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"The brain is a computer" is a damn problematic metaphor. I prefer to say that "the brain is an intuition machine". The term computer is conventionally understood to be a digital computer. It's the kind that is designed by minds and manufactured in assembly lines. It is a horrible metaphor.

"The activity of the intuition consists in making spontaneous judgments which are not the result of conscious trains of reasoning. These judgments are often but by no means invariably correct. . . . The exercise of ingenuity in mathematics consists in aiding the intuition through suitable arrangements of propositions, and perhaps geometrical figures or drawings." There are many misunderstandings AI researchers hold on to that lead them to dead ends. The most well-known one is the idea that has driven GOFAI (Good Old Fashion AI) since the 1950s: intelligence can be reducible to simulating logical reasoning.

One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.

One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.

One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.

One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.

There is more to building a sustainable Deep Learning solution than what is provided by Deep Learning frameworks like TensorFlow and PyTorch. These frameworks are good enough for research, but they don't take into account the problems that crop up with production deployment. I've written previously about technical debt and the need from more adaptive biological like architectures. To support a viable business using Deep Learning, you absolutely need an architecture that supports sustainable improvement in the presence of frequent and unexpected changes in the environment. Current Deep Learning framework only provide a single part of a complete solution.

A common incorrect assumption about the evolution of Artificial General Intelligence (AGI), that is self-aware sentient automation, will follow the path of ever more intelligent machines and thus accelerate towards a super intelligence once human level sentient automation is created. I'm writing this article to argue that this likely will not be the case and that there will be an initial divergence of two kinds of artificial intelligences. First, let us establish here that the starting point will come from present day Deep Learning technology. More specifically, I refer these as intuition machines (see: Intuition Machines a Cognitive Breakthrough). There will be a fork in the evolution of more intelligent machines.

With the emergence of Deep Learning as the dominant paradigm for Artificial Intelligence based systems, one open question that seems to be neglected is "What guidelines do we have in architecting software that uses Deep Learning?" If all the innovative companies like Google are on a exponential adoption curve to incorporate Deep Learning in every thing they do, then what perhaps is the software architecture that holds this all together? The folks at Google wrote a paper (a long time ago, meaning 2014), "Machine Learning: The High-Interest Credit Card of Technical Debt" that enumerates many of the difficulties that we need to consider when building software that consists of machine learning or deep learning sub-components. Contrary to popular perception that that Deep Learning systems can be "self-driving". There is a massive ongoing maintenance cost when machine learning is used.