TooKool4This

I don’t feel like reading what is probably AI generated content. But based on looking at the model fits where hyperbolic models are extrapolating from the knee portion, having 2 data points fitting a line, fitting an exponential curve to a set of data measured in %, poor model fit in general, etc, im going to say this is not a very good prediction methodology.

Sure is a lot of words though :)

> Why do we need triple the administrative staff that was utilized in the 1960s across industries like education, healthcare, etc.

Well for starters the population has almost 3x since the 1960s.

Mix in that we are solving different problems than the 1960s, even administratively and I don’t see a clear reason from that argument why a shitload of work is meaningless.

India is very different than Europe. It would be difficult to attribute outcomes between the 2 countries to just this single difference without considering the many other differences.

Yep but just an easy first approximation since the article didn’t have any numbers on the momentum of the particle. I’m sure if you wanted a really good answer you would need more than momentum and actually need to analyze the products of the collision like another poster suggested.

Also wanted to share this table since energy expressed in eV sounds like big numbers but it’s nice to understand that the definition of the eV is small in our usual definition of energy.

Google maps is very eager to re-route around traffic which ends up taking you down back roads (atleast in the US). Which are narrow, not controlled-access, poorly lit, and maybe windy and poorly maintained depending on where you are. And it does all this to save a couple of minutes at best. Everyone is using Google maps and getting the same rerouting suggestion so the backroads get filled and slow down eliminating any benefit.

Apple Maps is much more likely to use the “typical” option which is usually a controlled access freeway which are MUCH safer (in my opinion and I believe the traffic accident rates support it).

Like others have mentioned the entire field of sensor fusion deals with this problem. It is a very challenging problem to solve but it can be solved and it has been successfully used in spacecraft, aircraft, fighter jets, phones, AR/VR systems, and undoubtedly many others.

A basic approach is to have an uncertainty (or estimated uncertainty) for each of the sensing modalities. Then you use the uncertainties to weigh each sensing sample when deriving your estimated quantity (i.e. vehicle velocity for example). Assuming the uncertainties are correct, the resulting estimator can have variance lower than estimators deriving from a single sensor modality. Of course tuning sensor uncertainty values is a difficult problem in sensor fusion (and much more so when distributions are unknown) but it is definitely doable.

Repeating Elon's claim that sensor fusion is impossible/not-doable is entirely wrong. It is a technology that powers many different applications but its definitely not an easy thing to implement well.

Are you running an ad blocker by any chance? I turned off my ad blocker and the ads showed up.

Shouldn't you multiply rather than add IID events?

I think this is not the correct way of thinking of Moore's law. Like mentioned elsewhere, Moore's law is not so much a law as a self-fulfilling prediction. Semiconductor companies follow Moore's law (or recently an approximation) to stay competitive because their competitors do it and hence Moore's law continues. The cost of not marching along to Moore's law can mean you will get left in the dust (see Intel).

I think a better question to ask is whether the underlying economic factors behind continuous process tech improvements are healthy. Is there enough value-add to the final user by continuous process tech improvements? Are the costs for that improved process tech scaling with the value-add? And is the competitive landscape healthy? While that holds true, companies will keep looking for process tech improvements to give them a competitive edge.

In the 80-90s this was very much true but in recent decades it was to a lesser extent hence why we see consolidation/reduction in the number of foundries, foundry services to amortize the cost of older node-tech, and R&D going to companies/partnerships that can capture the most end user value-add (Apple/TSMC).

Looking forward I think the economics are very healthy with a design-house/foundry service model that we have right now so I would guess that Moore's law (or some approximation) will continue for the next decade. There are a lot of process tech innovation that can lead to better performance that are not necessarily scaling related. In fact, scaling transistors stopped being very useful a while back afaik due to the breakdown of Dennard's scaling.

I am not sure toughness has anything to do with ferromagnetic properties. But it seems like a bad idea anyways considering that very few materials are naturally ferromagnetic.