I was wondering why I don't get that animation. Turns out I turned on reduced motion in Windows 11, to get snappier UI and they actually set some accessibility tag correctly for the animation!
Same as for banks. Downloading some verification app with a confidence inspiring 1.2 rating on the app store, getting on a call with some random gig worker looking like they are taking the call in their living room and wiggling your ID around while giving a thumbs up is not the way I would like to prove my identity to a bank.
But there's no alternative. The EU digital identity wallet would be the alternative. You control and exactly see, what kind of information the bank is getting from you and you can be sure that it doesn't flow through some sketchy third-party identification service.
The thing is that LLMs can only ever go forward. It might go into one direction, once that direction is in the context it will realize that it doesn't work, but can't delete. So it has to backtrack (or double down, see the whole seahorse emoji breakdown).
Yep. I don't see 500 million being even close to enough to develop broad-spectrum preventatives that would be easy enough and safe enough to administer, to get over 67% of the population to take them.
There's this crowd on HN which is very vocal against academia. From what I've seen, the main points are that academia isn't efficient, most of the science coming out of academia is useless and that the whole system is just a waste of taxpayers money. Instead, what is often argued, all good research is done in private labs. Then pointing to SpaceX, Moderna, OpenAI, Google, etc.
And while it is very true that often the research coming out of Academia is useless, what is always neglected are the roots of the research done in private labs.
When Jürgen Schmidhuber and team published their work on Neural Nets back in 1991 it was also useless. Unless you had a supercomputer and very, very deep pockets you were not going to do anything with what came out of their lab.
But still, 30 years later here we are, standing on top of the shoulders of this useless research.
It's tech from the 80s. Look up the Nishika N8000 and Nimslo 3D.
Basically it's multiple lenses next to each other, each capturing a small slice on the 35mm film. Every lens has it's own shutter, which is triggered at exactly the same time.
This wasn't too involved and quite cheap to implement with analog tech in the 80s/90s, but if you want to do the same thing with digital there's quite a bit more to consider. Here's a cool video of someone building a digital stereo camera: https://www.youtube.com/watch?v=_aofxbH0elo
The hard part with digital boils down to: Cheap camera modules are hard to calibrate to the same parameters and sometimes impossible to set focus, so pictures look the same. And taking pictures takes quite a bit of processing power, so if you want to take 4 pictures at once it gets a bit tricky with just a cheap raspberry or similar.
That's pretty cool. In a similar but very different vein: A few years ago I took twenty years of daily satellite imagery and computed the mean color for countries and the world https://www.landshade.com/
But in doing that you really do notice how everything concerning colors is just a bit arbitrary. You get raw reflectances from a scientific sensor on a satellite with specific spectral bands and sensitivity within those bands. And then you try and map this scientific sensor to the sensor that is your eyes, to try and emulate what we would actually see if shot up into space.
A coding agent should make short work of that. However I'm a bit doubtful if the results would actually be meaningful.
I'm thinking that some of the LLM-isms are a bit more complex than just repeated phrases. It's often more that short, punchy writing style with quick setups and punchlines. But would be interesting nonetheless. I really think that some things (like "solving real problems" or "it's not"/"this isn't") would show up.
One thing I still struggle with in my head is how these vision embeddings can then be used to give LLMs eyes.
Because you somehow need a giant training set which describes images in natural language, no? Is that actually how it works, or is there some smart trick so you don't need to pay labellers a bunch of money to look at pictures and describe them.
If you ask him 100 times in a row I would bet that by the second, maybe third, time he will not answer with his song but with: what the fuck is wrong with you?
Nicely circling back to LLMs not being able to learn and form memories.
What training mechanism or model architecture provides the glue to go from human text to images?
Don't you need to have millions of really descriptively labelled images?