I think you should just focus on the road because most of us are just trying to get home safely to our families. Some of us are even biking beside the road on a lightly-protected bike lane.
I think you need to give some concrete examples, considering the US happily let its companies offshore a lot of work to China over the years, and Chinese funds own large chunks of American companies.
Generally speaking, I try to ensure that the LLM is using core abstractions throughout the codebase in a consistent manner. This makes it easier for me to review any changes it makes.
And even then - I still read the code it generates, and if I see a better way of doing something I just step in, write a partial solution, and then sketch out how the complete solution should work.
This line of thought is honestly a bit silly - uv is just a package manager that actually does its job for resolving dependencies. You’re talking about a completely orthogonal problem.
I see the value of the students, it just seems like an odd thing for a government to subsidize via NIH/NSF funding. We don’t really have anything analogous to that in Canada and it just seems awfully weird that it exists in the US without the “it’s older than the country” excuse that Oxford/Cambridge have.
You don’t need any rights to execute the feature. The user owns the book. The app lets the user feed the book into an LLM, as is absolutely their right, and asks questions.
I work on a much easier problem (physics-based character animation) after spending a few years in motion planning, and I haven’t really seen anything to suggest that the problem is going to be solved any time soon by collecting more data.
I think this is an interesting direction, but I think that step 2 of this would be to formulate some conjectures about the geometry of other LLMs, or testable hypotheses about how information flows wrt character counting. Even checking some intermediate training weights of Haiku would be interesting, so they’d still be working off of the same architecture.
The biology metaphor they make is interesting, because I think a biologist would be the first to tell you that you need more than one datapoint.