Poor Journalism doesn't help AI
There are bones to pick between me and most tech journalists. As always, there are exceptions, but many tech journalists simply don't seem to have an inkling of knowledge on the subjects they write on.
Many years ago, I visited an course on rhetoric - and if anything sticks in my mind from that evening class, it is this: "if all your knowledge covers a regular sheet of paper, that which you present to the outside world should not be bigger than a box of matches." What's the point of that? Well, if you spill all of your knowledge on a particular subject (in other words, if it is so little that you can do so in an article), then you'll be treading on thin ice when the questions start flying.
And while my expectation level of many of the "journalistic institutions" on the Web has dwindled over the years to make such encounters less painful, I would not have expected journalism so poor from a magazine like Scientific American. Apparently, even this iconic institution of educating the average American on science news has gone on the cheap. Specifically, this article about AI used in a new camera system sparked the goading in me.
Behold this statement of utter crap: "An artificial neural network is a group of interconnected computers configured to work like a system of flesh-and-blood neurons in the human brain." Wow. Really folks? The paragraph goes on to say that "the interconnections among the computers enable the network to find patterns in data fed into the system, and to filter out extraneous information via a process called machine learning."
While the article goes into great detail on the use of memristors in the device and indicates that "getting all of the components of a memristor neural network onto a single microchip would be a big step." Quite unfortunately, the article doesn't go into the direct advantages of using memristors as the hardware for running an AI. I can see the advantage of doing some pre-insight on a vision device (much as our optic nerve pre-processes vision input to feed more abstract concepts into the brain's vision center in the cerebral cortex. This isn't that new a concept, by the way, as this 2014 paper from Cornell University demonstrates.