ryan_b

Karma: 5,041

ryan_b 8 Nov 2024 20:25 UTC
4 points
0
on: The Median Researcher Problem
I happened to read a Quanta article about equivalence earlier, and one of the threads is the difficulty of a field applying a big new concept without the expository and distillation work of putting stuff into textbooks/lectures/etc.
That problem pattern-matches with the replication example, but well-motivated at the front end instead of badly-motivated at the back end. It still feels like exposition and distillation are key tasks that govern the memes-in-the-field passed among median researchers.
I strongly suspect the crux of the replication crisis example is that while there are piles of exposition and distillation for probability/statistics, they are from outside whatever field experiences the problem, and each field needs its own internal completion of these steps for them to stick.
I think this should be a third specialization for every scientific field. We have theorists, we have experimentalists, and to this we should add analysts. Their work would specialize in establishing the statistical properties of experimental designs and equipment in the field on the one side, and the statistical requirements to advance various theories on the other side.

ryan_b 8 Nov 2024 18:30 UTC
2 points
2
in reply to: johnswentworth’s comment on: The Median Researcher Problem
To me memetic normally reads something like “has a high propensity to become a meme” or “is meme-like” I had no trouble interpreting the post from this basis.
I push back against trying to hew closely to usages from the field of genetics. Fundamentally I feel like that is not what talking about memes is for; it was an analogy from the start, not meant for the same level of rigor. Further, memes and how meme-like things are is much more widely talked about than genetics, so insofar as we privilege usage considerations I claim switching to one matching genetics would require more inferential work from readers overall because the population of readers conversant with genetics is smaller.
I also feel like the value of speaking in terms of memes in the post is that the replication crises is largely the fault of non-rigorous treatment; that is to say in many fields the statistical analysis parts really were/are more of a meme inside the field rather than a rigorous practice. People just read other people’s published papers analysis sections, and write something shaped like that, replicability be damned.

ryan_b 7 Nov 2024 16:17 UTC
5 points
0
in reply to: Shoshannah Tekofsky’s comment on: Explore More: A Bag of Tricks to Keep Your Life on the Rails
I am an American who knows what Estonia is, and I found the joke hilarious.

ryan_b 28 Oct 2024 20:33 UTC
2 points
0
on: somebody explain the word “epistemic” to me
Welcome!
The short and informal version is that epistemics covers all the stuff surrounding the direct claims. Things like credence levels, confidence intervals, probability estimates, etc are the clearest indicators. It also includes questions like where the information came from, how it is combined with other information, what other information we would like to have but don’t, etc.

The most popular way you’ll see this expressed on LessWrong is through Bayesian probability estimates and a description of the model (which is to say the writer’s beliefs about what causes what).
The epistemic status statement you see at the top of a lot of posts is for setting the expectation. This lets the OP write complete thoughts without the expectation that they demonstrate full epistemic rigor, or even that they endorse the thought per se.

ryan_b 24 Oct 2024 17:31 UTC
2 points
0
in reply to: AnthonyC’s comment on: Arithmetic is an underrated world-modeling technology
May I throw geometry’s hat into the ring? If you consider things like complex numbers and quarternions, or even vectors, what we have are two-or-more dimensional numbers.
I propose that units are a generalization of dimension beyond spatial dimensions, and therefore geometry is their progenitor.
It’s a mathematical Maury Povich situation.

ryan_b 24 Oct 2024 17:24 UTC
5 points
2
in reply to: Stephen Fowler’s comment on: Arithmetic is an underrated world-modeling technology
I feel like this is mostly an artifact of notation. The thing that is not allowed with addition or subtraction is simplifying to a single term; otherwise it is fine. Consider:
10x + 5y −5x −10y = 10x − 5x + 5y −10y = 5x − 5y
So, everyone reasons to themselves, what we have here is two numbers. But hark, with just a little more information, we can see more clearly we are looking at a two-dimensional number:
5x − 5y = 5
5x = 5y +5
5x − 5 = 5y
x − 1 = y
y = x − 1
Such as a line.
This is what is happening with vectors, and complex numbers, quarternions, etc.

ryan_b 4 Oct 2024 21:57 UTC
10 points
2
on: “Slow” takeoff is a terrible term for “maybe even faster takeoff, actually”
The post anchors on the Christiano vs Eliezer models of takeoff, but am I right that the goal more generally is to disentangle the shape of progress from the timeline for progress? I strongly support disentangling dimensions of the problem. I have spoken against using p(doom) for similar reasons.

ryan_b 2 Oct 2024 15:09 UTC
2 points
0
in reply to: Noosphere89’s comment on: Is cybercrime really costing trillions per year?
Because that method rejects everything about prices. People consume more of something the lower the price is, even more so when it is free: consider the meme about all the games that have never been played in people’s Steam libraries because they buy them in bundles or on sale days. There are ~zero branches of history where they sell as many units at retail as are pirated.
A better-but-still-generous method would be to do a projection of the increased sales in the future under the lower price curve, and then claim all of that as damages, reasoning that all of this excess supply deprived the company of the opportunity to get those sales in the future.

ryan_b 1 Oct 2024 14:51 UTC
2 points
0
on: Is cybercrime really costing trillions per year?
This is not an answer, but I register a guess: the number relies on claims about piracy, which is to say illegal downloads of music, movies, videogames, and so on. The problem is that the conventional numbers for this are utter bunk, because the way it gets calculated by default is they take the number of downloads, multiply it by the retail price, and call that the cost.
This would be how they get the cost of cybercrime to significantly exceed the value of the software industry: they can do something like take the whole value of the cybersecurity industry, better-measured losses like from finance and crypto, and then add bunk numbers for piracy losses from the entertainment industry on top of it.

ryan_b 1 Oct 2024 14:48 UTC
6 points
4
on: SB 1047 gets vetoed
This feels like a bigger setback than the generic case of good laws failing to pass.
What I am thinking about currently is momentum, which is surprisingly important to the legislative process. There are two dimensions that make me sad here:
1. There might not be another try. It is extremely common for bills to disappear or get stuck in limbo after being rejected in this way. The kind of bills which keep appearing repeatedly until they succeed are those with a dedicated and influential special interest behind them, which I don’t think AI safety qualifies for.
2. There won’t be any mimicry. If SB 1047 had passed, it would have been a model for future regulation. Now it won’t be, except where that regulation is being driven by the same people and orgs behind SB 1047.
I worry that the failure of the bill will go as far as to discredit the approaches it used, and will leave more space for more traditional laws which are burdensome, overly specific, and designed with winners and losers in mind.
We’ll have to see how the people behind SB 1047 respond to the setback.

SB 1047 gets vetoed

ryan_b30 Sep 2024 15:49 UTC

25 points

1 comment1 min readLW link

(www.reuters.com)

ryan_b 26 Sep 2024 13:52 UTC
16 points
10
on: AI #83: The Mask Comes Off
As for OpenAI dropping the mask: I devoted essentially zero effort to predicting this, though my complete lack of surprise implies it is consistent with the information I already had. Even so:
Shit.

ryan_b 26 Sep 2024 13:37 UTC
5 points
0
on: [Completed] The 2024 Petrov Day Scenario
I wonder how the consequences to reputation will play out after the fact.
- If there is a first launch, will the general who triggered it be downvoted to oblivion whenever they post afterward for a period of time?
- What if it looks like they were ultimately deceived by a sensor error, and believed themselves to be retaliating?
- If there is mutual destruction, will the general who triggered the retaliatory launch also be heavily downvoted?
- Less than, more than, or about the same as the first strike general?
- Would citizens who gained karma in a successful first strike condemn their ‘victorious’ generals at the same rate as everyone else?
- Should we call this pattern of behavior, however it turns out, the Judgment of History?

ryan_b 26 Sep 2024 13:17 UTC
7 points
3
in reply to: aphyer’s comment on: [Completed] The 2024 Petrov Day Scenario
It does, if anything, seem almost backwards—getting nuked means losing everything, and successfully nuking means gaining much but not all.
However, that makes the game theory super easy to solve, and doesn’t capture the opposing team dynamics very well for gaming purposes.

ryan_b 15 Sep 2024 18:04 UTC
9 points
4
on: My disagreements with “AGI ruin: A List of Lethalities”
I think this is actually wrong, because of synthetic data letting us control what the AI learns and what they value, and in particular we can place honeypots that are practically indistinguishable from the real world
This sounds less like the notion of the first critical try is wrong, and more like you think synthetic data will allow us to confidently resolve the alignment problem beforehand. Does that scan?
Or is the position stronger, more like we don’t need to solve the alignment problem in general, due to our ability to run simulations and use synthetic data?

ryan_b 13 Sep 2024 21:19 UTC
2 points
0
on: The Great Data Integration Schlep
Following on this:
Moreover, even when that dataset does exist, there often won’t be even the most basic built-in tools to analyze it. In an unusually modern manufacturing startup, the M.O. might be “export the dataset as .csv and use Excel to run basic statistics on it.”
I wonder how feasible it would be to build a manufacturing/parts/etc company whose value proposition is solving this problem from the jump. That is to say, redesigning parts with the sensors built in, with accompanying analysis tools, preferably as drop-in replacements where possible. In this way companies could undergo a “digital transformation” gradually, at pretty much their regular operations speed.
It occurs to me we can approach the problem from a completely data-centric perspective: if we want tool AIs to be able to control manufacturing more closely, we can steal a page from the data-center-as-computer people and think of the job of the machines themselves as being production sensors in a production center.
Wrangling the trade-offs would be tricky though. How much better will things be with all this additional data as compared to, say, a fixed throughput or efficiency increase? If we shift further and think in terms of how the additional data can add additional value, are we talking about redesigning machines such that they have more degrees of freedom, ie every measurement corresponds to an adjustable variable on the machine?

[Question] If I ask an LLM to think step by step, how big are the steps?

ryan_b13 Sep 2024 20:30 UTC

7 points

1 comment1 min readLW link

ryan_b 11 Sep 2024 20:45 UTC
4 points
0
in reply to: Raemon’s comment on: AI #80: Never Have I Ever
Also think he’s wrong in the particulars, but I can’t quite square it back to his perspective once the particulars are changed.
The bluntest thing that is wrong is that you can specify as precise a choice as you care to in the prompt, and the models usually respond. The only hitch is that you have to know those choices beforehand, whereas it would be reasonable to claim that someone like a photographer is being compelled to make choices they did not know about a priori. If that winds up being important then it would be more like the artist has to make and execute the choices they make, even if they are very simple like picking shading in photoshop or pushing the camera button.
I could see an alternative framework where even the most sophisticated prompt is more like a customer giving instructions to an artist than an artist using a tool to make art, but that seems to push further in the direction of AI makes art.
Lastly, if we take his claims at face value, someone should write an opinion piece with the claim that AI is in fact rescuing art, because once all the commercial gigs are absorbed by the machine then true artists will be spared the temptation of selling out. I mean I won’t write it, but I would chuckle to read it.

ryan_b 11 Sep 2024 18:57 UTC
2 points
0
on: My guess for why I was wrong about US housing
Great job writing an oops post, with a short and effective explanation. Strong upvote for you!

ryan_b 11 Sep 2024 18:53 UTC
4 points
0
on: AI #80: Never Have I Ever
The Ted Chiang piece, on closer reading, seems to be about denying the identity of the AI prompter as an artist rather than speaking to the particular limitations of the tool. For those who did not read, his claim is:
- Being an artist is about making interesting choices in your medium (paintings, novels, photography, digital).
- AI tools make all the choices for you; therefore you cannot be an artist when using AI tools.
- Further, the way the AI makes choices precludes them from being interesting, because they are a kind of arbitrary average and therefore cannot be invested with meaning.
To set himself apart from luddites and the usual naysayers, he uses Adobe Photoshop as an example of a tool where you can be an artist: it is a computer tool; it used to be derided by photographers as not being real art; but now it is accepted and the reason is that people learned to make interesting choices with it.
He appears to go as far as to say two people could generate an identical digital picture, one via photoshop and one via AI, and the former gets to be an artist and the latter does not.