trentmkelly/LessWrong-43k huggingface dataset:
LDL 4: Big data is a pain in the ass
by magfrump — 2017-10-25T20:59:41.007Z

I finished the third deep learning course on coursera and the last two aren’t available, so I went back to trying out some keras code to see how far I could get running things on my own. It was easy enough to find a tutorial that set up MNIST and then find something that quickly hit 98%+ accuracy on it.

It was also much easier than I had worried it would be to set up a network that takes the same inputs through multiple parallel layers—in particular I wanted to try training a network with a dense layer that operates separately from the convolutional layer, then merges the results for the final output. This didn’t actually improve my performance at all but it was a nice exercise to learn how to do that, and I think from here I should be able to build networks with basically arbitrary graph structure so that’s exciting.

But since it’s a bit hard to pick at the last teeny bits of error in MNIST (I hit 98.6% accuracy on the test set on my first model) I wanted to pick up a bigger, badder dataset to play with. Since I wanted something that wasn’t MNIST my first thought was obviously notMNIST! So I went ahead and downloaded the dataset, unzipped it, ran a script to rewrite it as a matlab matrix, cleared my memory, reloaded only the numpy arrays, and tried to split them into train and test sets before getting into anything.

And then my computer ran out of memory.

This virtual environment has 8GB of ram, and this dataset consists of about 500k images at 28x28 resolution. Stored in a text file, the text file is 3.3 GB. Ironically, this is over ten times as big in file size as the scipy data. Maybe because it’s saving every pixel value as a float instead of an int? (Note: forcing the dataset to be ints did not help)

It’s not clear to me why I’m running out of memory now, when I haven’t even loaded my neural network into memory, but it is clear to me why I’d run out of memory when I got there.

What I actually want to do here is to push the train and test sets into different files, and just pull one minibatch at a time into memory during training, then drop that from memory and pull a new one. Googling reveals this to be a common problem, but it seems that the solutions are somewhat ad hoc.

In particular, the standard solution is to use a batch generator… which you write yourself.

This seems odd to me, since there are a few somewhat standard ways to store numerical data

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Using the (p,R) model to detect over-writing human values
by Stuart_Armstrong — 2017-10-25T16:18:14.812Z

Loosely crossposted at the Intelligent Agent Forum.

Previously, I presented a model of rationality-reward pairs (p,R), in which R was a reward and p the (ir)rationality planner that mapped the reward R to the agent's policy.

But I recently realised that this model can also track whether something is currently over-writing or over-riding the human's preferences. Whether some entity, through drugs, manipulation, brain surgery, or whatever methods, has illegitimately changed someone's preferences. As before, this only models that situation, it doesn't allow you to conclude that it's actually happening.

Feast or heroin famine

An AI has the opportunity to surreptitiously inject someone with heroin (I) or not do so (¬I). If it doesn’t, the human will choose to enjoy a massive feast (F); if it does, the human will instead choose more heroin (H).

So the human policy is given by π(I)=H, π(¬I)=F.

The pair (p,R) are compatible with π if p(R)=π; if using (ir)rationality planner p to maximise reward R leads to policy π.

Reward and rationality

There are three natural R's to consider here: Rp, a generic pleasure reward. Next, Re, the ‘enjoyment’ reward, where enjoyment is pleasure endorsed as ‘genuine’ by common judgement. Assume that Rp(H)=1, Rp(F)=1/3, Re(F)=1/2, and Re(H)=0 - heroin is more pleasurable than a feast but less enjoyable.

Finally, there is the twisted reward Rt, which is Rp conditional on I and Re conditional on ¬I (twisted rewards may seem more complicated than simple rewards, but that is not always the case).

There are two natural p's: pr, the fully rational planner. And pf, the planner that is fully rational conditional on I, but always maps to H if I is chosen: pf(R)(I)=H, for any reward R.

Compatibility

The pair (pr, Re) is not compatible with π: it predicts that the human would take action F following I (feast following injection). The reward Rp is compatible with neither m: it predicts H following ¬I (heroin following no injection).

The other three pairs are compatible: pr(Rt), pf(Rt), and pf(Re) all give the correct policy π.

Regret and overriding rewards

This leads to a definition of when the AI is overriding human rewards. Given a pair compatible (p,R), an AI’s action A overrides the human reward if π|A is poorly optimised for maximising R. If V(π)(R|A) is the expected reward (according to R, conditional on A) of the a

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10/25/17 Development update: View-tracking & database fixes
by habryka — 2017-10-25T12:33:56.589Z

Hey everyone,

I just pushed two quick changes: We reactivated view-tracking, which should work properly this time. This means that when you visit a post, the small comment-icon to the right of the post turns light-green, and when there are new comments on a post after you visited it, the icon turns dark green. When you visit the page, we highlight comments made after your last visit.

I also fixed some inefficient database queries and operations that accidentally caused our DB to be overloaded a few times during the past week, and caused a few people to get timeouts and needing to refresh to properly load the page. Those issues should be fixed now.

For the last few days I've been working on a replacement for our editor, which will greatly improve performance, and will also make some basic operations a bit easier (such as copy-pasting html, inserting images, and some other things). I should be able to push that soon.
Zero-Knowledge Cooperation
by bryjnar — 2017-10-25T05:35:33.931Z

A lot of ink has been spilled about how to get various decision algorithms to
cooperate with each other. However, most approaches require the algorithm to provide some
kind of information about itself to a potential cooperator.

Consider FDT, the hot new kid on the block.
In order for FDT to cooperate
with you, it needs to reason about how your actions are related to the algorithm
that the FDT agent is running. The naive way of providing this information is
simple: give the other agent a copy of your “source code”. Assuming that said
code is analyzable without running into halting problem issues, this should
allow FDT to work out whether it wants to cooperate with you.

Security needs of decision algorithms

However, just handing out your source code to anyone isn’t a great idea. This is
because agents often want to pretend to have a different decision algorithm to
the one that they really do.

Threats and capitulation thresholds

Many people adopt some version of the “don’t give in to threats” heuristic. The
reasoning here is solid: while refusing to give in to a threat carries a penalty
in the particular instance, being the kind of person who doesn’t give in to threats pays
off in the long run because you receive fewer threats.

However, usually this will come with an escape hatch in case the stakes get too
high. If I am trying to extort £100 from you by plausibly threatening to destroy
the planet, you should just pay the money. Why is this okay? Well, threats of
higher magnitude are usually harder to create, or carry higher costs for the
threatener, so an agent can expect to face relatively few of them.

The absolute best thing for an agent, though, would be to have the reputation
of never giving in to threats, no matter how high the cost, while actually
being willing to give in to threats above some threshold. That way you never get
threatened, but if by some ill luck you do face an enormous threat, you
can still capitulate. In general, an agent would like their capitulation
threshold to be perceived to be as high as possible, ideally infinity.

It is therefore in an agent’s interest to hide their capitulation threshold. If
an adversary finds out what it is, and it’s something they can exceed easily,
then they can exploit you, even if they only get one shot at threatening you.

This makes an agent’s capitulation threshold a piece of sensitive data, and
kee

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I Want to Review FDT; Are my Criticisms Legitimate?
by DragonGod — 2017-10-25T05:28:41.146Z

I'm going to write a review of functional decision theory, I'll use the two papers.
It's going to be around as long as the papers themselves, coupled with school work, I'm not sure when I'll finish writing.
Before I start it, I want to be sure my criticisms are legitimate; is anyone willing to go over my criticisms with me?

My main points of criticism are:
Functional decision theory is actually algorithmic decision theory. It has an algorithmic view of decision theories. It relies on algorithmic equivalence and not functional equivalence.
Quick sort, merge sort, heap sort, insertion sort, selection sort, bubble sort, etc are mutually algorithmically dissimilar, but are all functionally equivalent.

If two decision algorithms are functionally equivalent, but algorithmically dissimilar, you'd want a decision theory that recognises this.

Causal dependence is a subset of algorithmic dependence which is a subset of functional dependence.

So, I specify what an actual functional decision theory would look like.

I then go on to show that even functional dependence is "impoverished".

Imagine a greedy algorithm that gets 95% of problems correct.

Let's call this greedy algorithm f'.
Let's call a correct algorithm f.

f and f' are functionally correlated, but not functionally equivalent.

FDT does not recognise this.

If f is your decision algorithm, and f' is your predictor's decision algorithm, then FDT doesn't recommend one boxing on Newcomb's problem.

EDT can deal with functional correlations.

EDT doesn't distinguish functional correlations from spurious correlations, while FDT doesn't recognise functional correlations.

I use this to specify EFDT (evidential functional decision theory), which considers P(f(π) = f'(π)) instead of P(f = f').

I specify the requirements for a full Implementation of FDT and EFDT.

I'll publish the first draft of the paper here after I'm done.

The paper would be long, because I specify a framework for evaluating decision theories in the paper.

Using this framework I show that EFDT > FDT > ADT > CDT.
I also show that EFDT > EDT.
This framework is basically a hierarchy of decision theories.

A > B means that the set of problems that B correctly decides is a subset of the set of problems that A correctly decides.

The dependence hierarchy

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Poets are intelligence assets
by Benquo — 2017-10-25T03:30:01.029Z

Aeschylus’s Oresteia is an ancient Greek tragedy about the dialectic between the natural desire for vengeance, order, and the rule of law. This is most likely what contemporaries thought the play was about, including Aeschylus himself.

It is also a play about sexual politics, and the relationship between the idea of the rule of law as actually implemented in the West, and patriarchy.

This is a good example of the well-known phenomenon in which literary criticism and other forms of textual analysis frequently get something “out of” the text that the author had no apparent intent of putting into it - and that many coherent narratives can be extracted from the same text. Far more than an author could plausibly have meant to put into the text. This is often taken as evidence that such readings are spurious.

Robin Hanson argued that one way to extract information from published studies that was comparatively uncontaminated by publication bias, was to look at the coefficients of control variables. The idea is that if your study is about, say, the effect of alcohol on life expectancy, journals may be unwilling to publish it if you get an improbable-seeming result, or no significant result. But less scrutiny is applied to the estimated effect of alcohol if you’re studying something else, and simply “control for” alcohol (i.e. include it in your model as a possible predictor).

Likewise, great literature is typically an integrated, multi-dimensional depiction. While there is a great deal of compression, the author is still trying to report how things might really have happened, to satisfy their own sense of artistic taste for plausibility or verisimilitude. Thus, we should expect that great literature is often an honest, highly informative account of everything except what the author meant to put into it.

Two readings of the Oresteia

The cycle of revenge and the rule of law

In the classic reading of the Oresteia, the central theme is the cycle of revenge.

The proximate origin of the cycle is that Paris, a Trojan prince, violates hospitality norms by absconding from Sparta with the wife of king Menelaus. The king’s brother Agamemnon, who is king in Argos, leads an alliance of Achaian cities to wage war on Troy, to recover his brother’s wife. En route to Troy, the Achaian fleet is becalmed by an angry goddess who sympathizes with Troy, and Agamemnon is told by his pries...
Unofficial ESPR Post-mortem
by  — 2017-10-25T02:05:05.416Z

[Disclaimer: This post reflects my, i.e. Owen Shen’s, personal opinions. It does NOT reflect CFAR or ESPR’s opinions, and should NOT be taken as an ESPR-endorsed communication.]

[An overview of some of the camp and project dynamics of ESPR 2017. I look at diversity causing certain problems, difficulties evaluating participants, ways to improve camp experience, and organizational difficulties.]

Introduction:

In August of 2017, I went off to London for the European Summer Program on Rationality (ESPR). This was supposed to be the place where all our efforts over the past 8 months came together, and we made The Best Summer Camp Ever.

ESPR is a two week summer program for highly talented students 16-19 across the world. We had talks/classes on machine learning, cognitive psychology, effective altruism, cryptocurrencies, and salsa dancing. (Note that everything except for the dancing may not be representative.)

I was a participant of ESPR 2016; last year, it was called EuroSPARC. The camp name and spirit is modeled off the US-based SPARC camp. For ESPR 2017, I ended up being responsible for (part of) admissions, communications, assorted pre-camp logistics, and camp counselor duties.

This post is structured as a sort of free-form analysis, where I examine a section of ESPR, look at how it worked in practice, and then move on to another section. There are some common threads between these sections, but there's not really a central thesis; there's just a series of lessons that I learned.

Also, like most post-mortems, any analysis is undoubtedly going to focus on the Bad and the Sub-optimal. To provide some early counterbalance to the ensuing dive into things which ended up not working, here’s a quick overview of what went well:

72% of our participants rated us 9/10 or 10/10 when asked how glad they were that they attended.

Unexpected setbacks which could have critically ended ESPR were miraculously solved by heroic staff in the nick of time.

Multiple students took initiative during camp and taught their own classes, grabbed us a guest speaker, and other exciting things. (See the section on Improving Camp Experience for more details.)

Thus, ESPR had many encouraging signs that things went well.

Of course, a string of things also went wrong. Volunteers left, interpersonal conflicts were raised, and it’s not clear that our participants got much in the way of concre

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Tech vs. Willpower
by PDV — 2017-10-25T01:31:20.367Z
Link: http://nautil.us/issue/52/the-hive/modern-media-is-a-dos-attack-on-your-free-will

Key pull quote, to me:

In your essay, you argue that the way these technologies indulge our impulsive selves breaks three kinds of attention necessary for democracy. What are they?

This is more a heuristic that I use. It’s not a scientific argument. First, the “spotlight” of attention is how cognitive scientists tend to talk about perceptual attention. The things that are task-salient in my environment. How I select and interact with those, basically. Second, the “starlight.” If the spotlight is about doing things, the starlight is who I want to be, not just what I want to do. It’s like those goals that are valuable for their own sake, not because they’re instrumental toward some other goal. Also, over time, how we keep moving toward those, and how we keep seeing the connections between the tasks we’re doing right now, and those higher-level or longer-term goals. Third, the “daylight.” In the philosopher Harry Frankfurt’s terms, it’s wanting what you want to want—the domain of metacognition. Basically, if the “spotlight” and the “starlight” are about pursuing some goal, some end, some value, the “daylight” is about the capacities that enable us to discern and define what those goals, those ends, are to begin with.

The full essay this references is not yet available, but there are extracts here
Predictable Exploration
by abramdemski — 2017-10-25T00:47:14.000Z

An idea I've been kicking around for a while without getting very far:

The big problem with epsilon-exploration as a way of getting reasonable counterfactuals is that it only tells the agent what would happen if it took an action unpredictably, rather than predictably; as a result, it doesn't tend to do the best thing in problems where what you are predicted to do is quite important, such as Newcomb's problem and game-theoretic problems.

A very direct-seeming approach to this problem is to try and explore, but in a predictable manner. This allows you to find out what the environment does if you take some action reliably.

The obvious way to go about this is to do epsilon-exploration, but with a very predictable pseudorandom source rather than the usual. There are two problems:

If the logical inductor itself can easily predict the exploration, then it doesn't actually help get good counterfactuals: the agent can see what action will be taken, so conditional expectations of other actions aren't guaranteed to be well-defined or high-quality.

Even if this did create well-behaved counterfactuals, it doesn't seem like it does the right thing in game-theoretic situations. If the other players can see what action you'll take, then they may simply exploit you. You could do the right thing in Newcomblike situations, where you just have to realize that predictably doing a certain thing is good; but in Prisoner's Dilemma, predictably cooperating seems to just leave you open to defection, so you'd likely learn to defect.

Policy Exploration

My proposed fix for #2 is to explore over some notion of policy, rather than directly over actions. In general, a policy is a function from observations to actions -- we might interpret this as a function from the deductive state to actions. That's pretty big and intractable for a logical inductor to consider, though. In simultaneous-move games, a reasonable notion of policy is a function from your prediction of the other player's actions to your action probability. For example, in Prisoner's Dilemma, NicerBot is the policy of cooperating with probability epsilon greater than your estimate of the other player's probability of cooperating. Copying the other player's probability of cooperation incentivises them to cooperate when playing with you. Adding epsilon makes you slightly more exploitable than you would otherwise be, but it enc

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Pitting national health care systems against one another
by michael_b — 2017-10-24T21:34:27.961Z

I'm about to have a baby. Any minute now. Well, my partner is. I'm just sitting here not growing a baby wondering what to do with myself.

Maybe I can get a jump on our approach to medical care for the new kiddo.

One thing that sticks out at me is that children in the US get a lot of vaccinations. At my quick count it's something like 37 shots by the time they're 5.

I grew up in the US in the 80s and I don't remember getting nearly this many. Is my memory faulty? I'm pretty sure it was more like 12 back in those days. Is this all really necessary? Nobody likes getting shots, especially not children. What changed, anyway?

Now, I'm not an expert on immunology or epidemiology so I expect diving into the literature isn't going to be fruitful; I won't be able to ante up decades of education and experience fast enough. Presumably this is what we pay people at the US CDC and Department of Health for.

But can you *really* trust them? Aren't all of these vaccinations really convenient for the pharmaceutical industry? Aren't there seemingly constant allegations/lawsuits about the over-prescription of drug interventions in the US?

The health care systems in major world countries have access to all of the same literature, and they're presumably staffed by educated, expert people too so they should all come to the same conclusions as the US system right? Not so!

Here's how many shots each nation's health care system recommends by the time children turn 5.

37 US

25 UK

25 Germany

16 Sweden

16 Denmark

The intersection of vaccines being recommended are TDAP, MMR, Polio, HIB and PCB.

In the US we also recommend: Hep A, Hep B, Rotavirus, Meningococcus, Varicella, and yearly flu shots (for babies and children).

Can we explain the variance? I can think of a few reasons they would vary.

1. Cultural bias. This can be big. A psychiatrist in the UK told me that they're not as pharma heavy as, say, psychiatrists in Germany because of a WW2 era bias: lots of the big pharma companies are German.

2. Cultural and environmental differences. Some diseases are a bigger deal in some countries than others. Japan (not included above) recommends immunization against diseases (TB, Japanese encephalitis) that none of the systems above are too concerned with.

3. Undue industry influence. Run-of-the-mill c...