The Singularity is Near: When Humans Transcend Biology

by Ray Kurzweil

Scanning Using Nanobots. Although these largely noninvasive means of scanning the brain from outside the skull are rapidly improving, the most powerful approach to capturing every salient neural detail will be to scan it from inside. By the 2020s nanobot technology will be viable, and brain scanning will be one of its prominent applications. As described earlier nanobots are robots that will be the size of human blood cells (seven to eight microns) or even smaller.

Using high-speed wireless communication, the nanobots would communicate with one another and with computers compiling the brain-scan database. (In other words, the nanobots and computers will all be on a wireless local area network.)

A key technical challenge to interfacing nanobots with biological brain structures is the blood-brain barrier (BBB). In the late nineteenth century, scientists discovered that when they injected blue dye into an animal’s bloodstream, all the organs of the animal turned blue with the exception of the spinal cord and brain. They accurately hypothesized a barrier that protects the brain from a wide range of potentially harmful substances in the blood, including bacteria, hormones, chemicals that may act as neurotransmitters, and other toxins. Only oxygen, glucose, and a very select set of other small molecules are able to leave the blood vessels and enter the brain.

More recent studies have shown that the BBB is a complex system that features gateways complete with keys and passwords that allow entry into the brain.

Any design for nanobots to scan or otherwise interact with the brain will have to consider the BBB. I describe here several strategies that will be workable, given future capabilities. Undoubtedly, others will be developed over the next quarter century.

An obvious tactic is to make the nanobot small enough to glide through the BBB, but this is the least practical approach, at least with nanotechnology as we envision it today. To do this, the nanobot would have to be twenty nanometers or less in diameter, which is about the size of one hundred carbon atoms. Limiting a nanobot to these dimensions would severely limit its functionality.

An intermediate strategy would be to keep the nanobot in the bloodstream but to have it project a robotic arm through the BBB and into the extrace...

Another approach is to keep the nanobots in the capillaries and use noninvasive scanning.

Another type of noninvasive scanning would involve one set of nanobots emitting focused signals similar to those of a two-photon scanner and another set of nanobots receiving the transmission. The topology of the intervening tissue could be determined by analyzing the impact on the received signal.

Another type of strategy, suggested by Robert Freitas, would be for the nanobot literally to barge its way past the BBB by breaking a hole in it, exit the blood vessel, and then repair the damage.

Yet another approach is suggested by contemporary cancer studies. Cancer researchers are keenly interested in selectively disrupting the BBB to transport cancer-destroying substances to tumors.

We could bypass the bloodstream and the BBB altogether by injecting the nanobots into areas of the brain that have direct access to neural tissue.

Rob Freitas has described several techniques for nanobots to monitor sensory signals.48 These will be important both for reverse engineering the inputs to the brain, as well as for creating full-immersion virtual reality from within the nervous system.

“Olfactory and gustatory sensory neural traffic may be eavesdropped [on] by nanosensory instruments.”

“Pain signals may be recorded or modified as required, as can mechanical and temperature nerve impulses from … receptors located in the skin.”

For full-immersion virtual reality, it may be more effective to tap into the already-interpreted signals in the insula rather than the unprocessed signals throughout the body.

In order to reverse engineer the brain, we only need to scan the connections in a region sufficiently to understand their basic pattern. We do not need to capture every single connection.

Although a particular region of the brain may have billions of neurons, it will contain only a limited number of neuron types.

Once nanobot-based scanning becomes a reality, we will finally be in the same position that circuit designers are in today: we will be able to place highly sensitive and very high-resolution sensors (in the form of nanobots) at millions or even billions of locations in the brain and thus witness in breathtaking detail living brains in action.

If we were magically shrunk and put into someone’s brain while she was thinking, we would see all the pumps, pistons, gears and levers working away, and we would be able to describe their workings completely, in mechanical terms, thereby completely describing the thought processes of the brain. But that description would nowhere contain any mention of thought! It would contain nothing but descriptions of pumps, pistons, levers!

It is important that we build models of the brain at the right level. This is, of course, true for all of our scientific models.

It is often unnecessary to express higher-level results using the intricacies of the dynamics of the lower-level systems, although one has to thoroughly understand the lower level before moving to the higher one.

“In most cases, a system’s collective behavior is very difficult to deduce from knowledge of its components …. [N]euroscience is … a science of systems in which first-order and local explanatory schemata are needed but not sufficient.” Brain reverse-engineering will proceed by iterative refinement of both top-to-bottom and bottom-to-top models and simulations, as we refine each level of description and modeling.

“If the mind were simple enough for us to understand, we would be too simple to understand it.”

Although models have a long history in neuroscience, it is only recently that they have become sufficiently comprehensive and detailed to allow simulations based on them to perform like actual brain experiments.

If who we are is shaped by what we remember, and if memory is a function of the brain, then synapses—the interfaces through which neurons communicate with each other and the physical structures in which memories are encoded—are the fundamental units of the self …. Synapses are pretty low on the totem pole of how the brain is organized, but I think they’re pretty important …. The self is the sum of the brain’s individual subsystems, each with its own form of “memory,” together with the complex interactions among the subsystems. Without synaptic plasticity—the ability of synapses to alter the ease with which they transmit signals from one neuron to another—the changes in those systems that are required for learning would be impossible.

Although early modeling treated the neuron as the primary unit of transforming information, the tide has turned toward emphasizing its subcellular components.

Molecular and biophysical processes control the sensitivity of neurons to incoming spikes (both synaptic efficiency and post-synaptic responsivity), the excitability of the neuron to produce spikes, the patterns of spikes it can produce and the likelihood of new synapses forming (dynamic rewiring), to list only four of the most obvious interferences from the subneural level.

Experiments have also demonstrated a rich array of learning behaviors on the synaptic level that go beyond simple Hebbian models. Synapses can change their state rapidly, but they then begin to decay slowly with continued stimulation, or in some a lack of stimulation, or many other variations.

Other mechanisms are sensitive to overall spike timing and the distribution of potential across many synapses. Simulations have demonstrated the ability of these recently discovered mechanisms to improve learning and network stability.

“If a given connection is favorable, that is, reflecting a desirable kind of brain rewiring, then these synapses are stabilized and become more permanent. But most of these synapses are not going in the right direction, and they are retracted.”

“Our idea was that you actually don’t need to make many new synapses and get rid of old ones when you learn, memorize. You just need to modify the strength of the preexisting synapses for short-term learning and memory. However, it’s likely that [a] few synapses are made or eliminated to achieve long-term memory.”

Neurons (biological or otherwise) are a prime example of what is often called chaotic computing. Each neuron acts in an essentially unpredictable fashion. When an entire network of neurons receives input (from the outside world or from other networks of neurons), the signaling among them appears at first to be frenzied and random. Over time, typically a fraction of a second or so, the chaotic interplay of the neurons dies down and a stable pattern of firing emerges. This pattern represents the “decision” of the neural network. If the neural network is performing a pattern-recognition task (and such tasks constitute the bulk of the activity in the human brain), the emergent pattern represents the appropriate recognition.

the detailed arrangement of connections and synapses in a given region is a direct product of how extensively that region is used. As brain scanning has attained sufficiently high resolution to detect dendritic-spine growth and the formation of new synapses, we can see our brain grow and adapt to literally follow our thoughts.

This gives new shades of meaning to Descartes’ dictum “I think therefore I am.”

“You create your brain from the input you get.” It is not even necessary to express one’s thoughts in physical action to provoke the brain to rewire itself.

The long-term changes in the neurons occur only after the neurons are stimulated four times over the course of an hour. The synapse will actually split and new synapses will form, producing a permanent change that will presumably last for the rest of your life.

Brain-scanning studies are also revealing mechanisms to inhibit unneeded and undesirable memories, a finding that would gratify Sigmund Freud.

During this activity, regions in the frontal cortex that have been associated with memory repression showed a high level of activity, while the hippocampus, the region normally associated with remembering, was relatively inactive. These findings “confirm the existence of an active forgetting process and establish a neurobiological model for guiding inquiry into motivated forgetting,”

“The big news is that we’ve shown how the human brain blocks an unwanted memory, that there is such a mechanism, and it has a biological basis. It gets you past the possibility that there’s nothing in the brain that would suppress a memory—that it was all a misunderstood fiction.”

Most probably the human brain is, in the main, composed of large numbers of relatively small distributed systems, arranged by embryology into a complex society that is controlled in part (but only in part) by serial, symbolic systems that are added later. But the subsymbolic systems that do most of the work from underneath must, by their very character, block all the other parts of the brain from knowing much about how they work. And this, itself, could help explain how people do so many things yet have such incomplete ideas on how those things are actually done.

Common sense is not a simple thing. Instead, it is an immense society of hard-earned practical ideas—of multitudes of life-learned rules and exceptions, dispositions and tendencies, balances and checks.

The rapid success of turning the detailed data from studies of neurons and the interconnection data from neural scanning into effective models and working simulations belies often-stated skepticism about our inherent capability of understanding our own brains.

Although there is a great deal of detailed complexity and nonlinearity in the subneural parts of each neuron, as well as a chaotic, semirandom wiring pattern underlying the trillions of connections in the brain, significant progress has been made over the past twenty years in the mathematics of modeling such adaptive nonlinear systems.

We can understand the principles of operation of extensive regions of the brain by examining their dynamics at the appropriate level of analysis.

Given the relative crudeness of our scanning and sensing tools to date, the success in modeling, as illustrated by the following works in progress, demonstrates the ability to extract the right insights from the mass of data being gathered.

The cerebellum performs two types of transformations with these basis functions: going from a desired result to an action (called “inverse internal models”) and going from a possible set of actions to an anticipated result (“forward internal models”).

It provides a wide range of critical functions, including sensorimotor coordination, balance, control of movement tasks, and the ability to anticipate the results of actions (our own as well as those of other objects and persons).77 Despite its diversity of functions and tasks, its synaptic and cell organization is extremely consistent, involving only several types of neurons. There appears to be a specific type of computation that it accomplishes.

The cerebellum is responsible for our understanding of the timing and sequencing of sensory inputs as well as controlling our physical movements.

In the cerebellum, the basic wiring method is repeated billions of times. It is clear that the genome does not provide specific information about each repetition of this cerebellar structure but rather specifies certain constraints as to how this structure is repeated (just as the genome does not specify the exact location of cells in other organs).