Technological Singularity

Topics: Technological singularity, The Singularity Is Near, Transhumanism Pages: 5 (1771 words) Published: December 4, 2010
In a guest column, Computers vs. Brains on the Opinionator of The New York Times, Sandra Aamodt and Sam Wang analyzed some of the arguments by inventor Raymond Kurzweil, one of the leading inventors of our time, in his most recent futurist manifesto: “The Singularity Is Near: When Humans Transcend Biology” (2005). Kurzweil estimates that machines will inevitably be able to surpass our thinking capabilities within a few decades. Kurzweil's speculative reasoning has been heavily debated and challenged. In Aamodt and Wang's article they point out that there are fundamental differences between our brains and computers that makes Kurzweil's predictions improbable. The purpose of this essay is to evaluate the arguments of sides, Kurzweil's book and Aamodt and Wang's article. I will attempt to accomplish this by using various critical thinking methods such as defining, clarifying and explaining some of the history of the concepts and the debates involved. To understand the debate we must first clarify what is technological singularity. A technological singularity is the moment our technological development becomes so rapid that it makes the future after the singularity unpredictable. Writers on singularity, such as Raymond Kurzweil, define the concept in terms of the technological creation of super-intelligence (Kurzweil, 2005). The article points out that any comparison of the brain and computers misses the messy truth about the fundamental differences between them. The article provides various reasons why the brain is superior to computers and ways in which it is not. The debate focuses on differences on energy consumption, information processing strategies and capacity, and the pros and cons of artificial versus biological between brain and computers. The brain contains many systems that have evolved through natural selection for one task then was adopted for another. It is efficient for nature to adapt an old system that to build a new one. As such, the brain is composed of the brain stem, the limbic system and the brain cortex carrying out a complex communication with each other that we have yet to decipher. Engineer's however, have the advantage to start over to get it just right. A persistent problem, with not just artificial intelligence, but all machines, is the tendency of components to fail. Yet our biological neurons and synapses fail all the time, even under healthy conditions, but unlike computers, new neuron connections can form as well as break throughout a lifetime, this provides an infinite potential for new paths and brain activity. The human brain uses 12 watts, which is less than a typical refrigerator light while the memory of an artificial brain would use nearly a gigawatt, the amount currently consumed by all Washington, D.C. (Aamodt & Wang, 2009). To solve this challenge Kurzweil invoked Moore's Law. The law is named after Intel co-founders Gordon E. Moore, who originally described the trend in 1965. In this paper Moore noted that number the of transistors in integrated circuits had doubled every year from the invention of the integrated circuit in 1958 until 1965 and predicted that the trend would continue "for at least ten years" (Moore, 1965). His prediction has proved to be incredibly accurate, in part because most technology and electronic industries now use this principle to set targets for research and development (Disco & Meulen, 1998). For the last four decades memory and chip capacity has doubled every one or two years (est. 2025 to 2030). The article claims that Kurzweil overlooks Moore's Law power consumption per chip, which has also increased immensely since 1985 (Aamodt & Wang, 2009). While this is true, it implies that electrical power consumption will continue to grow whereas electrical power and storage technologies such as batteries, fuel cells and renewable energies will remain stagnant. The problem in this logic is that history and technology advancements do not evolve at a...
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