the temptation.

Another important but counterintuitive claim is that trying to suppress thoughts about a temptation (e.g. candy) makes it harder to resist the temptation. Whereas accepting that part of my mind wants candy (while remembering that I ought to follow a rule of eating less candy) makes it easier for me to resist the candy.

A careless author could have failed to convince me this is plausible. But McGonigal points out the similarities to trying to follow an instruction to not think of white bears – how could I suppress thoughts of white bears of some part of my mind didn’t activate a concept of white bears to monitor my compliance with the instruction? Can I think of candy without attracting the attention of the candy-liking parts of my mind?

As a result of reading the book, I have started paying attention to whether the pleasure I feel when playing computer games lives up to the anticipation I feel when I’m tempted to start one. I haven’t been surprised to observe that I sometimes feel no pleasure after starting the game. But it now seems easier to remember those times of pleasureless playing, and I expect that is weakening my anticipation or rewards.

Book review: The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t by Nate Silver.

This is a well-written book about the challenges associated with making predictions. But nearly all the ideas in it were ones I was already familiar with.

I agree with nearly everything the book says. But I’ll mention two small disagreements.

He claims that 0 and 100 percent are probabilities. Many Bayesians dispute that. He has a logically consistent interpretation and doesn’t claim it’s ever sane to believe something with probability 0 or 100 percent, so I’m not sure the difference matters, but rejecting the idea that those can represent probabilities seems at least like a simpler way of avoiding mistakes.

When pointing out the weak correlation between calorie consumption and obesity, he says he doesn’t know of an “obesity skeptics” community that would be comparable to the global warming skeptics. In fact there are people (e.g. Dave Asprey) who deny that excess calories cause obesity (with better tests than the global warming skeptics).

It would make sense to read this book instead of alternatives such as Moneyball and Tetlock’s Expert Political Judgment, but if you’ve been reading books in this area already this one won’t seem important.

Book review: The Righteous Mind: Why Good People Are Divided by Politics and Religion, by Jonathan Haidt.

This book carefully describes the evolutionary origins of human moralizing, explains why tribal attitudes toward morality have both good and bad effects, and how people who want to avoid moral hostility can do so.

Parts of the book are arranged to describe the author’s transition from having standard delusions about morality being the result of the narratives we use to justify them and about why other people had alien-sounding ideologies. His description about how his study of psychology led him to overcome his delusions makes it hard for those who agree with him to feel very superior to those who disagree.

He hints at personal benefits from abandoning partisanship (“It felt good to be released from partisan anger.”), so he doesn’t rely on altruistic motives for people to accept his political advice.

One part of the book that surprised me was the comparison between human morality and human taste buds. Some ideologies are influenced a good deal by all 6 types of human moral intuitions. But the ideology that pervades most of academia only respect 3 types (care, liberty, and fairness). That creates a difficult communication gap between them and cultures that employ others such as sanctity in their moral system, much like people who only experience sweet and salty foods would have trouble imagining a desire for sourness in some foods.

He sometimes gives the impression of being more of a moral relativist than I’d like, but a careful reading of the book shows that there are a fair number of contexts in which he believes some moral tastes produce better results than others.

His advice could be interpreted as encouraging us to to replace our existing notions of “the enemy” with Manichaeans. Would his advice polarize societies into Manichaeans and non-Manichaeans? Maybe, but at least the non-Manichaeans would have a decent understanding of why Manichaeans disagreed with them.

The book also includes arguments that group selection played an important role in human evolution, and that an increase in cooperation (group-mindedness, somewhat like the cooperation among bees) had to evolve before language could become valuable enough to evolve. This is an interesting but speculative alternative to the common belief that language was the key development that differentiated humans from other apes.

Book review: Thinking, Fast and Slow, by Daniel Kahneman.

This book is an excellent introduction to the heuristics and biases literature, but only small parts of it will seem new to those who are familiar with the subject.

While the book mostly focuses on conditions where slow, logical thinking can do better than fast, intuitive thinking, I find it impressive that he was careful to consider the views of those who advocate intuitive thinking, and that he collaborated with a leading advocate of intuition to resolve many of their apparent disagreements (mainly by clarifying when each kind of thinking is likely to work well).

His style shows that he has applied some of the lessons of the research in his field to his own writing, such as by giving clear examples. (“Subjects’ unwillingness to deduce the particular from the general was matched only by their willingness to infer the general from the particular”).

He sounds mildly overconfident (and believes mild overconfidence can be ok), but occasionally provides examples of his own irrationality.

He has good advice for investors (e.g. reduce loss aversion via “broad framing” – think of a single loss as part of a large class of results that are on average profitable), and appropriate disdain for investment advisers. But he goes overboard when he treats the stock market as unpredictable. The stock market has some real regularities that could be exploited. Most investors fail to find them because they see many more regularities than are real, are overconfident about their ability to distinguish the real ones, and because it’s hard to distinguish valuable feedback (which often takes many years to get) from misleading feedback.

I wish I could find equally good book for overuse of logical analysis when I want the speed of intuition (e.g. “analysis paralysis”).

Book Review: Simple Heuristics That Make Us Smart by Gerd Gigerenzer and Peter M. Todd.

This book presents serious arguments in favor of using simple rules to make most decisions. They present many examples where getting a quick answer by evaluating a minimal amount of data produces almost as accurate a result as highly sophisticated models. They point out that ignoring information can minimize some biases:

people seldom consider more than one or two factors at any one time, although they feel that they can take a host of factors into account

(Tetlock makes similar suggestions).

They appear to overstate the extent to which their evidence generalizes. They test their stock market heuristic on a mere six months worth of data. If they knew much about stock markets, they’d realize that there are a lot more bad heuristics which work for a few years at a time than there are good heuristics. I’ll bet that theirs will do worse than random in most decades.

The book’s conclusions can be understood by skimming small parts of the book. Most of the book is devoted to detailed discussions of the evidence. I suggest following the book’s advice when reading it – don’t try to evaluate all the evidence, just pick out a few pieces.