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AI continues to be hugely popular in the media, and while most articles continue to treat Big Data and Machine Learning as if they are the only game in town, we are starting to see more that recognize these narrow approaches don’t have a clear path to real AI or AGI.  Recently Forbes published a piece by Rob Toews who focuses on the big picture of AI. To Understand The Future of AI, Study Its Past This article divides AI into two opposing philosophies: connectionism and symbolism. From the historical perspective this is reasonable. Connectionism is what he means by today’s AI. Symbolism is what is sometimes called “Good Old-Fashioned AI.” Toews provides a good description of it: “Symbolic AI reached its mainstream zenith in the early 1980s with the proliferation of what were called “expert systems”: computer programs that, using extensive “if-then” logic, sought to codify the knowledge and decision-making of human experts in particular domains. These systems generated tremendous expectations and hype: startups like Teknowledge and Intellicorp raised millions and Fortune 500 companies invested billions in attempts to commercialize the technology.” I know. I was there. My first company, Talarian Corp, applied real-time expert systems to analyze spacecraft telemetry. Toews goes on to say, “Expert systems failed spectacularly to deliver on these expectations, due to the shortcomings noted above: their brittleness, inflexibility and inability to learn.” That’s an interesting way of looking at it. Usually the failure of expert systems is described as inability to scale. That is, [...]