Machines Mostly Steal Thoughts But Open A New Era Of Exploration
Man-made machines increasingly do things we previously considered thinking, but don't do anymore because now machines do them. I stole this recent thought more or less accurately from Danny Hillis, father of the Connection Machine and the Knowledge Graph. Stealing thoughts is a common activity in thought processes of both humans and machines. Indeed, when we humans are thinking, much of the content of our thoughts is coming from past experience or the documented experience of others. Very rarely we come up with something completely new. Our machines are not much different. What is called cognitive computing is in essence nothing else but a very sophisticated thought stealing mechanism, driven by a vast amount of knowledge and a complicated set of algorithmic processes. Such thought stealing processes, in both human(istic) thought and cognitive computing, are impressive, as they are not only capable to steal existing thoughts, but also potential thoughts that are reasonable or likely, based in a given corpus of knowledge.
Today, thought stealing machines can produce scholarly texts that are indistinguishable from "post-modern thought," computer science papers that get accepted in conferences, or compositions that experts cannot disambiguate from originals by classical composers. Like in weather forecast, machines are now capable to produce many different cognitive representations based on expectations derived from documents about the past or similar situations. Renaissance antiquarians would be delighted, as these machines are a triumph of the very methods that gave rise to modern archaeology and many other branches of science and research. But how impressed should we really be?
Our machines get more and more sophisticated, and so do their results. But, as we build better and better machines, we also learn more and more about nature. In fact, natural cognition is likely much more complex and detailed than our current incarnations of artificial intelligence or cognitive computing. For example, how sophisticated do we have to imagine natural cognition, when quantum coherence at room temperature can help common birds in our garden to sense the magnetic field? How complex do we have to imagine embodied cognition in common octopi, when it is possible to build Turing machines that are made exclusively out of artificial muscles? How should we answer these questions, when we are still very far from recording in full detail what is going on in our brains? My guess is, in 200 years our current thinking machines will look as primitive as the original mechanical Turk.
However sophisticated they may become, compared to the resolution and efficiency of natural cognition, our machines are still primitive. Similar to proto-biotic metabolism, our machines are below a critical threshold to real life. But our machines are powerful enough that we can enter a new era of exploration. Our machines allow us to produce many more thoughts than ever produced before, with innovation becoming an exercise of finding the right thought in the set of all possible thoughts. As much as having our own ideas, ingenuity will lie in the proper exploration of such ready-made sets of thought. Measuring the cognitive space of all possible thoughts will be as awe-inspiring as the exploration of the universe by astronomy. Maybe Mahler's potential 60th is as awesome as his 6th.
