There might not be a good abstraction. I've built a few harnesses for different types of workflows, and the details are so different I struggle to see a good abstraction. It's also not clear there should be - if you look at most complex software systems, it's a collection of smaller abstractions/tools/systems pulled together to achieve X.
isnt that how the modern telephone system works? digitize your voice, encode, transmit, decode, convert to analog audio. with buffering or keeping bits in memory all along the way?
There are similar exceptions. For instance, the copy of licensed content that your computer makes in RAM in order to play it is explicitly exempt from being treated as a copyright violation.
You've always been allowed to transcribe (ie, write) out a conversation. Recording is a different thing. A person can always claim, easily and believably, that the transcription is made up. It's just text.
From a legal perspective you can't record something in many us states. Using the microphone, keeping the bits in memory while you turn it into text, probably doesn't meet any reasonable definition of "record".
Maybe this will be true legally (we wont really know until it is tested in court), but I think there's a huge difference in practice.
Siri Recap is an electronic record of a conversation that includes timestamps. Imagine for example that during a scheduled meeting with my boss, he threatens to fire me for not doing something illegal. I now have strong evidence that this was said (even if it cant be officially attributed to him) during the time I was in a meeting with him. Not saying this will hold up in court, but it certainly is more damning that having written notes (not to mention the fact that my boss probably wouldn't say this when I am taking written notes).
Making that feature illegal may backfire spectacularly across the whole IT sector. What Apple does is it inputs voice data into an AI program and that outputs some transformed output. If a court would rule that output in any way matches the input (to be classified as a "record") then the whole ethics principle of stealing other people's data and funneling it through AI to make it company's own, would be at risk.
I imagine no single spineless impotent modern court would risk a wrath of our new benevolent AI overlords calling them all criminals they really are.
Maybe. Maybe not. Look at AI drug design - it's not really speeding up the important part - drug trials. There isn't really a coherent plan to use AI for the most complex part of drug discovery at all.
I work in pharma and I've advocated for directly training models that predict drug trial outcomes. I don't think we have the necessary support (data + algorithms) to produce accurate models in this space. I suspect that predicting drug trial outcomes is roughly isomorphic to understanding human biology at a fundamental level (and accepting that our existing models of how drugs work are extremely limited).
If you can create a graph of independent work, which you can with many such problems, agents can work together nicely. Again, thank Lean and the tooling around it.
It's really unclear that this entire line of work (training LLMs for proof writing) has much real value outside of writing math proofs. It is reasonably clear that, similar to Deep Blue at the time, people are extrapolating the results to general intelligence because the people who usually write proofs are insanely smart (just like world class chess players).
If you look closely at the gains in math, it's largely in proof writing. The reason is Lean, it's not some general intelligence jump, and the number of people actually working on proofs in life rounds to zero.
With Lean, math has become a really well suited problem for LLMs. We will likely see large gains for many years from here, just doing more and more rlvr, like continuously, non stop. No need to train from scratch. It really doesn't speak to the general intelligence of models though. It does speak to how good these things can become when a problem space has verifiable rewards, especially when you can verify one step at a time like Lean enables.
It's crazy how deep Microsoft's bench is (Lean was started there, vscode is another), for everything not directly related to the the ai models (hell, even github for data).
So interesting how everything played out, I remember in the early days when MS came out with the partnership with OpenAI it seemed like they were playing 5d chess and were poised to win big. And it all just fizzled out.
Well, amazon and apple haven't done great either. One might reasonably claim they weren't as well placed as goog or msft, but it might just be "big company can't do genuinely new thing".
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