It describes a lot of things, and I’m not sure which ones would be the right answer here. If anyone wants to read through and describe which one seems to best fit the corona problem, that’d be a better answer than just providing an enormous difficult-to-read monograph.
gwern
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March 2020 gwern.net newsletter
No, one shouldn’t. Playing a game of chance once or a thousand times does not influence the outcome of the next round (aka the gambler’s fallacy). If a bet is a good idea one time, it’s a good idea a thousand times. And if a bet is a good idea a thousand times, it is a good idea the first time. How could you consider betting a thousand times to be a good idea, if you think each individual bet is a bad idea?
Gambler’s ruin. The bets are the same, but you are not. If bets are rare enough or large enough, they do not justify they simplifying assumption of ergodicity (‘adding up to normality’ in this case) and greedy expected-value maximization is not optimal.
I’m not clear here what you mean by “AI”. Full AGI, or just AI like the present (various specialized systems, usually but not often subhuman capabilities)? If the former, it’s hard to speculate.
The latter, however, is already being commoditized and I include it as an example. FANG practically compete to see who can give away more source code & datasets & research and open-source everything, to the extent that when someone announces a release of a new reinforcement learning framework library I don’t even bother announcing it on
/r/reinforcementlearningunless there’s something special. FANG is not threatened by open-sourcing everything because all the benefits come from the integration into their ecosystem as part of your smartphone, running on the ASICs built into your smartphone, with access to all the data in your account and the private data in all the FANG datacenters, which power your upfront payments and then advertising eyeballs later. Someone who invents a better image classifier can not in any meaningful way threaten Google’s Android browser advertising revenues, but does help make Android image apps somewhat better and increases Google revenue indirectly, and so on. Thus, Google can release not just new research but the actual models themselves like EfficientNet, and it’s no problem. You can provide a free competitor. But Google provides something which is “better than free”.As far as ‘tool AI’ or ‘AI services’ go, this seems to be the overall theme. Most tasks themselves are not too valuable. The question is what can you build on top or around it, and what does it unlock? (https://www.overcomingbias.com/2019/12/automation-as-colonization-wave.html is interesting.)
If you want to get really hardcore in reading up on pool/group testing, there’s a recent monograph: “Group Testing: An Information Theory Perspective: chapter 3: Algorithms for Noisy Group Testing”, Aldridge et al 2019.
One question for WBE is whether we really need to upload a specific brain or if it is enough to use a generic brain as a template and a kind of informative prior to greatly speed up alternative AI techniques (an emerging paradigm I’m calling “brain imitation learning” for now).
https://magazine.atavist.com/promethea-unbound-child-genius-montana comes to mind as a cautionary example.
Happens all the time in decision theory & reinforcement learning: the average of many good plans is often a bad plan, and a bad plan followed to the end is often both more rewarding & informative than switching at every timestep between many good plans. Any kind of multi-modality or need for extended plans (eg due to upfront costs/investments) will do it, and exploration is quite difficult—just taking the argmax or adding some randomness to action choices is not nearly enough, you need “deep exploration” (as Osband likes to call it) to follow a specific hypothesis to its limit. This is why you have things like ‘posterior sampling’ (generalization of Thompson sampling), where you randomly pick from your posterior of world-states and then follow the optimal strategy assuming that particular world state. (I cover this a bit in two of my recent essays, on startups & socks.)
On the other hand, OpenAI Five (AN #13) also has many, many subtasks, that in theory should interfere with each other, and it still seems to train well.
True, but OA5 is inherently a different setup than ALE. Catastrophic forgetting is at least partially offset by the play against historical checkpoints, which doesn’t have an equivalent in your standard ALE; the replay buffer typically turns over so old experiences disappear, and there’s no adversarial dynamics or AlphaStar-style population of agents which can exploit forgotten area of state-space. Since Rainbow is an off-policy DQN, I think you could try saving old checkpoints and periodically spending a few episodes running old checkpoints and adding the experience samples to the replay buffer, but that might not be enough.
There’s also the batch size. The OA5 batch size was ridiculously large. Given all of the stochasticity in a DoTA2 game & additional exploration, that covers an awful lot of possible trajectories.
In Gmail, everything after
They also present some fine-grained experiments which show that for a typical agent, training on specific contexts adversely affects performance on other contexts that are qualitatively different.
Is cut off by default due to length.
If you’re interested in LW2′s typography, you should take a look at GreaterWrong, which offers a different and much more old-school non-JS take on LW2, with a number of features like customizable CSS themes. There is a second project, Read The Sequences.com, which focuses on a pure non-interactive typography-heavy presentation of a set of highly-influential LW1 posts. Finally, there’s been cross-pollination between LW2/GW/RTS and my own website (description of design).
Malaysia is up to 428 cases now and rising rapidly: https://www.bloomberg.com/news/articles/2020-03-15/malaysia-virus-cases-spike-after-outbreak-at-16-000-strong-event They’ve been averaging 24C with peaks of 36C. Not looking good for the heat hypothesis.
February 2020 gwern.net newsletter
As another decade has passed, his 2019 from 1999 predictions could be graded too: https://en.wikipedia.org/wiki/Predictions_made_by_Ray_Kurzweil#2019
Skimming them, there are a lot of total misses (eg computer chips being carbon nanotubes—not even on the horizon as of 2020, silicon is king), but there’s also a fair number of ones which came true only just recently. For example, a lot of the software ones, like speech recognition/synthesis/transcription and being able to wear glasses with real-time captioning and using machine translation apps in ordinary conversation, really only came true within the last few years, and I don’t think this would have been predicted in 1999 based purely on trend extrapolation from n-grams machine translation or HMM voice recognition. Likewise, even allowing for “genetic algorithms” being wrong, “Massively parallel neural nets and genetic algorithms are in wide use.” would have been dismissed. I thought while grading his 2010 predictions back then that Kurzweil’s errors seemed to be to overestimate how much hardware and biology would be to change, but his software-related projections generally much better, and that seems even truer of his 2019 predictions.
It’s still more of a draft than finished writeup. I’ll be sending out the newsletter when it’s officially done, as always.
An open question for me is whether it makes sense to not pre-emptively archive everything.
Update: I ultimately decided to give this a try, using SingleFile to capture static snapshots.
Detailed discussion: https://www.reddit.com/r/gwern/comments/f5y8xl/new_gwernnet_feature_local_link_archivesmirrors/ It currently costs ~5300 links/20GB, which is not too bad but may motivate me to find new hosting as I think it will substantially increase my monthly S3 bandwidth bill. The snapshots themselves look pretty good and no one has reported yet serious problems.… Too early to say, but I’m happy to be finally giving it a try.
“Scientists are curious and passionate and ready to argue”:
This psychological assessment owes nothing to surveys or personality testing; it pays no heed to the zodiac. Instead, researchers took the linguistic data from 200 tweets each of nearly 130,000 Twitter users across more than 3,500 occupations to assess their “personality digital fingerprints”.
They used machine learning to identify the traits and values that distinguish professions from each other.
This “21st century approach for matching one’s personality with congruent occupations,” dubbed the robot career adviser, is more reliable than existing career guidance methods based on self-reports through questionnaires, the researchers argue in a January paper in Proceedings of the National Academy of Sciences.
Personality characteristics of Twitter users were inferred using IBM Watson’s Personality Insights tool. The study focused on five specific traits (extraversion, agreeableness, conscientiousness, emotional stability, and openness) and five values (helping others, tradition, taking pleasure in life, achieving success, and excitement).
The analysis revealed that scientists combine low agreeableness and low conscientiousness with high openness.
“The combination is characteristic of people who tend to be unconventional and quirky, consistent with the image of scientists as curious and even sometimes eccentric boffins,” says one of the authors, Paul McCarthy+, an adjunct professor at the University of New South Wales (UNSW Sydney) in Australia. “In some ways it does confirm stereotypes.”
Within the sciences, a spectrum of personality traits emerged. Those dealing with more abstract or inanimate things (mathematicians, geologists) were more open than those in the life sciences (bio-statisticians, horticulturalists), who “tended to be more extroverted and agreeable”, says McCarthy.
Scientists and software programmers, whose personality characteristics aligned closely, were generally more open to experiencing a variety of new activities, tended to think in symbols and abstractions, and found repetition boring, the researchers found.
On the spectrum of occupations, scientists are especially different from professional tennis players. “Tennis professionals are a lot more agreeable and conscientious than all others in the study — especially scientists,” says McCarthy.
“It makes sense, because to be a tennis player, you have to be highly conscientious and be willing to take direction, whereas scientists are almost the complete opposite. They don’t take direction, their openness to experiences is very high, but so is their openness to being disagreeable.”
He noted that the occupation of research director had the highest median openness scores of any of the 3,513 occupations in the study.
My understanding was that distilling CNNs worked more-or-less by removing redundant weights, rather than by discovering a more efficient form of representing the data.
No. That might describe sparsification, but it doesn’t describe distillation, and in either case, it’s shameless goalpost moving—by handwaving away all the counterexamples, you’re simply no-true-Scotsmanning progress. ‘Oh, Transformers? They aren’t real performance improvements because they just learn “good representations of the data”. Oh, model sparsification and compression and distillation? They aren’t real compression because they’re just getting rid of “wasted information”.’
Draft lotteries provide randomized experiments. The most famous one is the Vietnam draft analysis by Angrist 1990 where he finds a 15% penalty to lifetime income caused by being drafted. There’s also little evidence of any benefit in Low-Aptitude Men In The Military: Who Profits, Who Pays?, Laurence & Ramberger 1991, which covers Project 100,000 among others, which drafted individuals who would not otherwise have been drafted either as a matter of policy or, in the ASVAB Misnorming, unrealized accident. (As Fredrik deBoer likes to say, if there’s any way it can possibly be a selection effect, then yeah, it’s a selection effect.)