Machine Learning: When and Where the Horses Went Astray?
Emanuel Diamant · Machine Learning · 2010
In this paper I have attempted to promote a new Thinking Machines design and development philosophy. The central point of my approach is a new definition of information, that is, a notion of information as a language-based description. Then, above it the notion of intelligence can be placed, defining intelligence as the system's ability to process information. The principles of information mining should be placed in the lower part of the construction. Thus, it seems to me, a proper frame for a rational Artificial or Machine Intelligence devices research and development enterprise can be established. Essentially, the declared focus of the paper's subject is the issue of Machine Learning, which is assumed to be a bundle of techniques used to support all information-processing machinery. But, as you know, Machine Learning as by now (and already for a very long time) is treated as an independent and stand alone discipline, totally detached from its original destination Thinking Machines research and development (Turing, 1950). The roadmap for this challenge was formulated at the Dartmouth College meeting in the summer of 1956 (McCarthy, et al. 1955). The date of this meeting is considered today as the Artificial Intelligence (AI) birthday. (The very name of AI was coined at this time by John McCarthy, one of the authors of the Dartmouth Proposal). At first, the excitement and hopes were really high, and the goals have seemed to be reachable in a short while. In the Panel Discussion at the Artificial General Intelligence (AGI) Workshop in 2006, Steve Grand has recalled that "Rodney Brooks has a copy of a memo from Marvin Minsky (another father of the Dartmouth Proposal), in which he suggested charging an undergraduate for a summer project with the task of solving vision. I don't know where that undergraduate is now, but I guess he hasn't finished yet" (Panel Discussion, 2006). Indeed, problems of Vision, as well as all other AI troubles, have turned out to be much more complicated and problematic than it looked out at the beginning. Within a decade or