No. Distillation trains on a teach model's logits or output tokens.
396 karma · joined July 26, 2019
They have always been behind. Why would this time be any different?
They control for the data being in-distribution
Their dataset also has examples of the problem being solved.
Mine is also smaller, due to patella tendinopathy.
What demographic are they?
Their algorithm is really built around their features. Specifically, temporal representations of user interest:
https://ieeexplore.ieee.org/document/9458799/
The features used by their algorithm tells you what a user is interested, historically.
Contrast this to Meta, which uses the social graph as their features. Imagine features like the number of times a user likes another author's / cluster's content.
Tiktok will serve you $TOPIC because you have $INTERACTED with $TOPIC historically.
Meta will serve you $TOPIC because you have $INTERACTED with $PEOPLE who post $TOPIC, historically.
Meta only coincidentally gives you what you like.
Tiktok knows what you like.
This is the difference. This is why IG is losing.
They clearly built something superior. And it can't seem to be matched by the biggest tech companies.
Prior to one of these hiccups, I hypothesized, given how shitty the codebase was, that they must be tracking this stuff poorly.
This led to an argument with my boss, who assumed things magically worked.
Days later, we received an email announcing an audit one one of these accounting discrepancies.
JPMC proposed using crypto, internally, to consistently manage cash flow.
Not sure if it went anywhere.
Are these low 15s or high 15s? Regardless, that's fantastic progress.
Do you respond better to intervals or longer aerobic efforts?
I ask because Ive always wondered if training should be specialized for fast/slow twitch runners.
Specifically, do you focus on strengths or weaknesses.
Let me follow you on Strava :p
That's all.
E.g. finance
In a sufficiently competitive space, good enough doesn't cut it.
I.e. One day we want to be able to backprop through the database.
Search systems face equivalent problems. The hierarchy of ML retrieval systems are separately optimized (trained). Maybe this helps regularize things, but, given enough compute / complexity, it is theoretically possible to differentiate through more of the stack.
Because he self-identifies with some definition of iq.
A Fourier transform is not a projection, it's a change of basis represented by a unitary transformation.
In California, the haves will have houses. The have nots will have ADUs.
Our world is structuring itself to (more) obviously reveal the wealth disparity.