Engineer at Donor to LW 2.0.

Wiki Contributions


Btw, some of the best sources of information on TSLA, in my view, are:

  1. the Tesla Daily podcast, with Rob Maurer
  2. Gary Black on Twitter

Rob is a buy-and-hold retail trader with an optimistic outlook on Tesla. I find him to be remarkably evenhanded and thoughtful. He's especially good at putting daily news stories in the context of the big picture.

Gary comes from a more traditional Wall Street background, but is also a TSLA bull. He tends to be a bit more short-term focused than Rob (I presume because he manages a fund and has to show results each year), but I find his takes helpful for understanding how institutional investors are likely to be perceiving events.

I continue to like TSLA.

The 50% annual revenue growth that they've averaged over the last 9 years shows no signs of stopping. And their earnings are growing even faster, since turning positive in 2020. (See fun visualization of these phenomena here and here.)

Admittedly, the TTM P/E ratio is currently on the high side, at 50.8. But it's been dropping dramatically every quarter, as Tesla grows into its valuation.

There are also some solutions discussed here and here. Though I'd assume Scott G is familiar with those and finds them unsatisfactory.

a lot of recent LM progress has been figuring out how to prompt engineer and compose LMs to elicit more capabilities out of them

A deliberate nod?

The assumption means the ballot asks for a ranking of candidates, possibly with ties, and no other information.

Note that this is only true for ranked methods, and not scored methods, like Approval Voting, Star Voting, etc.

There is a brief golden age of science before the newly low-hanging fruit are again plucked and it is only lightning fast in areas where thinking was the main bottleneck, e.g. not in medicine.

Not one of the main points of the post, but FWIW it seems to me that thinking could be considered the main bottleneck for medicine, if we can include simulation and modeling a la AlphaFold as thinking.

My guess is that with sufficient computation you could invent new treatments / drugs that are so overwhelmingly better than what we have now that regulatory or other bottlenecks would not be an issue. E.g. I expect a "slow aging by twenty years" pill would find its way around the FDA and onto the market pretty quickly (years not decades) if it actually worked.

Also, was the date in footnote 32 supposed to be 10/6?

The VPT comparison was added in an edit on 12/6, see this comment from Rohin Shah.

Dumb nitpick on an otherwise great post, but FYI you're using "it's" for "its" throughout the post and comments.

But it sure looks like tractable constant time token predictors already capture a bunch of what we often call intelligence, even when those same systems can't divide!

This is crazy! I'm raising my eyebrows right now to emphasize it! Consider also doing so! This is weird enough to warrant it!

Why is this crazy? Humans can't do integer division in one step either.

And no finite system could, for arbitrary integers. So why should we find this surprising at all?

Of course naively, if you hadn't really considered it, it might be surprising. But in hindsight shouldn't we just be saying, "Oh, yeah that makes sense."?

Dumb question — are these the same polytopes as described in Anthropic's recent work here, or different polytopes?

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