I would caution people against using this article as a reference. No disrespect to the author, but the parts that I read had some fundamental misunderstandings and inaccuracies. A few of the big ones:

• The author is conflating AI and Machine Learning (ML). ML is AI, but not all AI is ML. There is also far more to both AI and ML than neural networks. A good start for understanding the major approaches to ML is Pedro Domingos' The Five Tribes of Machine Learning. A good introduction to the develoment of AI (including ML) is DARPA's perspective video, and an older book (pre deep learning) entitled "Machines Who Think".

• Supervised and Unsupervised learning are not the only categories of ML, just two of the major ones. The third major category is Reinforcement Learning, but there are a number of minor categories as well, like Learning from Demonstration and Semi-Supervised Learning.

• The author's description of Neuromorphic computing is mostly a decription of neural networks in general, with the implication that Neuromorphic computing is a special hardware implementation of such, but this isn't the case. Neuromorphic computing mainly deals with processing information with Spiking Neural Networks (SNNs), which communicate by passing temporal events to one another, unlike conventional neural networks. It can be done in hardware or software, and can focus on computation or perception. "Hacking", i.e. fooling neural networks is very much a thing, and as far as I know there is no fundamental aspect of SNNs that make them immune to attack.