datastoat · HackerTrans

الشهر الماضيdiscuss

I believe that chain-of-thought reasoning blocks don't really correspond to what humans think of as reasoning. (See section 6.2.2 of the Fable/Mythos system card about "illegible reasoning", and the questions raised by the Apple paper on "The illusion of thinking".) I assumed they obscure the reasoning blocks because if users saw what's going on they'd be alarmed. Just as I'd probably be alarmed if I saw what was really going on in the heads of my colleagues ...

datastoat

قبل 5 أشهرdiscuss

Goodhart's Law of Specification: When a spec reaches a state where it's comprehensive and precise enough to generate code, it has fallen out of alignment with the original intent.

Of course there are some systems where correctness is vital, and for those I'd like a precise spec and proof of correctness. But I think there's a huge bulk of code where formal specification impedes what should be a process of learning and adapting.

datastoat

قبل 7 أشهرdiscuss

Wouldn’t it be handy if the browser could intelligently join this author’s sentences into paragraphs?! (in connection to the thread about Mozilla putting AI in the browser)

datastoat

قبل 10 أشهرdiscuss

Darwin would claim otherwise! Single cells plus random mutation plus selective copying = rocket ships.

datastoat

السنة الماضيةdiscuss

> They should also share their prompts

Here's a recent ShowHN post (a map view for OneDrive photos), which documents all the LLM prompting that went into it:

https://news.ycombinator.com/item?id=44584335

datastoat

السنة الماضيةdiscuss

Author: "5% chance of shipping something that only looked good by chance". One philosophy of statistics says that the product either is better or isn't better, and that it's meaningless to attach a probability to facts, which the author seems to be doing with the phrase "5% chance of shipping something".

Parent: "5% chance of looking as good as it did, if it were truly no better than the alternative." This accepts the premise that the product quality is a fact, and only uses probability to describe the (noisy / probabilistic) measurements, i.e. "5% chance of looking as good".

Parent is right to pick up on this, if we're talking about a single product (or, in medicine, if we're talking about a single study evaluating a new treatment). But if we're talking about a workflow for evaluating many products, and we're prepared to consider a probability model that says some products are better than the alternative and others aren't, then the author's version is reasonable.

datastoat

قبل سنتينdiscuss

Empirical Bayes is exactly what I was getting at. It's a pragmatic modelling choice, but it loses the theoretical guarantees about uncertainty quantification that pure Bayesianism gives us.

(Though if you have a reference for why empirical Bayes does give theoretical guarantees, I'll be happy to change my mind!)

datastoat

قبل سنتينdiscuss

Non-Bayesian NN training does indeed use regularizers that are chosen subjectively —- but they are then tested in validation, and the best-performing regularizer is chosen. Thus the choice is empirical, not subjective.

A Bayesian could try the same thing: try out several priors, and pick the one that performs best in validation. But if you pick your prior based on the data, then the classic theory about “principled quantification of uncertainty” doesn’t apply any more. So you’re left using a computationally unwieldy procedure that doesn’t offer theoretical guarantees.

datastoat

قبل سنتينdiscuss

I like Bayesian inference for few-parameter models where I have solid grounds for choosing my priors. For neural networks, I like to ask people "what's your prior for ReLU versus LeakyReLU versus sigmoid?" and I've never gotten a convincing answer.

datastoat

قبل سنتينdiscuss

The article explained that there are two roughly equal drivers: (1) Water is a better heat reserve than land, and winds tend to blow eastwards, so Europe gets air warmed by the sea and the US east coat gets colder air that's come from the land. (2) The joint effect of the altitude of the Rockies and the angular rotation of the earth mean that air currents are southeast over the Rockies and then northeast, so arctic air gets pulled down and then pushed back up over the US east coast.

datastoat

قبل 3 سنواتdiscuss

It'd be fun (and a bit scary) to use an LLM as a shell replacement. We'd give it the history of our commands as per the recent post [0], as well as their outputs, and it would turn natural-language commands into proper bash. The xkcd comic [1] would be solved instantly. "Tar these files, please." "Delete all the temporary files but please please please don't delete anything else." I'm sure people have implemented this, but my searching isn't good enough to find it.

[0] https://news.ycombinator.com/item?id=38965003

[1] https://xkcd.com/1168/

datastoat

قبل 3 سنواتdiscuss

According to the Wikipedia page for eszett [0] it evolved from "sz", as the name "eszett" suggests. (I only realized the link with "z" when I saw "tz" ligatures on street signs in Berlin.) Given that its typographic origin is sz, and given that its name literally says sz, I wish the spelling reformists had gone for sz rather than ss!

[0] https://en.wikipedia.org/wiki/%C3%9F

datastoat

قبل 3 سنواتdiscuss

They use Pyodide, a full Python interpreter in WASM: https://pyodide.org/en/stable/console.html

Pyodide includes manyuseful Python libraries including numpy, pandas, and matplotlib.

datastoat

قبل 3 سنواتdiscuss

Windows 8 (Metro) used semantic zoom. It's been a while, but I do remember that one of the apps that used it very nicely was Photos. A search for "windows metro semantic zoom" comes up with lots of articles about semantic-zoom-aware GridView controls etc.

Why isn't it commonplace? I think that touchscreen laptops are still too much a minority, and keyboard + mouse + monitor are too entrenched, for anyone to seriously attempt it again for a while. (A shame -- I'm one of the few who really liked the Windows 8 Metro interface.) I think that phones are too small for it to really work well. I don't know why it's not more popular on tablets.

datastoat

قبل 3 سنواتdiscuss

As an Australian I'm used to hearing "antipodean" and sometimes "antipodes", so "octopodes" sits well!

datastoat

قبل 3 سنواتdiscuss

That xkcd comic highlights the problem with observational (as opposed to controlled) studies. TFA is about A/B testing, i.e. controlled studies. It’s the fact that you (the investigator) is controlling the treatment assignment that allows you to draw causal conclusions. What you happen to believe about the mechanism of action doesn’t matter, at least as far as the outcome of this particular experiment is concerned. Of course, your conjectured mechanism of action is likely to matter for what you decide to investigate next.

Also, frequentism / Bayesianism is orthogonal to causal / correlational interpretations.

datastoat

قبل 3 سنواتdiscuss

I’m curious! Why “Bayesian”?

datastoat

قبل 3 سنواتdiscuss

I tried... I pointed out a problem and asked ChatGPT to fix it, unsuccessfully. I asked it for a proof of correctness, then pointed out a problem in its proof and asked ChatGPT to fix it, again unsuccessfully. (It's all in the notes I linked to.) Perhaps I'm just crummy at prompt engineering; or perhaps this is one of those questions where the only way to engineer a successful prompt is to know the answer yourself beforehand.

datastoat

قبل 3 سنواتdiscuss

You're right, ChatGPT is probabilistic. None of this is graded by the way -- it's all just for fun and bragging rights.

I've asked students to share their full dialog, both prompts and replies, so the whole class gets to see; and I'll invite one or two to talk through their attempts. This is all just a trick to make students engage with "how do you you spot bugs in a proof?", hopefully more than they would from just reading CLRS! Often, students engage well when they're hearing the material from other students.

datastoat

قبل 3 سنواتdiscuss

I'm trying something similar with an introductory Algorithms class.

After we go through Breadth First Search, there's a practical assignment where students are asked to modify the algorithm to return _all_ shortest paths. Then I ask ChatGPT for its solution, and students try to spot its mistakes.

Later, after going through the proof of correctness of Dijkstra's algorithm, I ask ChatGPT for a proof of correctness of its all-shortest-paths algorithm, and again students try to spot what's wrong in the proof. I want students to learn to tell the difference between a bullshit proof and a real proof; in the past I've given them bullshit proofs from real students in exams, but ChatGPT makes the point more nicely.

Finally, students are asked to figure out prompts that will make ChatGPT give a correct algorithm and proof. I haven't managed this myself! I'm looking forwards to seeing what students manage.

Here's a link to lecture notes, including the ChatGPT dialog: https://www.cl.cam.ac.uk/teaching/2223/Algorithm2/alg2-full....

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