I think AI skeptics have a strong bias to assume that human intelligence fundamentally functions differently from LLMs. They may be correct, but we don't have a strong enough understanding of human cognition to make the claim in as uncertain terms as the skeptical argument is unusually made. The training methods between human learning and machine learning are obviously fundamentally vastly different as are the infrastructure-level mechanics. These elements are likely never going to align, though with time the machine infrastructure may start to increasingly resemble human bio hardware. I bring this up because these known vast differences may account for a significant portion of the differences in expected output from human and machine processing. We don't understand the fundamental conceptual "black box" portions of either form of processing well enough to state definitely what is similar or dissimilar about those hazy areas. Somewhere within that not-well-understood area is what we collectively have vaguely defined "intelligence." But also within that area are all the other aspects that both humans and now machines are quite good at - prediction, fluency, translation. The challenge of lexicon and definition is potentially as difficult a task as is sharpening the focus of our understanding of the hazy black-box portion of both machine processing as well as human processing. Until all those are better defined I don't think we have a good measure for answering the question of machine intelligence either way.
The problem with this model is that if you aren't able to do due diligence yourself due to technical restrictions, how are your end users supposed to overcome the even greater technical restrictions on due diligence that using your service imposes?
Fundamentally, the problem is not the fact LogoFox is a logo maker. Because this applies also to logo designers. A due diligence is done on a case-by-case basis by someone specialized in doing this. We process thousands of logos, we naturally can't make a due diligence for every single one logo created.
I took the same sort of dispassionate approach, valuing the lives of the passengers above all else and staying the course otherwise. I was disappointed to discover the parsing of the results had no room for such methodology. Based my entirely algorithmic approach it was determined that I favored youth and fitness.
I'm curious to see how HIPAA regulations interact with this type of research. I would imagine it would be seriously limiting (though my actual knowledge of the laws is severely limited, so maybe not?)
As someone who's never coded in either go or nim, this seems to me to be the exact opposite ideology of golang. The metaprogramming is very cool, but I imagine sharing a code base that utilizes it heavily is a nightmare.
I think only time can tell if that sort of argument works. Your team's habits matter, but so does the rest of the ecosystem. Strangers with libraries too central to ignore might end up forcing inscruitable templates and macros on you.
As far as I can see, the Python community has done a pretty good job of using magic sensibly. The Ruby and C++ communities, not so much.
Yes time will tell. Its basically like giving someone Jedi powers with the risk they could fall to the dark side while Golang is like Han Solo just getting things done in a much less sophisticated yet practical manner.
