gwern
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January 2019 gwern.net newsletter
“Forecasting Transformative AI: An Expert Survey”, Gruetzemacher et al 2019
Yes, if it’s as simple as ‘spam clicks from imitation learning are too hard to wash out via self-play given the weak APM limits’, it should be relatively easy to fix. Add a very tiny penalty for each click to incentivize efficiency, or preprocess the replay dataset—if a ‘spam click’ does nothing useful, it seems like it should be possible to replay through all the games, track what clicks actually result in a game-play difference and what clicks are either idempotent (eg multiple clicks in the same spot) or cancel out (eg a click to go one place which is replaced by a click to go another place before the unit has moved more than epsilon distance), and filter out the spam clicks.
There’s also the CPU. Those <=200 years of SC2 simulations per agent aren’t free. OA5, recall, was ’256 GPUs and 128,000 CPU cores’. (Occasionally training a small NN update is easier than running many games necessary to get the experience to decide what tweak to make.)
It’s worth noting that NLP took a big leap in 2018 through simple unsupervised/predictive training on large text corpuses to build text embeddings which encode a lot of semantic knowledge about the world.
/r/reinforcementlearning discussion: https://www.reddit.com/r/reinforcementlearning/comments/ajeg5m/deepminds_alphastar_starcraft_2_demonstration/
“AlphaStar: Mastering the Real-Time Strategy Game StarCraft II”, DeepMind [won 10 of 11 games against human pros]
No one really knows. Calhoun did a bad job writing it up. When I went looking for details years back, I think the most in-depth primary source I found was like… 2 pages long.
I’ve wondered if what actually happened was just a contagious infection (sterility is a consequence of many infectious diseases), which given the population density should be near-inevitable. Even if he had checked for infections (it’s unclear if he did) it would be easy to miss a lot of organisms, which is why we still regularly run into new evidence of infectious contributions to various problems.
He also ran a number of mouse utopias, IIRC, and usually you hear only about the one which gave the ‘collapse’ narrative.… At this point, I assign it to the mental bucket of ‘wrong 1960s blankslatism like Rosenthal or Pygmalion effect or the Stanford Prison Experiment or Robber’s Cave but which will live on forever in pop science because their message is too appealing’.
Earlier discussion of the research: https://www.lesswrong.com/posts/pPZ27eZdBXtGuLqZC/what-is-up-with-carbon-dioxide-and-cognition-an-offer
“The Impact of Media Censorship: Evidence from a Randomized Field Experiment in China”, Chen & Yang 2018:
Media censorship is a hallmark of authoritarian regimes. We conduct a field experiment in China to measure the effects of providing citizens with access to an uncensored Internet. We track subjects’ media consumption, beliefs regarding the media, economic beliefs, political attitudes, and behaviors over 18 months. We find four main results:
free access alone does not induce subjects to acquire politically sensitive information;
temporary encouragement leads to a persistent increase in acquisition, indicating that demand is not permanently low;
acquisition brings broad, substantial, and persistent changes to knowledge, beliefs, attitudes, and intended behaviors; and
social transmission of information is statistically-significant but small in magnitude.
We calibrate a simple model to show that the combination of low demand for uncensored information and the moderate social transmission means China’s censorship apparatus may remain robust to a large number of citizens receiving access to an uncensored Internet.
As far as I can tell, his only public reaction is a line in Sparrow 2014: “A recent treatment of this topic by Shulman and Bostrom^6 calls the same technology ‘iterated embryo selection’ - a name that Matthews and Fujita et al may prefer.“. So, he never acknowledged being scooped.
Although after more looking into it, if we’re going to argue about priority, it looks like IES was actually first proposed a decade before MIRI did, in Haley & Visscher 1998′s “Strategies to Utilize Marker-Quantitative Trait Loci Associations”—their Figure 5c is unambiguously IES.
A new one: “Using EEG to characterise drowsiness during short duration exposure to elevated indoor Carbon Dioxide concentrations”, Snow et al 2018:
Drowsiness which can affect work performance, is often elicited through self-reporting. This paper demonstrates the potential to use EEG to objectively quantify changes to drowsiness due to poor indoor air quality. Continuous EEG data was recorded from 23 treatment group participants subject to artificially raised indoor CO2 concentrations (average 2,700 ± 300 ppm) for approximately 10 minutes and 13 control group participants subject to the same protocol without additional CO2 (average 830 ± 70 ppm). EEG data were analysed for markers of drowsiness according neurophysiological methods at three stages of the experiment, Baseline, High CO2 and Post-Ventilation. Treatment group participants’ EEG data yielded a closer approximation to drowsiness than that of control group participants during the High CO2 condition, despite no significant group differences in self-reported sleepiness. Future work is required to determine the persistence of these changes to EEG over longer exposures and to better isolate the specific effect of CO2 on drowsiness compared to other environmental or physiological factors.
Background: https://www.gwern.net/Embryo-selection#multi-stage-selection
Any selective breeding program operates similarly to this, and iterated embryo selection operates even more like this; if you consider the full life cycle from ethnicity to individual to assortative mating to embryo selection, much of the logic carries through (and considering such scenarios where there are multiple ‘stages’ is what made me appreciate the asymptotic advantage). Particle filters/swarm optimization algorithms operate similarly, in a sense, and evolutionary computation methods like CMA-ES operate pretty much exactly like this. This can also be a good intuition pump for pipeline scenarios like Scannell’s drug discovery pipeline model.
Visualizing the power of multiple step selection processes in JS: Galton’s bean machine
Littlewood’s Law and the Global Media
Evolution as Backstop for Reinforcement Learning: multi-level paradigms
The optional banner is harmless,
Revisiting this page now in 2019, I’d take more exception to this. For entirely unrelated reasons, I ran my own banner ad A/B test, and the results were far from harmless: https://www.gwern.net/Ads And this turns out to parallel experiments by both Pandora and Mozilla. Scuttlebutt has it there are more suppressed experiments also demonstrating long-term harm. (I’m running a followup experiment which I hope will show smaller effects but I don’t know what it is finding yet.)
Extrapolate the various estimates out to Craigslist and that’s a lot of potential global deadweight loss from sales/deals/rentals not happening.
https://www.reddit.com/r/DecisionTheory/search?q=flair%3ATextbook&restrict_sr=on is a starting point.