For most aspects my opinions don't change. But on coding, it has changed a lot. Two years ago I would just dismiss at the possibility that llm could write up a simple function. Now it has proven me wrong.
I don't know if "AI agrees" is reliable enough to count. Do you have a particular scientific article that you based on? And what are your example? Your post is mostly describing without concrete example so I can't follow.
An open model that is competitive to commercial models is a big deal if true. I hope someday I can find a way to run such high performance model locally on my laptop.
There is an article said that a DOGE member had write access as well (See [1]). But it was quickly changed back to read only. So there was a risk, but I can only hope nothing happened.
ImageNet dataset is the main thing AFAIK. But even so I find Dr. Li's contribution big enough. For a context, datasets for computer vision at her time were mostly small, so nn was rarely considered a good method for CV. Not until AlexNet won the challenge, and the world changes after that. I remember many people initially scoffed at ImageNet, arguing that the dataset was flawed and that a “bad” method like NN (AlexNet) could only win because of those flaws. Simply saying “paying” is an understatement because we also need to account for the academic politics of her time.
A little fun fact, even if most research papers nowadays try to propose new dataset, if we take imagenet and pretrain the backbone, we usually end up with a very strong baseline.
Btw, not sure why you think Karpathy has a bigger impact than Fei-Fei Li. I can't think what he is doing that is actually changing the playing field.
When I was a college student, I attended a conference regarding IoT. I still remember a developer of a face ID software for a safe box was so sure that their technology was secure because Apple deployed face ID on iPhone. Then a few days later, an article about fooling iPhone's face ID with a printed paper with the victim's face got published.
