auraai

4 ms·

1.
by auraai 2y ago
Their intelligence was clear to me growing up around them, without knowing anything about crows specifically. They clearly have intelligent interactions with each other and the environment, and have a "theory of mind" of people ap
2.
by auraai 2y ago
A model's need for data is a sort of reciprocal to its inductive bias strength. The more permissive is your model (can learn anything/fit noise perfectly), the more data you need to tune it to a useful state. Conversely, the more
3.
by auraai 2y ago
There's lots of room for cross-pollination between bio/life sciences and ML/AI. One key insight is the importance of what you pick as your primary representation of data (is everything a number, a symbolic structure, a probab
4.
by auraai 2y ago
This is pretty important in mathematical finance, where one moves from a real-world measure to a risk-neutral measure to make computations feasible. https://en.wikipedia.org/wiki/Girsanov_theorem https://en.
5.
by auraai 2y ago
> Poincaré once said that math isn't about numbers, but relationships between numbers He described math more poetically and profoundly: "the art of giving the same name to different things"
6.
by auraai 2y ago
We seem to be crawling out of the "uncanny valley" on the other side. Give it a couple more years, we've pretty much nailed the interpolation of human output (across conventional modalities).
7.
by auraai 2y ago
I don't think this type of method can scale indefinitely, it's essentially just "better" sampling within dense areas of knowledge space. It cannot help with better exploration outside these dense areas, because these exp