Is this from the original trilogy?
445 karma · joined April 8, 2023
So Newton was wasting everybody’s time with the gravity malarky after all?!
> ... search space grows so big that any non-expert will become lost in it.
And I am saying that's a fallacious argument: chess has an enormous search space too, and that does not imply AI can't beat humans.
Also
> You would beat anyone if you could calculate a few thousands moves per second, even more so if you had a neural network that discards suboptimal moves early.
So like the AI can? Your argument's dont add up.
> Enterprises with Google Cloud spend commitments will be able to draw down their GCP commitments for these purchases.
Either way, if they did outsource these launches, they would not be paying the cost, but the price (meaning cost + margin). And that is much more expensive then just buying it from SpX.
Vehicle Cost / tonne
Falcon 9 (booster recovery) ~$2 million
Ariane 64 ~$5 million
Starship (projected) <$500,000
All ratings agencies have their own scales, but broadly all ratings fall into one of two overarching categories, investment grade vs. junk.
The article explains that the rating ANT received was the highest within the junk range. So “highest junk rating” means within the junk category, it’s in the best sub-area. In other words, it’s just scratching below the investment grade rating.
This is important for institutional investors who can only buy investment grade, meaning if they can improve even one single grade up, they get access to a lot more debt.
Second, I think RSI will not be used like N -> N+1 where N is the frontier, since doing those experiments at scale is too expensive. My guess is they are doing N -> m_{1,…,99} where m << N, and then taking the insights from that and trying it at successively larger scales.
IIRC investments are any transactions which increase the assets in the balance sheet. Losses are outflows net of inflows.
In abstract terms, one is about stock and the other about flow of spend.
And most of the countries on this list probably fall under some French channel’s domain since they are dependencies (edit: or former colonies).
> The controversy grew out of accusations from a little-followed, anonymous social media account, which a columnist at Le Figaro, a conservative daily newspaper, later revealed he partly ran.
https://www.nytimes.com/2026/09/25/world/europe/thelyson-ore...
In other words, if Claude was a human employee with the freedom to do so, would accepting the contract have been the right choice?
The statement I quoted said “zero examples of intelligent species” doing something. It took me 2 seconds to think of a counterexample. At the very least it was poorly phrased, if not altogether wrong. Moreover, whether or not people are doing it voluntarily is orthogonal to the statement.
I agree with the gist of the article, but having such a *BOLD:* _False Statement_ does not do it any favors.
Many people ceed control of their path-finding to seeing-eye-dogs.
1) gross margins are 80% w/o revenue sharing.
2) gross margins are positive w/ revenue sharing.
So that means Anthropic is making money on every token, and customers are willing to pay 80% margins (some of which might go to e.g. Bedrock to serve the model).
Stupid metric. It‘s not because you spend more time that you travel farther.
s/
I am still rocking an iPhone 11 Pro Max and even after replacing the battery ~1 year ago, every page load in Safari would take +20 seconds (Chrome was still fast) and opening / closing any app took ~1 second.
iOS 27 has fixed all of it, it feels snappy again!
Note this claim is about „operating profit“, which commonly is the revenue - operating expenses (COGS, rent, payroll). This does not include RnD cost.
>Anthropic's gross margins are above 80% before accounting for revenue shared with distribution partners, including Amazon (AMZN.O), and the cost of training its model, the newspaper said.
Gross margin is typically (revenue - COGS) / revenue. Thus, both statements above seem generally in line with commonly accepted accounting standards.
Your comment is a little unclear, so it‘s hard to parse your exact question. But it seems you are conflating 3 things, PyTorch, RL and Gym/Training (?).
– PyTorch is a framework which lets you define neural network models.
– RL is a collection of methods to train neural networks (change the network parameters to improve its performance).
– An RL-Gym is a framework to apply the neural networks to some problem. This lets you collect the data necessary to later use the methods of RL to train your model.