It sounds as if the author is not well informed about the recent advancements and findings from the literature and research community on deep learning. > Howev

It sounds as if the author is not well informed about the recent advancements and findings from the literature and research community on deep learning. > However, while machine learning has made huge strides in many domains, it still has several areas of weakness that are very important when one is doing mathematics. Here are a few of them. Basically all of these claims are wrong. I'm not saying we have maybe perfectly solved them but we have solved them all to a great extend and it currently looks like if just scaling up further probably solves them all. > In general, tasks that involve reasoning in an essential way. See Google PaLM. Or most of the other recent big language models. > Learning to do one task and then using that ability to do another. Has been done many times before. But you could also again count the big language models as they can do almost any task. Or the big multi-modal models. Or then the whole area of multi-task learning, transfer learning. This all works very well. > Learning based on just a small number of examples. Few-shot learning, zero-shot learning, meta learning allows to do just that. > Common sense reasoning. Again, see PaLM or the other big language models. > Anything that involves genuine understanding (even if it may be hard to give a precise definition of what understanding is) as opposed to sophisticated mimicry. And again, see the big LMs. Sure, you can argue this is not genuine understanding. Or there is no real common sense reasoning in certain areas. Or whatever other shortcomings with current approaches. I would argue, even human intelligence falls short by many of that measures. Or humans also do just "sophisticated mimicry". Maybe you say the current models lack long-term memory. But we already also have solutions for that. Also see fast weights. The argument that humans just use a small number of examples to learn ignores all the constant stream of data we passively get through our life through our eyes, ears, etc. And also the evolutionary selection which sets the hyper parameters and many wiring paths of our brains.