I have rather neglected this blog recently, partly because of other distractions, including a stimulating FutureLearn course on the Hobbit (Home floresiensis), and partly because I am concentrating on writing up my ideas about the evolution of human intelligence. However I have just come across an article by Gary Marcus and Ernest Davis entitled "Eight (No Nine!) Things wrong with Big Data" which is well worth reading.
The issue I have with the big data approach to machine intelligence is that it is tackling the problem in a very different way to the human brain,
If we think about the evolution of the brain it started very small and incrementally became bigger over millions of years. And for each animal, including humans, the brains start with knowing very nothing apart from some pre-programmed instincts and its knowledge increases incrementally through life. The economics of evolution involve optimising the use of resources to maximise survival which will set limits to the size of the brain and the amount of time spent learning. In effect small amounts of "data" is beautiful as long as there is enough to be cost effective in the battle for survival.
Big data applications involve using vast amounts of data which is already available in digital form, such as the case of the Google language translator which uses million of document texts in different languages (so the data collection cost per byte is extremely low) and applies powerful statistical processes of a kind which clearly are not inbuilt into the human brain.
Of course in many cases the big data approach is invaluable in that it can do things humans are not be capable of doing. The important thing to realise is that the techniques used in processing big data can tell us virtually nothing about how the human brain works.