The Singularity is Near: When Humans Transcend Biology

by Ray Kurzweil

I believe that the way to create a brain-like intelligence is to build a real-time working model system, accurate in sufficient detail to express the essence of each computation that is being performed, and verify its correct operation against measurements of the real system.

The model must run in real-time so that we will be forced to deal with inconvenient and complex real-world inputs that we might not otherwise think to present to it.

In the context of such great complexity, it is possible that the only practical way to understand the real system is to build a working model, from the sensors inward, building on our newly enabled ability to visualize the complexity of the system as we advance into it. Such an approach could be called reverse-engineering of the brain…. Note that I am not advocating a blind copying of structures whose purpose we don’t understand, like the legendary Icarus who naively attempted to build wings out of feathers and wax. Rather, I am advocating that we respect the complexity and richness that is already well-understood at low levels, before proceeding to higher levels.

The software is not based on reproducing each individual neuron and connection, as is the cerebellum model described above, but rather the transformations performed by each region.

Watts has used his model as a preprocessor (front end) in speech-recognition systems and has demonstrated its ability to pick out one speaker from background sounds (the “cocktail party effect”). This is an impressive feat of which humans are capable but up until now had not been feasible in automated speech-recognition systems.

Categorization—the ability to differentiate, for example, between a person and a car or between a dog and a cat—is a more complex matter, although recently progress has been made.

We are often more focused on the hypothesis than the actual test, which explains why people often see and hear what they expect to perceive rather than what is actually there. “Hypothesis and test” is also a useful strategy in our computer-based pattern-recognition systems.

One group of what are called ganglion cells sends information only about edges (changes in contrast). Another group detects only large areas of uniform color, whereas a third group is sensitive only to the backgrounds behind figures of interest.

For more than thirty years Moravec has been constructing systems to emulate the ability of our visual system to build representations of the world. It has only been recently that sufficient processing power has been available in microprocessors to replicate this human-level feature detection, and Moravec is applying his computer simulations to a new generation of robots that can navigate unplanned, complex environments with human-level vision.

Operations of thought are like cavalry charges in a battle—they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.

Their “mirror system hypothesis” is that the key to the evolution of language is a property called “parity,” which is the understanding that the gesture (or utterance) has the same meaning for the party making the gesture as for the party receiving it; that is, the understanding that what you see in a mirror is the same (although reversed left-to-right) as what is seen by someone else watching you.

A closely related concept is that the ability to imitate the movements (or, in the case of human babies, vocal sounds) of others is critical to developing language.112 Imitation requires the ability to break down an observed presentation into parts, each of which can then be mastered through recursive and iterative refinement.

Recursion is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure and to continue this process iteratively. In this way, we are able to build the elaborate structures of sentences and paragraphs from a limited set of words. Another key feature of the human brain is the ability to make predictions, including predictions about the results of its own decisions and actions. Some scientists believe that prediction is the primary function of the cerebral cortex, although the cerebellum also plays a major role in the prediction of movement.

A related experiment was conducted recently in which neurophysiologists electronically stimulated points in the brain to induce particular emotional feelings. The subjects immediately came up with a rationale for experiencing those emotions. It has been known for many years that in patients whose left and right brains are no longer connected, one side of the brain (usually the more verbal left side) will create elaborate explanations (“confabulations”) for actions initiated by the other side, as if the left side were the public-relations agent for the right side.

The most complex capability of the human brain—what I would regard as its cutting edge—is our emotional intelligence. Sitting uneasily at the top of our brain’s complex and interconnected hierarchy is our ability to perceive and respond appropriately to emotion, to interact in social situations, to have a moral sense, to get the joke, and to respond emotionally to art and music, among other high-level functions. Obviously, lower-level functions of perception and analysis feed into our brain’s emotional processing, but we are beginning to understand the regions of the brain and even to model the specific types of neurons that handle such issues.

One difference is that humans have a larger cortex, reflecting our stronger capability for planning, decision making, and other forms of analytic thinking.

This type of “deep” interconnectedness, in which certain neurons provide connections across numerous regions, is a feature that occurs increasingly as we go up the evolutionary ladder.

a great deal of our thinking is directed toward our bodies: protecting and enhancing them, as well as attending to their myriad needs and desires.

Very recently yet another level of processing of what started out as sensory information from the body has been discovered.

It is important to point out that the spindle cells are not doing rational problem solving, which is why we don’t have rational control over our responses to music or over falling in love. The rest of the brain is heavily engaged, however, in trying to make sense of our mysterious high-level emotions.

I want to do something with my life; I want to be a cyborg. —KEVIN WARWICK

Another important application will be to actually interface our brains with computers, which I believe will become an increasingly intimate merger in the decades ahead.

Homo sapiens, the first truly free species, is about to decommission natural selection, the force that made us…. [S]oon we must look deep within ourselves and decide what we wish to become. —E. O. WILSON, CONSILIENCE: THE UNITY OF KNOWLEDGE, 1998

We may be like the young boy who loves to take things apart. He is bright enough to disassemble a watch, and maybe even bright enough to get it back together so that it works. But what if he tries to “improve” it? … The boy can understand what is visible, but he cannot understand the precise engineering calculations that determine exactly how strong each spring should be…. Attempts on his part to improve the watch will probably only harm it…. I fear … we, too, do not really understand what makes the [lives] we are tinkering with tick.

Anderson’s concern, however, does not reflect the scope of the broad and painstaking effort by tens of thousands of brain and computer scientists to methodically test out the limits and capabilities of models and simulations before taking them to the next step.

The process of understanding the principles of operation of the brain is proceeding through a series of increasingly sophisticated models derived from increasingly accurate and high-resolution data.

Databases of brain-scanning information and model building are also doubling in size about once per year.

Once the nanobot era arrives in the 2020s we will be able to observe all of the relevant features of neural performance with very high resolution from inside the brain itself. Sending billions of nanobots through its capillaries will enable us to noninvasively scan an entire working brain in real time.

the accelerating pace of brain reverse engineering makes it clear that there are no limits to our ability to understand ourselves—or anything else, for that matter.

The key to the scalability of human intelligence is our ability to build models of reality in our mind. These models can be recursive, meaning that one model can include other models, which can include yet finer models, without limit.

A complex system like a cell or the human brain cannot be understood simply by breaking it down into constituent subsystems and their components. We have increasingly sophisticated mathematical tools for understanding systems that combine both order and chaos—and there is plenty of both in a cell and in the brain—and for understanding the complex interactions that defy logical breakdown.

Our computers, which are themselves accelerating, have been a critical tool in enabling us to handle increasingly complex models, which we would otherwise be unable to envision with our brains alone.

That our intelligence is just above the threshold necessary to understand itself results from our native ability, combined with the tools of our own making, to envision, refine, extend, and alter abstract—and increasingly subtle—models of our own observations.

Uploading a human brain means scanning all of its salient details and then reinstantiating those details into a suitably powerful computational substrate. This process would capture a person’s entire personality, memory, skills, and history.

If we are truly capturing a particular person’s mental processes, then the reinstantiated mind will need a body, since so much of our thinking is directed toward physical needs and desires.

by the time we have the tools to capture and re-create a human brain with all of its subtleties, we will have plenty of options for twenty-first-century bodies for both nonbiological humans and biological human...

In theory one could upload a human brain by capturing all the necessary details without necessarily comprehending the brain’s overall plan. In practice, however, this is unlikely to work. Understanding the principles of operation of the human brain will reveal which details are essential and which details are intended to be disordered. We need to know, for example, which molecules in the neurotransmitters are critical, and whether we need to capture overall levels, position and location, and/or molecular shape.

In my view the most important element in uploading will be our gradual transfer of our intelligence, personality, and skills to the nonbiological portion of our intelligence.

In the 2020s we will use nanobots to begin augmenting our brains with nonbiological intelligence, starting with the “routine” functions of sensory processing and memory, moving on to skill formation, pattern recognition, and logical analysis.

Given the human brain’s plasticity, our thoughts literally create our brains through the growth of new spines, synapses, dendrites, and even neurons. As a result, Einstein’s parietal lobes—the region associated with visual imagery and mathematical thinking—became greatly enlarged.

As we re-create the human brain, we will not be limited in our ability to develop each skill. We will not have to compromise one area to enhance another. We can also gain insight into our differences and an understanding of human dysfunction.

It’s true that educating our AIs will be an important part of the process, but we can automate a lot of that and greatly speed it up. Also, keep in mind that when one AI learns something, it can quickly share that knowledge with many other AIs.

They’ll have access to all of our exponentially growing knowledge on the Web, which will include habitable, full-immersion virtual-reality environments where they can interact with one another and with biological humans who are projecting themselves into these environments.

These AIs don’t have bodies yet. As we have both pointed out, human emotion and much of our thinking are directed at our bodies and to meeting their sensual and sexual needs.

GEORGE 2048: Keep in mind, there won’t be a clear distinction between AIs and humans. MOLLY 2104: Yes, except for the MOSHs (Mostly Original Substrate Humans) of course.

We are daily giving [machines] greater power and supplying by all sorts of ingenious contrivances that self-regulating, self-acting power which will be to them what intellect has been to the human race.

We are ourselves creating our own successors. Man will become to the machine what the horse and the dog are to man; the conclusion being that machines are, or are becoming, animate. —SAMUEL BUTLER, 1863 LETTER, “DARWIN AMONG THE MACHINES”

no matter how successfully we fine-tune our DNA-based biology, humans will remain “second-class robots,” meaning that biology will never be able to match what we will be able to engineer once we fully understand biology’s principles of operation.

The most powerful impending revolution is “R”: human-level robots with their intelligence derived from our own but redesigned to far exceed human capabilities. R represents the most significant transformation, because intelligence is the most powerful “force” in the universe.

Underlying all of the wonders of life and misery of disease are information processes, essentially software programs, that are surprisingly compact.