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A recent publication<\/a> in the Optical Society\u2019s journal of Photonics Research demonstrates how certain computing tasks (referred to in the article as \u201cartificial neural computing\u201d) may be performed using a specially prepared sheet of glass (\u201cnanophotonic medium\u201d). For this paper, a neural network was codified into the glass by iteratively introducing impurities, effectively \u201ctraining\u201d it to the desired outcomes of the task \u2014 in the case of this research, to identify and recognize graphical numbers displayed to it.<\/p>\n As far as optics is concerned, this is an extraordinary accomplishment (at least it seems that way to an outsider like myself!). The applications of such a technology, if it reaches a state where it can be created quickly and to the needs of the moment, include a wide range of tasks such as facial recognition and fast, highly specific computation in low-power environments.<\/p>\n The limitations to this technology do make it likely it will apply to a small-ish domain, since without completely reconfiguring the glass (if such a thing is possible) the neural network remains applicable to the one task it was created for and no other.<\/p>\n Some news articles<\/a> have seized on this achievement and deemed it an artificial intelligence. I\u2019m no expert in the field, to be sure, but this titling worries me. Is every neural network an AI by default? Or is this really a highly complex, very specific tool? It can be calibrated, but only once. It can compute, but only in the sense that a mousetrap can compute when to spring \u2014 though on a completely different scale of complexity.<\/p>\n When you take the fundaments of such a system \u2014 any such system \u2014 you have a machine which takes an input via some sensing ability, performs computation on that input, and produces an output in the form of an action or record. In the technical sense, such a machine is sufficient to be called an intelligent agent, an AI. Whether the computation occurs by scattered light passing through a glass or by electricity flowing through a circuit \u2014 or for that matter, by gears turning \u2014 would seem not to make a difference to the fact that you have input, computation, output.<\/p>\n Granted, some systems may store the information in gears, relays, or platter, while a sheet of glass cannot (true for the most part; see for example https:\/\/www.sciencedaily.com\/releases\/2018\/07\/180711093109.htm<\/a>). Lack of storage does make some difference in terms of how we think about a computational system, but may it still be an AI?<\/p>\n I don\u2019t know, and I doubt if I\u2019m qualified to decide the answer to this question, but I do know a trend when I see one and like all trends, it needs temperance.<\/p>\n It\u2019s wonderful that technology has become so popularized and exciting, but stuffing achievements under uncertain headings to fit a trend may not really benefit anyone (except perhaps publishers and academics, who both need the funds).<\/p>\n Referring back to the news article mentioned earlier, I was particularly struck by how certain aspects of this achievement were stated. If you read the line stating \u201cIt can also recognise, in real time, when a number it is presented with changes\u201d you may be justifiably impressed that a sheet of glass can detect numbers, and skip by the minor detail that it would be nearly impossible for this process to happen in anything other than real time.<\/em><\/p>\n