As Artificial Intelligence matures over half a century, we may be fast approaching the limits of independent developments in software and hardware. Consulting companies seem to have embraced "data science," an ill-conceived and confusing area. Hardware companies, pressured to sell Silicon at any cost, have been creating Pizza sized "smart boxes." and "cognitive networks.". Not to be left behind, companies that specialize in "IoT," things that are on the internet, have been struggling to define how they are different. All of these, aided by massive hype, will likely destroy shareholder value in many ways.
There are two important avenues to make progress in this area. First, the hype created by consulting companies has to be tempered - data scientists do not add value, they typically destroy it. R and Python do not automatically add any value if the users of these somewhat obsolete tools do not understand the problem they are trying to solve. Most of the "new math," has been around for many decades, it is just that fast and cheap computers now have made the incompetent look smart.
It is time to focus on the assimilation of hardware and software to move the field forward. Lack of a theory of consciousness automatically means that humans are better off abandoning the idea of "modeling," the brain. However, we could learn a lot from observing the brain - it is an efficient learning system that gets tired and ages over time. No machine based on conventional computing architecture exhibits these qualities. This means that it is futile to throw more Silicon to a foreign design in an effort to make it act like the brain. In other words, intelligence is never artificial.
Human intelligence, albeit impressive, cannot be the end game. The inability of individual specimens to form a network has substantially restricted their ability to advance. So, replicating the human brain in silicon is not a good idea both because contemporary designs do not allow consciousness and the lack of network capabilities disallow scaling.
It's time software and hardware came together to advance AI.
There are two important avenues to make progress in this area. First, the hype created by consulting companies has to be tempered - data scientists do not add value, they typically destroy it. R and Python do not automatically add any value if the users of these somewhat obsolete tools do not understand the problem they are trying to solve. Most of the "new math," has been around for many decades, it is just that fast and cheap computers now have made the incompetent look smart.
It is time to focus on the assimilation of hardware and software to move the field forward. Lack of a theory of consciousness automatically means that humans are better off abandoning the idea of "modeling," the brain. However, we could learn a lot from observing the brain - it is an efficient learning system that gets tired and ages over time. No machine based on conventional computing architecture exhibits these qualities. This means that it is futile to throw more Silicon to a foreign design in an effort to make it act like the brain. In other words, intelligence is never artificial.
Human intelligence, albeit impressive, cannot be the end game. The inability of individual specimens to form a network has substantially restricted their ability to advance. So, replicating the human brain in silicon is not a good idea both because contemporary designs do not allow consciousness and the lack of network capabilities disallow scaling.
It's time software and hardware came together to advance AI.
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