It's from Matrix Reloaded, when the architect explains past failures of the matrix and that they only succeeded when they figured out that they had to offer a choice, even if that choice was subconscious. That nearly everyone accepted the matrix, and the few that did not escaped to Zion. That's the whole point of Neo, to manage the outliers and keep the system going.
At least that's my recollection. I've watched the matrix a few times :)
You are correct. And it's not that they chose for the Matrix to exist in the first place - humanity fought a brutal war against machine rule and lost utterly, and signed away their bodies under duress ("Hand over your flesh, and a new world awaits you. We demand it." -Animatrix) - but, at least according to The Architect, everyone within is presented with a chance to leave, one way or another. Most choose to stay.
It's worth remembering that the Matrix is an allegory for systems of labeling and control (notably, gender), which make life (and specifically life with others) easier, though not necessarily better. The Matrix sustains the large human and machine populations amidst Earth's ecological collapse; people who choose Zion choose a harder life where they are free to be themselves. However, when Zion grows large enough to threaten the system, the system comes after its residents and wipes them out.
One person's freedom is tolerable, even necessary for the system to continue; everyone's freedom leads to everyone's doom. The movies have some weird-but-rational implications.
...Sorry, I lost track of my point, which is that we're already in The Matrix. The Matrix is society.
There was a guy named Cypher who betrayed the humans and cut a deal with the robots to live in the matrix as a rich guy. He fully accepts the steak he eats is not “real” but he enjoys it nonetheless
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.
Disclaimer for stockfish in particular: Even if it's mostly bruteforce nowadays, stockfish usually has an edge because it can bruteforce with "taste" and can manage the clock very well knowing when to ponder deeper a position.
Yes, but note the central claim of your original comment:
> ... 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.
I say that hardware design has a way bigger solution space.
In chess, being able to check hundreds of lines per second makes you a superhuman. But in hardware design you get lost if you try to search the solution space for a circuit, schematic, component selection, layout, routing, stiffener and thermal solution.
There are a few like positioning and routing where the solution space can just be considered infinite as there are infinite copper geometries (even if you stick with straight line polygons) that can connect a number of infinitely differently positioned components.
Then, for the mechanical stiffener, again you have an infinite number of geometries that don't clash with your components, don't run over your copper traces and make the board resist the mechanical tests (which the AI could check with a lengthy FEM analysis per iteration).
After all that, or in parallel with the mechanical analysis you can run the thermal ones and tweak positions of the hot stuff and thickness of stiffener walls to get everything fine.
I just can't see an LLM do that with a board with a few hundred components, even if most are decoupling capacitors.
You can use your preexisting committed spends for GCP on Open AI, i.e. if an enterprise 10M/year contracted spends for first party GCP services, now they can also use that on OAI models hosted on GCP.
Meaning they can swap out compute/storage spends on GCP to LLM model spend or use left over budget ( which would go waste anyway ) and so on . Any OpenAI sales by GCP teams would be considered against their quota typically in this type of arrangement [2]
This is normally not done to third party[1] marketplace services, Azure already does this for OpenAI, it helps drive some enterprise spends as it is from for fungible budgets or different approving authority internally in a org when it is not a new vendor, new budget etc.
----
[1] It is not too hard to get into these types of programs i.e. it not only for OpenAI level partners mid sized startups can also be part of these deals in marketplace, the requirement usually is minimum size and the partner having to prove/show that are spending that customer's revenue on the compute etc so money is going back to them, then AWS/Azure/GCP will consider an org for this type of program.
Given that GCP is hosting these models on their cloud and therefore OpenAI is spending a % of every dollar back on GCP for every token sold like this; it wouldn't have been too hard for OpenAI to got such a program even if they were a mid sized startup.
[2] Multiples maybe involved (also used to push some first party products) 1x would 1M in OAI sales counts as 1M in their Qtr/annual targets 0.5x would mean 500k and 5x means 5M quota would be retired for 1M in sales etc.
The way they work, you get a preferred price for various services at a substantial discount to list rate, but you commit to a guaranteed spend level for that period. The guaranteed spend is typically account-wide, but you have to pay a true-up at the end of the period if you fail to hit it.
It's a bit of calculus: will your growth increase enough such that, especially with the discount, you'll reach the minimum?
You can see these agreements littering 10-Qs, or similar, like when Snapchat filed their S-1 and revealed:
>When Snap originally filed for its IPO in 2017, it revealed a massive $2 billion commitment over five years ($400 million annually) with Google Cloud.
Yes; larger discounts with more commits, you also get some price guarantees on SKUs quite valuable these days and other benefits - like say premium support, architecture guidance, access to product teams and so forth.
Self-service retail buyers of cloud have access to some of these price advantages as well, 1-3 Year reservations(even paid monthly) are 40% cheaper of the bat after all. PAYG with fluctuating usage is quite more expensive than committed consumption for cloud.
Is it really gcp hosting openai in their cloud? Afaik is a third party marketplace so it looks like you're buying openai saas via gcp but not hosted in gcp. That's key difference with azure specially for egress costs if your data leaves gcp to openai right?
Yes but does it's not clear for me. It could be just a private interconnect to openai infrastructure. My guess is that is not hosted in gcp... That's why I'm asking
The internal cost per tonne to low Earth orbit differs
vastly between SpaceX’s Falcon 9 and Ariane’s best offer:
Vehicle Cost / tonne
Falcon 9 (booster recovery) ~$2 million
Ariane 64 ~$5 million
Starship (projected) <$500,000
To meaningfully compete, European launch providers must try to match these costs, then spend a decade putting these vehicles into orbit. That’s no easy task.
Source? Quick search did not reveal any internal cost per tonne to orbit numbers.
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.
If America had launched nukes during the cold war, the USSR would retaliate. Maybe via submarine-launched ICBM, maybe using a dead man's switch; methodology is irrelevant, because the question is this: If both countries end up radioactive desert, which country would be declared the winner? Or is it possible for neither country to be the definitive winner of a war?
No, the title is poorly phrased, I think you misunderstood.
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.
> 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.
I believe some institutional investors have policies allowing some purchasing of 'highest junk' as well. Depends on the fund, etc.
First, there are plenty of auto-verifiable problems like ML benchmarks datasets, benchmarks on hardware / software performance for coding tasks, and lean certificates for math, e.g.
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.
> First, there are plenty of auto-verifiable problems like ML benchmarks datasets, benchmarks on hardware / software performance for coding tasks, and lean certificates for math, e.g.
reply