Readit NewsDo you have any sense of whether this type of model would work with children's speech? There are plenty of educational applications that would value a privacy-first locally deployed model. But, my understanding is that Whisper performs pretty poorly with younger speakers.
So anyway, should we be calling this "hairpaste for teeth", or "toothpaste from hair"?
This is a globally unique phrase, with nothing coming close other than this comment on the indexed web. It's also seemingly an original idea as I haven't heard anyone come close to describing a feeling (love or anything else) quite like this.
Food for thought. I'm not brave enough to draw a public conclusion about what this could mean.
"Love is the active concern for the life and the growth of that which we love."
“An important factor in the purchase of the F-35 by European governments was the idea that European defense would be built on a transatlantic basis in terms of strategy, institutions and capabilities,” she said, adding that “the Trump administration is in the process of dissolving the transatlantic link, and the purchase of American systems will therefore no longer have any added value for Europeans.”
“If you keep punching your allies in the face, eventually they’re going to stop wanting to buy weapons from you,” said a Western European defense official, granted anonymity to discuss the matter candidly. “Right now we have limited options outside of U.S. platforms, but in the long run? That could change in the coming decades if this combativeness keeps up.”
[1] https://www.politico.eu/article/punching-allies-in-the-face-...
It seems harder to prove that every real number can be constructed via such a method.
Is there a construction-based method that can produce ALL real numbers between, say, 0 and 1? This seems unlikely to me, since the method of construction would probably be based on some sort of enumeration, meaning that you would only end up with countably many numbers. But maybe someone else can help me become un-confused.
It's pretty easy: causal reasoning. Causal, not statistic correlation only as LLM do, with or without "CoT".
If you mean deterministic rather than probabilistic, even Pearl-style causal models are probabilistic.
I think the author is circling around the idea that their idea of reasoning is to produce statements in a formal system: to have a set of axioms, a set of production rules, and to generate new strings/sentences/theorems using those rules. This approach is how math is formalized. It allows us to extrapolate - make new "theorems" or constructions that weren't in the "training set".
One thing humans seem to be uniquely good at is picking goalposts that separate us from other species.