The Software of Intelligence
So far, I've been talking about the hardware of computing. The software is even more salient. One of the principal assumptions underlying the expectation of the Singularity is the ability of nonbiological mediums to emulate the richness, subtlety, and depth of human thinking. Achieving the computational capacity of the human brain, or even villages and nations of human brains will not automatically produce human levels of capability. By human levels I include all the diverse and subtle ways in which humans are intelligent, including musical and artistic aptitude, creativity, physically moving through the world, and understanding and responding appropriately to emotion. The requisite hardware capacity is a necessary but not sufficient condition. The organization and content of these resources--the software of intelligence--is also critical.
Before addressing this issue, it is important to note that once a computer achieves a human level of intelligence, it will necessarily soar past it. A key advantage of nonbiological intelligence is that machines can easily share their knowledge. If I learn French, or read War and Peace, I can't readily download that learning to you. You have to acquire that scholarship the same painstaking way that I did. My knowledge, embedded in a vast pattern of neurotransmitter concentrations and interneuronal connections, cannot be quickly accessed or transmitted. But we won't leave out quick downloading ports in our nonbiological equivalents of human neuron clusters. When one computer learns a skill or gains an insight, it can immediately share that wisdom with billions of other machines.
As a contemporary example, we spent years teaching one research computer how to recognize continuous human speech. We exposed it to thousands of hours of recorded speech, corrected its errors, and patiently improved its performance. Finally, it became quite adept at recognizing speech (I dictated most of my recent book to it). Now if you want your own personal computer to recognize speech, it doesn't have to go through the same process; you can just download the fully trained patterns in seconds. Ultimately, billions of nonbiological entities can be the master of all human and machine acquired knowledge.
In addition, computers are potentially millions of times faster than human neural circuits. A computer can also remember billions or even trillions of facts perfectly, while we are hard pressed to remember a handful of phone numbers. The combination of human level intelligence in a machine with a computer's inherent superiority in the speed, accuracy, and sharing ability of its memory will be formidable.
There are a number of compelling scenarios to achieve higher levels of intelligence in our computers, and ultimately human levels and beyond. We will be able to evolve and train a system combining massively parallel neural nets with other paradigms to understand language and model knowledge, including the ability to read and model the knowledge contained in written documents. Unlike many contemporary "neural net" machines, which use mathematically simplified models of human neurons, some contemporary neural nets are already using highly detailed models of human neurons, including detailed nonlinear analog activation functions and other relevant details. Although the ability of today's computers to extract and learn knowledge from natural language documents is limited, their capabilities in this domain are improving rapidly. Computers will be able to read on their own, understanding and modeling what they have read, by the second decade of the twenty-first century. We can then have our computers read all of the world's literature--books, magazines, scientific journals, and other available material. Ultimately, the machines will gather knowledge on their own by venturing out on the web, or even into the physical world, drawing from the full spectrum of media and information services, and sharing knowledge with each other (which machines can do far more easily than their human creators).
Reverse Engineering the Human Brain
The most compelling scenario for mastering the software of intelligence is to tap into the blueprint of the best example we can get our hands on of an intelligent process. There is no reason why we cannot reverse engineer the human brain, and essentially copy its design. Although it took its original designer several billion years to develop, it's readily available to us, and not (yet) copyrighted. Although there's a skull around the brain, it is not hidden from our view.
The most immediately accessible way to accomplish this is through destructive scanning: we take a frozen brain, preferably one frozen just slightly before rather than slightly after it was going to die anyway, and examine one brain layer--one very thin slice--at a time. We can readily see every neuron and every connection and every neurotransmitter concentration represented in each synapse-thin layer.
Human brain scanning has already started. A condemned killer allowed his brain and body to be scanned and you can access all 10 billion bytes of him on the Internet http://www.nlm.nih.gov/research/visi...ble_human.html.
He has a 25 billion byte female companion on the site as well in case he gets lonely. This scan is not high enough in resolution for our purposes, but then, we probably don't want to base our templates of machine intelligence on the brain of a convicted killer, anyway.
But scanning a frozen brain is feasible today, albeit not yet at a sufficient speed or bandwidth, but again, the law of accelerating returns will provide the requisite speed of scanning, just as it did for the human genome scan. Carnegie Mellon University's Andreas Nowatzyk plans to scan the nervous system of the brain and body of a mouse with a resolution of less than 200 nanometers, which is getting very close to the resolution needed for reverse engineering.
We also have noninvasive scanning techniques today, including high-resolution magnetic resonance imaging (MRI) scans, optical imaging, near-infrared scanning, and other technologies which are capable in certain instances of resolving individual somas, or neuron cell bodies. Brain scanning technologies are also increasing their resolution with each new generation, just what we would expect from the law of accelerating returns. Future generations will enable us to resolve the connections between neurons and to peer inside the synapses and record the neurotransmitter concentrations.
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