Maybe I'm only noticing the times when it messes things up, but it kinda seems like these auto-edits cause a lot of confusion that could be avoided if they were shown up-front to submitters, who would then have the option to undo them.
Or maybe judicious use of an LLM here could be helpful. Replace the auto-edits with a prompt? Ask an LLM to judge whether the auto-edited title still retains its original meaning? Run the old and new titles through an embedding model and make sure they still point in roughly the same direction?
oh interesting, TIL I can go edit my submission titles! That's useful, I've definitely submitted stuff and gotten a less-good title due to the automated fixes, so I'll have to pay attention to this next time
Oh now that would be a fun version 2 challenge: have all the clocks in one household synchronize such that they're all early by the same amount at any given time.
Easy enough for wifi enabled ones: a UDP broadcast to discover other clocks on the network, then sync how you will.
For non-wifi-enabled clocks, perhaps something like a CH572 would do the trick: a $0.20 RISC-V microcontroller with BLE support that all the clocks in the same vicinity could use to talk to each other.
You could really mess with your neighbors if they had the same clocks and you were within range...
I used to work at a place that had the famous Antoine de Saint-Exupéry quote painted near the elevators where everyone would see it when they arrived for work:
Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.
They are not. Turbine engines require much higher quality manufacturing and tolerances and operate at much higher speeds and pressures. There is more to it than the perceived number of moving parts.
Distilling from a closed model like GPT-4 via API would be architecturally crippled.
You’re restricted to output logits only, with no access to attention patterns, intermediate activations, or layer-wise representations which are needed for proper knowledge transfer.
Without alignment of Q/K/V matrices or hidden state spaces the student model cannot learn the teacher model's reasoning inductive biases - only its surface behavior which will likely amplify hallucinations.
In contrast, open-weight teachers enable multi-level distillation: KL on logits + MSE on hidden states + attention matching.
My dumb ass sat there for a good bit looking at the example in the first link thinking "How does a 30-60 Hz webcam have enough samples per cycle to know it's 77 BPM?". Then it finally clicked in my head beats per minute are indeed not to be conflated with beats per second... :).
When you get to a company that's that big, the roles are much more finely specialized.
I forget the title now, but we had someone who interfaced with our team and did the whole "talk to customers" thing. Her feedback was then incorporated into our day-to-day roadmap through a complex series of people that ended with our team's product manager.
So people at Google do indeed do this, they just aren't engineers, usually aren't product managers, frequently are several layers removed from engineers, and as a consequence usually have all the problems GP described.
Note that this particular problem has existed for well over a decade. It's atrocious, but let's not pretend it's anything new.
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