Which seems massively worse than a real local device in fact 2x is probably untenable to the point of uselessness because actually privacy sensitive matters need actual privacy that can't be defeated by your government telling Google to serve you compromised js and spy on you anyway and most people don't give 2 shits about privacy so they won't pay 10% more let alone 2x.
I'm glad people fund things that are only of interest to nerds but this will never be useful.
I think you have completely wrong use cases in mind. You will not use this for normal compute workloads.
Typical use cases are for doing biometric authentication without giving your biometric information, or sensitive queries using medical information.
Apple has homomorphic encryption in image search. You can use your own photos encrypted into the cloud to search for landmarks in the image without revealing photos.
People can also coordinate and compare information without sharing sensitive data.
Yeah, for simple calculations on medical data or whatnot, FHE is a highly useful tool. I do wonder, will indistinguishability obfuscation or witness encryption ever be a viable scheme?
But for some fields, almost EVERYTHING they touch is sensitive. I've been reviewing PPML for wildlife management purposes, which my brother is involved in and my other brother, a ML expert, may want to be involved in.
With wildlife management, you're dealing with health issues, like rabies outbreaks. That requires privacy. You want to preserve customer confidentially because it's often embarassing. And private property cameras and sensors can leak information about private citizens or kids in a neighborhood without appropriate social and technical protections.
There are hundreds of fields like this. Not just healthcare and policing.
We are likely to see a lot of the hardware required for this to move out into space data centers for batch jobs at least.
And along with that calls for reduced RF and light pollution like StarLink.
And that is going to be helped by a large number of angry liberal citizens who are being riled up about data centers. And that anti-tech rhetoric is already leading to violent responses and debate.
Which hurts the liberal cause for universal healthcare. Conservatives see angry liberal anti-tech actions and tarnish calls for healthcare reform and other liberal causes.
The people who are going to benefit the most from cheaper PPML in orbiting data centers are in many ways making it harder for the rest of us.
It's not a small issue, and I wish I had had the reputation to reduce the anger.
It seems unrelated to PPML. But PPML is a clever political gas pedal to get space control.
You lose rollbacks, superior syncing, redundancy and data integrity assurance and add complexity for what deceased ssd wear which is hardly an actual problem.
The only people in a position to show this are the idiots responsible for the current broken system. Deep analysis will have to come after regime change
Define insanely complex and deep in a way that isn't illiterate hand waving.
Most humans are dumber than a box of rocks. Here in Seattle we had one of many light rail-related fuckups where they had to replace part of the line with buses. People piled into the front of one when it was full. When people got out they never moved back. As the driver struggled to close the door and people struggled to get in the wad of people never moved back to fill the ample space.
Chatgpt was smarter than the average person a while ago
> People piled into the front of one when it was full. When people got out they never moved back. As the driver struggled to close the door and people struggled to get in the wad of people never moved back to fill the ample space.
This does not demonstrate a lack of intelligence. It demonstrates laziness and a lack of interest in spreading apart. Or just lack of consideration (or even malice) on the part of those at the back of the wad.
> Chatgpt was smarter than the average person a while ago
This is an absurd claim that fundamentally misunderstands what it means to be "smart". Reasoning that would get you to this conclusion would equally well apply to Google's search engine over a decade ago.
I’m not talking about the actions we take or how we might perform at certain tasks, I’m talking about how our brains actually work. My point is that we have no idea how I’m able to imagine an apple and see it in my mind’s eye. It’s basically biological magic to us at this point.
There are processes at work there that we don’t even have the language to describe.
Not only that, but we do it with a processor that is basically required to operate in a narrow temperature band below 40C, using a mere 86 billion neurons (although the equivalence with either machine-learning "neurons" or LLM parameters is not at all clear) operating on a few dozen watts; and with this we operate many other systems besides language processing. It's not clear that our reasoning process requires language, either.
(86 billion is the number ChatGPT, ironically enough, has given me a couple of times. I remember hearing for a long time that it was estimated to be somewhere in the ballpark of 100 billion. This is not my field of study.)
I recently read a blogpost from a human neuroscience student who came across this question (https://ccli.substack.com/p/the-biggest-mystery-in-neuroscie...), and she looked up the study behind this number (https://ora.ox.ac.uk/objects/uuid:1f559b3b-97fd-48c2-b2ac-29...), and it seems that the actual current state of knowledge is that the number is somewhere in the range 60-100 billion, with probably some of that variation being biological and some coming from uncertainty in the measurement techniques, hard to tell, because all the data comes from nine brains.
For now, caring about a topic (or having somebody you trust care for you) still gets you better information than asking an LLM.
I'm glad people fund things that are only of interest to nerds but this will never be useful.