https://www.lesswrong.com/posts/u9Kr97di29CkMvjaj/evaluating...

Long Intel but as Dylan (and many others) noted they should have stopped the dividend a year ago.

I don't have an exact list but I think you'll see differences of that order in the years following big acquisitions like AMD / Xilinx for this sector.

On the second point about parabolic orbits I also find that probably relatively low risk because we are only talking about a fraction of an orbit for a collision to occur so unless the debris field was massive the chance of another collision is probably still low. Remember when we are modelling orbit collisions normally we are often talking over 25+ years - 100,000 + orbits.

I think the main problem is busy orbits (e.g. sun-synchronous polar orbits at popular altitudes) where most of the debris remains roughly in the same orbit following an acute collision but has a lot of other potential collision targets. Also as satellites are disabled by a collision they lose the ability to avoid other objects already in the same crowded orbit - i.e. the fraction of objects able to take avoidance decreases increasing the chance that future collisions are from 2 incapacitated satellites, removing the possibility of avoidance.

Smaller image models had the same problems with logical inconsistency just because they didn't have sufficient general understanding of how visual concepts.

The same is almost certainly true of video - early smaller models will likely create janky movements/motion, however once they've seen enough video to understand how a person walks, how a scene is framed etc.. there's no reason we couldn't get to the same level of maturity as today's image models.

I think the real issue will come from labelling - most video is only going to be labelled simply with basic info/captions without detailed descriptions of the camera pan, movement of subjects. The amount of text required to accurately describe a scene is much larger than a still image and I'm not sure how once would go about collecting this.

Even ignoring the infohazard angle if they published everything immediately that would escalate the race. By sitting on their capabilities and waiting for others to publish (e.g. PaLM, Imagen vs GPT-3, DALL-E) they are at least only playing catch up.

Take a look into scaling laws and alignment concerns, this is a very real challenge and existential risk not some crackpot theory.

There are very good reasons for withholding SOTA models, primarily from the info hazard angle and avoiding escalating the capabilities race which is basically the biggest risk we have right now.

Google / Deepmind have actually made some good decisions to try and slow down the race (such as waiting to publish).

To learn logical concepts just from images seems entirely impractical, like we can't rely on having enough images such that models can understand words coherently as language. You could draw a picture of a sign that says "children crossing" not because you can understand and remember exactly what an image of such a sign would look like, but because you have an understand of English and the character set that would let you reproduce it. If you tried to learn to create the same sign in Arabic you'd either need to see a huge number of signs to learn from or (more likely) build a language model for Arabic.

The kind of abstract understandings that we know we can train in language models just aren't learned by image transformers at this scale (or likely any practical scale). A language model could easily understand: "A red cube is stacked on top of a blue plate, a green pyramid is balanced on the red cube" and infer things like the position of the pyramid relative to the blue plate, image models quickly fall over with such examples.

An interesting nascent (and hacky) example of the benefits of combining models is people are using language models like GPT-3 to create better prompts for image models.

The idea that ASML would somehow be more worthy of merit if Nikon/Canon also succeeded is weird.

Would the moon landing me more impressive if Russia and Japan also managed it?

Highway systems create massive economic benefits that are relatively simple to calculate, just in the raw # hours saved which massively increases productivity. This can be well captured by the state through taxes or (less frequently) by private industry through tolls - i.e. the people that direct their construction.

Other mega projects have much less certain benefits, or the benefits are harder to capture by those with the ability to allocate resources because incentives aren't sufficiently aligned.

If BTC mining relied on pieces of litter being collected you'd get people spending $millions on factories to produce litter and vehicles to distribute it just so more could be collected. That sounds crazy to type out, but it's no stranger than the situation we currently find ourselves in. The most advanced tools man has ever created are being used to create chips with no practical value, but that are good for converting energy into tokens through pointless calculations.

I don't believe it is possible to create a proof of work system that is not a "proof of waste" once the reward is high.

Spare CPU cycles felt like a waste, that becomes dedicated processing, that becomes dedicated hardware, that eventually became designing custom hardware.

These Proof of Waste systems aren't really a negative feedback loop, ultimately asset prices determine mining yield. Capacity will keep being added to the network until the difficulty rises such that you are required to destroy $99.99 of resources for $100 of crypto. Until that point it's worth buying more hardware and energy as you get a positive expected return.