https://chatgpt.com/share/69dc5030-268c-83e8-92c2-6cef962dc5...

In general, the idea of a "market rate" for any given property depends fundamentally on a system of property rights actually being enforced.

Any given Senator is to some extent constantly in a favor-trading game with executive branch officials. People from the President on down need congressional cooperation to get their pet provisions into bills, programs funded, nominees approved, etc. A Senator can tell a White House official "I'd love to help you with that, however I have this issue with this agency not responding to my requests". Assuming it's a reasonable thing, whoever at the agency is in charge of this then gets an irate call from their boss's boss's boss ordering them to cooperate.

Of course this mostly doesn't actually get played out, because everyone understands the dynamic that defying senatorial requests will ultimately cost the President in terms of cooperation on other issues. So the norm is mostly to comply with reasonable requests, unless you're quite sure that it's a top-level priority where the White House really wants to take a stand.

https://youtube.com/watch?v=TzN148WQ2OQ

By far the catchiest song about eel mating you will encounter today.

Suppose the government charges you with murder, searches your house, and finds your sex toy collection. At trial they present some elaborate thesis about how you used a sex toy to kill someone, but do not convince the jury, so you're found not guilty. The public has a legitimate interest in judging that the trial was handled with integrity and that the correct verdict was reached. They do not have a legitimate interest in judging you based on whatever private information presented at trial might in some way embarrass you (eg, photos of your sex toy collection). On balance, it could be that the public-record interest does in fact justify making public the evidence of the sex toys, but you have to justify it on those terms. The transparency is not itself intended to be punitive.

Any unjust policy (including just dispensing with trials altogether and allowing the executive to arbitrarily break up companies) will get to the 'desirable' outcome in some cases. That doesn't make it a just policy.

The specific allegation in the post is that the Trump administration will not appeal the verdict because Sundar gave $1M to Trump's inauguration. As far as I know, the government has not yet indicated whether it will appeal, so the claim that "Trump just paid him back, 40,000 times over" is in fact not true. (whether it becomes true at some point in the future, it was a falsehood at the time the author wrote it). It's also quite plausible that a Republican administration wouldn't appeal the verdict just due to being more pro-business in general, even without explicit corruption. But it's precisely because we have such a corrupt executive that it becomes all the more important to stick up for the rule of law. The correct response to authoritarianism is not to advocate for more authoritarianism!

The public record argument is fine; it's just a different argument than the extrajudicial punishment advocated by the original post.

The argument that we should cheer on the use of government power to target a specific company, to selectively expose their dirty laundry as punishment for a crime they have not been convicted of, is what I found noxious in the original post.

Regardless of what you think of Google or this case specifically, this is an argument for authoritarianism: that it is legitimate for the government to "punish" any company at will, based only on them falling into political disfavor.

> ... the only punishment Google would have to bear from this trial would come after the government won its case, when the judge decided on a punishment (the term of art is "remedy") for Google.

Yes, this is called the rule of law. Punishment comes through the courts, after a guilty verdict. The government has to actually win the argument as to what remedies would be proportionate under the law. In this case the judge didn't buy it. It's fine to disagree with his reasoning (or with the law), but the fantasizing about extrajudicial punishment here is frankly un-American.

https://en.wikipedia.org/wiki/Gospel_of_Matthew#Author_and_d...

https://en.wikipedia.org/wiki/Gospel_of_John#Authorship

"Supervised learning" for LLMs generally means the system sees a full response (eg from a human expert) as supervision.

Reinforcement learning is a much weaker signal: the system has the freedom to construct its own response / reasoning, and only gets feedback at the end whether it was correct. This is a much harder task, especially if you start with a weak model. RL training can potentially struggle in the dark for an exponentially long period before stumbling on any reward at all, which is why you'd often start with a supervised learning phase to at least get the model in the right neighborhood.

If you want to be picky, it's true that the direct analogue of continuous optimization would be discrete optimization (integer programming, TSP, etc) rather than decision problems like SAT. But there are straightforward reductions between the two so it's common to speak of optimization problems as being in P or NP even though that's not entirely accurate.

Of course, this isn't really relevant to the subjective experience of taking ibogaine at its typical dose, which by all accounts is strange in ways that go beyond the classical psychedelics.

I don't think the article supports this. All we know is that sama appeared cooperative when the board fired him. This was probably a reasonable posture for him to adopt regardless of his actual intentions at the time.

(yes, I've tried psychedelics; they're fascinating and super promising, but at least for me, not transformative in the way that fluoxetine was)

No individual depression treatment works for everyone. SSRIs are not a magic bullet. Neither are psychedelics. But if you're depressed and haven't tried SSRIs, you owe it to yourself and everyone in your life to at least test the hypothesis that they might help.

Scott Alexander's page on SSRIs is a great, relatively objective resource, from a psychiatrist who regularly prescribes them: https://lorienpsych.com/2020/10/25/ssris/

  If I could build it again, I’d start with automating the evaluation of forecasts. It’s silly to build models if you’re not willing to commit to an evaluation procedure. I’d also probably remove most of the automation of the modeling. People should explicitly make these choices.

Even in the age of deep learning, I do think these relatively classical Bayesian models have a lot of value for many applications. Time-series forecasting tends to be a case where:

- you don't have a ton of iid data points (often, only a single time series),

- you'd like forecasts with principled uncertainty estimates, e.g., credible intervals, giving you a range of scenarios to plan for,

- you often do have a pretty good idea of what features are relevant to the process you're predicting, and

- you want to understand in detail what features the forecast is accounting for (and what it might be missing),

all of which play to the strengths of more classical, structured statistical models, compared to more data-hungry black-box deep learning models. So the basic ideas in Prophet and similar tools do still have a lot of relevance going forward, IMHO.