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"But it’s in the recommendations for adapting to technological change that this book really falls short. The program for winning the future, it turns out, consists of encouraging entrepreneurship and improving education. The former, the authors say, will allow us to discover a bounty of new ways of employing people, through the magic of Hayekian tacit knowledge and Schumpeterian creative destruction. And an improved education system will ensure that the general population has the necessary human capital to participate in this magical new economy. This is a remarkably thin vision, redolent of the kind of popular techno-libertarianism that flourished at the height of the dot-com bubble, and it’s no more compelling now than it was then."
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But if technology really is dramatically reducing the need for human labor, then we have an opportunity to think bigger and better, getting beyond merely trying to scrape up new skills and new jobs for the displaced proletariat. If you’re a regular reader, you know where I’m going with this by now; as somebody said of one of my earlier renditions on this theme, “we get it–Peter Frase hates work”. Totally missing from Race Against the Machine is any consideration that we might take some of our productivity gains in the form of free time rather than income. Nowhere do the authors even contemplate reducing the length of the work week and work year, or accepting a lower labor-force participation rate. Thus, despite constantly reminding us of all the ways in which technology has improved our standard of living and transformed society, Brynjolfsson and McAfee never question the centrality of wage labor in its current form: they never consider that there is any alternative to a society in which everyone expects, and is expected, to spend the bulk of their life as a 40 (or more) hour per week wage laborer, or as a profit-maximizing “entrepreneur”.
"What's the connection between how many bits we can send over a channel and how accurately we can classify documents or fit a curve to data? Is there any connection between decision trees, prefix codes and wavelet transforms? What about error-correcting codes, graphical models and compressed sensing? "
Here, then, is my question: Are you and I machines? Are we analyzable without remainder into a collection of mechanisms whose operation can be fully explained by the causal operation of physical and chemical laws, starting from the parts and proceeding to the whole? It might seem so, judging from the insistent testimony of those whose work is to understand life.
"The great “what will we do when the machines take over” debate continues, but surprisingly little attention has been paid to the arguments of (licensed speculative economists) science fiction writers, who have been engaged in this debate for some decades at least."
The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
Peter D. Turney
Institute for Information Technology
National Research Council Canada
I asked myself this question because I sense that how we organize today, using machine rules, seems to drive great dysfunction. The core of humanity, the family is eroding. Our great institutions seem increasingly incapable of serving us.
The self-organizing map (SOM) is a subtype of artificial neural networks. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space. This make
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