It is a well known phenomenon in haskell or OOP in general that just reading can cause systems to change their execution paths. Which is why it's not sufficient to make write/setters private, but reads/getters too.
However in the case of SuperLog the path to system change is quite direct
"We fix bugs.
Superlog prepares a resolution PR for every incident. If Confidence Gate fails, it posts findings for the investigating team and pulls in the engineers who can add context."
The system literally pushes (or Pull requests, whatever, github is dead anyways) a code change.
> In distillation, you take a set of prompts you are interested in, and record the big LLM's outputs, then train your small model to produce the same output as the big LLM.
Why use the bigger LLM outputs for this and not human outputs? If we assume that human responses to prompts are better than sota models (in some cases they are) then why use the big model at all?
Have you seen what annotations cost? It can be on the order of $50/annotation for a reasonable document, some agentic annotations can cost over $1000 each, whereas a model response might cost $0.10, or maybe $20 for an agentic session. Plus all of that takes a ton of effort to collect.
You can set up model distillation as a weekend batch job.
It's no secret they've been tracking people's faces as much as they can.
The morning of Pretti I was on Lyndale and there were two men wearing "press" jackets with DLSRs taking pictures of people's faces in the crowd. They were eventually recognized and yelled out, but it was quite an unnerving feeling.
Right, but this electron box led to one of the largest (if not the largest) media revolution that has transformed the course of humanity in a frightening way we're still trying to grapple with.
Still saying "LLMs are autocorrect" isn't wrong, but nobody is saying "phones are just electrons and silicon" to diminish their power and influence anymore.
The people controlling what went on the screens were unreliable and nondeterministic. The algorithm on facebook/instagram is nondeterministic and I hope I don't have to convince you of the impact these algorithms have.
As far as I'm concerned, the nondeterminism argument is fruitless
_Nobody_ has the right take. Believe it or not, being seemingly laissez-faire about something can be a well evaluated and rigorous position. I highly doubt that OP doesn't care about the potential negative ramifications of AI, and it's frankly disingenuous and confusing to see every clause interpreted in the worst way possible.
Each clause you've highlighted has a nugget of truth, but that nugget is not inherently negative, it's just a different perspective which you aren't picking up on.
I'm still trying to understand how I feel about this so this is a bit of a napkin ramble;
I can't help but feel like they've missed the mark a bit on some of the imagery from the mission that's been published so far.
One of the most compelling shots from the mission, to me, was Reid Wiseman's IPhone footage from within the capsule while Earth was being eclipsed[0].
At the start there's a moment you can see the window frame and the Moon all together. Seeing the moon in context of their vantage point within the the context of the capsule gave me the awe I had as a kid again, more than almost any shot that's come out this mission. I actually felt like I was in the capsule looking at a massive, sterile cold sphere.
I understand wanting to take a nice and centered DLSR picture of... _The Moon_ when you're floating by it, but frankly I've seen thousands of those. They're doing a flyby in a capsule in space, I want to have a taste of how the moon exists from _that_ context. What is it like being ~4,000 from the Moon's surface? Take a crappy 0.5x video from your phone showing the inside, then stick it front of the window. Let the Moon be contextualized from your vantage point. I wont be able to make out every crater and basin and the colors might be off from your eye's view, but I will be able to understand what they are seeing. Everyone has an intuitive understanding and feeling of an IPhone's optics and image pipeline, in some ways seeing the Moon through that is more real and relatable than any mirrorless DLSR + color correction.
This being said I don't want to take away from the accomplishment, I'm terribly excited about space exploration and it getting more light in the zeitgeist.
Agreed with the sentiment regarding that footage. This felt closer to something you could actually experience rather than another beautiful DSLR shot. Briefly seeing the inside of the capsule, the autofocus taking a long time, the iPhone image processing vibe... Felt 100x more real.
this is one of the great insights of photography and we can all apply it. no one gives a shit about another picture of the leaning tower of pisa in your photo album. its the weird, candid, accidental shots that are most interesting and enduring. but its easy to not grasp that in the moment - the tower is what you're there to see, and its stunning, so of course it's what you photograph. those spaces in between though, like that iphone video, are what secretly transcend
Slightly off topic, but when I read about these archeological discoveries being made thanks to custom software, ML or the like - Who is writing this code?
To me these projects would be so fun to work on, but this domain seems so far out of a tradition SWE track. Are the researchers just cobbling the code together themselves? Cross department collaboration within the university? I'd love to have a hand in things like this.
i rolled into tech because of archeology. started using GIS for site mapping and need for customization just got me going. ended up going to school again for compsci.
generally, students from other departments are writing the code, but current day most archaeologists can work with ready-made packages (model builders etc..) now too.
I guess the change in voltages, arrangement of registers, filling of buffers in the network stack are changing but... what?
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