v_krishna

Obviously you need lots of GPUs to run large deep learning models. I don’t see how that’s a fault of the developers and researchers, it’s just a fact of this technology.

In deep learning generally open source doesn’t include actual training or inference code. Rather it means they publish the model weights and parameters (necessary to run it locally/on your own hardware) and publish academic papers explaining how the model was trained. I’m sure Stallman disagrees but from the standpoint of deep learning research DeepSeek definitely qualifies as an “open source model”

Same here. Sonic fiber and I’ve had 0 issues. Pay less now than I did for much much slower cable internet before and I think we’ve had 1 maintenance and 1 outage in the last 4 years. Just checked speed test on my modem and it 941 down 942 up.

Grimm was a somewhat cheesy cop drama along these lines…

Many (14?) years back I attended a conference (now I can’t remember what it was for, I think a complex systems department at some DC area university) and saw a lady give a talk about using agent based modeling to do computational sociology planning around federal (mostly navy/army) development in Hawaii. Essentially a sim city type of thing but purpose built to help aid in public planning decisions. Now imagine that but the agents aren’t just sets of weighted heuristics but instead weighted heuristic/prompt driven LLMs with higher level executive prompts to bring them together.

I fully agree with this, would have written something similar but was eating lunch when I made my former comment. I also think there’s a big part of pragmatics that comes from embodiment that will become more and more important (and wish Merleau-Ponty was still around to hear what he thinks about this)

A lot of semantic NLP tried this and it kind of worked but meanwhile statistical correlation won out. It turns out while humans consider semantic understanding to be really important it actually isn’t required for an overwhelming majority of industry use cases. As a Kantian at heart (and an ML engineer by trade) it sucks to recognize this, but it seems like semantic conceptualization as an epiphenomenon emerging from statistical concurrence really might be the way that (at least artificial) intelligence works