Medical expenses would be a combination of cost of insurance / treatment (PPP relevant) and government transfers in a country with partially or fully public healthcare, which is why it's so important to do both.
I see. That paper is looking a median income from a PPP perspective but without accounting for taxes and transfers, which is a tiny bit unorthodox in this exact context which tries to get into the weeds slightly more than just standard PPP adjusted GDP per-capita or something like that. It's valid statistically, but typically if you're trying to adjust for cost of living it makes a lot of sense to use the amount of actual income the individuals have left over after paying taxes and receiving transfers from social programs. Otherwise you'll have a number which accounts for differences in prices but not differences in the proportion of "income" which is actually retained by the earner. This is what that wikipedia article I linked is reporting. Comparing two different countries with very different tax/social program policies with PPP adjustments but without tax/transfer adjustments is less than ideal.
It's not wrong, it's just not very useful. It's typically a more intermediary statistic rather than a final one when doing this type of comparison.
Yes, since the weights being updated are a small subset of the overall total it's manageable. Just like how each separate conversation currently requires you to store a separate KV cache, you'd need to store the fast weights separately. Both KV cache and fast weight content stores have to be conversation specific, so just setting a bit of extra RAM aside for "memory" isn't really a new ask, just a different format for an old problem.
The idea of periodically stopping to write blocks of recent context into a fast-weight state is interesting, but I think it liked it better when E2E-TTT[1] did it. It's a more flexible and elegant continuous learning approach.
Essentially it goes "You know how your model can remember its training data? Well, what if you treated its recent context like more training data and updated (some of) the weights using (mostly) the same process used to train it?"
The end result is very good at remembering things but also really good at adapting to new unseen distributions.
>Its turn around time is ridiculous, it has to be maintained with specialized equipment/hangers, along with external contractor assistance.
>Compared to the Gripen, as an example, which can land on a freeway and be up in the air again in a few minutes.
I have no idea where people got the idea that the F-35 requires a major refit after each sortie or that it needs climate controlled hangars, but there's literally no truth to any of it.
The turnaround time for an F-35 after a mission in a wartime scenario isn't going to be much different from any other older fighter jet. Refuel, rearm, get back in the air.
One of the key requirements for the F-35 programs was to minimize extra care needed for the RAM (Radar Absorbent Material). Unlike older stealth aircraft the F-35's ram is "baked in" to the aircraft skin, rather than being a coating. The F-117 and B-2 require climate controlled hangars because their coatings are old and delicate, the F-22 doesn't, but needs regular touch-ups for its coating, the F-35 is just left sitting outside most of the time regardless of where it's operating, a desert, the arctic, a jungle, the deck of a ship, you just leave it out there. The only common maintenance done on the F-35's RAM is replacing a relatively small amount of special RAM tape which is usually used around the edges of the access panels which are opened for other types of maintenance.
Cerebras has effectively 100% yield on these chips. They have an internal structure made by just repeating the same small modular units over and over again. This means they can just fuse off the broken bits without affecting overall function. It's not like it is with a CPU.
That's what it's running on. It's optimized for very high throughput using Cerebras' hardware which is uniquely capable of running LLMs at very, very high speeds.
Takes much longer to build, requires a much larger up-front investment, and requires a lot more land.
The footprint needed when trying to generate this much power from solar or wind necessitates large-scale land acquisition plus the transmission infrastructure to get all that power to the actual data center, since you won't usually have enough land directly adjacent to it. That plus all the battery infrastructure makes it a non-starter for projects where short timescales are key.
Precision bombing during WW2 was not possible at the required scale. To put a bomb precisely on target back then you needed something like a dive bomber, a tactic which is incompatible with strategic-scale bombing. Even "precise" methods using advanced analog computers like the Norden bombsight could only do so much.
>Under combat conditions the Norden did not achieve its expected precision, yielding an average CEP in 1943 of 1,200 feet (370 m)[1]
This means that 50% of bombs fell within 1,200 feet of the target, which is an absolutely awful accuracy if you're trying to hit anything specific.
This was further compounded during the campaign against Japan by the heavy reliance of Japanese wartime industry on cottage industries which were dispersed almost randomly within Japanese population centers, rather than being located within specialized industrial districts. From a purely strategic standpoint which is only concerned with destroying the enemy's ability to make war, the most effect way to disrupt these kinds of industry with 1945 technology was essentially to burn every building in the city to the ground. Other options were simply ineffective.