I’m so confused, how are you supposed to perform aggregation without reduce? This is like saying “I like plus and times, but I don’t like divide because it’s hard.” I mean sure, but you need it??
Not in Elixir. Unless you use tail recursion to simulate the loop. I love reduce and the other functions of the Enum module, so I have 165 calls to reduce in my code base (plus 338 map and 116 filter). It's the swiss army knife of functional programming and I don't see any problem with its usage.
We haven’t decided that—Substrait[0] is still the IR for query plans across different execution engines—but projects like QueryBrew are a great side effect of what a SOTA planner can accomplish.
The root of all these is the culture in mathematics (and science in general) to only reward those who “get there first”. This creates a perverse incentive to compete. When no one can out compete a tireless swarm of AI, no one gets rewarded any more.
But nothing’s stopping anyone to still work out an alternative proof, or a more elegant proof, or just trying to prove for the sake of understanding, just like doing homework without looking at the solution. It’s just that you can’t get paid doing that anymore.
People saying “he should have opted out” are missing the point. OpenAI can and should check their training data for leakage in the face of big breakthroughs like these. It’s the burden of the author to appropriately cite their sources.
It’s like a scientist refusing to give another one credit and say “sucks to be you, you shouldn’t have shared your idea with me”.
We checked and determined it was impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.
If prompts were submitted earlier than that and training was not opted out, there's a chance they made their way into our training pipeline in some form. But this would be a droplet in an ocean and unlikely to have made any difference, imo.
You guys should seriously offering a clear way of working with (semi-)confidential data for particulars. Regardless of what is actually done internally, toggling off an opt-in isn't reassuring enough, which is why people are having these worries.
Option 2 feel reassuring enough, but is out of reach of particulars.
Option 1 is not. In part because it is opt-out (will it turn back on on its own like my Facebook privacy settings?), and not always respected (sending feedback can mean your chat is used?). Also because disabling "Improve model for everyone" is very vague.
There simply needs to be a setting like "my data is confidential", in which case there clear guarantees like there are for ZDR.
As an example, I've seen people speculate that while input prompts and output tokens are discarded, thinking traces are retained for training, which could leak information. I doubt this is true, but it shows that the policy is not unambiguous and reassuring enough to remove all doubt.
I was thinking about this some more, and perhaps the best solution for subscription plans would be to charge more for real privacy. In which case breaking that privacy would be committing fraud. Just a thought.
It's rather convenient for OpenAI that user logs are de-identified before being fed into training, so they can say "there's no way for us to check if we plagiarized our user's work, because user data is private".
It's also difficult to imagine any competent AI researcher would overlook the possibility of training data leaking into the test, especially given that they know the users have been using their model to work on the same problem, and that they jumped on the problem after hearing rumors of the breakthrough.
The remaining 1% is usually uncomfortable if not down right painful.
But yes, I agree a query optimizer is valuable. Luckily there’s nothing stopping us from implementing one, as Prela is algebraic and all optimization techniques for SQL carry over.
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