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.
> 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.
Why does it matter if it's not for consumer applications? GNSS is used for many more applications (and arguably more critical) than consumer applications (agriculture, mapping & surveying, aeronautics, shipping, etc.)
Also just want to mention that, yes, integration errors accumulate when using intero-receptive sensors but if errors are small enough (white noise, various biases, sample rates, quantization, etc.) from the inertial sensors an odometry solution might be adequate until an extero-receptive sensor can localize the sensor within an external frame.
This can shift the discussion from solving a problem that has no solution (i.e. how do I integrate a signal with white noise without any error) to an engineering problem (i.e. what error parameters allow the odometry to be accurate within x% over some timeframe).
>The end goals of the TIMU program are the demonstration of a single-chip IMU which maintains an accumulated position error of less than 1 nmi/hour with device volume of less than 10 mm3 and power consumption of less than 200 mW.
(My job is related to estimating location of things).
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.
The financialization of everything to extract incremental value is exhausting. I feel its also very detrimental to society when every positive interaction has to involve money.
Go to a theme park: buy a ticket. Then have the option to buy a fast-pass otherwise you will be in line all day. But wait, that only covers certain attractions and other ones need a faster fast-pass. [1]
Food delivery: pay a fee to the delivery network, separate tip for the driver. Then some places will hide a fee by raising the prices of things ordered through the delivery network vs. calling in. Then how much you tip might determine if your food arrives warm or cold. [2]
And so many more examples.
I've started patronizing businesses and products that don't nickel and dime at every step. I guess the easiest thing to do is vote with our wallets.
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.
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.
Very high field magnets are critical for MRIs, tokamak fusion reactors, mass spectrometers, and probably many other uses if we consider pure science as “useful”.
Actually particle accelerators somewhat funded the early investment in high field superconducting magnets which might now appear to yield improvements in fusion energy Q factors due to the strong dependence on field intensity [1]
Agreed! The list of highest growth and highest decline cities by page view is almost entirely small cities. This is a pointless stat because of course the small cities are going to see the largest change from even the smallest of trends.
I’m skeptical of the rest of the analysis if simple things like that are not considered.
Any location estimator worth its salt is already doing this through the use of a multi-rate EKF. On top of that, it takes into account the vehicle dynamics (i.e. cars can't move perfectly horizontally) to improve estimates.
The "novelty" here is the ML approach although I am not sure if that is particularly novel as well.
It’s also incorrect to assume the expected value of the equity is zero. It’s much more useful to model it as E(x)=x*p(x), especially if you have some useful information on estimating p(x) that a person working at a startup might.
Yeah maybe. Are we talking about PBS overall or the Newshour segment?
In PBS Newshour the upfront report by Judy is very much fact based. I agree though that the various mini-documentary parts of the segment are less direct-fact based reporting and bias starts to creep in.
Anyways, I'm not too interested in starting an internet politics fight. The main takeaway from my post is that we should reevaluate what we are getting out of following the news obsessively.
An hour of PBS Newshour a day. Very much fact based reporting with maybe a slight left leaning bias. Digest it and move on and live your life.
Besides that, I just turn off the news spigot. I'm slowly coming to the realization that watching news was kind of becoming my version of watching reality TV shows. There is of course value in being informed but outside of that, a single person can't meaningfully take action on all the things being reported on. So I see less and less reason to keep up with the news besides just being generally informed.
I think a better way to consume news might be to get a few "general" news tidbits through your regular news outlets. And then get more focused news on topics you personally care about and are willing to take action on through non-traditional news outlets.
Sure is a lot of words though :)