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.

However I often see this going from "there's issues" to discounting academia altogether and positioning private labs as a good or only alternative.

After all, most people in the open science collaboration which published the seminal paper kicking off the replication crisis were from academia.

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.

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.

From what I can tell they actually have a bit more robust science behind their algorithm (and a lot less questions to answer)

But the data is open source, so if you want to dig around my badly documented code and raw satellite data yourself, be my guest: https://github.com/jonasViehweger/LandShade/tree/main/data

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.

There's some really cool science around that if you're a color nerd: https://www.sciencedirect.com/science/article/pii/S003442571...

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.

I'd expect to see a huge increase in "solving real problems" over the last months.

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.

Nicely circling back to LLMs not being able to learn and form memories.

The author did not find a solution to trigger file write earlier/more frequently.

Just sentences like this:

> This isn’t just a problem for far-off countries of which we know little, like the EU and the US and China. Here in the UK [...]

So good! I feel like I'm becoming an old cynic but if it's the tenth time on the day that I read an overdramatized "It's not X, it is Y" in an article, actually good writing just hits different.