Seems to me the purpose of all these releases, credits, pricing changes, harness changes, unpredictable token usages for the same task, etc. is to keep customers completely befuddled so that it's impossible to compare AI products. It's like hiring a consultant who sends invoices every month that aren't related to hours worked or project progress, but are whatever the consultant feels like billing, and you're expected to keep quiet and and keep paying.
I compare online marketplaces to ISPs: we don't expect ISPs to filter the traffic going over their network to make sure it complies with all laws, because that would be impossible. Having actually run a marketplace in a previous life, it's a similar situation. How is a marketplace with millions of products supposed to vet each individual product? About the only thing a marketplace can do is provide some kind of customer feedback mechanism, make sure customers can get refunds easily and quickly, and kick off merchants and buyers that have too many complaints (though they will probably just register again under a different name).
To limit inequality, cap the CEO's total compensation to 100x (or whatever) the lowest-paid workers' compensation.
Google says: "at the 100 publicly traded U.S. companies with the lowest median worker pay, this ratio (CEO to worker pay) is much higher, averaging 632 to 1.
Also: "From 1978 to 2024, inflation-adjusted CEO pay grew over 1,094%, while typical worker compensation increased by only 26%"
Consistent hashing helps solve this. You don't divide into shards based on the id, but on the consistent hash of the id. Then when you have to add shards, it's easier to distribute data to the new shards. If you have 3 shards and expand to 4, only 1/4th of the data from each shard gets moved to the new shard.
I dunno... if we gave an AI all of these medical records as training data, wouldn't it be trained to give the same answers as the doctors already gave, without knowing whether those diagnoses were correct or not?
'Mythos “broke into almost all of our classified systems, not in weeks, but in hours.”'
And the government's response was to limit access to US citizens? I don't believe this for a minute. If Mythos could actually break into all these systems, the government would declare it a national security risk and it would never see the light of day for anyone outside government staff with security clearance.
My sister has a Windows 10 laptop she used for her accounting business. One day it decided not to boot, saying there was no boot device. I took the laptop home, took the SSD out (Samsung 1TB), put it in an external USB case, plugged it into another Windows laptop, and it showed up in Explorer. Weird.
I had another brand-new, identical Samsung SSD, so I hooked both the old and new drive up to a Linux laptop (with USB cases) and tried to dd the old drive to the new drive. That mostly worked, but VERY VERY slowly: it would run fast for 5 seconds and then have no activity for 30 seconds. I had a fan blowing on the old drive to keep it cool because it was running very hot.
The dd copy would eventually fail and then I'd restart it with appropriate iseek and oseek values. I also did a cmp /dev/zero with the new disk to verify that it was all zeroes (it was brand new), and that allowed me to use conv=sparse on the dd. The reason for that was to avoid writing to ever sector of the new disk; I didn't want to copy sectors from the old drive that had never been accessed (she only used about 250GB of the 1TB).
It took a couple of days and about 5 restarts to finish the copy, but it did work, and as a precaution, I made another copy of the drive and ran a cmp of the original drive and the 2nd copy (also having to restart cmp several times). Since that compare worked, I knew that all 3 drives had identical content. The new drive worked fine in her laptop and she was mighty glad to see her Windows login screen.
The thing that made this work, IMO, is that Linux has a longer timeout for errors than Windows apparently does, especially during the boot sequence. Plus Linux allows adjusting the drive timeout, so if the device is doing error recover, which is sometimes slow, it gives it time to finish rather than reporting an error.
One of my theories was that the bad SSD was overheating, but if that was the case, a cold boot should have worked, with the failures only coming later.
The other theory is that one of the chips on the SSD failed, so the drive was having to use the ECC codes to correct for the missing information, and the correction process was taking longer than Windows boot would tolerate.
Checkout the insane amount of money Goodwill makes because of people getting rid of their "junk". There are 151 independent Goodwill organizations and all of them have a CEO, usually making 6 figures a year.
I don't know much about the AI field, but it seems to me that trying to train any model to be all things to all people is a really dumb idea. It requires huge financial resources and is causing extreme shortages/market distortions in
any resource used by an AI company - RAM, SSDs, data centers, etc.
In the real world, you don't hire a plumber and expect him to also do your landscaping, fix your car, and tailor your clothes. It would seem like a much better use of resources if I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp.
As humans, we've done pretty well by specializing. I hope this gets explored more with smaller, focused AI models vs the current path of one model to rule them all that can only be run in a data center the size of a country.
Similarly, it's better (for me) to use the right ring finger for Backspace rather than the pinky. The pinky requires moving the whole arm whereas the ring finger just needs a wrist flick to reach Backspace.
As a musician (piano), I've never been able to listen to music while working: it's too distracting, even without lyrics. That makes sense to me because for musicians, hearing just the music still makes your brain want to focus on the structure of it, time signature, rhythm patterns, interesting chords, key changes, etc. - things that a non-musician isn't so intellectually aware of, even if they like them in the music.
> So, back way before ChatGPT era, the folks over at AI safety/X-risk think sphere worked out a pretty compelling argument that two AGIs never need to fight, because they are transparent to each other (can read each other's goal functions off the source code), so they can perfectly predict each other's behavior in what-if scenarios, which means they can't lie to each other. This means each can independently arrive at the same mathematically optimal solution to a conflict, which AFAIR most likely involves just merging into a single AI with a blended goal set, representing each of the competing AIs original values in proportion to their relative strength. Both AIs, the argument goes, can work this out with math, so they'll arrive straight at the peace treaty without exchanging a single shot. In such case, your plan just doesn't work.
I never got a surprise bill myself, but reading a few cases like this motivated me to cancel my GCS account and remove my CC. Now if I try to use it it fails immediately with an error.
As author of HashBackup, I know people are using it with GCS, and I'd like to be able to test against it, but not enough to swallow a large surprise Google bill.