trentmkelly/LessWrong-43k huggingface dataset:
The news attention span
by damiensnyder — 2021-07-09T01:44:08.108Z
Link: https://www.damiensnyder.com/blog/the-news-attention-span

Every day, every daily news source sends out headlines to their readers via newspapers and the web. Most of the time, the top headlines have no relation to the previous day's headlines. Sometimes this is not true, such as during an election or a disaster, but even then, stories rarely remain on the front page for more than a couple days.

This implies that the most important thing to hear about changes constantly from day to day. I won't pretend that doesn't make sense. If you already heard about something yesterday, you probably don't need to hear about it again today, unless something changed in the situation.[1]

But the most important issues do not change as often as the headlines. If headlines are supposed to give information people can act on, it is strange that my best course of action is so disjointed. One day I am supposed to donate money to a political campaign, on the next I am supposed to protest big tech, and today I should write a letter to my senator about Afghanistan.[2] Perhaps I dream of a newspaper whose front page headline every day is:

The problem with such a newspaper is that they would go out of business. After all, if the headline has been the same for the last month, even if it is the most important action item in the world, people will stop learning anything from it. Your newspaper would be more of an oldspaper.

On the other hand, if you do not repeat the same point every week, people will completely forget about it and nothing will change. Remember when everyone was talking about Gamestop in January? It's still at $200 a share, six months later, but major news sites have stopped reporting on it. Do we pretend that ransomware, critical race theory in K-12, or other issues of the day will not meet the same fate?

I think most people don't pretend to that. The news has a second function, besides informing concerted political action: it determines what people talk about at the dinner table, what they post about on Nextdoor, and what slogan they put in their Instagram bio. People get bored of talking about the same thing every day, which is why few people bring up "malaria this" and "clean drinking water that" in every conversation they have. The Discourse is not always about doing something in the world. Most people will never be activists or megadonors. They are just trying to show support for the right beliefs when they talk to their peers. ...
Intermittent Distillations #4: Semiconductors, Economics, Intelligence, and Technological Progress.


by Mark Xu — 2021-07-08T22:14:23.374Z

The Semiconductor Supply Chain: Assessing National Competitiveness (Saif M. Khan, Alexander Mann, Dahlia Peterson)

The Semiconductor Supply Chain: Assessing National Competitiveness

Summary

This report analyzes the current supply chain for semiconductors. It particularly focuses on which portions of the supply chain are controlled by US and its allies and China. Some key insights:

The US semiconductor industry is estimated to contribute 39 percent of the total value of the global semiconductor supply chain.

The semiconductor supply chain is incredibly complicated. The production of a single chip requires more than 1,000 steps and passes through borders more than 70 times throughout production.

AMD is currently the only company with expertise in designing both high-end GPUs and high-end CPUs.

TSMC controls 54% of the logic foundry market, with a larger share for leading edge production, e.g., state-of-the-art 5 nm node chips.

Revenue per wafer for TSMC is rapidly increasing, while other foundries are seeing declines.

The Netherlands has a monopoly on extreme ultraviolet (EUV) scanners, equipment needed to make the most advanced chips.

The Netherlands and Japan have a monopoly on argon floride (ArF) immersion scanners, needed to make the second most advanced chips.

The US has a monopoly on full-spectrum electronic design automation (EDA) software needed to design semiconductors.

Japan, Taiwan, Germany and South Korea manufacture the state-of-the-art 300 mm wafers used for 99.7 percent of the world's chip manufacturing. This manufacturing process requires large amounts of tacit know-how.

China controls the largest share of manufacturing for most natural materials. The US and its allies have a sizable share in all materials except for low-grade gallium, tungsten and magnesium.

China controls ~2/3rds of the world's silicon production, but the US and allies have reserves.

The report also analyzes US competitiveness at very detailed levels of the supply chain, which I didn't read that carefully. Tables:

Opinion

One perspective on the economy is that it's running a vast, distributed computation to allocate supply to demand in a relatively efficient manner. Examining the details on one of the supply chains underpinning a half a trillion dollar industry is relatively awe-inspiring. The only thing I'm currently aware of that is as complicated as computer hardware is ...
Jackpot! An AI Vignette
by Ben Goldhaber — 2021-07-08T20:32:22.528Z

I started noticing the world had gotten weirder when my grandmother messaged the family that she wanted to do her 80th birthday party in Reno.

Don't get me wrong, Reno is great - biggest little city in the world! - but we're New Yorkers, why on earth fly to a desert? If you want to rough it, lets just go to Jersey.

When we asked, she replied that she had gone there with her friends and had a, well, she called it spiritual experience in the Goldstrike! casino. "It was like heaven, I forgot all my cares; I was one with the universe".

"... Can you find enlightenment in Atlantic City?"

Apparently not. The specific game she liked, Nirvana Slots, was only available in Nevada. The slot machine was paired with a lightweight BCI helmet and AI feedback software that optimized for a state of flow. It would modulate the experience of the game - when wins would occur, when losses would occur, the sounds and lights and haptic feedback - such that the gamblers mindset would stay as close as possible to the 'zone', that wonderful place where they don't exist. You get to stay in the zone until you run out of money.

I looked up the game on Wikipedia and saw that with the BCI helmet and cushioned, reclinable chair the slot machine looked more like a fighter pilot ejection seat than a traditional one armed bandit. The similarity was more than skin deep - the article noted that technology was originally developed to augment the combat effectiveness of drone pilots, so they could stay focused on the mission for longer. It was effective, but the side effects were off putting -- pilots ended up flying longer routes and bombing more targets just to stay in the machine longer

I also learned that the reason we needed to fly to Reno is that Nirvana is only legal in Nevada. AI controlled reward protocols were banned in most states after the Farmville Legacy launch caused several hospitalizations from dehydration and starvation, but there was a carve out for non-internet based gambling. While most states had banned it, Nevada was still a place where freedom rang out, where you could go to an e-brothel with deepfaked celebrity VR and then go to a casino and play your favorite AI powered reel spinner.

Nirvana was wildly popular among the slotpullers and the casino owners. As long as the statistical frequency of wins and losses was maintained, the gaming board didn't care when the person won or lo...
Whistle Synth Pi
by jefftk — 2021-07-08T19:00:04.367Z

I made my whistle-controlled synthesizer into a stand-alone device:

Melody example:

Bass example:

Playing bass by singing falsetto:

It consists of:

Raspberry Pi 3 computer ($35)

Case ($7)

USB sound card ($8)

Keypad ($10)

3.5mm to 1/4" cable ($4)

XLR to 3.5mm cable ($7)

It also needs a micro SD card, power (2A, USB), and a microphone. I'm
using ones I already had, but it doesn't need anything fancy. If you
wanted to make a special-purpose device, I think you could get the
materials down below $20; there is nothing especially expensive here,
and I'm not using most of the features of the Pi.

The code is open source: github.

Comment via: facebook
BASALT: A Benchmark for Learning from Human Feedback
by Rohin Shah — 2021-07-08T17:40:35.045Z
Link: https://bair.berkeley.edu/blog/2021/07/08/basalt/

Copying the abstract of the paper:

The last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are now being incorporated into commercial products. With this comes an additional challenge: how can we build AI systems that solve tasks where there is not a crisp, well-defined specification? While multiple solutions have been proposed, in this competition we focus on one in particular: learning from human feedback. Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.

The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. These tasks are defined by a paragraph of natural language: for example, "create a waterfall and take a scenic picture of it", with additional clarifying details. Participants must train a separate agent for each task, using any method they want. Agents are then evaluated by humans who have read the task description. To help participants get started, we provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline that leverages these demonstrations.

Our hope is that this competition will improve our ability to build AI systems that do what their designers intend them to do, even when the intent cannot be easily formalized. Besides allowing AI to solve more tasks, this can also enable more effective regulation of AI systems, as well as making progress on the value alignment problem.

I also mention this in the latest Alignment Newsletter, but I think this is probably one of the best ways to get started on AI alignment from the empirical ML perspective: it will (hopefully) give you a sense of what it is like to work with algorithms that learn from human feedback, in a more realistic setting than Atari / MuJoCo, while still not requiring a huge amount of background or industry-level compute budgets.

Section 1.1 of the paper goes into more d...
[AN #155]: A Minecraft benchmark for algorithms that learn without reward functions
by Rohin Shah — 2021-07-08T17:20:02.518Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.

HIGHLIGHTS

BASALT: A Benchmark for Learning from Human Feedback (Rohin Shah et al) (summarized by Rohin): A typical argument for AI risk, given in Human Compatible (AN #69), is that current AI systems treat their specifications as definite and certain, even though they are typically misspecified. This state of affairs can lead to the agent pursuing instrumental subgoals (AN #107). To solve this, we might instead build AI systems that continually learn the objective from human feedback. This post and paper (on which I am an author) present the MineRL BASALT competition, which aims to promote research on algorithms that learn from human feedback.

BASALT aims to provide a benchmark with tasks that are realistic in the sense that (a) it is challenging to write a reward function for them and (b) there are many other potential goals that the AI system “could have” pursued in the environment. Criterion (a) implies that we can’t have automated evaluation of agents (otherwise that could be turned into a reward function) and so suggests that we use human evaluation of agents as our ground truth. Criterion (b) suggests choosing a very “open world” environment; the authors chose Minecraft for this purpose. They provide task descriptions such as “create a waterfall and take a scenic picture of it”; it is then up to researchers to create agents that solve this task using any method they want. Human evaluators then compare two agents against each other and determine which is better. Agents are then given a score using the TrueSkill system.

The authors provide a number of reasons to prefer the BASALT benchmark over more traditional benchmarks like Atari or MuJoCo:

1. In Atari or MuJoCo, there are often only a few reasonable goals: for example, in Pong, you either hit the ball back, or you die. If you’re testing algorithms that are meant to learn what the goal is, you want an environment where there could be many possible goals, as is the case i...
The Efficient Market Hypothesis in Research
by libai — 2021-07-08T17:00:20.221Z

A classic economics joke goes like this:

Two economists are walking down a road, when one of them notices a $20 bill on the ground. He turns to his friend and exclaims: "Look, a $20 bill!" The other replies: "Nah, if there's a $20 on the bill on the ground, someone would've picked it up already."

The economists in the joke believe in the Efficient Market Hypothesis (EMH), which roughly says that financial markets are efficient and there's no way to "beat the market" by making intelligent trades.

If the EMH was true, then why is there still a trillion-dollar finance industry with active mutual funds and hedge funds? In reality, the EMH is not a universal law of economics (like the law of gravity), but more like an approximation. There may exist inefficiencies in markets where stock prices follow a predictable pattern and there is profit to be made (e.g.: stock prices fall when it's cloudy in New York). However, as soon as someone notices the pattern and starts exploiting it (by making a trading algorithm based on weather data), the inefficiency disappears. The next person will find zero correlation between weather in New York and stock prices.

There is a close parallel in academic research. Here, the "market" is generally efficient: most problems that are solvable are already solved. There are still "inefficiencies": open problems that can be reasonably solved, and one "exploits" them by solving it and publishing a paper. Once exploited, it is no longer available: nobody else can publish the same paper solving the same problem.

Where does this leave the EMH? In my view, the EMH is a useful approximation, but its accuracy depends on your skill and expertise. For non-experts, the EMH is pretty much universally true: it's unlikely that you've found an inefficiency that everyone else has missed. For experts, the EMH is less often true: when you're working in highly specialized areas that only a handful of people understand, you begin to notice more inefficiencies that are still unexploited.

A large inefficiency is like a $20 bill on the ground: it gets picked up very quickly. An example of this is when a new tool is invented that can straightforwardly be applied to a wide range of problems. When the BERT model was released in 2018, breaking the state-of-the-art on all the NLP benchmarks, there was instantly an explosion of activity as researchers raced to apply i...
Practical anthropics summary
by Stuart_Armstrong — 2021-07-08T15:10:44.805Z

This is a simple summary of how to "do anthropics":

If there are no issues of exact copies, or advanced decision theory, and the questions you're asking aren't weird, then use SIA. And by "use SIA", I mean "ignore the definition of SIA, and just do a conventional Bayesian update on your own existence". Infinite universes won't be a problem (any more than they are with conventional probabilities). And this might not increase your expected population by much.

First of all, there was the realisation that different theories of anthropic probability correspond to correct answers to different questions - questions that are equivalent in non-anthropic situations.

We can also directly answer "what actions should we do?", without talking about probability. This anthropic decision theory gave behaviours that seem to correspond to SIA (for total utilitarianism) or SSA (for average utilitarianism).

My personal judgement, however, is that the SIA-questions are more natural than the SSA-questions (ratios of totals rather than average of ratios), including the decision theory situation (total utilitarianism rather than average utilitarianism). Thus, in typical situations, using SIA is generally the way to go.

And if we ignore exact duplicates, Boltzmann brains, and simulation arguments, SIA is simply standard Bayesian updating on our existence. Anthropic probabilities can be computed exactly the same way as non-anthropic probabilities can.

And there are fewer problems than you might suspect. This doesn't lead to problems with infinite universes - at least, no more than standard probability theories do. And anthropic updates tend to increase the probability of larger populations in the universe, but that effect can be surprisingly small - 7 to 32 given the data we have.

Finally, note that anthropic effects are generally much weaker than Fermi observation effects. The fact that we don't see life, on so many planets, tells us a lot more than the fact we see life on this one.
Anthropics and Fermi: grabby, visible, zoo-keeping, and early aliens
by Stuart_Armstrong — 2021-07-08T15:07:30.891Z

When updating the probability of life across the universe, there are two main observations we have to build on:

Anthropic update: we exist on Earth.

Fermi observation: (we exist on Earth and) we don't see any aliens.

I'll analyse how these two observations affect various theories about life in the universe. In general, we'll see that the anthropic update has a pretty weak effect, while the Fermi observation has a strong effect: those theories that benefit most are those that avoid the downgrade from the Fermi, such as the Zoo hypothesis, or the "human life unusually early" hypothesis.

Grabby and visible aliens

I've argued that an anthropic update on our own existence is actually just a simple Bayesian update; here I'll explain what that means for our updates.

This paper talks about grabby aliens, who would expand across the universe, and stop humans from evolving (if they reached Earth before now). As I've argued, "we exist" and "we have not observed X" are statements that can be treated in exactly the same way. We can combine them to say "there are no visible aliens anywhere near", without distinguishing grabby aliens (who would have stopped our existence) from visible-but-not grabby aliens (who would have changed our observations).

Thus the Fermi observation is saying there are no grabby or visible aliens nearby[1]. Recall that it's so comparatively easy to cross between stars and galaxies, so advanced aliens would only fail to be grabby if they coordinated to not want to do so.

Rare Earth hypotheses

Some theories posit that life requires a collection of conditions that are very rarely found together.

But rare Earth theories don't differ much, upon updates, from "life is hard" hypotheses.

For example, suppose T0 say that life can exist on any planet with rate ρ, while the rare Earth hypothesis, T1, says that life can exist on Earth-like planets with rate p, while Earth-like planets themselves exist with rate r.

But neither the Fermi observation nor the anthropic update will distinguish between these theories. The Fermi observation posits that there are no visible aliens close to Earth; the anthropic updates increases the probability of life similar to us. We can see T1 as T0 with a different prior on ρ=pr (induced from the priors on p and r), but both these updates affect ρ.

Now, T0 and T1 can be distinguished by observation (seeing dead planets with Earth...
Covid 7/8: Delta Takes Over
by Zvi — 2021-07-08T13:00:00.630Z

Delta is now two thirds of sequenced samples from the past week, so we can be confident that it has taken over, and soon will be most cases in America, and soon after that most cases around the world. That’s bad news, but given we know the case numbers, it sort of is good news. It means we’ve already ‘taken the hit’ to the reproduction rate, mostly, and if we can stabilize things one final time, then unless there’s a new even worse variant then the last scare is over.

Things are not yet stable, but they continue to echo what happened this time last year, and things continue to be quite hot. If things cool down a bit (in temperature terms), several weeks pass and these trends continue, then they’ll be more clearly where we are at going forward, and we’ll be counting on further vaccinations and on past vaccinations kicking in. We should have enough momentum there to still get us over the finish line, but in many places it’s going to be close.

Let’s run the numbers.

The Numbers

Predictions

I forgot that July 4th was coming, so my deaths prediction was dumb. Whoops.

Prediction from last week: Positivity rate of 2.7% (up 0.3%) and deaths decline by 5%.

Result: Positivity rate of 2.9% (up 0.5%) and deaths decline by 20% likely due to July 4th.

Prediction for next week: Positivity rate of 3.3% (up 0.4%) and deaths increase by 7%.

Predicting deaths this week is weird, and I’m predicting an increase because I think this week’s count is artificially low due to the holiday. So there will be some catch-up reporting and some reversion, even if the new rise in cases won’t have caught up to us yet. For the positivity rate, it jumped a lot the last day, but I have to assume it will keep rising for now. As it stops being constrained, the positivity rate will once again become a useful measure of our situation.

Deaths

Date	WEST	MIDWEST	SOUTH	NORTHEAST	TOTAL
May 27-June 2	527	838	1170	456	2991
June 3-June 9	720	817	915	431	2883
Jun 10-Jun 16	368	611	961	314	2254
Jun 17-Jun 23	529	443	831	263	2066
Jun 24-Jun 30	550	459	706	186	1901
Jul 1-Jul 7	459	329	612	128	1528

I’d love for this to be real, but it’s probably not. It’s July 4th, and I keep forgetting to look ahead to the holidays in the next week before I make predictions. There’s no way this full decline is real, and it’s hard to know where the real number was.

My brain continues to not understand how it being the 4th of July causes three ...