Ray Kurzweil - How to Create a Mind - The Secret of Human Thought Revealed

Similarly, the vast expanse of artistic expression of all kinds that celebrates love and its myriad forms dating back to antiquity is also an end in itself. Our ability to create these enduring forms of transcendent knowledge—about love or anything else—is precisely what makes our species unique.

The neocortex is biology’s greatest creation. In turn, it is the poems about love—and all of our other creations—that represent the greatest inventions of our neocortex.

A mother rat will build a nest for her young even if she has never seen another rat in her lifetime. 1 Similarly, a spider will spin a web, a caterpillar will create her own cocoon, and a beaver will build a dam, even if no contemporary ever showed them how to accomplish these complex tasks. That is not to say that these are not learned behaviors. It is just that these animals did not learn them in a single lifetime—they learned them over thousands of lifetimes. The evolution of animal behavior does constitute a learning process, but it is learning by the species, not by the individual, and the fruits of this learning process are encoded in DNA.

To appreciate the significance of the evolution of the neocortex, consider that it greatly sped up the process of learning (hierarchical knowledge) from thousands of years to months (or less). Even if millions of animals in a particular mammalian species failed to solve a problem (requiring a hierarchy of steps), it required only one to accidentally stumble upon a solution. That new method would then be copied and spread exponentially through the population.

We are now in a position to speed up the learning process by a factor of thousands or millions once again by migrating from biological to nonbiological intelligence. Once a digital neocortex learns a skill, it can transfer that know-how in minutes or even seconds. As one of many examples, at my first company, Kurzweil Computer Products (now Nuance Speech Technologies), which I founded in 1973, we spent years training a set of research computers to recognize printed letters from scanned documents, a technology called omni-font (any type font) optical character recognition (OCR). This particular technology has now been in continual development for almost forty years, with the current product called OmniPage from Nuance. If you want your computer to recognize printed letters, you don’t need to spend years training it to do so, as we did—you can simply download the evolved patterns already learned by the research computers in the form of software. In the 1980s we began on speech recognition, and that technology, which has also been in continuous development now for several decades, is part of Siri. Again, you can download in seconds the evolved patterns learned by the research computers over many years.

Ultimately we will create an artificial neocortex that has the full range and flexibility of its human counterpart. Consider the benefits. Electronic circuits are millions of times faster than our biological circuits. At first we will have to devote all of this speed increase to compensating for the relative lack of parallelism in our computers, but ultimately the digital neocortex will be much faster than the biological variety and will only continue to increase in speed.

When we augment our own neocortex with a synthetic version, we won’t have to worry about how much additional neocortex can physically fit into our bodies and brains, as most of it will be in the cloud, like most of the computing we use today. I estimated earlier that we have on the order of 300 million pattern recognizers in our biological neocortex. That’s as much as could be squeezed into our skulls even with the evolutionary innovation of a large forehead and with the neocortex taking about 80 percent of the available space. As soon as we start thinking in the cloud, there will be no natural limits—we will be able to use billions or trillions of pattern recognizers, basically whatever we need, and whatever the law of accelerating returns can provide at each point in time.

In order for a digital neocortex to learn a new skill, it will still require many iterations of education, just as a biological neocortex does, but once a single digital neocortex somewhere and at some time learns something, it can share that knowledge with every other digital neocortex without delay. We can each have our own private neocortex extenders in the cloud, just as we have our own private stores of personal data today.

Last but not least, we will be able to back up the digital portion of our intelligence. As we have seen, it is not just a metaphor to state that there is information contained in our neocortex, and it is frightening to contemplate that none of this information is backed up today. There is, of course, one way in which we do back up some of the information in our brains—by writing it down. The ability to transfer at least some of our thinking to a medium that can outlast our biological bodies was a huge step forward, but a great deal of data in our brains continues to remain vulnerable.

One approach to building a digital brain is to simulate precisely a biological one. For example, Harvard brain sciences doctoral student David Dalrymple (born in 1991) is planning to simulate the brain of a nematode (a roundworm). 2 Dalrymple selected the nematode because of its relatively simple nervous system, which consists of about 300 neurons, and which he plans to simulate at the very detailed level of molecules. He will also create a computer simulation of its body as well as its environment so that his virtual nematode can hunt for (virtual) food and do the other things that nematodes are good at. Dalrymple says it is likely to be the first complete brain upload from a biological animal to a virtual one that lives in a virtual world. Like his simulated nematode, whether even biological nematodes are conscious is open to debate, although in their struggle to eat, digest food, avoid predators, and reproduce, they do have experiences to be conscious of.

At the opposite end of the spectrum, Henry Markram’s Blue Brain Project is planning to simulate the human brain, including the entire neocortex as well as the old-brain regions such as the hippocampus, amygdala, and cerebellum. His planned simulations will be built at varying degrees of detail, up to a full simulation at the molecular level. As I reported in chapter 4, Markram has discovered a key module of several dozen neurons that is repeated over and over again in the neocortex, demonstrating that learning is done by these modules and not by individual neurons.

Markram’s progress has been scaling up at an exponential pace. He simulated one neuron in 2005, the year the project was initiated. In 2008 his team simulated an entire neocortical column of a rat brain, consisting of 10,000 neurons. By 2011 this expanded to 100 columns, totaling a million cells, which he calls a mesocircuit. One controversy concerning Markram’s work is how to verify that the simulations are accurate. In order to do this, these simulations will need to demonstrate learning that I discuss below.