trentmkelly/LessWrong-43k huggingface dataset:
Why sigmoids are so hard to predict
by Stuart_Armstrong — 2021-03-18T18:21:51.203Z

tl;dr: Predicting the behaviour of a sigmoid-like process is not fitting the parameters of a logistic curve. Instead, it's trying to estimate the strength of the dampening term - a term that might be actually invisible in the initial data.

A logistic curve is a function that starts out growing like an exponential, but then diminishing returns hits in, and the curve plateaus:

The logistic curve has a specific equation f(x)=L1+e−k(x0−x); it has a turning point at x0 (where it goes from accelerating growth to decelerating growth) and an (asymptotic) maximum at L.

The broader category of "curves that look like logistic curves" are sigmoids, and they appear all over the place - population growth, virus spreading, and so on.

I've seen a few examples and arguments as to why it's so hard to predict the turning points and maximums of a sigmoid; I wrote one myself.

But all these arguments, including my own, didn't really satisfy me. The fact is, a logistic curve has three free parameters, so three perfect samples will pin it down entirely. So the arguments then turn on how a little bit of noise can make these parameters very uncertain.

And those argument are correct: small uncertainties in the data do imply very different outcomes. I illustrated this by plotting the standard logistic curve with turning point at 0 and maximum at 1, in blue, versus the exponential function 0.51e0.69x (which has no turning points and no maximum), in purple:

Then all the arguments and demonstrations are more formal ways of saying "hey, these curves look similar, they're hard to tell apart". If the noise is independent, however, then you will eventually be able to distinguish the two curves if you get enough data points.

General sigmoids

I then started thinking about non-independent noise, and other similar ideas about how these curves would behave in the real world. But that was missing the key question: how do we know that the purple curve is not a sigmoid? It starts off as a pure exponential, but nothing stops me from completing it like this:

The purple curve is not a logistic curve, but it's a perfectly respectable sigmoid. The wikipedia sigmoid article lists a variety of sigmoid functions such as "Logistic functions", "Arctangent functions", "Gudermannian function", "Error functions", "Generalised logistic functions", "Smoothstep functions", and the wonderfully vague but huge category of "Som...
Book Club: Software Design for Flexibility
by stuhlmueller — 2021-03-18T15:42:59.376Z

Gerry Sussman's new book on how to build adaptive systems just came out. It's called "Software Design for Flexibility: How to Avoid Programming Yourself into a Corner" and it covers similar ground to Sussman's advanced symbolic programming class which I liked a lot. The class itself is a kind of follow-up to SICP.

I'm reading the book over the next 8 weeks. If you want to read it together, and share notes and toy implementations, add your name to this doc.

Excerpt:

Our goal in this book is to investigate how to construct computational systems so that they can be easily adapted to changing requirements. One should not have to modify a working program. One should be able to add to it to implement new functionality or to adjust old functions for new requirements. We call this additive programming. We explore techniques to add functionality to an existing program without breaking it.
Acetylcholine = Learning rate (aka plasticity)
by Steven Byrnes — 2021-03-18T13:57:39.865Z

What is “learning rate”, and why should we expect a learning-rate-modulation mechanism in the brain?

What is “learning rate”? As many readers know, learning algorithms involve a large number of parameters (“weights” in ML, or synapse locations and strengths in brains) that get changed while the learning algorithm runs. These parameters store the information that the algorithm has learned. “Learning rate” is a multiplier on this change process—the higher the learning rate, the more aggressively you change the parameters each step.

So if the learning rate is zero, you don’t change the parameters at all (and it’s no longer a learning algorithm!). If the learning rate is really high, you remember information more reliably with fewer repetitions. But it’s not all good: if the learning rate is too high, you get problems like (depending on the algorithm) instability, more tendency to overwrite old memories, more tendency to overfit (i.e., to “learn” things that are just random noise rather than robust patterns in the environment), and so on.

(Learning rate is an exact synonym of “plasticity”, as far as I can tell.)

Why might an organism benefit from using different learning rates in different situations? Well, as above, the learning rate involves a tradeoff between different considerations, and it would be awfully surprising if the all-things-considered best learning rate winds up being exactly the same for someone sitting by a campfire vs someone fighting a lion. For example, intuitively, when fighting a lion, you’re in a situation where you’re making lots of life-or-death decisions. You probably want an unusually high learning rate here, so that next time you’re fighting a lion you’ll do a much better job of understanding what’s going on. By contrast, sitting by the campfire, maybe it’s not so important that you remember everything, and the balance of considerations pushes towards a low learning rate, which again has better properties in avoiding overwriting old memories, avoiding overfitting, etc.

(I think there’s a connection to pedagogy here. Everyone knows that students retain information better when doing something arousing, like arguing with someone, vs when they’re bored and inactive. I bet the brain sets its learning rate to a higher setting in the first case! But I think there are other things going on here too—for example, emotional memories will get replaye...
Is RL involved in sensory processing?
by Steven Byrnes — 2021-03-18T13:57:28.888Z

(UPDATE 1½ YEARS LATER: I’ve learned a lot since writing this post, and you shouldn’t assume that I still endorse everything herein.)

I’m biased: I have a strong prior belief that reinforcement learning should not be involved in sensory processing in the brain. (Update: I meant "directly involved", see comment.) (Update 2: I should have said "low-level sensory processing", see discussion of inferotemporal cortex here.) The reason is simple: avoiding wishful thinking.

If there’s a positive reward for looking at beautiful ocean scenes, for example, then the RL solution would converge towards parsing whatever you look at as a beautiful ocean scene, whether it actually is or not!

Predictive learning (a.k.a. self-supervised learning), by contrast, seems perfect for the job of training sensory-processing systems. Well, really weighted predictive learning, where some prediction errors are treated as worse than others, and where top-down attention is providing the weights. (Or something. Not sure about the details here.) Anyway, predictive learning does not have a “wishful thinking” problem; plus there’s a nice information-rich supervisory signal that provides full error vectors (

which are used for error-driven learning

) which are easier to learn from than 1D rewards. (Imagine a high school teacher grading essays. If they say “That essay was bad; next time try using shorter paragraphs”, that’s like an error gradient signal, telling the student how to improve. If they just say “That essay was bad”, that’s like a reward signal, and now making progress is much harder!!) So that’s been my belief: I say reward learning has no place in the sensory-processing parts of the brain.

So later on, I was excited to learn that the basal ganglia (which plays a central role in reinforcement learning) sends signals into the frontal lobe of the brain—the home of plans and motor outputs—but not to the other lobes, which are more involved in sensory processing. (Update: There's at least one exception, namely inferotemporal cortex; I guess the division between RL / not-RL does not quite line up perfectly with the division between frontal lobe / other lobes.) (Update 2: I have a lot more to say about the specific case of inferotemporal cortex here.) Anyway, that seemed to confirm my mental picture.

Then I was reading

Marblestone Wayne Kording 2016

(let’s call it MWK), and was gobsmacked—y...
Covid 3/18: An Expected Quantity of Blood Clots
by Zvi — 2021-03-18T13:50:00.599Z

This week’s Covid news was that most of Europe suspended administration of the AstraZeneca vaccine over reports of blood clots. This was ludicrously stupid several times over. There was always going to be something that happened to correlate with vaccination days to some extent, somewhere, over some time period. The number of blood clots experienced after vaccination wasn’t even higher than the base rate you would otherwise expect. And even if all the observed clots were extra, all were caused by the vaccine, all were fatal, and that represented the overall base rate, and we ignore all population-level benefits and economic issues, the vaccine would still be worth using purely for personal health and safety by multiple orders of magnitude.

The WHO and EMA said there was no evidence there was an issue.

None of that mattered, as one by one countries suspended injections as part of a blame avoidance strategy. As a result vaccinations are hold, thousands (or more) will die as a direct result, with many European countries seeing things getting worse rather than better and facing possible new restrictions, and with a permanent new weapon in the arsenal of vaccine skeptics that we’ll have to hear about for decades, long after this is proven to be a non-concern.

Meanwhile, in the United States, deaths are happily way down, but case numbers have stopped dropping due to the rise of the new strains, and will likely start ticking upwards once again for a while. Whether or not this will count as a last surge/wave is unclear, it looks like the strains aren’t as additionally infectious as we feared and vaccinations are going well, so it might not be so bad.

Also, we (myself and the anonymous donor) awarded the Covid Microgrants, for details see the section on that below.

Let’s run the numbers.

The Numbers

Predictions

Prediction (WaPo numbers): Positivity rate will be 4.2% (unchanged) and deaths will fall by 12%.

Results from WaPo Covid page, which I picked last week as the data source:

Positivity rate was indeed close to unchanged, but I see a contradiction with the Wikipedia data. The Washington Post source says there were 26% fewer tests and an essentially unchanged positive rate. Wikipedia reports essentially a flat number of positive tests. Those two things can’t both be true at the same time, so someone has this wrong. John Hopkins has 4.7% positive rate right now, ...
Spirits vs Terms as Arbitration Norms
by rai — 2021-03-18T05:07:03.860Z

Here's a cautionary tale about a friendly bet going wrong and how to avoid the same mistake in your own bets.

When I was in Brazil with a friend, I had a grand time teaching him to say peanut butter in Portuguese (manteiga de amendoim). He struggled to remember it and butchered the pronunciation and so it turned the whole situation into a great inside joke.

Fast forward a year later,we're staying with each other again and I bet him that he wouldn't be able to remember how to say it by midnight at 20:1 odds. The bet was on.

As we went about our day, every now and then we'd think of it, he’d struggle, but say it was starting to come back to him.

Around midnight I called him into my room and asked him for his final answer.

"Pasta de amendoim." he said.

This kicked off a heated debate about who was right. We each were shocked at some of the other's arguments. They seemed so obviously wrong. We're great friends so even though feelings were heightened, we could still laugh about it.

I held that it wasn't the right answer because I was looking for "peanut butter" while he said "peanut paste". Peanut Paste is what is written on some peanut butter jars but I argued that that clearly wasn't the intent of the bet because there was no inside joke to that effect. Why would I make a bet about random words distinct from the ones I was attempting to teach him day in, day out. He argued that the exact phrasing of the bet was "How do you say peanut butter in Portuguese?" (which is true) and that a third-party would surely rule in his favor. I said that if the third-party had more than just a dictionary, if they had the entire context of our Brazil trip, they would rule in my favor. He still disagreed because he said the context didn't matter because of the way I worded the bet.

I left the situation feeling bad about all the potential bets we might not make if this became the most salient betting experience we had.

Weeks later we revisited it and we finally saw what each other meant. What we disagreed on was norms of arbitration. We both understood that when we shook hands on the bet, a ritual was complete and there was a magical "bet". However, he saw the words as the bet in and of themselves, conferring all the liabilities to the participants that a legal contract would in a court of law. That's understandable. I imagined that this would be arbitrated by a warmer, more human method o...
What are fun little puzzles / games / exercises to learn interesting concepts?
by Mati_Roy — 2021-03-18T03:26:00.948Z

Things in the category of the 2-4-6 puzzle where you need to guess the rule and can try as many examples as you want to figure out the puzzle, and it teaches 'gung jr ner cebar gb pbasvezngvba ovnf' (https://rot13.com/).

Please describe the puzzle / game / exercise and its solution / strategy / take-away separately in order to avoid spoiling it.

Am particularly interested in cognitive biases, but don't want to restrict the answer-space to those.
The best things are often free or cheap
by Adam Zerner — 2021-03-18T02:57:15.012Z

I've been watching Chef's Table on Netflix recently.

Even though those restaurants are crazy expensive, I've been thinking that it's something I want to experience. I'd like to know just how high of a level you can take food to.

Then I had the thought:

What about areas other than food? Maybe I should experience how good things can get in other areas too?

As I'm thinking about what those other areas might be, it's seeming like often times, the best things are free. Here are some examples.

Music: Whether you're partial to Tupac, Mozart, or The Beatles, you can probably find it for free. If not, you can pay a dollar or whatever on iTunes, or twenty bucks for a CD (or whatever digital format they currently use).

Art: Art isn't really my thing, but after a little bit of googling, it looks like you can find most stuff online.

Writing: Novels, essays, blogs, nonfiction, poetry - it's usually available for free online or at the library.

Software: If you enjoy reading and learning from beautiful code, there's a lot of highly acclaimed stuff in the world of open source that you can check out.

Education: For textbooks, sometimes they're available for free, sometimes you have to pay for them. But $100-200 will buy you an amount of content that you can mull over and dig into for a very long time. For courses, universities like MIT and Stanford have been offering the actual lectures and course materials for free for a while now.

Nature: For me personally, I live about 20 minutes away from the Hoover Dam, and four hours from the Grand Canyon. The Hoover Dam is free, and the Grand Canyon is a small fee. Others aren't quite so lucky, but many are only a road trip away.

Sports: Other than boxing's PPV, you can watch the greatest in the world on TV. If you want to go back and watch the classics, you can usually find it on YouTube. And if you want analysis, you can find places like Back Picks scattered around the corners of the internet.

Movies: I've never heard of a movie that costs more that twenty bucks.

Stand up comedy: Lots of specials are available online, but you haven't lived until you've been to the Comedy Cellar in NYC! It only costs ~$25.

Philosophy: Special shout out to the Stanford Encyclopedia of Philosophy!

Is there anything I'm forgetting? Of course. This isn't meant to be an exhaustive list. But I think that a back of the napkin excercise like this points pre...
Product orientation
by DirectedEvolution — 2021-03-18T00:41:04.661Z

When I applied to graduate schools, I methodically went through over 50 bioinformatics program websites and US News and World Report to find their tuition data and ranking, then threw it all into a spreadsheet. I included cost of living, the length of the program, and ran a linear regression to figure out what school offered the cheapest cost vs. the best ranking.

When I have a small computational task I need to do - generating random combinations from two lists, stripping formatting from copied text, finding slant anagrams - I am far more likely to code it in Python than to try and find a pre-existing app.

Since I frequently need to use Ctrl+F in the large PDF textbooks I use for school, and this takes forever on my underpowered laptop, I've developed a homebrew process. I print each chapter to PDF as I go along, which involves using my TI-84 to determine the page number offsets in the PDF file vs. the page number in the table of contents.

When I schedule events, I send plaintext emails, trying to be careful about timezones.

I don't usually think of myself as a "DIY" person. I hate sewing. I hate cooking. I hate changing the oil on my car. But I do like programming and fussing around with spreadsheets, while I really don't like reading technical manuals and watching "how-to" and product advertisement videos. Money is definitely still a constraint for me. And most of my work involves computers, so when I have a new problem to solve, it generally is a software problem.

Now, you might think of this as the DIY spirit, being an inventor, a hacker, an entrepreneurial type. But that's not quite what I'm trying to get at here.

Because what accompanies all this is this deep-set trait:

I. DON'T. LIKE. SHOPPING.

I don't like going to stores. I don't like browsing on Amazon.com. I don't like Instagram, which is basically a market. I don't like farmer's markets. I don't like sales. I don't like sales pitches, and when a salesperson approaches me in the store I have perfected body language that screams "leave me alone!"

I never did like shopping, I don't now, and I am hoping I can make this change in the future, because I'm dissatisfied with it.

So I want to reframe this in a way that appeals to my nerdiness. The tendency to shop, to browse, and to enjoy it is product orientation, a way of developing product knowledge.

And product knowledge is absolutely crucial for living, and liv...
The Age of Imaginative Machines
by Yuli_Ban — 2021-03-18T00:35:56.212Z

A previous post of mine detailed the rise of "synthetic media" and discussed the burgeoning field in terms that now read as if from the 20th century. At the time of writing, the most advanced publicly available language modeler was the recently unveiled GPT-2 with its now quaint 1.5 billion data parameters.

Things have changed.

Indeed, I've now begun to use synthetic media for my own endeavors, such as generating photorealistic character portraits and creating landscapes with Artbreeder— pieces that would have cost hundreds of dollars' worth of effort even two years ago, I can now have for no cost on my end and with the only difficulty and concession being the need to fiddle with GANs to create only a rough approximation that lacks the ability to add any defining nuances with ease. And this is using technology that very well may be obsolete within the year.

It came my attention that essays from later that same year— reflecting my shock at the sheer rate of change— are hidden by poor metadata and their host websites delisting them. So I've decided to transfer one essay here, except now with a few updates to reflect the changes and further mullings given since the summer of 2019.

As recently as five years ago, the concept of “creative machines” was cast off as impossible- or at the very least, improbable for decades. Indeed, the phrase remains an oxymoron in the minds of most. Perhaps they are right. Creativity implies agency and desire to create. All machines today lack their own agency. Yet we bear witness to the rise of computer programs that imagine and “dream” in ways not dissimilar to humankind.

Though lacking agency, this still meets the definition of imagination.

To reduce it to its most fundamental ingredients: Imagination = experience + abstraction + prediction. To get creativity, you need only add “drive”. Presuming that we fail to create artificial general intelligence in the next ten years (an easy thing to assume because it’s unlikely we will achieve fully generalized AI even in the next thirty), we still possess computers capable of the former three ingredients.

Someone who lives on a flat island and who has never seen a mountain before can learn to picture what one might be by using what they know of rocks and cumulonimbus clouds, making an abstract guess to cross the two, and then predicting what such a “rock cloud” might look like. This is the...
"Objective vs Social Reality" vs "Simulacra 1/3"
by Raemon — 2021-03-17T22:15:14.383Z

Mild rant:

a) I think "Simulacrum Levels 1 - 4" are terrible names and we should be looking to replace them, generally.

b) usually, there's not really a good reason to bring "simulacrum" into it. The concept of simulacrum levels makes sense when you're talking about how how relations-to-reality change over time, or how people relate to it in different ways. But I don't think it makes sense to bring it up every time the distinction between physical/objective-level reality and social reality. Social Reality is a perfectly good name that I think is at least relatively transparent about what it means.

This mild rant brought to you by this comment which was one-straw-too-many for me.
AstraZeneca vaccine shows no protection against Covid-19 variant from Africa
by Gytis Daujotas — 2021-03-17T18:45:06.175Z

A recently published study has found that the AstraZeneca vaccine does not protect against the "African variant" of nCoV-19, which is a worrying outcome, since the paper believes that we probably won't be able to manufacture a specifically targeted new vaccine for the rest of 2021.

CONCLUSIONS

A two-dose regimen of the ChAdOx1 nCoV-19 vaccine did not show protection against mild-to-moderate Covid-19 due to the B.1.351 variant.

https://www.nejm.org/doi/10.1056/NEJMoa2102214#.YFJIkr3ju7k.twitter