trentmkelly/LessWrong-43k huggingface dataset:
Utility need not be bounded
by Richard_Kennaway — 2020-05-14T18:10:58.681Z

A few months ago I disagreed with Sniffnoy about whether the theorem regarding Savage's axioms for probability and utility, that utility must be bounded, is a good reason for believing that utility must be bounded. Sniffnoy said yes, because it follows from the axioms, I said no, therefore there is a flaw in the axioms. (The fact that the theorem does follow from the axioms is not an issue.) I concluded that conversation by saying I'd have to think about it.

This I have done. I have followed Jaynes' dictum that when infinities lead to problems, one must examine the limiting process by which those infinities were arrived at, which almost invariably dissolves the problem. The flaw in Savage's system is easy to find, easy to describe, and easy to rectify. I have devised a new set of axioms such that:

Probability and utility are constructed from the preference relation by the same method as Savage.
Every model of Savage's axioms is a model of these axioms and constructs the same probability measure and utility function.
The new axioms also have models with acts and outcomes of unbounded utility.
Acts of infinite utility (such as the St. Petersburg game) are admitted as second-class citizens, in much the same way that measurable functions with infinite integral are in measure theory.
More pathological infinite games (such as St. Petersburg with every other payout in the series reversed in sign) are excluded from the start, but without having to exclude them by any criterion involving utility. (Utility is constructed from the axioms, so cannot be mentioned within them.) Like measurable functions that have no integral, well, that's just what they are. There's no point in demanding that they all should.

This removes all force from the argument that because Savage's axioms imply bounded utility, utility must be bounded. (There are other axiom systems that have that consequence, but I believe that my construction would apply equally to them all.) If one prefers Savage's axioms because they have that consequence, one must have some other reason for believing that utility must be bounded, or the argument is circular.

There are a few details of proofs still to be filled in, but I don't think there will be any problems there. Any expert on measure theory could probably dispose of them with a theorem off the shelf. Because of this ...
Suggestions for making research as trustworthy, intelligible and quickly available as possible
by mickofemsworth — 2020-05-14T14:47:52.571Z

There's a lot of research papers out there - some good, some bad, some intelligible, some unintelligible, some disseminated quickly, some slowly. Now, with covid-19, it is more important than ever that we get to know whether a research paper should be trusted, and as many as possible of the relevant people should be able to understand it as quickly as possible. The phrases in italics refer to key problems with the system for creating and disseminating knowledge. The purpose of this note is to explain how a few publications of mine are relevant to tackling these problems.

Just after deciding to write this note, I received a "nature briefing" email from the journal Nature summarising some of their latest highlights. One thing that caught my eye was a paper entitled "Suppression of COVID-19 outbreak in the municipality of Vo’, Italy" (E. Lavezzo et al. Preprint at medRxiv http://doi.org/ggsmcj). According to the Nature summary: "On 21 February, the town of Vo’ reported Italy’s first COVID-19 death, leading authorities to ban movement in the town and end public services and commercial activities there for two weeks. Andrea Crisanti at Imperial College London and his colleagues swabbed almost every resident of Vo’ for viral RNA at the beginning and end of the lockdown. ... The team found that some 43% of the people infected with SARS-CoV-2 in the town reported no fever or other symptoms. The researchers observed no statistically significant difference in potential infectiousness between those who reported symptoms and those who did not."

The paper in question was posted on https://www.medrxiv.org/ which describes itself as a preprint server for health sciences. There is a warning on the top of each page that the paper is "not certified by peer review". This seems to imply that once it has been "certified" - by a couple of "peers" chosen by the editor of the journal - then its contents must be right, trustworthy or whatever. This assumption is implicit in the journalistic practice of giving the name of the journal a research paper appears in with the implied "must the right, it's published in this prestigious journal". The paper was posted on 18 April 2020. I am writing this two weeks later and there has been no update: surely given the urgency ...
What newsletters are you subscribed to, and why?
by Rudi C — 2020-05-14T14:47:17.584Z

I've found myself increasingly feeling the need for curated newsletters on the topics I care about from interesting people in the field, but I have little clue where to look for them.

I currently follow TLDR newsletter(in Telegram because that’s more convenient than in email), because I find the news it delivers worth my while, and I haven’t found a better alternative. I also use rss feeds to see posts with +500 scores from Hackernews and the top posts of lobste.rs.
Small Data
by lsusr — 2020-05-14T04:29:52.455Z

Probabilistic reasoning starts with priors and then updates them based off of evidence. Artificial neural networks take this to the extreme. You start with deliberately weak priors, then update them with a tremendous quantity of data. I call this "big data".

In this article, I use "big data" to mean the opposite of "small data". By this, "big data" refers to situations with so much training data you can get away with weak priors. Autonomous cars are an example of big data. Financial derivatives trading is an example of small data.

The most powerful recent advances in machine learning, such as neural networks, all use big data. Machine learning is good at fields where data plentiful, such as in identifying photos of cats, or where data can be cheaply manufactured, such as in playing videogames. "Plentiful data" is a relative term. Specifically, it's a measurement of the quantity of training data relative to the size (complexity) of the search space.

Do you see the problem?

Physical reality is an upper bound on data collection. Even if "data" is just a number stored momentarily on a CPU's register there is a hard physical limit to how much we can process. In particular, our data will never scale faster than O(x4) where x is the diameter of our computer in its greatest spacetime dimension. O(x4) is polynomial time.

Machine learning search spaces are often exponential or hyperexponential. If your search space is exponential and you collect data polynomially then your data is sparse. When you have sparse data, you must compensate with strong priors. Big data uses weak priors. Therefore big data approaches to machine learning cannot, in general, handle small data.

Statistical Bias

Past performance is no guarantee of future results.

Suppose you want to estimate the mean variance σ2 of a Gaussian distribution. You could sample n points and then compute the mean variance of them.

σ=√∑ni=1(xi−¯x)2n

If you did you'd be wrong. In particular, you'd underestimate the mean variance by a factor of nn−1. The equation for standard deviation s corrects for this and uses n−1 in the denominator.

s=√∑ni=1(xi−¯x)2n−1

An estimate of the variance of a Gaussian distribution based solely on historical data, without adjusting for statistical bias bias will underestimate the mean variance of the underlying distribution.

s2=nn−1σ2

Underestimating mean variance by a factor of nn−1 can be solved by th...
Will the world hit 10 million recorded cases of COVID-19? If so when?
by jmh — 2020-05-13T17:26:07.232Z

It wasn't that long ago that I suspect if someone claimed there would be 10 million reported cases of COVID-19 most people would be skeptical. Now, maybe not so much.

It looks like we're heading there -- perhaps much higher.

Doing just a real dirty, back of the paper napkin type calculation looks like about 45 days to go from .5 million around March 26 to 4 million on May 10. So something like an 8 fold increase in 45 days.

That kind of puts us on track for 10 million in maybe 15 days -- end of May?

But will the linear looking global trend keep the same slope?
Running Wired Ethernet
by jefftk — 2020-05-13T17:20:02.643Z

After talking excitedly about

how much better a
wired connection has made my video calls,

housemates who work remotely
were interested in having ethernet to their desks. My dad lent me his
RJ-45 tools, and I took a few hours on Friday to run cables.

I started with what ended up being the hardest cable, from a second
floor bedroom down to the basement. Ideally I would have done the
vertical portion when I had everything open doing the bathroom work,
but at that point I still thought wired ethernet was obsolete. I was
able to get it through the area around the plumbing stack using a
snake, though, and it wasn't too bad. At the top I needed to drill
horizontally through a thick wall, originally the outside of the
house, now the wall between the house and the addition. I had an
installer bit which turned out to have a cable-pull hole, which was
really helpful. The desk was on the far side of the room from where
the cable came in, and I decided to be lazy and run the cable along
the baseboard, using 7mm cable
tacks to keep it in place.

The tidiest way to do this would be to put an RJ-45 jack in the wall,
but that's more work than I was up for. Instead I just crimped on a
connector directly. Pass-through connectors are definitely worth it:
it's much easier than getting the wires all exactly the right length,
and I'm less likely to accidentally swap a pair.

The second run was from a room that's was an addition to the house.
There's a crawl-space under it, but it's accessible only through a
small window about six feet off the basement floor. I've been in it
several times, and it's pretty annoying. I was complaining about this
at dinner before running the cable, and Lily was very excited to learn
our house had a crawl space. She wanted to go explore it, and wasn't
willing to wait until tomorrow. I suggested she put on sturdy
clothes, and while she was changing I hurried to go drill a hole and
get the cable fed into the space. I lifted her up so she could climb
in, and she was very excited to see what it was like. While she was
in there she was happy to go get the end of the cable for me, which
meant I didn't need to get myself in and out.

When I was putting her to bed that night I thanked her again, and she
told me "I had a little spark of fear, but the bigger sparks inside me
were adventure, excitement, and helpfulness."

The ...
[AN #99]: Doubling times for the efficiency of AI algorithms
by Rohin Shah — 2020-05-13T17:20:02.637Z

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).

HIGHLIGHTS

AI and Efficiency (Danny Hernandez et al) (summarized by Flo): Given the exponential increase (AN #7) in compute used for state-of-the-art results in ML, one might come to think that there has been little algorithmic progress. This paper presents strong evidence against that hypothesis. We can roughly measure algorithmic progress by tracking the compute needed to achieve a concrete performance benchmark over time. Doing so yields doubling times in efficiency (time until only half of the initial compute was needed for the same performance) of around 16 months for ImageNet, which is faster than Moore's law. Other tasks like translation as well as playing Go and Dota 2 exhibit even faster doubling times over short periods. As making a task feasible for the first time arguably presents more algorithmic progress than improving the efficiency of solving an already feasible task, actual progress might be even faster than these numbers suggest. However, the amount of data points is quite limited and it is unclear if these trends will persist and whether they will generalize to other domains. Still, the authors conjecture that similar trends could be observed for tasks that received large amounts of investment and have seen substantial gains in performance.

Combining these results with the increased available compute over time, the authors estimate that the effective training compute available to the largest AI experiments has increased by a factor of 7.5 million (!) in 2018 relative to 2012.

A focus on efficiency instead of top performance allows actors with limited amounts of compute to contribute. Furthermore, models that reach a particular benchmark quickly seem like strong candidates for scaling up. This way, more efficient algorithms might act as a catalyst for further progress. There is a public git repository to keep better track of algorithmic efficiency.

Flo's opinion: Even though access to compute has surely helped with increased efficiency in ways that I would not really label as algorithmic progress (for example by enabling research...
Legends of Runeterra: Early Review
by Zvi — 2020-05-13T10:50:00.696Z

Legends of Runeterra has been getting a generally favorable reception. The game is highly relevant to my interests as someone who plays a lot of collectible card games and is making a collectible card game of my own called Emergents, together with Brian David-Marshall, that will be ready for its first Alpha test soon.

Thus, after the latest round of prompting to check the game out, I have checked the game out. I figured I should report back.

Legends of Runeterra has some very cool things that it does well.

It also has some things that, from my perspective, it does poorly.

That doesn’t mean those things that I personally disliked are bad or wrong! It means they made my initial experience worse, slash lowered my expectations for my future personal experiences. I am a weird customer.

I notice that I am having the hit from ‘this is a new card game with new decision points and new cards and new decks.’ Which I’m always happy to experience. There’s some potentially interesting tactical games of chicken, and the spell mana mechanic is nice. Plus, the game plays smoothly and looks gorgeous. Always a plus.

And yet, already I find myself thinking about games as something the game assigns to me, as work, to complete quests and unlock cards. I don’t feel the urge to play for the sake of playing – although I do feel a bit of ‘I have only my phone and I can play a game of this, whereas I can’t play Magic or Slay the Spire right now so maybe?’. I don’t have the same kind of ‘just one more game’ urge I have with Magic: The Gathering, or in the first few months with Hearthstone, or Artifact or Slay the Spire. But it’s always tinged with ‘…grind out some rewards’ which always makes me feel sick about feeling it. There’s no larger scale motivation to keep playing, as the ladder gives essentially no rewards. As ladders often do – I have no idea why ladder rewards are reliably super stingy in such free to play economies.

Strategically, there isn’t much there, there. What there is is mostly tactical guessing games and games of chicken. The games blur together and all feel the same after only a few dozen total, and I wish my opponents would ‘get on with it.’

Overall, a mixed bag.

I don’t want to discourage those looking for a new game. Check it out! It’s free, the joy of seeing what they’re up to makes the first few hours reasonable (and, again, totally free), and you’ll know quickly if w...
Studies On Slack
by Scott Alexander — 2020-05-13T05:00:02.772Z

I.

Imagine a distant planet full of eyeless animals. Evolving eyes is hard: they need to evolve Eye Part 1, then Eye Part 2, then Eye Part 3, in that order. Each of these requires a separate series of rare mutations.

Here on Earth, scientists believe each of these mutations must have had its own benefits – in the land of the blind, the man with only Eye Part 1 is king. But on this hypothetical alien planet, there is no such luck. You need all three Eye Parts or they’re useless. Worse, each Eye Part is metabolically costly; the animal needs to eat 1% more food per Eye Part it has. An animal with a full eye would be much more fit than anything else around, but an animal with only one or two Eye Parts will be at a small disadvantage.

So these animals will only evolve eyes in conditions of relatively weak evolutionary pressure. In a world of intense and perfect competition, where the fittest animal always survives to reproduce and the least fit always dies, the animal with Eye Part 1 will always die – it’s less fit than its fully-eyeless peers. The weaker the competition, and the more randomness dominates over survival-of-the-fittest, the more likely an animal with Eye Part 1 can survive and reproduce long enough to eventually produce a descendant with Eye Part 2, and so on.

There are lots of ways to decrease evolutionary pressure. Maybe natural disasters often decimate the population, dozens of generations are spend recolonizing empty land, and during this period there’s more than enough for everyone and nobody has to compete. Maybe there are frequent whalefalls, and any animal nearby has hit the evolutionary jackpot and will have thousands of descendants. Maybe the population is isolated in little islands and mountain valleys, and one gene or another can reach fixation in a population totally by chance. It doesn’t matter exactly how it happens, it matters that evolutionary pressure is low.

The branch of evolutionary science that deals with this kind of situation is called “adaptive fitness landscapes”. Landscapes really are a great metaphor – consider somewhere like this:

You pour out a bucket of water. Water “flows downhill”, so it’s tempting to say something like “water wants to be at the lowest point possible”. But that’s not quite right. The lowest point possible is the pit, and water won’t go there. It will just sit in the little puddle forever, because it woul

...
Kids and Time
by jefftk — 2020-05-12T11:40:02.644Z

Note: this is written about normal times. With the pandemic
closing schools and daycares a lot of parents have far less time than
usual, and while we're lucky that this has not affected us it's not what I'm looking
at this here.

When I compare my life before and after kids, the biggest change is
probably how much less free time I have. Looking back at time
tracking numbers (2011, 2015, 2017a, 2017b) the big change is
having ~40% of my waking time be with the kids around. This is time I
enjoy, but it's also time I'm using for very different things.

One place you can see this is in how much I've ended up writing. Very
roughly, the more time I have with nothing else I need to do, the more
I write. Here are monthly blog posts here over the years:

Initially, when Lily was born it didn't really decrease my free time
very much. I had twelve weeks of leave from work, and a lot of my
existing social time was the kind of thing I could do with a small
cuddly kid on my lap. While I expected that
this would mean less writing, it mostly didn't.

This changed when we bought a house that needed a lot of work. I think if we had bought the
house first, however, I would be writing this the other way around: it
was the combination of the two that took up all my previously free
time.

Then we had a second kid, and while the house situation became less
work kids started taking more time. This wasn't really a problem: I
like spending time with the kids, and I also like working on the
house. But it meant I had less slack and often had things I would like to write about (or other things I would
like to do) that I didn't have time for. Spikes in the graph
correspond to things like vacations, work travel, and unemployment.

In the last year I've been feeling like I have more time again. The
biggest chunk of this has gone into musical
exploration, but I've also been playing with a lot of other
things. The two largest changes were wrapping up the last (so far?)
major house project and moving the
kids to an earlier bedtime when Lily started kindergarten. This is
over a much larger background trend, though, with the kids (6y and 4y)
getting more independent.

At this point they get themselves up (Lily when she feels like it and
Anna when her wake-up light
turns on), get dressed, use the bathroom, and go play downstairs.
If we're not up yet they'll ...
What would flourishing look like in Conway's Game of Life?
by sudhanshu_kasewa — 2020-05-12T11:22:31.089Z

I'm thinking of designing a reinforcement learning environment based on Conway's Game of Life (GoL). In it, at every timestep, an agent can change the state of some cells.

As is the case with most interesting RL problems, agent behaviour would be determined by the reward function.

In this scenario, I see some issues with simple reward functions:

1) Total life:

Something like this glider gun would represent a technically correct unbounded score, with a stream of lonely travellers in an interstaller abyss. Say we want to stick to a finite memory.

2) Highest density:

This is generation 50 of the Max spacefiller. The agent might find some way of maintaining the striped pattern from the middle. Uninspiring, to say the least; like
***spoiler alert*** being batteries in The Matrix.

Other reward functions might consider variation over time.

3) Some function with a penalty for static life:

Consider Karel's p177:

This oscillator has a period of 177 time steps. There's another one with p312, but I couldn't find a good-enough gif of it. Such patterns would likely game this reward specification too.

I've thought of a few other simple functions, which all look flawed in some obvious ways.

That's not to say there is some ungameable reward function. But I wonder, if each cell symbolically represented some small unit of sentience (say a single person, or a family, or a planet) what would flourishing look like?