The development and promulgation of Ethical AI precepts are being pursued to hopefully prevent society from falling into a myriad of AI-inducing traps. For my coverage of the UN AI Ethics principles as devised and supported by nearly 200 countries via the efforts of UNESCO, see. In a similar vein, new AI laws are being explored to try and keep AI on an even keel. One of the latest takes consists of a set of proposed AI Bill of Rights that the U.S.
I say this because I find it worrisome and quite disturbing from an AI Ethics perspective that many AI researchers and AI scientists tend to blur the line between artificial neural networks of a computational bent and the biological or wetware neural networks that sit inside our noggins. They are two completely different constructs.
Generative AI tends to then have an underlying array of these mathematical or computational nodes arranged into what is commonly said to be an artificial neural network. This in turn is arranged typically into various layers. The data training of generative AI involves establishing the calculations and such that will take place within the artificial neural network, based on pattern-matching of scanned text across the Internet.
If you wanted me to trace laboriously through the artificial neural network of the generative AI, I could tell you exactly which numbers went into each of the artificial neurons or nodes. I could also tell you precisely which numbers flowed out, going from each artificial neuron to each other one, and ultimately led to those generated words “Landed safely”. This is a straightforward aspect of mechanically tracing the flow of numbers.
Sorry, you are having the wool pulled over your eyes. The problem is that the generative AI that has generated the explanation of what the generative AI was doing, well, it is yet another fanciful concoction. You have no means of ascertaining that the generative AI-generated explanation has anything at all to do with the actual internal flowing of the numbers. It is once again considered a contrived explanation.
The approach consists of first identifying which generative AI app you want to try and examine. This is referred to as the Subject Model. Next, via the use of GPT-4, a second model is devised that tries to explain the Subject Model. This second model is referred to as the Explainer Model. Finally, once there is a logical explanation concocted that might or might not be applicable, a third model is used to simulate whether the explanation seems to work out.
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