But, how is that different to any of its other output? I am sure there are many references to concepts of self-preservation in its training set that models will mimic just like all other concepts in its training set.
Hardware is hard. It doesn’t always have the transparent composability that software has because you hit physics and the real world.
The example already given makes the point. Work has been great on the M1 but my understanding is that this has not translated all that well to e.g. M2, M3 and M4.
If someone replies to your comment then I think it’s entirely fair that they take your point in the context in which it was intended. Otherwise, if they are not interested in the point then simply don’t reply to it.
No-one enjoys being taken out context.
But I do accept that given the hostility of replies I didn’t make my point very effectively. In a nutshell, the original comment was that it’s surprising a small team like DeepSeek can compete with OpenAI. Another reply was more succinct than mine: that it’s not surprising since following is a lot easier than doing SOTA work. I’ll add that this is especially true in a field where so much research is being shared.
That doesn’t in itself mean DeepSeek aren’t a very capable bunch since I agree with a better reply that fast following is still hard. But I think most simply took at it as an attack on DeepSeek (and yes, the comment was not very favourable to them and my bias towards original research was evident).
Anthropic was founded in part from OpenAI alumni, so to some extent it’s true for them too. And it’s still taken them over 3 years to get to this point.
Of course not. But in this context the point was simply that it’s not exactly a fair comparison.
I’m reminded how hard it is to reply to a comment and assume that people will still interpret that in the same context as the existing discussion. Never mind.
I would extend the same reasoning to Mistral as DeekSeek as to where they sit on the innovation pipeline. That doesn’t have to be a bad thing (when done fairly), only to remain mindful that it’s not a fair comparison (to go back to the original point).
Disrespecting copyright and personal data is good for users? I guess I disagree. I would say that it’s likely great for the company’s users, but not so great for everyone else (and ultimately, humankind).
Also don’t forget that if you think some of the big names are playing fast and loose with copyright / personal data then DeepSeek is able to operate in a regulatory environment that has even less regard for such things, especially so for foreign copyright.
Except it’s not really a fair comparison, since DeepSeek is able to take advantage of a lot of the research pioneered by those companies with infinite budgets who have been researching this stuff in some cases for decades now.
The key insight is that those building foundational models and original research are always first, and then models like DeepSeek always appear 6 to 12 months later. This latest move towards reasoning models is a perfect example.
Or perhaps DeepSeek is also doing all their own original research and it’s just coincidence they end up with something similar yet always a little bit behind.
My hunch is that Apple might give users a choice, perhaps during the initial onboarding process. That would be damaging for Google even insofar as I would guess many users probably don’t even realise that there _is_ a choice.
I doubt that Apple would want to give their competitor a free one-up in this space, so a choice for such a critical thing may be reasonable.