trentmkelly/LessWrong-43k huggingface dataset:
What's a good way to test basic machine learning code?
by Kenny — 2021-03-11T21:27:15.535Z

I originally 'scoped' this question based on my inspiration, i.e. a set of example data and their expected answers for various machine learning algorithms or their code components. In other words, a set of test cases that could be used to test implementations of those algorithms or components.

I took the Coursera course Machine Learning (by { Stanford University / Andrew Ng}) and it was great!

I took it because I wanted some 'finger practice' of writing machine learning code and actually running it on my own computer. The course was perfect for that!

One thing in particular that was nice is that the exercises/quizzes/tests that required writing code would have some kind of automated 'endpoint' whereby you could submit your code for testing. (That kind of 'test kata' practice is a really fun way to learn any kind of programming topic, e.g. a new programming language.)

I found these links, on the above course's forum on Coursera, linking to some of the info about the exercises:

Machine Learning - Discussions | Coursera

Machine Learning - Discussions | Coursera

The course uses Octave, which was fine.

But I want to basically repeat the course – but using completely different tools. (I want to play with this library in particular.)

What are the best alternatives to the course I took, or similar resources that would be helpful in implementing basic machine learning algorithms, but with arbitrary programming tools?
Learn and practice using Occam's Razor with exercises
by icompetetowin — 2021-03-11T20:29:58.972Z
Link: https://newsletter.decisionschool.org/p/occams-razor

I want to be great at making decisions.

But it's hard to practice and even harder to get feedback.

I've tried books, courses, and talks.

While some provide exercises, these resources are mostly informative.

This makes it hard to know if I really understood what i've learned.

And more importantly, if I can apply it.

That's why I decided to create the

Decision School

.

An online platform dedicated to providing exercises (with feedback) on decision making.

While the platform is being built, I will post exercises through this newsletter / blog.

Feedback is greatly appreciated.

Thank you!
Killing Moloch with "Nudge tech"?
by nathanlippi — 2021-03-11T20:28:58.957Z

Context

I come from the SaaS startup world and I intend to help kill some inadequate equilibria in the next few years, specifically around the attention economy.

I'll outline what I'm thinking, below, and would appreciate any help from the LW community:

Specific questions I hope to answer:

From a god's-eye-view, where is the project likely to fail?
How might you approach accomplishing a similar goal? What would you change?
What should I read / who should I talk with to learn more?

Project Description

"Turn tech from addicting to revitalizing, with nudge tech"

A continually-improving "nudge tech" that digitally overlays computers/smartphones, and gently changes the quality of life for those individuals and populations who opt in.

It will lessen the pull of companies who legally but unethically get people to self-sabotage (e.g. phone addiction, sleep deprivation, empty content and empty calories)
It will minimize the tribalistic tendency of humans to 'other' populations by popping algorithmic bubbles
It will increase serendipity and innovation in its users by exposing them to relevant and timely diverse viewpoints and opportunities

It will be continually improved by open-source plugin contributions; plugins will be tested automatically and successful ones will be rolled out to those who opt in.

Nudge tech - definition

Nudge tech is technology that changes the probability of specific human behaviors by modifying 1+ of the following:

What behavior prompts a person gets
The difficulty of accomplishing a behavior
The reward/punishment from a given behavior

'Nudge tech', is used by tech companies like Facebook, Netflix, etc. to keep users engaged and therefore make money. But nudge tech can be used to defend against exploitation by these same companies.

That's it! I'm looking forward to your thoughts.
Four Motivations for Learning Normativity
by abramdemski — 2021-03-11T20:13:40.175Z

I have been pretty satisfied with my desiderata for learning normativity, but I haven't been very satisfied with my explanation of why exactly these desiderata are important. I have a sense that it's not just a grab-bag of cool stuff; something about trying to do all those things at once points at something important.

What follows are four different elevator pitches, which tell different stories about how it all hangs together. Desiderata are bolded.

Conceptual Difficulties with Outer Alignment

The classic problem of outer alignment is that we have no perfect loss function, so we can't just go optimize. The problem can be understood by thinking about Goodhart and how optimization amplifies. The classic response to this is value uncertainty and value learning, but wireheading, human manipulation, and no-free-lunch results make it seem plausible that we have the same problem one level up: we still don't know how to specify a perfect loss function for what we care about, and imperfect loss functions can still create big problems.

So, just like value-learning tackles the initial problem head-on by suggesting we manage our uncertainty about values and gain knowledge over time, learning at all levels suggests that we tackle the meta-problem directly, explicitly representing the fact that we don't have a perfectly good loss function at any level, but can manage that uncertainty and learn-to-learn over time.

Humans can only give explicit feedback at so many meta-levels, so between-level sharing is critical for any meaningful learning to take place at higher meta-levels. Otherwise, higher meta-levels remain highly uncertain, which itself makes learning at lower levels almost impossible (since you can't learn if you have high uncertainty about learning-to-learn).

A consequence of having no perfect loss function is no perfect feedback; no evidence about what the system should do can be considered absolute. A helpful measure for coping with this is to support uncertain feedback, so that humans can represent their uncertainty when they provide feedback. Ultimately, though, humans can have systematic biases which require reinterpretable feedback to untangle.

Even with all these tools, some forms of feedback would be difficult or impossible to articulate without process-level feedback: the ability to tell the system that specific patterns of thinking are good or bad, without need...
Resolutions to the Challenge of Resolving Forecasts
by Davidmanheim — 2021-03-11T19:08:16.290Z

One of the biggest challenges in forecasting is formulating clear and resolvable questions, where the resolution reflects the intent of the question. When this doesn't happen, there is often uncertainty about the way the question will be resolved, leading to uncertainty about what to predict. I want to discuss this problem, and in this post, point out that there are a variety of methods which are useful for resolving predictions.

But first, the problem.

What is the Problem?

The OpenPhil / Good Judgement COVID-19 dashboard

provides an example. The goal was to predict the number of cases and deaths due to COVID. The text of the questions was "How many X will be reported/estimated as of 31 March 2021?" and the explanation clarified that "The outcome will be determined based on reporting and estimates provided by Johns Hopkins of X..."

Early on, the question was fairly clear - it was about what would happen. As time went on, however, it became clearer that because reporting was based on very limited testing, there would be a significant gap between the reported totals, and the estimated totals. Discussions of what total to predict were then partly side-tracked by predictions of whether the reports or the predictions would be used - with a very large gap between the two.

Solving the Problem?

This is a very general problem for forecasting, and various paths towards solutions have been proposed. One key desiderata, however, is clear; whatever resolution criteria are used, they should be explicitly understood beforehand. The choice of which approach to be clear you are using, however, is still up for debate - and I’ll present various approaches in this post.

Be Inflexible

We can be inflexible with resolution criteria, and always specify exactly what number or fact will be used for the resolution, and never change that. To return to the example above, the COVID prediction could have been limited to, say, the final number displayed on the Johns Hopkins dashboard by the resolution date. If it is discontinued, it would use the final number displayed before then, and if it is modified to no longer display a single number, say, providing a range, it would use the final number before the change is introduced .

Of course, this means that even the smallest deviation from what you expected or planned for will lead to the question resolving in a way other than representing the ou...
Partnership
by Ruby — 2021-03-11T17:17:02.266Z

This is the sixth speech in the wedding ceremony of Ruby & Miranda. See the Sequence introduction for more info. The speech was given by Miranda.

Oliver descends from podium.

Brienne: Ruby and Miranda have been part of this community and shared these values with us for a long time. The purpose of this ceremony is to declare an even stronger bond between the two of them: a partnership. I call upon Miranda to remind us what partnership means.

Miranda ascends podium.

IMAGE 5 PROJECTED ON PLANETARIUM

The nearby star-forming region around the star R Coronae Australis.

Miranda commences speech.

What is a marriage? Fundamentally, when two people choose to join forces together, to support and care for each other. In fact, a marriage is a special case of a whole class of situations where humans choose to band together. When two people do this, we call it a partnership. When many people do, we call it a community. We are a social species, a species that forms pair-bonds and alliances and teams. In some sense, this is what we are for. For many people, perhaps most people, the bonds they form with others fill a deep need–for security, comfort, safety, or many other things. The types of unions we know of are myriad. Families. Friendships. But a partnership of two, based on love, might be one of the purest examples–two people held together not by shared genes, not by convenience, not by politics, but by choice.

Why is partnership so valuable? Two people might have the same values; they might care about working hard, or being kind to others, or growing stronger and learning constantly. They might share the same vision of an ideal world. They might have spent years staring at the world’s darkness, the parts that were furthest from their ideals, banging their head against the unsolved problems, and chosen goals–and those might be the same goals. If two people are trying to accomplish the same things with their lives, it makes sense to do it together.

Why does it make sense to do it together? There are benefits more concrete and direct than the emotional needs met. Skills and personalities complement each other. In a team, whether of two people or of a dozen, division of labor and each person’s comparative advantage can split seemingly insurmountable problems into workable pieces. Two people can each love their work, and be grateful to the other, willing to do the “nas...
Community
by Ruby — 2021-03-11T17:14:52.635Z

This is the fifth speech in the wedding ceremony of Ruby & Miranda. See the Sequence introduction for more info. The speech was given by Oliver Habryka.

Ruby descends from podium.

Brienne: We have given voice to Ruby and Miranda’s central values. These are the ideals upon which they will build their life together: universal flourishing and endless growth. But they are not the only ones who share those values.

This ceremony is not held in private. It is held before all of us, a community of purpose, created not by accident of location or genetics, but by the goals and values we share with Ruby and Miranda. We are here to support them in their commitment, to hold them to it, and to join them in it. I now call upon Oliver to speak of this community.

Oliver ascends podium.

IMAGE 4 PROJECTED ON PLANETARIUM

The Pleiades is an open star cluster containing middle-aged, hot B-type stars.

Oliver commences speech.

In the last half hour I observed something beautiful. And it wasn’t only the the wonderful images of the night sky. It wasn’t only the epic and beautiful speeches that we just listened to. It was something, that many of you might not have even noticed. But for me it was the most impressive thing this evening.

When Nate talked about the darkness above us. How we are threatened to be crushed under the weight of a universe that does not care about us. I saw genuine fear and worry in all of you.

When Tara read Jess’ piece about the progress of the human race. About the people that lived, the people that died, the beauty of this culture, this life, this city and… all of this. About how not all hope is lost, and we are still marching on. I saw warmth and caring in all of you.

And when Riva painted a picture of the future. A future without suffering, without limitations, and without the shackles of time. I saw so many of us looking up at this artificial sky, some clenching our fists, longing for the stars, knowing that what separates us from them, is just a few hundred years. When Riva spoke, I saw joy, and hope, and a dedication to fight for our dreams, in all of you.

And ultimately. When Ruby talked about how we, as a community, need to get stronger. How we are not yet set up for the tasks ahead of us. How we will overcome every challenge that lies ahead of us, or die trying. When he spoke, I saw that never ending urge to improve in all of yo...
Parameter vs Synapse?
by Tomás B. — 2021-03-11T15:30:59.745Z

In terms of AI timelines, the biggest question I haven't seen addressed is the computational equivalence of a synapse vs a parameter in modern neural nets. This seems like a very important input for any prediction on when we will have human-level AI.

Moravec's estimates of a retina vs the then-current edge-detection methods are sort of worthless under the assumption that AI will be built using modern learning methods, because feature engineered code is plausibly much more compute efficient than learned policies on tasks that are comprehensible to human programmers, and Moravec compared the retina with the former.

To pump this intuition, if we assume that 1 synapse == 1 parameter, then Moravec's estimates are more than 6 orders of magnitude too low. The size of the largest models we are able to train, on computers that are more powerful than Moravec predicted are needed for AGI, is at most 10 billion parameters, which is about as many synapses as the retina has. Though, it would be more accurate to say we are memory bandwidth constrained, as we train neural networks at much higher temporal resolution than human brains.

A very interesting question, then, is how many parameters does a modern learned model need to have plausibly similar capabilities to a human retina. This seems hard to investigate but not impossible. If we look at the first few layers of a conv net, for example, they seem to be doing the sort of edge detection that the retina does.

I think this would be a high-leverage thing to investigate as both the parameter > synapse and parameter < synapse are not implausible at first glance. Some people think the brain is doing something mysterious and better than backpropagation. Some people think it is just doing a worse, Rube Goldberg approximation of backpropagation. Artificial spiking neural networks perform really poorly compared standard stuff, which may imply paramater > synapse, etc.

If we assume AGI will come from scaling up current methods (rather than, as Moravec predicted, very efficient programs that imitate the aggregate function of thousand-neuron assemblies) then this question is very pertinent to any prediction. Is anyone here aware of any work on this?
Behavioral Sufficient Statistics for Goal-Directedness
by adamShimi — 2021-03-11T15:01:21.647Z

Note: this is a new version -- with a new title -- of my recent post "A Behavioral Definition of Goal-Directedness". Most of the formulas are the same, except for the triviality one that deals better with what I wanted; the point of this rewrite is to present the ideas in a perspective that makes sense. I'm not proposing a definition of goal-directedness, but just sufficient statistics on the complete behavior that make a behavioral study of goal-directedness more human-legible.

I also use this new version as a first experiment in another approach to feedback: this post includes a lot of questions asked through the elicit prediction feature. A lot. I definitely tried to overshoot the reasonable number to add, to compensate my tendency to never use them. But don't worry: whether or not there were too many questions will be the subject of another question at the end!

Introduction

In a

previous post

, I argued for the study of goal-directedness in two steps:

Defining goal-directedness: depends only on the complete behavior of the system, and probably assumes infinite compute and resources.
Computing goal-directedness: depends on the internal structure, and more specifically what information about the complete behavior can be extracted from this structure.

Intuitively, understanding goal-directedness should mean knowing which questions to ask about the complete behavior of the system to determine its goal-directedness. Here the “complete” part is crucial; it simplifies the problem by removing the need to infer what the system will do based on limited behavior. Similarly, we don’t care about the tractability/computability of the questions asked; the point is to find what to look for, without worrying (yet) about how to get it.

Elicit Prediction (forecast.elicit.org/binary/questions/GUv6153YY)

Despite these simplifications, the behavioral approach still suffers from one massive problem: it's not human-legible. We don't know what to do with this mass of loosely structured information, and have slim hopes of finding the right angle or question by sheer luck.

This post addresses this problem: it proposes human-legible sufficient statistics on this complete behavior that should be enough to deconfuse and clarify most questions about goal-directedness. The next posts then use these statistics to explore a formal understanding of goal-directedness.

Eventually w...
Animal faces
by KatjaGrace — 2021-03-11T08:50:11.484Z

[Epistemic status: not reflective of the forefront of human undersetanding, or human understanding after any research at all. Animal pictures with speculative questions.]

Do the facial expressions of animals mean anything like what I’m inclined to take them to mean?

On the one hand, we can’t have been the first creatures to have the innovation of facial expressions. And the same kind of animal is often seen to have different facial expressions, which has got to happen for a reason. But on the other hand, the whole race of dolphins just looks vaguely elated, and surely that can’t track the collective personality of the species.

I suppose the answer is that many animals do have meaningful facial expressions, and even that broadly ‘smiling’ matches our broadly ‘smiling’, but that for the most part, they don’t match in their subtleties, and species-wide features don’t track anything.

That all sounds very reasonable, but can I really look at this creature and not see it as happy?

Perhaps I can’t, but my hard-to-quash beliefs are just wrong. How tragic in that case to have a visual right on one’s face—standing in the way of our ever looking each other in the eye and understanding at all, even if we were to spend a lifetime together.

And seals—they seem to look extremely contented more than half the time, but not all of the time. So it’s not that they are automatically contented-looking. What does it mean?

Are these creatures not contented?

Do the facial expressions of humans mean anything like what I’m inclined to take them to mean?

I tend to read slightly different smiles with a lot of flavor and significance, for instance.

But when I look in the mirror, my face doesn’t look like what I mean it to look like. At least not reliably and with fine specifically. I’m way better than chance on ‘smile’ when intending to smile, but ‘friendly smile’ can easily end up as ‘ridiculous to the point of questioning how anyone takes me seriously’ smile, or ‘somewhat intimidating smile’.

And people’s default faces have features that my brain interprets as expression—if one person looks more grouchy or judgmental or open or lighthearted across the board than another, does that mean something? My automatic interpretation of things thinks so, but it seems very questionable.

~
Are dogs bad?
by qbolec — 2021-03-11T08:11:18.462Z

This is my response to Trapped Priors As A Basic Problem Of Rationality by Scott Alexander.

I felt that the discussion in the post and comments lack some references to lessons learned from Yudkowsky's Causal Diagrams and Causal Models and Fake Causality and One Argument Against An Army which is that you can't bounce back and forth between verdict and assumptions double-counting the evidence in self-reinforcing loop.

Also, I posit that you have to be careful to distinguish separate concepts of "prior on this particular encounter going badly" and "prior on the fraction of the encounters which will go badly" etc. Scott's post seems to hide both under the same phrase "prior that dogs are terrifying".

I've made a colorful charts showing the story of three encounters with dogs, which illustrate how I believe one should update their prior (and meta-prior) and what can go badly if you let your final verdicts influence them instead:

https://docs.google.com/spreadsheets/d/1vQfK-g0zAMEcXwbbPbAWWMTpHprE1jpf6bmwDjtJkHo/edit?usp=sharing

One important feature of the epistemology algorithm I propose above, is that you can admit you were wrong about your past judgments: after exposure to many friendly puppies and updating your prior on "fraction of good dogs" up sufficiently, you may reevaluate your previous verdicts ("see them in a new light") and decide that perhaps they were also good dogs. And there is no risk of this causing a loop in the reasoning, because there are no edges from "verdicts" to "the prior" in this algorithm.

There are many open questions, though. Like for example: should one also update the operational definition of word "bad" over time - in my example, initially we assume that "bad dog" will make us feel threatened 2/3 of the time, and this remains fixed throughout the lifetime. But perhaps this should also update, adding one more layer of Bayes computations. (Perhaps this is exactly the part which is "broken" in people suffering from phobia - perhaps they have a different value than 2/3, or let it drift too much or too little w.r.t. rest of us?). I don't know how to do it, without risking that "bad" and "good" will lose their meaning over time. The 1/3 vs 2/3 split might also change for a different reason: perhaps over time we learn something about our sensors' reliability, their mapping to reality, etc. This might introduce another layer of Bayes. I'...