Earlier this month, Twitter was aflame with rumors that OpenAI’s new Strawberry project was about to go live. This could be it! Artificial general intelligence! Maybe! Possibly! (The gossip is that…
Does this mean they’re not going to bother training a whole new model again? I was looking forward to seeing AI Mad Cow Disease after it consumed an Internet’s worth of AI generated content.
If you change the tokenizer you have to retrain from scratch, but you can do so with the old, unpolluted data.
It’s genius if you think about it,* you can waste energy and tell your investors it’s a new better model, while staying upstream from the river you pollute.
* at least for consultants, compute providers and other middle men.
I remember one time in a research project I switched out the tokeniser to see what impact it might have on my output. Spent about a day re-running and the difference was minimal. I imagine it’s wholly the same thing.
*Disclaimer: I don’t actually imagine it is wholly the same thing.
Does this mean they’re not going to bother training a whole new model again? I was looking forward to seeing AI Mad Cow Disease after it consumed an Internet’s worth of AI generated content.
If you change the tokenizer you have to retrain from scratch, but you can do so with the old, unpolluted data.
It’s genius if you think about it,* you can waste energy and tell your investors it’s a new better model, while staying upstream from the river you pollute.
* at least for consultants, compute providers and other middle men.
I remember one time in a research project I switched out the tokeniser to see what impact it might have on my output. Spent about a day re-running and the difference was minimal. I imagine it’s wholly the same thing.
*Disclaimer: I don’t actually imagine it is wholly the same thing.
there’s a research result that the precise tokeniser makes bugger all difference, it’s almost entirely the data you put in
because LLMs are lossy compression for text
latent space go brrrr