Ditto on the hate, technical, but important distinction here, they support open-weight ML. They do not release training source code or data sets to actually make your own (granted you’d need millions in video cards to do it, but still). Open-source gets thrown around a lot in AI, presumably virtue signalling, but precious few walk the walk.
Never underestimate the value of getting hordes of unpaid workers to refine your product. (See also React, others)
Agreed, and the chance of it backfiring on them is indeed pleasingly high. If the compute moat for initial training gets lower (e.g. trinary/binary models) or distributed training (Hivemind etc) takes off, or both, or something new, all bets are off.
The compute moat for the initial training will never get lower. But as the foundation models get better, the need for from-scratch training will be less frequent.
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Ditto on the hate, technical, but important distinction here, they support open-weight ML. They do not release training source code or data sets to actually make your own (granted you’d need millions in video cards to do it, but still). Open-source gets thrown around a lot in AI, presumably virtue signalling, but precious few walk the walk.
Never underestimate the value of getting hordes of unpaid workers to refine your product. (See also React, others)
I understand the distinction, but it’s still waaay better than what
OpenIAIClosedAI is doing.Also people are really good at reverse engineering. Open weights models can be fine tuned or adapted. I am trained a Llama 3 Lora not that long ago.
Agreed, and the chance of it backfiring on them is indeed pleasingly high. If the compute moat for initial training gets lower (e.g. trinary/binary models) or distributed training (Hivemind etc) takes off, or both, or something new, all bets are off.
The compute moat for the initial training will never get lower. But as the foundation models get better, the need for from-scratch training will be less frequent.