In addition to this historical perspective, we can examine the question from a mathematical perspective and arrive at the same conclusion: *
There is an identity known as the data processing inequality, which states that if you have a dataset X with information about a quality Y, then processing the dataset X cannot increase the information regarding Y. AI is a form of processing so AI algorithms can only reduce the information in the dataset, not increase it.
The dataset is the source of the information that makes AI work. Therefore, in order to increase information, we must increase data. Assuming we have access to unlimited data, the limit on increasing the dataset’s size is the rate at which we can process it. The bottleneck in improving AI is the processing throughput.
May we expect a continuation of the boom-bust cycle for AI, and thus another spring after the predicted winter? If we assume that AI has advanced primarily due to processor improvements, then continued improvement is contingent on Moore’s law, which states that the number of transistors on a processor circuit doubles every two years. However, for the transistor density to increase, the size of the transistors must decrease.
Because there is a limit to how small transistors can be, there is a limit to the applicability of Moore’s law. Further, because Moore’s law is an exponential law, the numbers multiply rapidly and we could hit the physical limit rather suddenly. Current indications are that Moore’s law’s speed has already slowed or even ceased to be a true description of the information technology (IT) industry today… More.
Also by Eric Holloway: How can we measure meaningful information
Has neuroscience disproved thinking?
Eric Holloway has a Ph.D. in Electrical & Computer Engineering from Baylor University. He is a current Captain in the United States Air Force where he served in the US and Afghanistan He is the co-editor of the book Naturalism and Its Alternatives in Scientific Methodologies. Dr. Holloway is an Associate Fellow of the Walter Bradley Center for Natural and Artificial Intelligence.