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How to Create a Mind

RAY KURZWEIL
2012

Introduction
  • The story of human intelligence starts with a universe that is capable of encoding information. This was the enabling factor that allowed evolution to take place.
  • We are capable of hierarchical thinking, of understanding a structure composed of diverse elements arranged in a pattern, representing that arrangement with a symbol, and then using that symbol as an element in a yet more elaborate configuration. We call these patterns ideas. And we call this vast array of recursively linked ideas knowledge.
  • It is only because of our tools that our knowledge base has been able to grow without limit. Our first invention was the story: spoken language that enabled us to represent ideas with distinct utterances. With the subsequent invention of written language. An evolutionary process inherently accelerates as a result of its increasing levels of abstraction. Its products grow exponentially in complexity and capability. I call this phenomenon the Law of Accelerating Returns (LOAR), and it pertains to both biological and technological evolution.
  • The Web is itself a powerful and apt example of the ability of a hierarchical system to encompass a vast array of knowledge while preserving its inherent structure.
  • If understanding language and other phenomena through statistical analysis does not count as true understanding, then humans have no understanding either.
  • The operating principle of the neocortex is arguably the most important idea in the world, as it is capable of representing all knowledge and skills as well as creating new knowledge.
Thought Experiments
  • Our memories are sequential and in order. They can be accessed in the order that they are remembered. We are unable to directly reverse the sequence of a memory. There are no images, videos, or sound recordings stored in the brain. Our memories are stored as sequences of patterns. Memories that are not accessed dim over time.
  • We can recognize a pattern even if only part of it is perceived (seen, heard, felt) and even if it contains alterations. Our recognition ability is apparently able to detect invariant features of a pattern - characteristics that survive real-world variations.
  • Our conscious experience of our perceptions is actually changed by our interpretations. Consider that we see what we expect to see. We are constantly predicting the future and hypothesizing what we will experience. This expectation influences what we actually perceive. Predicting the future is actually the primary reason that we have a brain.
  • Each of our routine procedures is remembered as an elaborate hierarchy of nested activities. The same type of hierarchy is involved in our ability to recognize objects and situations. The use of hierarchies allows us to reuse patterns.
A Model of the Neocortex: The Pattern Recognition Theory of the Mind
  • The neocortex is responsible for our ability to deal with patterns of information and to do so in a hierarchical fashion. Animals without a neocortex (basically nonmammals) are largely incapable of understanding hierarchies.
  • The neocortex is responsible for sensory perception, recognition of everything from visual objects to abstract concepts, controlling movement, reasoning from spatial orientation to rational thought, and language-basically, what we regard as "thinking."
  • Due to its elaborate folding, the neocortex constitutes the bulk of the human brain, accounting for 80 percent of its weight.
  • Extensive experimentation has revealed that there are in fact repeating units within the neuron fabric of each column. It is my contention that the basic unit is a pattern recognizer and that this constitutes the fundamental component of the neocortex. There is no specific physical boundary to these recognizers, as they are placed closely one to the next in an interwoven fashion, so the cortical column is simply an aggregate of a large number of them. These recognizers are capable of wiring themselves to one another throughout the course of a lifetime, so the elaborate connectivity (between modules) that we see in the neocortex is not prespecified by the genetic code, but rather is created to reflect the patterns we actually learn over time.
  • Human beings have only a weak ability to process logic, but a very deep core capability of recognizing patterns.
  • The pattern recognition theory of mind that I present here is based on the recognition of patterns by pattern recognition modules in the neocortex. These patterns (and the modules) are organized in hierarchies.
  • Each pattern (which is recognized by one of the estimated 300 million pattern recognizers in the neocortex) is composed of three parts:
    • Part one is the input, which consists of the lower-level patterns that compose the main pattern. The descriptions for each of these lower-level patterns do not need to be repeated for each higher-level pattern that references them.
    • The second part of each pattern is the pattern's name. In the neocortex the "name" of a pattern is simply the axon that emerges from each pattern processor; when that axon fires, its corresponding pattern has been recognized.
    • The third and final part of each pattern is the set of higher-level patterns that it in turn is a part of. Each recognized pattern at one level triggers the next level that part of that higher-level pattern is present. In the neocortex, these links are represented by physical dendrites that flow into neurons in each cortical pattern recognizer.
  • There are likely to be hundreds of such recognizers firing, if not more. The redundancy increases the likelihood that you will successfully recognize each instance.
  • An important attribute of the PRTM is how the recognitions are made inside each pattern recognition module. Stored in the module is a weight for each input dendrite indicating how important that input is to the recognition. The pattern recognizer has a threshold for firing (which indicates that this pattern recognizer has successfully recognized the pattern it is responsible for).
  • The neocortex predicts what it expects to encounter. Envisaging the future is one of the primary reasons we have a neocortex. These predictions are constantly occurring at every level of the neocortex hierarchy. We often misrecognize people and things and words because our threshold for confirming an expected pattern is too low.
  • In addition to positive signals, there are also negative or inhibitory signals which indicate that a certain pattern is less likely to exist. When a pattern recognizer receives an inhibitory signal, it raises the recognition threshold, but it is still possible for the pattern to fire.
  • Our memories are in fact patterns organized as lists (where each item in each list is another pattern in the cortical hierarchy) that we have learned and then recognize when presented with the appropriate stimulus. In fact, memories exist in the neocortex in order to be recognized.
  • Our thoughts are largely activated in one of two modes, undirected and directed, both of which use these same cortical links. In the undirected mode, we let the links play themselves out without attempting to move them in any particular direction.
  • In directed thinking we attempt to step through a more orderly process of recalling a memory (a story, for example) or solving a problem. This also involves stepping through lists in our neocortex, but the less structured flurry of undirected thought will also accompany the process.
  • Memories grow dimmer with time as the amount of redundancy becomes reduced until certain memories become extinct.
  • The ability to recognize patterns even when aspects of them are transformed is called feature invariance, and is dealt with in the following ways:
    • First, there are global transformations that are accomplished before the neocortex receives sensory data.
    • The second method takes advantage of the redundancy in our cortical pattern memory. Especially for important items, we have learned many different perspectives and vantage points for each pattern.
    • The third and most powerful method is the ability to combine two lists. One list can have a set of transformations that we have learned may apply to a certain category of pattern; the cortex will apply this same list of possible changes to another pattern. That is how we understand such language phenomena as metaphors and similes.
  • There is a limit, however, to useful redundancy. There is a mathematical solution to this optimization problem called linear programming, which solves for the best possible allocation of limited resources (in this case, a limited number of pattern recognizers) that would represent all of the cases on which the system has trained. Linear programming is designed for systems with one-dimensional inputs, which is another reason why it is optimal to represent the input to each pattern recognition module as a linear string of inputs.
  • To summarize what we've learned so far about the way the neocortex works:
    • Dendrites enter the module that represents the pattern. Even though patterns may seem to have two- or three-dimensional qualities, they are represented by a one-dimensional sequence of signals. The pattern must be present in this (sequential) order for the pattern recognizer to be able to recognize it. Each of the dendrites is connected ultimately to one or more axons of pattern recognizers at a lower conceptual level that have recognized a lower-level pattern that constitutes part of this pattern.
    • The module computes the probability that the pattern it is responsible for is present.
    • When this pattern recognizer recognizes its pattern (based on all or most of the input dendrite signals being activated), the axon (output) of this pattern recognizer will activate.
    • If a higher-level pattern recognizer is receiving a positive signal from all or most of its constituent patterns except for the one represented by this pattern recognizer, then that higher-level recognizer might send a signal down to this recognizer indicating that its pattern is expected. Such a signal would cause this pattern recognizer to lower its threshold, meaning that it would be more likely to send a signal on its axon (indicating that its pattern is considered to have been recognized) even if some of its inputs are missing or unclear.
    • Inhibitory signals from below would make it less likely that this pattern recognizer will recognize its pattern.
    • Inhibitory signals from above would also make it less likely that this pattern recognizer will recognize its pattern.
    • For each input, there are stored parameters for importance, expected size, and expected variability of size. The module computes an overall probability that the pattern is present based on all of these parameters and the current signals indicating which of the inputs are present and their magnitudes. A mathematically optimal way to accomplish this is with a technique called hidden Markov models.
  • Dreams are examples of undirected thoughts.
  • To the extent that a dream does not make sense, we attempt to fix it through our ability to Confabulate.
  • There is one key difference between dream thoughts and our thinking while awake. One of the lessons we learn in life is that certain actions, even thoughts, are not permissible in the real world.
  • Cultural rules are enforced in the neocortex with help from the old brain, especially the amygdala.
  • We learn, for example, that breaking a cultural norm even in our private thoughts can lead to ostracism, which the neocortex realizes threatens our well-being. If we entertain such thoughts, the amygdala is triggered, and that generates fear, which generally leads to terminating that thought.
  • It is as if our brain realizes that we are not an actual actor in the world while dreaming. Freud wrote about this phenomenon but also noted that we will disguise such dangerous thoughts, at least when we attempt to recall them, so that the awake brain continues to be protected from them.
  • Relaxing professional taboos turns out to be useful for creative problem solving. It is also reasonable to conclude that the patterns that end up in our dreams represent important matters to us and thereby clues in understanding our unresolved desires and fears.
The Biological Neocortex
  • Canadian psychologist Donald O. Hebb (1904–1985) made an initial attempt to explain the neurological basis of learning. In 1949 he described a mechanism in which neurons change physiologically based on their experience, thereby providing a basis for learning and brain plasticity: "Let us assume that the persistence or repetition of a reverberatory activity (or ‘trace') tends to induce lasting cellular changes that add to its stability. . . . When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased."2 This theory has been stated as "cells that fire together wire together" and has become known as Hebbian learning.
  • It is clear that brain assemblies can create new connections and strengthen them, based on their own activity.
  • The pattern recognition theory of mind that I articulate in this book is based on a different fundamental unit: not the neuron itself, but rather an assembly of neurons, which I estimate to number around a hundred. The wiring and synaptic strengths within each unit are relatively stable and determined genetically.
  • Learning takes place in the creation of connections between these units, not within them, and probably in the synaptic strengths of those interunit connections.
  • All of the pathways of the brain taken together fit together in a single exceedingly simple structure. They basically look like a cube. They basically run in three perpendicular directions, and in each one of those three directions the pathways are highly parallel to each other and arranged in arrays.
  • The brain starts out with a very large number of "connections-in-waiting" to which the pattern recognition modules can hook up.
  • The most powerful argument for the universality of processing in the neocortex is the pervasive evidence of plasticity (not just learning but interchangeability): In other words, one region is able to do the work of other regions, implying a common algorithm across the entire neocortex.
  • There are several reasons, however, why a skill or an area of knowledge that has been relearned using a new area of the neocortex to replace one that has been damaged will not necessarily be as good as the original. First, because it took an entire lifetime to learn and perfect a given skill. Second, it too has been carrying out vital functions, and will therefore be hesitant to give up its neocortical patterns to compensate for the damaged region. And thirdly, particular types of patterns will flow through specific optimized regions (such as faces being processed by the fusiform gyrus).
  • The basic algorithm of the neocortical pattern recognition modules is equivalent across the neocortex from "low-level" modules, which deal with the most basic sensory patterns, to "high-level" modules, which recognize the most abstract concepts.
  • Signals go up and down the conceptual hierarchy. A signal going up means, "I've detected a pattern." A signal going down means, "I'm expecting your pattern to occur," and is essentially a prediction.
  • Each pattern is itself in a particular order and is not readily reversed. Even if a pattern appears to have multidimensional aspects, it is represented by a one-dimensional sequence of lower-level patterns. A pattern is an ordered sequence of other patterns, so each recognizer is inherently recursive. There can be many levels of hierarchy.
  • There is a great deal of redundancy in the patterns we learn, especially the important ones.
  • The AI field was not explicitly trying to copy the brain, but it nonetheless arrived at essentially equivalent techniques.
The Old Brain
  • Although we experience the illusion of receiving high-resolution images from our eyes, what the optic nerve actually sends to the brain is just a series of outlines and clues about points of interest in our visual field. We then essentially hallucinate the world from cortical memories that interpret a series of movies with very low data rates that arrive in parallel channels.
  • The most significant role of the thalamus, however, is its continual communication with the neocortex. The pattern recognizers in the neo-cortex send tentative results to the thalamus and receive responses principally using both excitatory and inhibitory reciprocal signals.
  • Profound damage to the main region of the thalamus bilaterally can lead to prolonged unconsciousness. Directed thinking-the kind that will get us out of bed, into our car, and sitting at our desk at work-does not function without a thalamus. The thalamus relies on the structured knowledge contained in the neocortex. It can step through a list (stored in the neocortex), enabling us to follow a train of thought or follow a plan of action.
  • The issue of whether the thalamus is in charge of the neocortex or vice versa is far from clear, but we are unable to function without both.
  • Each brain hemisphere contains a hippocampus. Since sensory information flows through the neocortex, it is up to the neocortex to determine that an experience is novel in order to present it to the hippocampus. The hippocampus is capable of remembering these situations, although it appears to do so primarily through pointers into the neocortex.
  • The capacity of the hippocampus is limited, so its memory is short-term. It will transfer a particular sequence of patterns from its short-term memory to the long-term hierarchical memory of the neocortex by playing this memory sequence to the neocortex over and over again.
  • The Cerebellum carries out "Gordian knot" type solutions to what would otherwise be an intractable mathematical problem, also known as basis functions. The cerebellum is an old-brain region that once controlled virtually all hominid movements.
  • Most of the function of controlling our muscles has been taken over by the neocortex, using the same pattern recognition algorithms that it uses for perception and cognition. The neocortex does make use of the memory in the cerebellum. The neocortex can call upon the cerebellum to use its ability to compute real-time basis functions to anticipate what the results of actions would be that we are considering but have not yet carried out (and may never carry out), as well as the actions or possible actions of others. It is another example of the innate built-in linear predictors in the brain.
  • One region that is associated with pleasure is the nucleus accumbens. In humans, other regions are also involved in pleasure, such as the ventral pallidum and, of course, the neocortex itself. Pleasure is also regulated by chemicals such as dopamine and serotonin.
  • It is the job of our neocortex to enable us to be the master of pleasure and fear and not their slave.
  • The amygdala is also part of the old brain and is involved in processing a number of types of emotional responses, the most notable of which is fear. In premammalian animals, certain preprogrammed stimuli representing danger feed directly into the amygdala, which in turn triggers the "fight or flight" mechanism. In humans the amygdala now depends on perceptions of danger to be transmitted by the neocortex.
  • Thinking takes place in the new brain (the neocortex), but feeling takes place in both. There is a continual struggle in the human brain as to whether the old or the new brain is in charge. The old brain tries to set the agenda with its control of pleasure and fear experiences, whereas the new brain is continually trying to understand the relatively primitive algorithms of the old brain and seeking to manipulate it to its own agenda. The amygdala is unable to evaluate danger on its own-in the human brain it relies on the neocortex to make those judgments.
Transcendent Abilities
  • Our emotional thoughts also take place in the neocortex but are influenced by portions of the brain ranging from ancient brain regions such as the amygdala to some evolutionarily recent brain structures such as the spindle neurons, which appear to play a key role in higher-level emotions. The spindle neurons have highly irregular shapes and connections. They are the largest neurons in the human brain, spanning its entire breadth. They are deeply interconnected, with hundreds of thousands of connections tying together diverse portions of the neocortex. These cells are particularly active when a person is dealing with emotions such as love, anger, sadness, and sexual desire.
  • There are relatively few spindle cells. Other mammals, except apes, lack them completely. Spindle cells do not exist in newborn humans but begin to appear only at around the age of four months and increase significantly in number from ages one to three.
  • A key aspect of creativity is the process of finding great metaphors-symbols that represent something else. The neocortex is a great metaphor machine, which accounts for why we are a uniquely creative species.
  • Finding a metaphor is the process of recognizing a pattern despite differences in detail and context.
The Biologically Inspired Digital Neocortex
  • 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 non-biological intelligence. Once a digital neocortex learns a skill, it can transfer that know-how in minutes or even seconds. 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. Last but not least, we will be able to back up the digital portion of our intelligence.
  • 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.
  • Markram and his team are basing their model on detailed anatomical and electrochemical analyses of actual neurons. After simulating one neocortical column, Markram was quoted as saying, "Now we just have to scale it up." Scaling is certainly one big factor, but there is one other key hurdle, which is learning.
  • The simulated brain will need to have sufficient content in its simulated neocortex to perform tasks. There are two obvious ways this can be done. One would be to have the brain learn this content the way a human brain does. The other approach is to take one or more biological human brains that have already gained sufficient knowledge to converse in meaningful language and to otherwise behave in a mature manner and copy their neocortical patterns into the simulated brain. There is a third approach, simplify molecular models by creating functional equivalents at different levels of specificity, ranging from my own functional algorithmic method (as described in this book) to simulations that are closer to full molecular simulations.
  • The purpose of a brain simulation project such as Blue Brain and Modha's neocortex simulations is specifically to refine and confirm a functional model. Molecular simulations will help us to perfect that model and to fully understand which details are important.
  • Neural Nets: The Perceptron was built from what he claimed were electronic models of neurons. Input consisted of values arranged in two dimensions. Each point of a given input was randomly connected to the inputs of the first layer of simulated neurons. Every connection had an associated synaptic strength, which represented its importance, and which was initially set at a random value. Each neuron added up the signals coming into it. If the combined signal exceeded a particular threshold, the neuron fired and sent a signal to its output connection; if the combined input signal did not exceed the threshold, the neuron did not fire, and its output was zero. The output of each neuron was randomly connected to the inputs of the neurons in the next layer.
  • The key to a neural net, therefore, is that it must learn its subject matter, just like the mammalian brains on which it's supposedly modeled. Connections that are consistent with the correct answer are made stronger. Those that advocate a wrong answer are weakened. Over time the neural net organizes itself to provide the correct answers without coaching.
  • Limitations in the range of material that the Perceptron was capable of learning quickly became apparent. It did a fairly good job of autoassociation (that is, it could recognize the letters even if I covered parts of them), but fared less well with invariance (that is, generalizing over size and font changes, which confused it).
  • A Perceptron was inherently incapable of determining whether or not an image was connected. This is a task that humans can do very easily, and it is also a straightforward process to program a computer to make this discrimination.
  • Vector Quantization can be used to reduce realtime sound samples to clusters of vectors such that the data can be simplified for one-dimensional pattern recognizers.
  • The Russian mathematician Andrei Andreyevich Markov (1856–1922) built a mathematical theory of hierarchical sequences of states. The model was based on the possibility of traversing the states in one chain, and if that was successful, triggering a state in the next higher level in the hierarchy. Markov's model included probabilities of each state's successfully occurring. He went on to hypothesize a situation in which a system has such a hierarchy of linear sequences of states, but those are unable to be directly examined-hence the name hidden Markov models.
  • The lowest level of the hierarchy emits signals, which are all we are allowed to see. The network in a neural net system is fixed and does not adapt to the input: The weights adapt, but the connections do not. In the Markov model system, if it was set up correctly, the system would prune unused connections so as to essentially adapt the topology.
  • Evolutionary (Genetic) Algorithms The key to a genetic algorithm is that the human designers don't directly program a solution; rather, we let one emerge through an iterative process of simulated competition and improvement. Another major requirement for the success of a GA is a valid method of evaluating each possible solution.
  • When using GAs you must, however, be careful what you ask for. Beware of "overfitting" the problem. There is a danger that such a system will overgeneralize to the specific examples contained in the training sample. By making random adjustments to the input, the more invariant patterns in the data survive, and the system thereby learns these deeper patterns.
  • LISP (LISt Processor) is a computer language that is capable of hierarchical processing. The neocortex is engaged in list processing of a symbolic nature very similar to that which takes place in a LISP program. But the neocortex programs itself.
  • Self-organizing methods such the pattern recognition theory of mind are needed to understand the elaborate and often ambiguous hierarchies we encounter in real-world phenomena, including human language. An ideal combination for a robustly intelligent system would be to combine hierarchical intelligence based on the PRTM (which I contend is how the human brain works) with precise codification of scientific knowledge and data.
  • A Strategy for Creating a Mind:
  • Start by building a pattern recognizer that meets the necessary attributes.
  • Make as many copies of the recognizer as we have memory and computational resources to support.
  • Each recognizer computes the probability that its pattern has been recognized.
  • The recognizer triggers its simulated axon if that computed probability exceeds a threshold.
  • Recognition of the pattern sends an active signal up the simulated axon of this pattern recognizer. This axon is in turn connected to one or more pattern recognizers at the next higher conceptual level.
  • Each pattern recognizer also sends signals down to pattern recognizers at lower conceptual levels whenever most of a pattern has been recognized, indicating that the rest of the pattern is "expected." Each pattern recognizer has one or more of these expected signal input channels.
  • The pattern recognizers are responsible for "wiring" themselves to other pattern recognizers up and down the conceptual hierarchy.
  • Start with a large number of possible connections and then prune the neural connections that are not used.
  • Enable our system to create as many new levels of hierarchy as needed.
  • Allow for the system to flexibly create its own topologies based on the patterns it is exposed to while learning.
  • Accommodate substantial redundancy of each pattern, especially ones that occur frequently. This allows for robust recognition of common patterns and is also one of the key methods to achieving invariant recognition of different forms of a pattern.
  • Develop rules regarding redundancy, recognition thresholds, and the effect on the threshold of a "this pattern is expected" indication. Optimize parameters using a genetic algorithm.
  • Hierarchical pattern recognition systems will only learn about two-preferably one-hierarchical levels at a time. Previously learned levels would provide a relatively stable basis to learn the next level.
  • Provide a critical thinking module, which would perform a continual background scan of all of the existing patterns, reviewing their compatibility with the other patterns (ideas) in this software neocortex.
The Mind as Computer
  • A computer can run any algorithm that we might define because of its innate universality (subject only to its capacity). The human brain, on the other hand, is running a specific set of algorithms. Its methods are clever in that it allows for significant plasticity and the restructuring of its own connections based on its experience, but these functions can be emulated in software.
  • There are four key concepts that underlie the universality and feasibility of computation and its applicability to our thinking:
    • The first is the ability to communicate, remember, and compute information reliably. American mathematician Claude Shannon (1916–2001) came along and demonstrated how we can create arbitrarily accurate communication using even the most unreliable communication channels. This is redundancy. Simply repeating information is the easiest way to achieve arbitrarily high accuracy rates from low-accuracy channels, but it is not the most efficient approach. Shannon's paper, which established the field of information theory, presented optimal methods of error detection and correction codes that can achieve any target accuracy through any nonrandom channel.
    • The second important idea on which the information age relies is "Strong" interpretations of the Church-Turing thesis. The basic idea is that the human brain is likewise subject to natural law, and thus its information-processing ability cannot exceed that of a machine (and therefore of a Turing machine). Turing had shown that at its essence, computation is based on a very simple mechanism. Because the Turing machine (and therefore any computer) is capable of basing its future course of action on results it has already computed, it is capable of making decisions and modeling arbitrarily complex hierarchies of information.
    • Our third major idea: the von Neumann model includes a central processing unit, where arithmetical and logical operations are carried out; a memory unit, where the program and data are stored; mass storage; a program counter; and input/output channels.
    • The fourth important idea: go beyond the conclusion that a computer could not think creatively.
  • English mathematician and inventor Charles Babbage's (1791–1871) Analytical Engine, which he first described in 1837, featured a stored program via punched cards borrowed from the Jacquard loom. Its random access memory was based entirely on mechanical gears.
  • Babbage's computer did result in the creation of the field of software programming. English writer Ada Byron (1815–1852), Countess of Lovelace and the only legitimate child of the poet Lord Byron, was the world's first computer programmer. She wrote programs for the Analytical Engine.
  • Von Neumann applied the concept of the universality of computation to conclude that even though the architecture and building blocks appear to be radically different between brain and computer, we can nonetheless conclude that a von Neumann machine can simulate the processing in a brain. The converse does not hold, however, because the brain is not a von Neumann machine and does not have a stored program. Its algorithm or methods are implicit in its structure.
  • Von Neumann presciently notes that the speed of neural processing is extremely slow, on the order of a hundred calculations per second, but that the brain compensates for this through massive parallel processing-another unobvious and key insight.
  • Von Neumann was quoted as having said that "the ever accelerating progress of technology and changes in the mode of human life give the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue." If von Neumann's insight is correct, there is an essential equivalence between a computer with the right software and a conscious mind.
  • It is important to note that the design of an entire brain region is simpler than the design of a single neuron. Models often get simpler at a higher level-consider.
Thought Experiments of the Mind
  • Many observers consider consciousness to be a form of performance-for example, the capacity for self-reflection, that is, the ability to understand one's own thoughts and to explain them.
  • Philosopher John Searle: "We know that brains cause consciousness with specific biological mechanisms... The essential thing is to recognize that consciousness is a biological process like digestion, lactation, photosynthesis, or mitosis... The brain is a machine.
  • The Australian philosopher David Chalmers (born in 1966) has coined the term "the hard problem of consciousness" to describe the difficulty of pinning down this essentially indescribable concept. Chalmers does not attempt to answer the hard question but does provide some possibilities:
    • One is a form of dualism in which consciousness per se does not exist in the physical world but rather as a separate ontological reality. Consciousness then exists essentially in another realm, or at least is a property separate from the physical world. This explanation does not permit the mind (that is to say, the conscious property associated with the brain) to causally affect the brain.
    • Another possibility that Chalmers entertains, which is not logically distinct from his notion of dualism, and is often called panprotopsychism, holds that all physical systems are conscious, albeit a human is more conscious than, say, a light switch.
  • My own view, which is perhaps a subschool of panprotopsychism, is that consciousness is an emergent property of a complex physical system. Another way to conceptualize the concept of consciousness is as a system that has "qualia." One definition of the term is "conscious experiences." It is remarkable that such common phenomena in our lives are so completely ineffable as to make a simple confirmation, like one that we are experiencing the same qualia, impossible.
  • Another definition of qualia is the feeling of an experience.
  • The Hameroff-Penrose thesis is that the microtubules in the neurons are doing quantum computing and that this is responsible for consciousness. Even if such a phenomenon as quantum computing in the brain did exist, it would not necessarily be linked to consciousness.
  • There is a conceptual gap between science, which stands for objective measurement and the conclusions we can draw thereby, and consciousness, which is a synonym for subjective experience. The question as to whether or not an entity is conscious is therefore not a scientific one.
  • There are two ways to view the questions we have been considering - converse Western and Eastern perspectives on the nature of consciousness and of reality.
    • In the Western perspective, we start with a physical world that evolves patterns of information.
    • In the Eastern view, consciousness is the fundamental reality; the physical world only comes into existence through the thoughts of conscious beings.
  • The East-West divide on the issue of consciousness has also found expression in opposing schools of thought in the field of subatomic physics. I call this the Buddhist school of quantum mechanics, because in it particles essentially don't exist until they are observed by a conscious person.
  • There is another interpretation of quantum mechanics: The field representing a particle is not a probability field, but rather just a function that has different values in different locations.
  • The Austrian British thinker Ludwig Wittgenstein (1889–1951) work spanned this East-West divide. His Tractatus Logico-Philosophicus and the logical positivism movement assert that physical reality exists separate from our perception of it, but that all we can know of that reality is what we perceive with our senses, which can be heightened through our tools, and the logical inferences we can make from these sensory impressions.
  • French philosopher and mathematician René Descartes quoted his famous "I think, therefore I am" and is generally interpreted to extol rational thought, in the sense that "I think, that is I can perform logical thought, therefore I am worthwhile." Descartes was troubled by what is referred to as the "mind-body problem": Namely, how does a conscious mind arise from the physical matter of the brain? "I think, that is to say, a subjective experience is occurring, so therefore all we know for sure is that something (call it I) exists." He could not be certain that the physical world exists, because all we have are our own individual sense impressions of it, which might be wrong or completely illusory. We do know, however, that the experiencer exists.
  • On the one hand, it is foolish to deny the physical world. Even if we do live in a simulation, as speculated by Swedish philosopher Nick Bostrom, reality is nonetheless a conceptual level that is real. On the other hand, the Eastern perspective that consciousness is fundamental and represents the only reality that is truly important is also difficult to deny.
  • American psychology researcher Michael Gazzaniga (born in 1939) has conducted extensive experiments on what each hemisphere in split-brain patients is thinking. Gazzaniga's tests offer an interesting perspective on the issue of consciousness, they speak even more directly to the issue of free will. In each of these cases, one of the hemispheres believes that it has made a decision that it in fact never made.
  • Experiments by physiology professor Benjamin Libet (1916–2007) indicated that the motor cortex was preparing to carry out the task about a third of a second before the subject was even aware that she had made a decision to do so. Libet himself concluded that our awareness of decision making appears to be an illusion, that "consciousness is out of the loop." If the subject is unaware of when she is aware of making a decision, then who is?
  • Rather than free will, Ramachandran suggests we should talk about "free won't", that is, the power to reject solutions proposed by the nonconscious parts of our neocortex.
  • Since there is a reasonable consensus among philosophers that free will does imply conscious decision making, it appears to be one prerequisite for free will. However, to many observers, consciousness is a necessary but not sufficient condition. If our decisions conscious or otherwise are predetermined before we make them, how can we say that our decisions are free? This position, which holds that free will and determinism are not compatible, is known as incompatibilism.
  • The compatibilists argue, essentially, that you're free to decide what you want even though what you decide is or may be determined.
  • Daniel Dennett, for example, argues that while the future may be determined from the state of the present, the reality is that the world is so intricately complex that we cannot possibly know what the future will bring. We can identify what he refers to as "expectations," and we are indeed free to perform acts that differ from these expectations. That, Dennett argues, is sufficient for free will.
  • There is another split in the philosophy of quantum events that has a bearing on our discussion of free will, one that revolves around the question: Are quantum events determined or random?
  • The most common interpretation of a quantum event is that when the wave function constituting a particle "collapses," the particle's location becomes specific. The opposing interpretation is deterministic: specifically, that there is a hidden variable that we are unable to detect separately, but whose value determines the particle's position.
  • Most quantum physicists seem to favor the idea of a random resolution according to the probability field, but the equations for quantum mechanics do allow for the existence of such a hidden variable.
  • According to the probability wave interpretation of quantum mechanics, there is a continual source of uncertainty at the most basic level of reality.
  • It is true that under this interpretation of quantum mechanics, the world is not determined, but our concept of free will extends beyond decisions and actions that are merely random.
  • Most incompatibilists would find the concept of free will to also be incompatible with our decisions' being essentially accidental. Free will seems to imply purposeful decision making.
  • Dr. Wolfram proposes a way to resolve the dilemma. He presents the idea of cellular automata. A cellular automaton is a mechanism in which the value of information cells is continually recomputed as a function of the cells near it. Dr. Wolfram's primary thesis is that the world is one big class IV cellular automaton. The future state of class IV cellular automata cannot be predicted without simulating every step along the way. Therefore the future state of the universe is completely unknowable even though it is deterministic. For Dr. Wolfram, this is sufficient to allow for free will.
  • Fundamentally we (our identitity) is not the stuff that makes up our bodies and brains. We are a pattern that changes slowly but has stability and continuity, even though the stuff constituting the pattern changes quickly.
The Law of Accelerating Returns Applied to the Brain
  • Computation is the most important example of the law of accelerating returns, because of the amount of data we have for it, the ubiquity of computation, and its key role in ultimately revolutionizing everything we care about.
  • Once a technology becomes an information technology, it becomes subject to the LOAR.
  • Most inventions and inventors fail not because the gadgets themselves don't work, but because their timing is wrong, appearing either before all of the enabling factors are in place or too late, having missed the window of opportunity.
  • Technologies build on themselves in an exponential manner, and this phenomenon is readily measurable if it involves an information technology.
These notes were taken from Ray Kurzweil's book How to Create a Mind


© 2020 Cedric Joyce