Your first paragraph is spot on. Some people are hell-bent on developing AGI (and these people should exist), and there are some doing SOTA-chasing by tweaking

Your first paragraph is spot on. Some people are hell-bent on developing AGI (and these people should exist), and there are some doing SOTA-chasing by tweaking hyperparameters to the extreme and overfitting the data (these works are _mostly_ useless), but most people do not realize the amount of AI we have right now, right this day, is enough bring in fundamental and permanent paradigm changes in as many fields as you can think of. And you are onto something when you say that "empirical records" can be learned by ML models, and a huge amount of grunt work is required for that. Every tom, harry, and dick can overfit to MNIST today, it used to be hard someday. And note the amount of grunt work it took to build MNIST- thousands of human hours. Building Datasets does not pay you back instantly, but it has benefits for all. I know some people who are paying money out of their own pockets and creating niche Datasets and making them available under MIT Lisence. Hope that more and more companies and people do that for really niche fields. And I don't completely agree with not understanding things that DL models do and relying on them. We have started to understand much of it. ML interpretation is a field with mot much success but I am hopeful. We did not know jack about how very deep CNNs work, but that changed with the Zeiler-Fergus paper[0]. Later with things like Grad-CAM[1]. Now we are trying to understand latent representation fully. We (even I) can create GAN generated pictures with different haircolor, different kinds of glasses, etc. from scratch. I read not more than two days ago that Microsoft and Peking Uni researchers found a way to identify "knowledge neurons" in unsupervized pretrained embeddings in NLP and they can edit facts with that [2]. So, I am optimistic about our "interpretation" future. [0]: https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf [1]: https://arxiv.org/pdf/1610.02391.pdf https://arxiv.org/pdf/1610.02391.pdf [2]: https://arxiv.org/pdf/2104.08696.pdf https://arxiv.org/pdf/2104.08696.pdf