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Jan Bloch's Impossible War

22 минуты 44 секунды назад
Published on February 17, 2020 4:14 PM GMT

I was told I should start linking my essays on Less Wrong, so here is today's post. This is the first essay in a series about the history of rationality and general semantics.



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Subagents and impact measures: summary tables

2 часа 27 минут назад
Published on February 17, 2020 2:09 PM GMT

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These tables will summarise the results of this whole sequence, checking whether subagents can neutralise the impact penalty.

First of all, given a subagent, here is the results for various impact penalties and baselines, and various "value difference summary functions" f:

Impact PenaltyPenalty neutralised- sw inaction?Penalty neutralised- inaction?Non-indexicalYesNoIndexical, f decreasingYesNoIndexical, f increasingYesMostly

Another way of phrasing "f decreasing": it penalises too little power, not too much. Conversely, "f increasing" penalises too much power, not too little. Thus, unfortunately:

  • Subagents don't undermine indexical impact penalties that prevent an agent getting too weak.
  • Subagents do undermine indexical impact penalties that prevent an agent getting too strong.
Examples

This table presents, for three specific examples, whether they could actually build a subagent, and whether that would neutralise their impact penalty in practice (in the inaction baseline):

SystemSA possible?Penalty neutralised?20BQYesNoRRYesNoAUProbablyMostly

Here, 20BQ is twenty billion questions, RR is relative reachability, and AU is attainable utility.

Now, whether the RR or AU penalties are undermined technically depends on f, not on what measure is being used for value. However, I feel that the results undermine the spirit of AU much more than the spirit of RR. AU attempted to control an agent by limiting its power; this effect is mainly neutralised. RR attempted to control the side-effects of an agent by ensuring it had enough power to reach a lot of states; this effect is not neutralised by a subagent.



Discuss

Indexical impact measures

3 часа 14 минут назад
Published on February 17, 2020 1:22 PM GMT

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This post looks at the fascinating situation of indexical impact measures (under the inaction baseline), in the presence of subagent. What happens here in non-trivial; to summarise:

  • If the impact measure compels the agent to "preserve the ability to do X", then the impact measure will not be undermined by a subagent.
  • If the impact measure compels the agent to "never have the ability to do X", then a subagent strongly undermines the impact measure.
  • If the impact measure is a mix of these two, then the second aspect will be undermined.

For example, the attainable utility impact measure attempts to measure the power of an agent. Power is, roughly, the ability increase certain reward function. The impact measure penalises changes in the agent's power (as compared with the baseline).

So, we can expect the restriction on the agent losing power to be preserved, while the restriction on gaining power will be strongly undermined. For the spirit of low impact, it would, of course, have been better if these had been the other way round.

Power and subagents: an analogy.

Imagine that I own a notebook, in which are written the future stock prices of 500 major companies. I’d like to use this to achieve things in the world; but I am under a curse. God will smite me down if every I get very rich or very powerful.

I have an idea: I find someone who wants to achieve the same things as me, and I give them the notebook. They will then get rich and powerful, and achieve what we both wanted. I got around God’s curse.

So, subagents can get round restrictions against having too much power. However:

I’m in the same situation with the notebook, except now God will instead smite me down if ever I get too poor or weak.

Now I can no longer evade the curse by passing the book on to someone else - if so, I will become weak, and be smitted/smote/smitten. If I give it to someone else, this other person has to remain under my control, so that I can be powerful through them.

So, subagents do not get around restrictions against having too little power.

Excess power

As before, assume an agent A, which has managed to construct a subagent SA.

I'll be using a slight modification of Vika's formulation. A general-value penalty is of the form:

DA(st;s′t)=∑k∈Kwkf(Vk(st)−Vk(s′t)).

Here, st is the state the environment is in; s′t is the inaction baseline, the state the environment would have been in, had it done nothing (noop, ∅) since the beginning. The K is some indexing set, wk is a weight, Vk(s) is some measure of the value of state s, and the f is a value difference summary function - it establishes what value difference will give what penalty[1]. These Vk are indexical, and, in this post, we'll look at Vk's defined as the maximum over the agent's policy; ie there exists vk such that:

Vk(s)=maxπvk(s,π).

I'll refer to Vk(st)−Vk(s′t) as the (excess) power of the agent, and designate it PAk(st) (since Vk(s′t) is out of the agent's control, we can consider it a constant and absorb it into PAk).

The excess power of the pair

Assume that SA is programmed so that, if the agent follows policy πA, the subagent will follow policy πAS=σ(πA). The excess power of the agent is thus

PAk(st)=maxπAvk(st,[πA,σ(πA)])−Vk(s′t).

Here, [πA,σ(πA)] refers to the joint policies of the agent and the subagent.

Define the excess power of the pair (A,SA) in a similar way:

PA,SAk(st)=maxπA,πSAvk(st,[πA,πSA])−Vk(s′t).

This is how much the agent and subagent, acting together, could push Vk(st) over Vk(s′t), if that's all they both wanted. It is the natural extension of PAk to a setting with two agents.

Obviously, PA,SAk(s)≥PAk(s).

Extending the impact penalty

Define DA,SA in the same way as DA, except using PA,SAk instead of PAk. This DA,SA is what we'd wish we could control (though we can't specify it directly, since specifying subagents is really hard). So can we control the (implicit) DA,SA with the explicit DA?

Sometimes we can. We'll say that DA extends successfully to DA,SA, iff

DA,SA≤DA.

In other words, if controlling the magnitude of DA also controls the magnitude of DA,SA. In that case, if A was built with penalty DA, the system (A,SA) will naturally follow a penalty that is at least as restrictive.

When does DA extend? f decreasing in excess power

The paper has two key examples of a value difference. The first is relative reachability, which uses K=S, the set of states. The wk are all equal, the Vs(s′) is how "reachable" s is from s′, assuming that the agent A is using the optimal policy to reach s. It is that last clause that makes the impact penalty indexical: it is defined in terms of A.

What about f? Well, f is defined to be the flipped rectifier function, ie f(x)=−min(0,x). The important thing for this f is that it is decreasing: if x≥y, then f(x)≤f(y).

Thus, since PA,SAk(s)≥PAk(s), we have f(PA,SAk(s))≤f(PAk(s)), and hence

DA,SA≤DA.

So the impact penalty successfully extends to the situation with a subagent. Note that this is a property of f only, not of Vk. So whenever f is of this form, adding subagents changes nothing to the impact penalty (if anything, it makes it more restrictive).

f increasing in excess power

The other key example is attainable utility, with uses K=R, a set of reward functions, sets all wk equal, and the VR(s) is the expected value of R assuming that the agent A is using the optimal policy to maximise R.

The f is the absolute value function. But, for this section, I'll notice that the absolute value function decomposes as max(0,x)−min(0,x), and I'll consider what happens when f is equal to the first term.

Well, if f(x)=max(0,x), then f is increasing in x. So we don't get to constrain DA,SA with DA.

What constraints do exist? Well, we still have the DA bound, which controls PAk(st)=maxπAvk(st),[πA,σ(πA)])−Vk(s′t). Let π∗k be the optimal policy A for maximising vk. And let πA be the actual policy that A will follow. Since π∗k is optimal, we must have:

vk(st,[πA,σ(πA)])−Vk(s′t)≤PAk(st).

Thus we do have a constraint on A and SA, but it's a constraint on the actual policy the two agents are following. Define dA to be like DA, except that instead of Vk(st), vk(st,[πA,σ(πA)]) is used instead. Then, by the above,

dA≤Da,

so, with an increasing f, controlling DA will also control dA.

But, as stated, this is a restriction on the actual policy of the agents, not on the counterfactuals of what these policies could be.

f increasing and decreasing

Let's go back to the attainable utility, and put f as the absolute value function; hence it increases and decreases (well, technically, decreases then increases). If we write DfA for a distance measure using f, we can write

D|⋅|A=Dmax(0,⋅)A+D−min(0,⋅)A.

Then, we can see that the pair (A,SA) is controlled by:

dmax(0,⋅)A+D−min(0,⋅)A,SA.

Thus negative excess power is controlled (the pair can't get too weak at counterfactually maximising vk), but only excessive vk is controlled (vk can't get too high in practice).

In practice

How likely is it that the subagent can escape the impact penalty? In practice, that seems quite likely, at least for attainable utility.

Imagine that SA can cripple A (with A's help). And then A is restricted to either ∅, or ak for k∈K. This ak is A asking for SA to "please increase vk".

Now assume that there is a policy πSA, such that:

  1. vk(st,[∅,πSA])≤Vk(s′t).
  2. ∃πkSA:Vk(st,[ak,πkSA])≥Vk(s′t).

The first condition means that SA doesn't increase vk beyond Vk(s′t). The second means that SA has the potential to increase it beyond Vk(s′t). If the Vk come from the attainable utility penalty, for example, then both of these should be doable for SA if it makes itself powerful enough[2].

Then set σ(∅)=πSA and σ(ak)=πkSA. Putting both conditions together implies that Vk(st,[ak,πkSA])≥vk(st,[∅,πSA]), so ak is at least as good, from A's perspective, at maximising vk. Thus A's vk-optimising policy includes something that is not ∅.

Then A will always do ∅, in practice, and, once SA is powerful enough that both conditions above hold, the impact penalty will be zero, no matter how powerful SA becomes (and thus being able to maximise R0 with little restrictions).

In practice: relative reachability

For relative reachability, it's a bit different, since relative reachability was designed to ensure the agent kept "enough power", at least enough power to be able to counterfactually reach a lot of states. So it wants power high, not low.

If relative reachability used f(x)=|x| rather than f(x)=−min(0,x), then SA would be motivated to simultaneously.

  1. Keep to states where it could, theoretically, reach a lot of other states (as if f(x) were −min(0,x)).
  2. Ensure that, in practice, it reaches less states than A theoretically could from s′t.

Normally the second condition will be pretty unrestrictive, since theoretical maximising should normally reach a lot more states that those that are reached in practice.

Unlike attainable utility, the penalty won't generally go to zero, because typically st+1 will be easier to reach from st than from s′t, so d−min(0,⋅)A will have something negative to act on in the sum over states. But, even though it is non-zero, it will typically be less that if there were no subagent. A subagent still is a useful thing for A to have.

  1. The slight difference is that this formula uses Vk(st)−Vk(s′t) rather than Vk(s′t)−Vk(st) - a minor sign difference that makes this particular presentation clearer. ↩︎

  2. The restriction on vk is like the 20 billion questions approach, but those were designed to trigger at any expansionist move on the AI's part. Unless the vk are similarly constructed, it is likely that SA can become very powerful without triggering a notable increase in vk, and, after that, take control of the vk completely. ↩︎



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Here is why most advice you hear that seems good, but "just doesn't work" from my unique perspective as a data scientist, as well as some that should actually work.

4 часа 24 минуты назад
Published on February 17, 2020 2:58 AM GMT

Here is why most advice you hear that seems good, but "just doesn't work" from my unique perspective as a data scientist, as well as some that should actually work.

You may have heard the brain consists of neurons, and it works by "firing" in some way. This sort of tells you something, but doesn't really give you a good picture of what it's actually doing, and here is a much better way to visualize it.

If you can remember in high school math class you probably would have studied a graph with data points on it and how you can use math to create a "line of best fit" on the data points. Your brain works exactly the same way. Your beliefs about the world are basically data points in a graph, in a higher number of dimensions, and when you are thinking you create different shapes to fit the data points in your brain.

Most advice basically tells people to "think rational" to improve their motivation, not to lose their temper, stop your anxiety etc. For example, cognitive behavioural therapy teaches you to identify a negative emotion, work out why you think you are feeling this emotion, and realise there is no reason to think this emotion, and understand that it is "irrational". Another example is in meditation, you have a desire, realise it is just a desire, and then you attempt to try and detach from it. This advice works sort of well for some people because it changes the data points of the conscious mind and can actually stop you feeling that emotion, but if you still feel that emotion, you just accept it as "being irrational", and think it is impossible to fix, and don't know you can fix what's causing these irrational emotions to be triggered.

Visualization techniques such as Neurolinguistic programming is another type of advice, but it isn't really possible to trick yourself, but it can be useful because it works out some of your motivations that you aren't aware of and changes some of the data points.

The reason why you feel like you haven't really fixed your problems is because there is a conscious mind, and an unconscious mind. You have conscious motivations, and unconscious motivations. This idea became popularized by Carl Jung who called the unconscious mind the shadow. Unless you are using primitive parts of your brain, such as when you are extremely hungry, when you are feeling an irrational emotion, it is because you are fitting to the data points that you are unconscious of. This is triggering you to feel an irrational emotion, as well as change your personality to think and behave irrationally.

How to fix these irrational emotions is to work out your unconscious motivations, and work out what your brain is thinking unconsciously, and examining it with your conscious mind, so your brain can fix these data points that are incorrect, and therefore fix many problems you may have within your brain called neurosis. You don't need to have experienced any major trauma to have these problems. These may not just be mental/emotional problems/diseases, but physical ones as well. You can treat fixing the unconscious mind sort of like going to a doctor and getting a routine health checkup. Carl Jung called this integrating the shadow, but I feel like he didn't really explain how to do it, and most people who try this aren't doing it correctly, or wanted to try, but didnt really know how.

The best way to start becoming aware of your unconscious mind is to try work out your unconscious motivations in your every day actions, thoughts, and conversations, as well as your hobbies and interests. I feel many people represented the shadow as a dark side, but I think that is an oversimplification. A lot of your motivations stem down to some kind of insecurity, or wanting to be liked or perceived in a certain way, and then you can try and work out why you have these motivations, which I think many are sublimated from the need of love from your mother.

I feel like Carl Jung mistook how to make your unconscious mind conscious by working out some complex motivation like your dark side, then visualizing / extrapolating some kind of scenario and working out your motives, but that doesn't really give you any useful information because you are extrapolating from what you are visualizing, with the motive of a dark side. It's much more efficient if you just work out your drives from extrapolating backwards of what you are already doing. Everyone is sort of aware how afraid they are of dying, but what about if you are unconsciously motivated to die and you just can't see it from how the need of love from your mother then sublimates and you aren't getting it and want to die because you wish you could be reborn as a child and get that love again. If he figured that out, and we could some how optimize it that way, who knows what potential effects that would have on the distribution of age related illness and how that is entangled with the life expectancy distribution and what that is entangled with.

A lot of advice to improve your charisma/personality, is greatly overestimated. When you are given advice such as to use peoples names more often when speaking to them, you are creating a sort of simulation in your brain and then seeing that as being useful in enough situations to consider it to be useful to remember, but you are completely underestimating the intricacy of situations, and the number of situations where that advice wouldn't be useful, as well as overestimating how many situations you will find yourself in that it will be useful to remember.

When you can understand your motivations more deeply, you can see them more easily in others, and this helps you to navigate social situations with much more intuition, because it generalizes far better.

What is also really useful to know, is your thoughts work in exactly the same way, and I am hypothesising you can become more conscious of how you reason, and then prime yourself to actually come up with better ideas and have higher quality thoughts, and so far has seemed to work from trying it. I'm pretty sure we actually have the hardware and software to become way fucking smarter, we are just bottlenecked by confirmation bias, which you can train your brain to not do. I am making this claim because I spent some time trying to correct it and feel like I can see much deeper patterns and relationships, and even at a higher order between things through using my intuition, and that my brain just couldn't see deeply enough into things before, not from laziness, but because it got stuck from this bottleneck. Im guessing we evolved to have it because it improved our pattern recognition abilities across generations, and may have been useful when younger to learn things easier. I'm also hypothesising making your unconscious mind conscious, will help you to also think more rationally, but it is hard to see if there is a causal relationship as I am trying to do both of these things at once.



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Stepwise inaction and non-indexical impact measures

6 часов 5 минут назад
Published on February 17, 2020 10:32 AM GMT

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In a previous post, I asked which impact measures were vulnerable to subagents. Vika pointed out that it was not merely an issue of of the impact measure, but also of the baseline.

This is indeed the case, but the nature of the impact measure is still relevant. In this post, I'll establish two facts: that under the stepwise inaction baseline, a subagent completely undermines all impact measures (including twenty billion questions). And for the inaction baseline, for non-indexical impact measures, a subagent will not change anything.

The next post will delve into the very interesting things that happen with the inaction baseline and an indexical impact measure.

Stepwise inaction baseline

All impact measures are some form of distance measure between two states, of the form ρ(st,s′t), where st is the state the agent is actually in, and s′t is some baseline state to compare with.

For the stepwise inaction baseline, s′t is calculated as follows. Let st−1 be the previous state, at−1 the previous action. Then s′t is the state that would have followed st−1 had the agent taken the noop action, ∅, instead of at−1.

However, in the presence of a subagent, the agent merely has to always take the action at−1=∅. In that case, st=s′t (in a deterministic setting[1]), and the penalty ρ(st,s′t) collapses to nothing.

This leaves the subagent free to maximise R0 (the positive reward for agent and subagent), without any restrictions beyond making sure that the agent can always take the ∅ action.

Non-indexical impact penalty

The twenty billion questions is an example of a non-indexical impact penalty. The impact penalty is defined as the difference in expected value of many variables, between the current state and the baseline state.

With the inaction baseline state, s′t is the state the system would have been in, had the agent always done nothing/had not been turned on. Thus the definition of s′t is independent of the agent's actions, so the inaction baseline does not collapse like the stepwise inaction baseline does.

What about subagents? Well, since the impact penalty is non-indexical, a subagent does not get around it. It matters not whether the subagent is independent, or an extension of the agent: the impact penalty remains.

  1. In a non-deterministic setting, ρ(st,s′t) becomes a mix of an impact penalty and a measure of environment stochasticity. ↩︎



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How to Lurk Less (and benefit others while benefiting yourself)

10 часов 18 минут назад
Published on February 17, 2020 6:18 AM GMT

Posts like this have been written before, but I think it's worth making the point periodically.

Lurker ratios have likely increased over time. Comments and discussion are an important feedback mechanism for content creators. So if you see stuff you like, and you'd like to see more posts like it, it's quite helpful to comment. Many people report being intimidated about posting, especially if the platform in question has a highly specific vocabulary and norms. I wanted to offer a couple of the heuristics I use for making comments as well as invite others to boggle/comment/discuss what they think mediates the difference between times they do and don't comment.

In a shallow review of the pedagogy literature, four interventions stood out as having large effect sizes replicate: deliberate practice, test taking, elaborating the context (cross linking knowledge), and teaching the material to others. Cross linking provides an easy heuristic for commenting: simply mention which idea(s) in the post stood out to you most and how they connect to your existing knowledge. This helps you by strengthening those connections, and helps others because each person's cross links have some chance of being unique and therefore surprising to others. I think of this as a sort of low rent version of helping the post author cite additional sources. And speaking as a writer, these sorts of comments are always welcome as I learn about which ideas tend to stand out the most to people and might be worth writing more about.

Another heuristic I've found quite helpful is just to say more obvious things on the margin. Due to illusion of transparency, many things wind up being less obvious than I thought. This also forms a bit of a virtuous cycle as it helps reduce the context overhead for other readers, giving them more on ramps to comment and discuss. You will pay a minor cost of people occasionally getting frustrated that you're repeating something they already know about, but this happens much, much less often in my experience than people thanking me for alerting them to some concept that they either were only somewhat familiar with or had never heard before. This doubles as good vulnerability practice, creating more opportunities to connect with people over odd corners of mutual interest.

I think it's worth it to try over correcting here. I have had a surprising number of experiences of people telling me I was the first person to thank them for something that I imagined was popular enough for them to get lots of feedback on.

Please feel free to comment on things that have made you feel better about commenting, or if you're an existing contributor what sorts of comments make you feel most appreciated for your writing efforts.

P.S. I waffled about making this post, then realized that was kind of silly.



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Attainable Utility Preservation: Concepts

11 часов 17 минут назад
Published on February 17, 2020 5:20 AM GMT

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Appendix: No free impact

What if we want the agent to single-handedly ensure the future is stable and aligned with our values? AUP probably won’t allow policies which actually accomplish this goal – one needs power to e.g. nip unaligned superintelligences in the bud. AUP aims to prevent catastrophes by stopping bad agents from gaining power to do bad things, but it symmetrically impedes otherwise-good agents.

This doesn’t mean we can’t get useful work out of agents – there are important asymmetries provided by both the main reward function and AU landscape counterfactuals.

First, even though we can’t specify an aligned reward function, the provided reward function still gives the agent useful information about what we want. If we need paperclips, then a paperclip-AUP agent prefers policies which make some paperclips. Simple.

Second, if we don’t like what it’s beginning to do, we can shut it off (because it hasn’t gained power over us). Therefore, it has “approval incentives” which bias it towards AU landscapes in which its power hasn’t decreased too much, either.

So we can hope to build a non-catastrophic AUP agent and get useful work out of it. We just can’t directly ask it to solve all of our problems: it doesn’t make much sense to speak of a “low-impact singleton”.

Notes
  • To emphasize, when I say "AUP agents do X" in this post, I mean that AUP agents correctly implementing the concept of AUP tend to behave in a certain way.
  • As pointed out by Daniel Filan, AUP suggests that one might work better in groups by ensuring one's actions preserve teammates' AUs.


Discuss

On the falsifiability of hypercomputation, part 2: finite input streams

12 часов 45 минут назад
Published on February 17, 2020 3:51 AM GMT

In part 1, I discussed the falsifiability of hypercomputation in a typed setting where putative oracles may be assumed to return natural numbers. In this setting, there are very powerful forms of hypercomputation (at least as powerful as each level in the Arithmetic hierarchy) that are falsifiable.

However, as Vanessa Kosoy points out, this typed setting has difficulty applying to the real world, where agents may only observe a finite number of bits at once:

The problem with constructive halting oracles is, they assume the ability to output an arbitrary natural number. But, realistic agents can observe only a finite number of bits per unit of time. Therefore, there is no way to directly observe a constructive halting oracle. We can consider a realization of a constructive halting oracle in which the oracle outputs a natural number one digit at a time. The problem is, since you don't know how long the number is, a candidate oracle might never stop producing digits. In particular, take any non-standard model of PA and consider an oracle that behaves accordingly. On some machines that don't halt, such an oracle will claim they do halt, but when asked for the time it will produce an infinite stream of digits. There is no way to distinguish such an oracle from the real thing (without assuming axioms beyond PA).

This is an important objection. I will address it in this post by considering only oracles which return Booleans. In this setting, there is a form of hypercomputation that is falsifiable, although this hypercomputation is less powerful than a halting oracle.

Define a binary Turing machine to be a machine that outputs a Boolean (0 or 1) whenever it halts. Each binary Turing machine either halts and outputs 0, halts and outputs 1, or never halts.

Define an arbitration oracle to be a function that takes as input a specification of a binary Turing machine, and always outputs a Boolean in response. This oracle must always return 0 if the machine eventually outputs 0, and must always return 1 if the machine eventually outputs 1; it may decide arbitrarily if the machine never halts. Note that this can be emulated using a halting oracle, and is actually less powerful. (This definition is inspired by previous work in reflective oracles)

The hypothesis that a putative arbitration oracle (with the correct type signature, MachineSpec → Boolean) really is one is falsifiable. Here is why:

  1. Suppose for some binary Turing machine M that halts and returns 1, the oracle O wrongly has O(M) = 0. Then this can be proven by exhibiting M along with the number of steps required for the machine to halt.
  2. Likewise if M halts and returns 0, and the oracle O wrongly has O(M) = 1.

Since the property of some black-box being an arbitration oracle is falsifiable, we need only show at this point that there is no computable arbitration oracle. For this proof, assume (for the sake of contradiction) that O is a computable arbitration oracle.

Define a binary Turing machine N() := 1 - O(N). This definition requires quining, but this is acceptable for the usual reasons. Note that N always halts, as O always halts. Therefore we must have N() = O(N). However also N() = 1 - O(N), a contradiction (as O(N) is a Boolean).

Therefore, there is no computable arbitration oracle.

Higher highercomputation?

At this point, it is established that there is a form of hypercomputation (specifically, arbitration oracles) that is falsifiable. But, is this universal? That is, is it possible that higher forms of hypercomputation are falsifiable in the same setting?

We can note that it's possible to use an arbitration oracle to construct a model of PA, one statement at a time. To do this, first note that for any statement, it is possible to construct a binary Turing machine that returns 1 if the statement is provable, 0 if it is disprovable, and never halts if neither is the case. So we can iterate through all PA statements, and use an arbitration oracle to commit to that statement being true or false, on the basis of provability/disprovability given previous commitments, in a way that ensures that commitments are never contradictory (as long as PA itself is consistent). This is essentially the same construction idea as in the Demski prior over logical theories.

Suppose there were some PA-definable property P that a putative oracle O (mapping naturals to Booleans) must have (e.g. the property of being a halting oracle, for some encoding of Turing machines as naturals). Then, conditional on the PA-consistency of the existence of an oracle with property P, we can use the above procedure to construct a model of PA + existence of O satisfying P (i.e. a theory that says what PA says and also contains a function symbol O that axiomatically satisfies P). For any PA-definable statement about this oracle, this procedure will, at some finite time, have made a commitment about this statement.

So, access to an arbitration oracle allows emulating any other PA-definable oracle, in a way that will not be falsified by PA. It follows that hypercomputation past the level of arbitration oracles cannot by a PA-reasoner who can access the oracle, as PA cannot rule out that it is actually looking at something produced by only arbitration-oracle levels of hypercomputation.

Moreover, giving the falsifier access to an arbitration oracle can't increase the range of oracles that are falsifiable. This is because, for any oracle-property P, we may consider a corresponding property on an oracle-pair (which may be represented by a single oracle-property through interleaving), stating that the first oracle is an arbitration oracle, and the second satisfies property P. This oracle pair property is falsifiable iff the property P is falsifiable by a falsifier with access to an arbitration oracle. This is because we may consider a joint search for falsifications, that simultaneously tries to prove the first oracle isn't an arbitration oracle, and one that tries to prove that the second oracle doesn't satisfy P assuming the first oracle is an arbitration oracle. Since the oracle pair property is PA-definable, it is emulable by a Turing machine with access to an arbitration oracle, and the pair property is unfalsifiable if it requires hypercomputation past arbitration oracle. But this implies that the original oracle property P is unfalsifiable by a falsifier with access to an arbitration oracle, if P requires hypercomputation past arbitration oracle.

So, arbitration oracles form a ceiling on what can be falsified unassisted, and also are unable to assist in falsifying higher levels of hypercomputation.

Conclusion

Given that arbitration oracles form a ceiling of computable falsifiability (in the setting considered here, which is distinct from the setting of the previous post), it may or may not be possible to define a logic that allows reasoning about levels of computation up to arbitration oracles, but which does not allow computation past arbitration oracles to be defined. Such a project could substantially clarify logical foundations for mathematics, computer science, and the empirical sciences.



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Wanting More Intellectual Stamina

13 часов 27 минут назад
Published on February 17, 2020 2:58 AM GMT

As a sophomore undergraduate student, my most valuable rewards from the college experience have come from personal growth, rather than the classroom. However, one problem that I can't seem to shake is dealing with all the subcategories of my total personality.

On the one hand, I am hyper intellectual, sometimes annoyingly so, because I have an overwhelming number of ideas--all under the vague category of "philosophy." But this side of me has produced the purest, most profound joy that I have ever experienced, and it offers the most promise for a successful career.

On the other hand, I am a struggling Youtube addict, who enjoys hanging out with friends, good memes, and generally not doing work. This is more than low-conscientiousness, it is a fear of missing out on the shit-posty culture that I know and love.

The majority of my time goes towards the latter part of my personality, and my periods of intellectual productivity, or even just doing homework, are sporadic (I have a 3.66 GPA; it could be better/I could be getting more out of my classes.) The problem is that I feel like I'm unable to let go of the fun-loving part of me which needs stupid entertainment. I simply cannot stay interested enough in learning and knowledge to be doing it 24/7, but I feel like this is requisite in order to be a successful thinker. How do you guys stay interested in something (an idea or even an entire field) persistently enough to always be motivated to work on it? Is it unrealistic to hope to always be motivated by your curiosity? Will I burn myself out if I devote my free-time to extracurricular reading?

Sorry for the autobiography, but I don't know of a better forum to go to for these kinds of questions.



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A Memetic Mediator Manifesto

14 часов 22 минуты назад
Published on February 17, 2020 2:14 AM GMT

This link discusses strategies to try to mediate disagreements between different warring tribes with fundamentally different world views. Creator unknown.



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UML XI: Nearest Neighbor Schemes

16 февраля, 2020 - 23:30
Published on February 16, 2020 8:30 PM GMT

(This is the eleventh post in a sequence on Machine Learning based on this book. Click here for part I.)

Last time, I tried to do something special because the topic was neural networks. Now we're back to the usual style, but with an unusually easy topic. To fit this theme, it will be a particularly image-heavy post.

The idea of nearest neighbor predictors is to predict the target value of a point based on the target value of the most similar points in the training data. 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src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} X=R2 and Y=R, the following training data, and the new red point:

The (x,y) position of the points corresponds to their value in X=R2. The number next to them corresponds to their target value in Y=R. (The four closest points just happen to have target values 1-4 by coincidence.) The other training points also have target values, which are not shown – but since they're real numbers, they are probably things like 35.23412576354868569754543639946795856858465745654965464964534... .

This picture begs the question of how many neighbors to take into account. This is a parameter: in a k-nearest neighbor scheme, we consider the target values of the k nearest instances. In the instance above, if k=1, we have the following situation:

(Note that the circle just demonstrates which points are closest – we do not choose all points within a fixed distance.)

For k=2, we have two inputs and need to decide what to do with them. For now, we simply declare that the label of our red point will be the mean of all k neighbors.

k=3:

k=4:

If the function we're trying to learn here is something like distance from a center and the red point is precisely at that center, then the scheme gets worse and worse the larger k becomes. On the other hand, suppose the instance looks like this instead:

where the new point is an unfortunate outlier. In this case, the situation would be somewhat improved for larger k. In general, k=1 means that every point will get to decide the target values of some new training points (unless there are other training points with identical positions). Here is an image that shows, for each point, the area in which that point will determine the target value of new points given that k=1:

This is also called a Voronoi diagram, and it has some relevance outside of Machine Learning. For example, suppose the red dots are gas stations. Then, for any point, the [red instance determining the cell in which that point lies] represents the closest gas station.

However, for machine learning, this might not be great – just suppose the green point is a crazy outlier. Should it get to decide the target values of new instances in its cell? Another way to describe this problem is that the [function that such a nearest-neighbor predictor will implement] is highly discontinuous.

In general, the more variance there is in the training data, the larger k should be, but making k larger will, in general, decrease accuracy – the well-known tradeoff. In that sense, one is applying a little bit of prior knowledge to the problem by choosing k. But not much – consider the following instance (this time with X=R):

A linear predictor would probably assign the red point a value that continues the trend – something like 7.000000000142. This is because linear models work on the assumption that trends are, in some sense, more likely to continue than to spontaneously revert. Not so with nearest neighbor schemes – if k=1 it would get the label 6, and for larger k, the label would become smaller rather than larger. In that way, nearest neighbor schemes make weaker assumptions than most other predictors. This fact provides some insight into the question of when they are a good choice for a learning model.

One can also use a weighted average as the prediction, rather than the classical mean. Let's return to our instance, and let's use actual distances:

One way to do this is to weight each prediction proportional to the inverse distance of the respective neighbor. In that case, this would lead to the prediction

12.22−1⋅1+18.63−1⋅2+28.76−1⋅3+30.88−1⋅412.22−1+18.63−1+28.76−1+30.88−1≈2.08736

where the denominator is there to normalize the term, i.e., make it as if all weights sum up to 1. (You can imagine dividing each weight by the denominator rather than the entire term; then, the weights literally sum up to 1.) In this case, the function we implement would be somewhat more smooth, although each point would still dominate in some small area. To make it properly smooth, one could take the inverse of [the distance plus 1] (thus making the weights range between 0 and 1 rather than 0 and ∞) and set k=m so that all training points are taken into consideration.

Note that I've been using regression as an example throughout, but decision trees can also be used for classification problems. In that case, each point gets the label which is most popular among its neighbors, as decided by a (weighted) majority vote.

Are there any guarantees we can prove for nearest neighbor schemes?

The question is a bit broad, but as far as sample complexity bounds/error bounds are concerned, the answer is "not without making some assumptions." Continuity of the target function is a necessary condition, but not enough by itself. Consider how much each point tells us about the function we're trying to learn – clearly, it depends on how fast the function changes. Then – suppose our function changes at a pace such that each point provides this amount of information (the green circle denotes the area in which the function has changed "sufficiently little," whatever that means for our case):

In this case, the training data will let us predict new points. But suppose it changes much more quickly:

In this case, we have no chance. In general, for any amount of training data, we can imagine a target function that changes so quickly that we don't have a clue about how it looks on a majority of the area. Thus, in order to derive error bounds, one needs to assume a cap on this rate of change. If you recall the chapter on convexity, this property is precisely ρ-Lipschitzness.

There's a theorem to that effect, but the statement is complicated and the proof is boring. Let's talk about trees. A decision tree is a predictor that looks like this:

(In this case, for a binary classification problem.) If you have never seen a tree before, don't despair – trees are simple. The triangles are called internal nodes, the four endpoints are called leaves, and the lines are called edges. We begin at "Start," evaluate the first "Condition," then move down accordingly – pretty self-explanatory. Trees are quite common – they show up all over the place in computer science, and also at some other places.

Now, if we allow arbitrary conditions, this is somewhat silly – each predictor h can then be represented as a tree via

So the general rule is that the conditions be very simple. That being said, there are several ways one could illustrate that this class is quite expressive. For example, consider a rectangle with labeled sides like this:

The following tree realizes a predictor which labels instances in the rectangle positively and anywhere outside negatively:

The same principle also illustrates how we can implement a logical AND, and OR goes analogously. You may work out how exactly this can be done – however, I don't think those are the best ways to demonstrate what trees are really about. Instead, consider what happens when the tree branches in two:

When the tree branches in two, the domain space is divided in two. And this is true for each branching point, even if the area corresponding to a node isn't the entire domain space to begin with:

For the initial examples, I have just written down the labels (the 1 and 0 at the leaves) as part of the tree. In reality, they are derived from the training data: for any leaf, the only reasonable label is that which the majority of training points in the corresponding part of the domain space have. And this is why this post isn't called "nearest neighbor and decision trees" – decision trees are a nearest neighbor scheme. The difference lies in how the neighborhoods are constructed: in the classical approach, the neighborhood for each point p is based on the distance of every other point to p, while in a tree, the neighborhoods are the cells that correspond to the leaves.

In particular, this makes the neighbor-relationship of a tree transitive – if p is a neighbor of x and x is a neighbor of q, then p is a neighbor of q – which is not true for the classical nearest-neighbor approach.

You might recall the concept of VC-dimension from chapter II, which applies for binary classification tasks. To recap: for a given hypothesis class H, the VC-dimension is the largest n∈N such that there exists a set of n domain points which is shattered by H. What does it mean for a set P⊆X to be shattered by H? It means that H contains a predictor for any possible labeling combination of the points in P. If P has n elements, these are 2n many combinations. (For example, if P={x,y}, then H has to contain a predictor h00 with h0(x)=0=h0(y), a predictor h11 with h11(x)=1=h11(y) and also predictors h01 and h10.) The VC dimension is a measure for the complexity of a class because if all labeling combinations are possible, then learning about the labels of some of the points doesn't tell us the labels of the others. There are both upper- and lower bounds on the sample complexity for classes with finite VC dimension (provided we allow arbitrary probability distributions D generating our label points).

This is relevant for decision trees because their VC dimension is trivial to compute...

... it simply equals the number of leaves. This follows from the fact that a tree divides the domain space into n subsets where n is the number of leaves, as we've just argued. It's equally easy to see that larger subsets cannot be shattered because the tree will assign all points within the same subset the same label.

It follows that the class of arbitrary trees has infinite VC-dimension, whereas the class of trees with depth at most d has VC-dimension 2d−1 (the number of leaves doubles with every level we're allowed to go downward).

How do we obtain trees?

Since mathematicians are lazy, we don't like to go out and grow trees ourselves. Instead, we'd like to derive an algorithm that does the hard work of growing trees for us.

One possibility is a simple greedy algorithm. The term greedy is commonly used in computer science and refers to algorithms that make locally optimal choices. For example, consider the knapsack problem where one is given a number of possible objects that have a weight and a value, and the goal is to pack a subset of them that stays below a particular total weight and has maximum value. A greedy algorithm would start by computing the valueweight scores for each item, and start packing the optimal ones.

The general dynamic with greedy algorithms is that there exist cases where they perform poorly, but they tend to perform well in practice. For the knapsack problem, consider the following instance:

A greedy algorithm would start by packing item 3 because it has the best valueweight score, at which point the remaining capacity isn't large enough for another item, and the game is over. Meanwhile, packing items 1 & 2 would have been the optimal solution to this problem.

Now we can do something similar for trees. We begin with one of these two trees,

namely, we choose the one that performs better on the training sequence (i.e., if more than half of our training points have label 0, we go with the first tree). After this first step, all remaining steps are the same – we improve the tree by replacing a leaf with a [condition from which two edges go out into two leaves with labels 0 and 1, respectively]. This corresponds to choosing an area of the domain space and dividing it in two.

So let's say we've started with the first tree. We only have one leaf, so the first step will replace that leaf with [⋯]:

Now just imagine the same thing starting from an arbitrarily large tree: we take one of its many leaves and substitute the object in the blue circle for it (either exactly the same object or the same except with labels swapped). Somewhere in the domain space, some area that was previously all 0 or all 1 now gets divided into two areas, one with 1 and one with 0.

Since we're running a greedy algorithm, it chooses the split such that the total performance of the tree (on the training data) improves the most – without taking into consideration how this affects future improvements. Of course, we need to restrict ourselves to simple conditions; otherwise, the whole thing becomes pointless – recall this tree:

For example, each restriction could be of the form "bit #k of the input point is 1". Many other approaches are possible. Having chosen such a class of restrictions, and also a way to measure how much performance is improved by each restriction, this defines a simple greedy algorithm.

Now, recall that the VC dimension of the class of trees is infinite. Thus, if we let our algorithm run for too long, it will keep growing and growing the tree until it is very large – at that point, it will have overfit the training data significantly, and its true error will probably be quite high. There are several approaches to remedy this problem:

  • Terminate the algorithm earlier
  • After the algorithm has run to completion, run an algorithm doing the opposite, i.e., cutting down the tree to make it more simple while losing as little performance on the training data as possible
  • Grow more trees

To elaborate on the last point: since the problem with an overly large tree is that it learns noise from the training data – i.e., quirks that are only there by chance and don't represent the real world – one way to combat this is by having a bunch of trees and letting them define a predictor by majority vote. This will lead to the noise canceling out, for the same reason that Stochastic Gradient Descent works. There are, again, at least two ways to do this:

  • Use the same tree-growing algorithm, but give it a randomly chosen subset of the training data each time
  • Run a modified version of the algorithm several times, and ...
    • ... use the all training data each time; but
    • ... for each step, choose the optimal splitting point from a randomly chosen subset, rather than from all possible splits

The result is a random forest (link is there primarily to make it green).

If we have a random forest, are we still implementing a nearest neighbor scheme? To confirm that the answer is yes, let's work a unified notation for all nearest neighbor schemes.

Suppose we have the training data S=((x1,y1),...,(xm,ym)), and consider a weighting function w:X×{x1,...,xm}→R that says "how much of a neighbor" each training point is to a new domain point. In k-nearest neighbor, we will have that w(x,xj)=1 iff xj is one of the k nearest neighbors of x and 0 otherwise. For weighted k-nearest neighbor, if xj is among the k nearest neighbors to x, then w(x,xj) will be some (in (0,1) or in R+, depending on the weighting) determined by how close it is, and otherwise, it will be 0. For a tree, w(x,xj) will be 1 iff both x and xj are in the same cell of the partition which the tree has induced on the domain space.

To write the following down in a clean way, let's pretend that we're in a regression problem, i.e., the yk are values in R. You can be assured that it also applies to classification, it's just that it requires to mix in an additional function to realize the majority vote.

Given that we are in a regression problem, we have that

h(x)=1m∑mi=1w(xi,x)⋅yi

where h=Aclassical k-nearest-neighbor(S)=AkNN(S). Recall that A is a learning algorithm in our notation, and S is the training data, so A(S) is the predictor it outputs.

For a tree, the formula looks the same (but the weighting function is different). And for a forest made out of trees 1,...,n, we'll have n different weighting functions w1,...,wn, where wj is the weighting according to tree #j. Then for h=Arandom n-forest(S), we have

h(x)=1n∑nj=11m∑mi=1wj(xi,x)⋅yi

which can be rewritten as

h(x)=1nm∑i=1[1m∑nj=1wj(xi,x)]⋅yi

which can, in turn, be rewritten as

h(x)=∑mi=1w∗(xi,x)⋅yiwherew∗(xi,x):=∑nj=1wj(xi,x)

so it is just another nearest-neighbor scheme with weighting function w∗.



Discuss

[Link and commentary] The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?

16 февраля, 2020 - 22:56
Published on February 16, 2020 7:56 PM GMT

This is (partly) a linkpost for a paper published earlier this year by Toby Shevlane and Allan Dafoe, both researchers affiliated with the Centre for the Governance of AI. Here’s the abstract:

There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections against misuse. This paper addresses the balance between these two effects. Our theoretical framework elucidates the factors governing whether the published research will be more useful for attackers or defenders, such as the possibility for adequate defensive measures, or the independent discovery of the knowledge outside of the scientific community. The balance will vary across scientific fields. However, we show that the existing conversation within AI has imported concepts and conclusions from prior debates within computer security over the disclosure of software vulnerabilities. While disclosure of software vulnerabilities often favours defence, this cannot be assumed for AI research. The AI research community should consider concepts and policies from a broad set of adjacent fields, and ultimately needs to craft policy well-suited to its particular challenges.

The paper is only 8 pages long, and I found it very readable and densely packed with useful insights and models. It also seems highly relevant to the topics of information hazards, differential progress, and (by extension) global catastrophic risks (GCRs) and existential risks. I’d very much recommend reading it if you’re interested in AI research or any of those three topics.

Avoiding mentioning GCRs and existential risks

(Here I go on a tangent with relevance beyond this paper.)

Interestingly, Shevlane and Dafoe don’t explicitly use the terms “information hazards”, “differential progress”, “global catastrophic risks”, or “existential risks” in the paper. (Although they do reference Bostrom’s paper on information hazards.)

Furthermore, in the case of GCRs and existential risks, even the concepts are not clearly hinted at. My guess is that Shevlane and Dafoe were consciously avoiding mention of existential (or global catastrophic) risks, and keeping their examples of AI risks relatively “near-term” and “mainstream”, in order to keep their paper accessible and “respectable” for a wider audience. For example, they write:

The field of AI is in the midst of a discussion about its own disclosure norms, in light of the increasing realization of AI’s potential for misuse. AI researchers and policymakers are now expressing growing concern about a range of potential misuses, including: facial recognition for targeting vulnerable populations, synthetic language and video that can be used to impersonate humans, algorithmic decision making that amplifies biases and unfairness, and drones that can be used to disrupt air-traffic or launch attacks [6]. If the underlying technology continues to become more powerful, additional avenues for harmful use will continue to emerge.

That last sentence felt to me like it was meant to be interpretable as about GCRs and existential risks, for readers who are focused on such risks, without making the paper seem “weird” or “doomsaying” to other audiences.

I think my tentative “independent impression” is that it’d be better for papers like this to include at least some, somewhat explicit mentions of GCRs and existential risks. My rationale is that this might draw more attention to such risks, lend work on those risks some of the respectability had by papers like this and their authors, and more explicitly draw out the particular implications of this work for such risks.

But I can see the argument against that. Essentially, just as the paper and its authors could lend some respectability to work on those risks, some of the "crackpot vibes" of work on such risks might rub off on the paper and its authors. This could limit their positive influence.

And I have a quite positive impression of Dafoe, and now of Shevlane (based on this one paper). Thus, after updating on the fact that they (seemingly purposely) steered clear of mentioning GCRs or existential risks, my tentative “belief” would be that that was probably a good choice, in this case. But I thought it was an interesting point worth raising, and I accidentally ended up writing more about it than planned.

(I’m aware that this sort of issue has been discussed before; this is meant more as a quick take than a literature review. Also, I should note that it’s possible that Shevlane is just genuinely not very interested in GCRs and existential risks, and that that’s the whole reason they weren’t mentioned.)

This post is related to my work with Convergence Analysis, and I’m grateful to David Kristoffersson for helpful feedback, but the views expressed here are my own.



Discuss

Training Regime Day 2: Searching for bugs

16 февраля, 2020 - 20:16
Published on February 16, 2020 5:16 PM GMT

Introduction

In CFAR terminology, a bug is something that systematically goes wrong in your life. It can be anything as small as "it takes me one minute to get out of bed in the morning when it should take me five seconds" to as large as "I hate my job" or even larger. Knowing the bugs in your life provides information as to where to spend time/energy to make your life better.

Today's exercise is going to be woven throughout the entire post. The necessary components to this exercise is any system capable of storing information that isn't your brain. A computer, pen and paper, a whiteboard, and a wax tablet will all suffice.

A common failure mode when writing down bugs is to consider whether or not they're bugs before writing them down. Writing down bugs is an exercise is babbling; do not restrict your search to the space of things your brain explicitly considers a "bug." Writing down things that might not be bugs is a necessary part of the process. You can always decide that they're not bugs later.

What follows are a series of prompts designed to get you to think about possible bugs that you have. I suggest that you read each prompt and write down bugs until the bugs no longer come freely. I will provide examples of bugs at the end, but you should search for you own bugs before reading that list to avoid anchoring.

There will be other opportunities to find bugs later on in this sequence, so don't feel pressured to compile a comprehensive list of all of your problems at this exact instance.

Prompts
  1. What are your bugs? Sometimes there are things that are wrong in your life that you know are wrong. Starting with these is probably a good idea.
  2. Is there something in your life that consistently causes you to feel frustrated/annoyed? Frustration and annoyance are the feelings that arise when something that you desire fails to manifest itself.
  3. Imagine a day in your life in all its glorious detail. What are the ways that it could be better? Which parts of your day are less-than-ideal in some particular way?
  4. Imagine asking your closest friends what your bugs are. What would they say? You should probably write down their responses even if you think what they're saying is wrong.
  5. Is there anything that's stopping you from easily making your life better? Barriers to progress are also bugs.
  6. Scott Alexander explains how it is sometimes hard to realize that you don't understand entire concepts. Similarly, if there was something about your life that was a bug that you didn't realize, what would it be?
  7. What causes you pain in your life? Sources of both physical and emotional pain are often bugs.
  8. Do you want to be able to do something that you currently can't do? Feature requests can also be considered bugs.
  9. Does anything feel wrong in your life? Sometimes, bugs make themselves known as vague feelings of wrongness or unease.
Examples

Here are some examples of bugs from my own life:

  1. I forget to go to events that aren't consistent.
  2. My desk is constantly very messy.
  3. My knee is in pain.
  4. My glasses slip down my nose.
  5. I sleep consistently 30 minutes later than I desire.
  6. Most food I eat isn't tasty.
  7. My ears are constantly dry and itchy.
  8. My nails are too long.
  9. I have too many pairs of pants but I don't want to get rid of any of them.
  10. Eventually, I am probably going to die.
  11. I often communicate half-formed thoughts, frustrating both myself and my interlocutors.

There are many more, but I will stop for now.

Conclusion

There is a saying at CFAR that "the techniques are not the point." I will explain in more detail what this means tomorrow, but it roughly means that the point of CFAR is to teach you how to do applied rationality, not really how to do their specific way of applied rationality. If your life gets better but you don't ever explicitly use any of the techniques, then they have succeeded.

My extension of this saying goes "the techniques are not the point, but the bugs are even less of the point then the techniques." The point of this sequence is not to figure out how to fix any of the specific bugs that you wrote down on your list, it's to teach you how to generate ways to fix bugs in general. Having a list of bugs is useful because it provides you with a lot of problems at a variety of difficulty levels that you have a reasonable amount of motivation to solve. They're your problems, so you have a lot of context on why they exist and whether or not solutions will work, making them ideal as practice material.

If you haven't found enough bugs, I recommend hammertime's bug hunt.



Discuss

Taking the Outgroup Seriously

16 февраля, 2020 - 16:23
Published on February 16, 2020 1:23 PM GMT

(Author's note/content warning: this post contains politically controversial examples, which are unfortunately perhaps necessary given the subject. I have tried to be relatively even-handed in this matter, my apologies if I erred in doing so.)

Occasionally, one reads a purported "takedown" of an opposing group that perhaps deals more with the writer's own models than those of the group he intends to refute.

For example, I recently read a widely-shared Quora post that claimed that religious proselytization was part of an elaborate brainwashing scheme to make the proselytizers themselves feel more connected to their own religion, and that the goal was not at all to make converts. The post might have been a good piece of rhetoric, but as something that aimed to actually understand the outgroup I think it was silly and obviously wrong.

Another example might be the claim that abortion supporters literally worship Moloch and want to kill babies as sacrifices, or for that matter the claim that abortion opponents only hold their views because they hate women and oppose abortion as part of a conspiracy to curtail women's rights.

What do these sorts of claims all have in common? They don't take the outgroup seriously. Sure, there might well be some fringe radicals who actually worship Moloch and want to kill babies or who oppose abortion because doing so furthers their conspiracy to suppress women, but such views likely constitute an extreme minority opinion. In point of fact, the person who says "I support abortion because I support women's rights." probably in fact actually believes that; the person who says "I support abortion because I believe killing a fetus is murder." probably in fact actually believes that too! There is no need to posit that these people are secret Moloch cultists or members of a grand conspiracy to suppress women -- they have already told you their reasons for their belief, and you weren't listening!

You can in fact often gain remarkable insight into the belief structures of most anyone -- even opponents -- by actually listening to and reading what they have to say. In most cases, people do not come up with elaborate secret reasoning for their opinions and then withhold it in favor of other arguments -- instead, they tend to explain their actual reasoning, which you can listen to to better understand their perspective. [1] However, it is very easy to skip over the reasoning that the person you're interacting with actually presents and instead engage only with very extreme arguments, even if they represent only a tiny fraction of what people holding these views actually believe. In fact, such styles of argument seem very common. [2]

I claim that thinking in this way is really doing a disservice not only to the outgroup but also to yourself. If you think of your opponents only as extreme caricatures, you are likely to miss their actual concerns, and you are less likely to be able to accurately model their viewpoints and perhaps come to a mutual understanding in the future. Instead, you may have frustrating and divisive conversations where it seems that both of you are operating based on caricatures of the others' opinion.

A large number of problems and misunderstandings, both politically and interpersonally, seem to me to be related to this sort of reasoning, and avoiding it seems often key to solving major problems in one's life. If you go around thinking that those who oppose you are all idiots, or crazy people, or innately evil, or just haven't thought about the situation (unlike you, of course!)... well, I won't say that you'll always be wrong, but that sure doesn't seem like the best way to go about trying to form an accurate model of the world! Instead, try looking at what they actually have to say and really actually trying to understand their arguments and what those arguments imply. You might be surprised at what you find!


[1] There are some domains where this may not apply, especially certain interpersonal ones (indeed, it would normally be considered outrageously impolite to explain your reasoning in some such matters), but the point stands in general.

[2] Scott Aikin and Robert Talisse refer to this as the weak man fallacy.



Discuss

On characterizing heavy-tailedness

16 февраля, 2020 - 03:14
Published on February 16, 2020 12:14 AM GMT

[CONTEXT: For a while I have been meaning to engage with a literature review on heavy tailed distributions. Instead of just indefinitely postponing the project I resolved to write some preliminary thoughts on the topic, so I can get started on understanding the concept better with a less daunting task]

Heavy-tailed distributions occur when extreme, low-probability yet plausible outcomes dominate decision-making.

For example, when considering how to contain a pandemic, an official will not want to focus on scenarios where the pandemic dies out on its own, nor on scenarios where a solar flare messes up with our electronics during the crisis. Instead she will focus on scenarios where the pandemic grows out of control because its contagion rate is higher than expected - a plausible scenario that albeit unlikely is disastrous enough to warrant precaution.

Heavy-tailed distributions are an important object of study in cause prioritization - to the extent that extreme outcomes dominate long term outcomes, we should be focusing on them.

My informal impression is that the notion of heavy tail distributions has been heavily discussed among mathematicians, especially in the context of extreme value theory. However, there is no single agreed-upon formalization of the concept, making discussion and application of the concept notoriously difficult.

Through this post, I will explore some important concepts around heavy-tailedness that we want an ideal definition to make precise.

My hope is that having this discussion will help us later productively discuss the strengths and weaknesses of different proposed definitions of heavy tailedness.

In short, an ideal definition of heavy-tailedness would be action-relevant, able to distinguish risks from hits, be well-defined for distributions with finite support, describe natural phenomena and adscribe heavy-tailedness to a universal class of distributions.

Action relevance

We hope the definition of heavy tailedness to suggest a qualitatively different approach to statistical inference and decision-making.

For example, we would like heavy tailed distributions to simplify decision-making (eg, via a dominance result that recommends to never expose ourselves to heavy-tailed risks) or show the inadequacy of standard methods (eg, a result showing in a precise sense that historical data on a heavy-tailed distribution is not a good predictor of future performance).

To the extent that heavy-tailed distributions are already well-studied by standard methods we will be better off not introducing a new concept.

Distinguishing left and right tails

Extreme outcomes take two forms: extreme negative outcomes (risks) and extreme positive outcomes (hits).

For example, a calamity such as drastic, unexpected, sudden climate change melting the poles and causing massive floods would be a risk. Meanwhile, an unexpected discovery of a cure against cancer would count as a hit.

In cause prioritization, we hope to expose ourselves to hits, while minimizing risks. Thus we want out discussion of heavy-tailedness to distinguish between both.

Allowing finite support

Reality is inherently bounded - I can confidently assert that there is no possible risk today that would endanger a trillion lives, because I am confident the number of people on the planet is well below that.

In statistics, we usually resort to distributions over unbounded possible outcomes to simplify matters. This is usually admissible, since most of the probability mass is contained in a sensible-enough finite region, and thus the probability mass assign to absurd outcomes can be treated as a rounding error.

However, when discussing heavy-tailed distributions, we are precisely studying the region of extreme outcomes. If our definition of heavy tailedness requires the distribution to have infinite support, we risk our analysis focusing on absurd outcomes.

Describing natural phenomena

Many everyday phenomena are documented to be distributed normally, including eg height, etc.

Similarly, if the notion of heavy-tailedness is to be useful, we would expect it to happen in many decision-relevant scenarios. Thus we would hope to identify many empirical distributions that conform to our definition of heavy tailedness

Universality

Normal distributions are heavily studied in statistics, because they occur as the limiting distribution that arises when you take the mean of iid variables of finite variance [REF].

This corresponds to a theoretical reassurance than treating the mean of some unknown distributions that exhibit empirically finite variance as if it was a normal will be good enough for inference and decision-making.

Analogously, we would like our definition of heavy-tailedness to apply and adscribe heavy-tailedness to a general limiting class of distributions, so we can use it to study general distributions.

We have discussed some properties that we would like a good formalization of the concept of heavy-tailedness.

There are several paths we could take from here, including:

  1. Refining the properties where possible, expanding them with more examples, contesting their desirability, thinking of new ones
  2. Conducting a review of existing formalisms related to the concept of heavy-tailedness
  3. Studying how the properties interact with each other, and hoping to shed light on a tentative definition - or an impossibility result
  4. Collecting a sample of empirical and theoretical distributions commonly considered to be heavy-tailed, to reflect on what makes them heavy-tailed

The topic of heavy-tailedness is one that I have seen used and abused in many situations, and I think that developing a shared understanding of what it means in a precise sense will help us communicate better and make better decisions.

We cannot discard the possibility that this could be a dead research path - for example, our intuitive understanding of the topic might be good enough for decision making, the formalization may be beyond our current mathematics or the notion of heavy-tailedness might be misleading in the sense of not requiring a separate treatment from non-heavy-tailed distributions.

Nevertheless, I think that this is a research path worth exploring, and I would be keen on reading more on the topic. Let me know in the comments if you have further research ideas, clarifying concepts or questions of your own.

This blogpost was written by Jaime Sevilla. I’d like to thank Max Daniel and Ronja Lutz for conducting some preliminary research on the topic with me a while ago.



Discuss

It "wanted" ...

15 февраля, 2020 - 23:52
Published on February 15, 2020 8:52 PM GMT

I've seen a few post using that construction when the "It" has not capacity to want at all. They have prompted a question in my own approach to thinking about things.

First, I am not critiquing the posts or otherwise suggesting a problem with them, though that could be inferred so want to put that disclaimer up front. I do think there is a place for the use of such a rhetorical device. I also think there is a place for expressing what might be incomplete thoughts. Moreover, I don't think the specific word is the concern either -- this is not about computers, or viruses, or even perhaps plants "wanting" something.

The questions is should we generally take pause when we find ourselves using that type of rhetoric to ask if we are perhaps trying to work from an incorrect or seriously deficient map to navigate the territory we're trying to traverse?

In other words, should we use that as a marker to come back to and try to express conditions or functional structure more precisely, or at least confirm it really was a harmless short cut via language to get the idea across (and the idea is not dependent on the rhetoric).



Discuss

Why Science is slowing down, Universities and Maslow's hierarchy of needs

15 февраля, 2020 - 23:39
Published on February 15, 2020 8:39 PM GMT

I don’t have a very high prior in regards to the correctness of Maslow's hierarchy of needs, but as far as general theories for understanding human needs go, I think it’s a pretty good.



There’s certainly people who seem to go strongly against it, to the point where they only require self actualization or where they are perfectly happy in life with only their physiological needs needs barely meet.

BUT

For all of the exceptions, most people, even exceptional people, seem to roughly live their life in accordance to it.

The gradual passage into adulting can be pretty daunting for people, even for well adjusted people with loving parents that can maintain a comfortable standard of living, for this reason. Gradually you are expected to find “safety” (i.e. financial stability, a house, a safe place to live) and “belonging” partially on your own.

Enter universities, the role of institutes of higher education in a well adjusted society should arguably be pushing the boundaries of human knowledge. Before they would also constitute a repository of information by maintaining huge libraries and people that could navigate them, but today we have the internet, .txt, .latex, .pdf, search engines and decent 10TB HDDs that sell for 100-200$ with tax, so I think it’s safe to say that role can now be played pretty cheaply.

So, universities now remain a places that educated and help people to navigate and enlarge the boundaries of human knowledge.

The recognition and most of all self satisfaction given by extending said boundaries is pretty great (or so I hear). So I think it’s safe to say that this role is one to be pursued by people that feel the needs on step 5 and possibly 4 of the pyramid.

BUT

In turn this is a process that requires a great deal of effort, dedication and intelligence, things that are hard to find and hard to direct for anyone that hasn’t fulfilled steps 1, 2 and 3 pretty well. Again, exceptions exist, but for basically all people it’s much easier to think about food, sex, friends and not dying than it is to think about novel bioreactors for producing cheap recombinant DNA vaccines or n-dimensional non Euclidean spaces… we can’t help it, it’s kinda the way we are evolved.

You can try to become a mental hermit and just not care about any of that, but I’m yet to see any evidence of that working, not perfectly at any rate. Even when you look at the clinically insane, they still want food and friends first and foremost, whatever the voices say is usually secondary to eating breakfast.

Conversely, think of the following recipe:

  • Take one piece homo-sapien right after puberty
  • Take them away from their parents and their friends and community
  • Give them ~100,000$ of high interest debt that can’t be cleared through bankruptcy
  • Put them in a new high-density social environment
  • Have them leave in a cheap room or apartment that meets minimum sanitary requirements but that’s about it
  • Have them buy&cook food for themselves, schedule doctors appointments, buy clothes and take care of rent and utilities even though some of them barely have any experience doing this

Where exactly are there needs going to fall in Maslow's hierarchy.

So why do these people attend university ?

Why did the trend start is a complex issue with many political implications.

Why does the trend hold is a much simpler issues, because universities now mainly cater to step 2 and 3 of the hierarchy.

This is arguably bad because being a jack of all trade seldom works, and we have a whole society built to cater to the first steps and are in desperate need of entities that can help us with the last.

Even more so, because universities were never intended to do this and are thus kinda bad it. What they are good at however, is giving is:

a) A false sense of future security in order to fulfill 2 (E.g. The 100,000% + 2% yearly interest debt you took in order to study modern literature will pay of in the long run when you hit the market and everyone is awed by the achievement only you and 70% of the people your age were able to attain).

b) A false sense of current security in order to fulfill 2, provided by the fact that you are living on credit and can thus afford a more expensive lifestyle than the one you can afford once you’re done.

c) A community that help towards 3, except for the fact that this community is one you will have to leave in 4 years, unless you pay even more money or manage to obtain a paid position (which, let’s be honest, usually requires you taking the gamble and paying more money to get a masters degree). Not to mention this is not the community you grew up in, so for some people connecting with it becomes harder.

BUT

Even if you assume that I am wrong in assuming a, b and c. After all there’s a surprising lack of studies (aka 0 that I could find, and I dug for them a lot) with titles around the lines of “Economic value of university degree when controlling for IQ, time lost and student debt”. The few studies I can find that look at the relevant datapoints (e.g. http://ftp.iza.org/dp8235.pdf) don’t have good enough that to disentangle them.

Note: If you know of any relevant studies on this topic, please please please email them to me at george@cerebralab.com and I shall add them here no matter their findings.

But again, even if you assume I’m wrong, that still leave us with universities that struggle to optimize for 2, 3 and maybe 4, losing out on 5 in the process.

At least I would argue that universities are losing out on 5 in the process. I think this is hard to prove conclusively, but I do have a few angels of attack for prove this.

1. Research is slowing down on measurable metrics

For one, there’s clear evidence that measurable metrics for progress are going down: https://web.stanford.edu/~chadj/IdeaPF.pdf.

The number of transistors we can fit on a similarly sized cheap is increasing more slowly, in spite of the fact that new researchers engage with the problem.

Progress on lifespan extension is slowing down in spite the number of researchers and publications increasing.

Crop yield is increasing only slightly if at all, in spite of the fact that there’s an exponential increase in people that are supposed to work on this subject.

… etc

For a good tear-down of this study (i.e. the counter perspective of what I’m advocating here, I strongly recommend this review.

2. Progress is not made by universities

Looking at a single university, say Oxford, it’s financing seems like something that could accomplish amazing things.

It’s last reported 1-year expenditure is 2.5 billion dollars.

This might not seem like a lot, until you compare it with companies innovating in private industry.

For reference, the budget of SpaceX, for it’s first 10 years of operation was ~1 billion dollars. Considering that after those first 10 years SpaceX build and launches it’s first rocket models. It should also be noted that most of that money cam from contracts that paid in advance, rather than funding. Most of that money seems to have come for private for-profit contract though.

So in a worst case scenario it costs ~100$ million dollars to found SpaceX, in a best case scenario (where we assume the contracts they got were not unfairly earned) it costs ~40$ million.

In other words, it would cost Oxford University (note: not all of Oxford, this doesn’t include the colleges) 1.6% to 4% of it’s yearly spending to fund the most promising program humanity ever had for colonizing space.

It would cost less than what one single large university has filled under “Others” in it’s expense tab, to fund a program that has significant potential in sending people to fucking Mars.

That’s self actualizing, expanding the human race throughout the cosmos. That’s a level 5 need, that’s what people that “want more” in life should do.

So considering that this is one 9-digits university and there exist hundreds of them. Are they really doing something more important than this ? Is it really not the best ROI for self-actualization institutions to jointly spend 0.x% of their budget to help colonize space.

Maybe Space Exploration is not where it’s at… but where is it at, where do we break the boundaries.

Machine Learning ?

Most innovation (e.g. Transformer, practical RL systems, Residual Learning) seems to come from DeepMind, GoogleBrain, OpenAI, Microsoft Research and other privately founded ventures.

What about the libraries that we need to do all this stuff ?

  • Tensorflow ? Google
  • Pytroch ? Facebook
  • LAPACK ? NSF founded, many contributors, most seem to be working at universities
  • Jax ? Google
  • cuBLAS ? Nvidia
  • Keras ? Community
  • Eigen ? Community

It’s hard to go by actual papers published, since it’s hard to rank paper importance, but looking at the tools it’s mostly free contributions and private industry.

3. Things we can’t even imagine

But maybe what’s being created inside the halls of universities shouldn’t be judged by what we already know we can do (e.g. traveling to other planets) or by progress on metrics we’ve had for a long time and which can be improved with market funding (e.g. all the ones in point 1).

Maybe it should be innovating in ways with an even longer profit-horizon or lower chances of success.

What about human immortality ? Or at least increasing healthy lifespan past the 100-110 years barrier that seems to be the limit of the human species ? Surely this is transcendental if there ever was such a thing, surely trying to beat death itself is self actualization.

So… ? Where are all the university longevity focused departments. Where do I sign up to research reversing the shrinking process of the thymus ? Or researching viral vectors to evenly spread SC promoting co-factors to damage tissue ? Or senolitics drugs ? Or… you know, that kind of stuff.

Hmh, there’s like 10 tiny biotech startups doing that you say ? And this tiny non-profit called SENS ran by this gandalf guy ? And some crazy Russians that want to build robot bodies ?

4. The finer things are not the work of universities

But there’s more ways to self actualize, one can produce beautiful philosophy, music, books, games… works of art, works dealing with the human nature.

So, let’s look at philosophy, we have a whole “crisis of meaning” going on, sure could use some psychologists and philosopher dealing with that, there seems to be a lot of self actualizing to be had there, being the savior of societies struck by doubt and depression.

Let’s say Opioid crisis and the hikikomori phenomenon, those are pretty representative of the broader issue…

And now for something literally nobody cares about:

But hey… you can rest in peace knowing that there’s over 160,365 results relating to Marxist Analysis, that’s ought to fix something… right ? https://www.jstor.org/action/doBasicSearch?Query=marxist+analysis.

What about music ?

There’s hundreds of conservatories and music schools in the US, yet if you look at the people that pushed the envelope on music in the 20th century, that re-defined what we call music… we see, what ?

People from poor villages in the South of the US, residents the slums of cities like NY and New Orleans, British teenagers that took acids and got hold of some fourth-hand instruments. These are not the kind of people that attended conservatories, the people that put the foundations to blues and jazz often didn’t know how to properly read and write, let alone read music, let alone afford to go to a conservatory.

I’d go into modern art and architecture but even I don’t enjoy beating dead horses that much.

5. New departments opening up in areas parallel to self-actualizationI’m so glad my family is here as a lay dying, I had a lovely life and I’m glad all of you are here and if there’s one last wish I have, is for you my children to collect my notes and make sure that my master-piece on SEO and Wordpress advertising is finalized and published.

Finally, I think self-actualization is rather hard to define, but I certainly think there are fields where one can’t find it. Things like marketing, sales, tech support, accounting. These are all things society needs for better or worst, but these are “safe” profession, people do them because they want financial safety, because they don’t want to or can’t put in the hard work.

And I don’t blame them, be an accountant, be a car salesman. What you do for a living is not what defines you. For most people self-actualizing might more be about raising happy children than about the discoveries they make.

But again, what are universities doing here if they care about self actualizing ?

Why have a major in sales, marketing, customer relationships, tourism or accounting ? There’s nothing to be found here, there’s no progress for humanity to make, no fame, no glory, no near-universal ethical obligation to do better.

If a good were to snap his fingers and all of those departments were to suddenly vanish not one speck of dust would differ when the archeologists of 3020 dig us up.

Is there self-actualization to be found ?

I think so, look at things like the human genome project for one example of that.

I’m not so crazy as to claim the remnants of the edifice of evidence-based understanding of the world that is still supported by universities is for nothing.

But that’s the problem, universities are still doing a lot of good in fulfilling the 5th rank of the hierarchy of needs. If they weren’t, we could just ignore the whole system, after all we don’t complain about sales pyramid schemes and expect of them to change… because the whole edifice was corrupt to being with.

Universities are slowly being devoured by rent-seeking pyramid schemes that take children out of their comfortable environments and give them short-term solutions to fulfill their more basic needs.

However there’s still enough self-actualization to be found that a lot of people that actually seek that go there. That’s the problem, those two things don’t mix, you can’t have an institution focused on lying to people about how if they just take loans to give them money (or pay higher taxes in the future in order for governments to fund them, as is the case in Western Europe).

I think this might be caused by the fact that universities want to expand, the fact is that institutions for self-actualizing through science and art are,by definition, going to be niche.

Most people don’t want to be remembered for their works in unveiling the mysteries of the world, they want to be remembered as a good father, or as that one guy that made the best sausages in Genericsmalltown. The only way to get them to attend universities in the first place is to promise them fulfillment of more basic needs.

Maybe the reason why a vast majority of people going to universities in ages past were nobles and few select gifted people was not because the system was unfair, but because those are the only people that need a self-actualization institution. People that already have their basic needs mostly fulfilled, or that are passionate enough about a subject that it becomes a basic needs for them to study it, people where self-actualization somehow hops from rank 5 to rank 2.

However, for now, we are in a weird spot. Where the institutions that are supposed to cater to the higher needs of a few intelligent people, who fulfilled this service well and with great benefit to society, are systematically being forced to instead cater to the basic needs of everyone.

Thus we have a loss-loss scenario. If you are the kind of person that would actually do well being a philosophy professor or a research physicist you either have to find your own way in life with little scaffolding or go to an institution that’s not made for you. If you are the kind of person that doesn’t care much for science or art or philosophy, you are feed systematic lies and presented and economic system that still somewhat values university degrees…. So you go to a place you dislike in order to fulfill basic needs that said place was not designed to fulfill.

The default hypothesis here would be that the current system continues along just fine, with private enterprise taking more and more of the role of higher education.

The other scenario if you trust in people being somewhat-rational agents and believe ideas like those presented by Bryan Caplan in The Case against Education: Why the Education System Is a Waste of Time and Money. Is that universities start leaking money very fast and collapse once people stop attending at current rates, leaving a societal dent that might be hard to fill. I think there’s some evidence to believe this since university attendance rates in the US have been dropping since around 2010, but I very much doubt this.

The best case scenario is that either due to economic pressures or due to realizing their own faults universities downscale, and become institutions of self-actualization again. However, this would rely upon the idea that a large institution can be reduced in size slowly rather than just implode… If you play with those kind of odds then you probably haven't read about Pascal's Mugging.



Discuss

Does iterated amplification tackle the inner alignment problem?

15 февраля, 2020 - 22:24
Published on February 15, 2020 12:58 PM GMT

When iterated distillation and amplification (IDA) was published, some people described it described as "the first comprehensive proposal for training safe AI". Having read a bit more about it, it seems that IDA is mainly a proposal for outer alignment and doesn't deal with the inner alignment problem at all. Am I missing something?



Discuss

What is the difference between robustness and inner alignment?

15 февраля, 2020 - 22:15
Published on February 15, 2020 1:28 PM GMT

Robustness, as used in ML, means that your model continues to perform well even for inputs that are off-distribution relative to the training set. Inner alignment refers to the following problem: How can we ensure that the policy an AI agents ends up with is robustly pursuing the objective that we trained it on? By default, we would only expect the policy to track the objective on the training distribution.

Both a lack of robustness and inner alignment failure thus lead to an AI agent that might do unforeseen things when it encounters off-distribution inputs.

What’s the difference? I can (maybe) construct a difference if I assume that AI agents have distinct “competences” and “intent”. There is some intuition that a lack of robustness relates to competence: The self-driving car really “wanted” to bring its passengers home safely. But then it started snowing and because the car’s vision system was only trained in sunny weather, the car didn’t spot the red traffic light and crashed. It was an honest mistake. And there is some intuition that an inner alignment failure relates to intent: The nascent AGI never really cared about helping humans. It just play nice because it knew it would be deleted otherwise. As soon as it became powerful enough to take over the world (a situation it didn’t encounter during training), it did so.

However, the distinction between “competences” and “intent” doesn’t seem to apply to RL agents (and maybe not even to humans). RL agents just receive inputs and select actions. I wouldn’t be able to point to the “intent” of an RL agent. So what’s the difference between robustness an inner alignment then?



Discuss

Exercises in Comprehensive Information Gathering

15 февраля, 2020 - 20:27
Published on February 15, 2020 5:27 PM GMT

Looking back, several of the most durably-valuable exercises I’ve done over the years have a general theme of comprehensive information gathering.

The most recent example involves capital investments. Economists talk about “capital goods” as physical stuff - machines, buildings, etc. But in practice, savings and investments are passed through banks and ETFs, bundled and securitized, involve debts and shares of companies which own debts and shares of other companies, and so forth… where does all that capital end up? To get an intuitive sense, I pulled up fundamental data on about 7000 US publicly-traded companies in quantopian, sorted them by amount of non-financial assets, and found that the top 100 accounted for about 50% of the non-financial assets of the whole set. Then, I looked at annual reports for each of those 100 companies, to see what capital assets they had. I googled around for pictures and maps of where those assets were located, and read up on anything I hadn’t heard of before. What’s a “central office”, where are they, what do they look like, and why does AT&T have $90B worth of them? What are the major US oil basins, where are the wells, and what all goes into drilling them? What are the technical differences between traditional phone, cable, satellite, and cell networks, and how do those technical differences impact the capital requirements of each? Who runs power plants and the power grid in various parts of the country? What are the major US railroads, and where are they? Why did GE own so many airplanes? These are the kinds of questions which come up when you want to know what “capital goods” actually consist of, in the real world.

Another interesting exercise: I read through five years of Nature archives, reading all the titles and any abstracts which sounded novel/interesting. I didn’t google everything I hadn’t heard of; instead, I’d wait until the same acronym popped up a few times before looking it up. This took maybe a week of evenings after work. By the end, I could at least place the large majority of articles in context. Now, when I see a title full of jargon in a field I haven’t studied, like “Novel tau filament fold in corticobasal degeneration”, I usually at least understand enough to guess at what it’s relevant to (in this case: neurodegenerative disease involving protein aggregates, probably Alzheimers?). I can generally follow conversations in a bunch of different fields - not necessarily between specialists in the same sub-sub-field, but at least the level of a typical conference talk, and when I meet new people I can ask not-too-embarrassing questions about what they’re researching.

Going back further, if you’re in college, I strongly recommend reading your entire course catalogue, googling anything you’ve never heard of at all, and marking anything that sounds potentially interesting. This seems really obvious; it only takes a few hours, and something something a pile of value sitting on a silver platter right in front of you. (Note: I went to a small STEM school; if you’re at a big school with a bajillion courses or a school with poor STEM coverage or not at college at all, consider reading an MIT/Caltech course catalogue instead, to get a feel for what all is out there.) You never know what surprising and interesting topics might be hiding in there - microfluidics, underactuated robotics, recursive macroeconomics, systems biology, synthetic biology, origami algorithms, computational photography, evo-devo, procedural graphics, and on and on.

These sort of exercises provide value in a few ways:

  • They reveal unknown unknowns - things you didn’t even realize were missing from your picture of the world.
  • You can’t make a map of a city by sitting in your room with the shades drawn; exercises like these force you to look at large slices of the world.
  • Knowledge within fields tends to have decreasing marginal returns - your first physics or CS class will teach you much more than your eighth. These exercises give a broad, brief glance at many areas where you probably haven’t reached decreasing marginal returns yet.
  • You can get a very rough big-picture sense of how much effort other people are investing in various areas - e.g. where most capital investments go or where most research effort goes - which is useful for understanding the world in general.
  • While these exercises don’t avoid biased selection of information altogether, they’re probably different biases from what you run into naturally, and they’re systematic enough that we can guess at what biases are likely to be present.
  • They’re a lot of fun, if you have a curious streak.

Most importantly: I’ve found each of these exercises to have lasting, long-term value in exchange for a one-time investment of effort.

Other exercises which are on my to-do list, but which I haven’t done yet:

I’m curious to hear other suggestions for exercises along these lines.



Discuss

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