| 
			  
			  
			  
			
			 
			by Priya Ganapati  
			August 16, 2010 
			from
			
			Wired Website 
			
			Spanish version 
			  
			Reverse-engineering the human brain so we can simulate it using 
			computers may be just two decades away, says 
			
			Ray Kurzweil, 
			artificial intelligence expert and author of the best-selling book
			
			The Singularity is Near.
 
 It would be the first step toward creating machines that are more 
			powerful than the human brain.
 
			  
			These supercomputers could be 
			networked into a cloud computing architecture to amplify their 
			processing capabilities. Meanwhile, algorithms that power them could 
			get more intelligent.  
			  
			Together these could create the ultimate 
			machine that can help us handle the challenges of the future, says 
			Kurzweil. This point where machines surpass human intelligence has 
			been called the “singularity.”
			 
			  
			It’s a term that Kurzweil helped 
			popularize through his book. 
				
				“The singular criticism of the 
				singularity is that brain is too complicated, too magical and 
				there’s something about its properties we can’t emulate,” 
				Kurzweil told attendees at the Singularity Summit over the 
				weekend.    
				“But the exponential growth in 
				technology is being applied to reverse-engineer the brain, 
				arguably the most important project in history.” 
			For nearly a decade, neuroscientists, 
			computer engineers and psychologists have been working to simulate 
			the human brain so they can ultimately create a computing 
			architecture based on how the mind works.
 Reverse-engineering some aspects of hearing and speech has helped 
			stimulate the development of artificial hearing and speech 
			recognition, says Kurzweil. Being able to do that for the human 
			brain could change our world significantly, he says.
 
 The key to reverse-engineering the human brain lies in decoding and 
			simulating the cerebral cortex - the seat of cognition. The human 
			cortex has about 22 billion neurons and 220 trillion synapses.
 
 A supercomputer capable of running a software simulation of the 
			human brain doesn’t exist yet. Researchers would require a machine 
			with a computational capacity of at least 36.8 petaflops and a 
			memory capacity of 3.2 petabytes - a scale that supercomputer 
			technology isn’t expected to hit for at least three years, according 
			to IBM researcher Dharmendra Modha.
 
			  
			Modha leads the cognitive computing 
			project at IBM’s 
			
			Almaden Research Center.
 By next year, IBM’s ‘Sequoia’ supercomputer should be able to offer 
			20 petaflops per second peak performance, and an even more powerful 
			machine will be likely in two to three years.
 
				
				“Reverse-engineering the brain is 
				being pursued in different ways,” says Kurzweil. “The objective 
				is not necessarily to build a grand simulation - the real 
				objective is to understand the principle of operation of the 
				brain.” 
			Reverse engineering the human brain is 
			within reach, agrees Terry Sejnowski, head of the 
			computational neurobiology lab at the Salk Institute for Biological 
			Studies.
 Sejnowski says he agrees with Kurzweil’s assessment that about a 
			million lines of code may be enough to simulate the human brain.
 
 Here’s how that math works, Kurzweil explains:
 
				
				The design of the brain is in the 
				genome. The human genome has three billion base pairs or six 
				billion bits, which is about 800 million bytes before 
				compression, he says.  
			Eliminating redundancies and applying 
			loss-less compression, that information can be compressed into about 
			50 million bytes, according to Kurzweil. About half of that is the 
			brain, which comes down to 25 million bytes, or a million lines of 
			code.
 But even a perfect simulation of the human brain or cortex won’t do 
			anything unless it is infused with knowledge and trained, says 
			Kurzweil.
 
				
				“Our work on the brain and 
				understanding the mind is at the cutting edge of the 
				singularity,” he says.
 
			  |