The human brain has some capabilities that the brains of other animals lack. Num Pages: 390 pages, black and white line drawings and halftones. What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? In a tour de force of analytic thinking, Bostrom lays a foundation for understanding the future of humanity and intelligent life. This seminal book injects the topic of superintelligence into the academic and popular mainstream. Environmental science, engineering & technologyĭescription for Superintelligence: Paths, Dangers, Strategies Paperback.Civil engineering, surveying & building.Electronics & communications engineering.
Industrial chemistry & manufacturing technologies.Book Review: A Short History of Progress by Ronald.Book Review: Superintelligence by Nick Bostrom.That is both our benefit and our challenge. Our species is where it is because our intelligence has explored more of our space faster and to greater effect. Evolution is also a search algorithm, although a much slower one. We explore physical spaces, new foods to eat, new places to be, new ways to raise families, new ways to defeat our enemies. They are just an outward-looking minority evolved for the group's longer term survival. But the aggregate of our individual decisions looks much like a search algorithm. A few dare to challenge conventional thinking aimed at in-group stability. A few, a very few, spend their days trying to think rationally, or exploring the ramifications of rational laws on our near future.
We make decisions that keep our in-groups stable, by and large. We do not make rational decisions, we make emotional ones, even when we try not to. The actions of individuals, both male and female, are inextricably linked to our cognitive biases. Frankly, it is even true of any large software program that has entered maintenance failure, which is almost always an admission by a development team that the program has become too complex for them to reliably change. Bostrom's conception of software is at least as old as the Apple Newton. This is equally true in Numenta's Cortical Learning Algorithm ( CLA), and will be true of any eventual model of the human brain. Ask a Google engineer exactly how their program recognizes a face, or a road, or a cat, and they will have no idea. So-called "deep learning", which generally just means neural networks of more than historical complexity due to the application of more computing power, just exacerbates the problem of understanding.
We can train a neural network, but we cannot typically deconstruct the resulting weighted algorithm to figure out how a complex recognition task is performed. There are already several forms of artificial intelligence where we simply do not understand how they work.
0 kommentar(er)
