Deepseek and Qwen are catching up fast and at this point in the race it doesn’t matter so much if it takes weeks or even months to catch up to whatever the US releases, but cost is definitely starting to play a much bigger role. Neither country is close enough to AGI yet.
Its believed (probably more just fact) that a lot of the open source models leverage larger models. Either directly through distiallation (meaning training a model directly off of another) or by using the larger models for knowledge distiallation (basically using the larger models as “ground truth” for training workflows).
Either way, this means the open source models would always be “a few months behind” as they rely on the progress of the closed source models to improve themselves.
From what I’ve read, it seems like a lot of the companies monitor API calls and will posion responses for suspicious traffic. This basically means they’ll modify the output to be harmful to the scrapper.
Outside of that they can block accounts/IPs, limit what is returned (just the final answer vs all reasoning steps, etc.). However it’s a bit like all cyber security and an evolving game of cat and mouse.
I’m not caught up on all the details, but as I said this has been an ongoing game of cat and mouse with people looking to leverage other models to improve their own (this isn’t just a qwen vs Claude thing and everyone if pointing fingers at everyone). If I had to guess it’s only been more recently that AI companies are actively fighting these issues, but even then I imagine they’re not 100% effective.
Deepseek and Qwen are catching up fast and at this point in the race it doesn’t matter so much if it takes weeks or even months to catch up to whatever the US releases, but cost is definitely starting to play a much bigger role. Neither country is close enough to AGI yet.
Tbf, qwen is a distilled version of claude :')
Really? What do you mean?
Its believed (probably more just fact) that a lot of the open source models leverage larger models. Either directly through distiallation (meaning training a model directly off of another) or by using the larger models for knowledge distiallation (basically using the larger models as “ground truth” for training workflows).
Either way, this means the open source models would always be “a few months behind” as they rely on the progress of the closed source models to improve themselves.
I see. Is there some way to protect models from being trained on them aside from just removing it from the market?
From what I’ve read, it seems like a lot of the companies monitor API calls and will posion responses for suspicious traffic. This basically means they’ll modify the output to be harmful to the scrapper.
Outside of that they can block accounts/IPs, limit what is returned (just the final answer vs all reasoning steps, etc.). However it’s a bit like all cyber security and an evolving game of cat and mouse.
How was Qwen previously able to train on Claude? Prompt injection?
I’m not caught up on all the details, but as I said this has been an ongoing game of cat and mouse with people looking to leverage other models to improve their own (this isn’t just a qwen vs Claude thing and everyone if pointing fingers at everyone). If I had to guess it’s only been more recently that AI companies are actively fighting these issues, but even then I imagine they’re not 100% effective.