How to Create a Mind by Ray Kurzweil

• The mammalian brain has a distinct aptitude not found in any other class of animal. 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. This capability takes place in a brain structure called the neocortex, which in humans has achieved a threshold of sophistication and capacity such that we are able to call these patterns ideas.
• Through An unending recursive process we are capable of building ideas that are ever more complex. We all this vast array of recursive linked ideas knowledge.
• Only Homo sapiens have a knowledge base that itself evolves, grows exponentially, and is passed down from one generation to another.
• It is only because of our tools that our knowledge base has been able to grow without limit.
• Reverse-engineering the human brain may be regarded as the most important project in the universe.
• If understanding language and other phenomena through statistical analysis does not count as true understanding, then humans have no understanding either.
• It turns out that the distinction between being determined and being predictable is an important one.
• Fundamentally, the brain does store and process information, and because of the universality of computation there is more of a parallel between brains and computers than may be apparent.
• 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.
• We also have some difficulty starting a memory in the middle of a sequence.
• Typically we are unable to visualize people we’ve only casually come across to draw or describe them sufficiently but would have little difficulty in recognizing a picture of them. This suggests that there are no images, videos, or sound recordings stored in the brain. Our memories are stored as a sequence 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.
• Once your mind has fixed on an understanding, however, it may be difficult to see the other perspective.
• Your brain’s interpretation of the image actually influences your experience of it.
• Thus our conscious experience of our perception is actually changed by our interpretations.
• This implies that 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.
• We do not need to relearn the concept of a noose and a mouth each time we are introduced to a new face.
• Animals without a neocortex (basically non mammals) are largely incapable of understanding hierarchies. Understanding and leveraging the innately hierarchical nature of reality is a uniquely mammalian trait and results from mammals unique possession of this evolutionarily recent brain structure.
• 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.
• Human beings have only a weak ability to process logic, but a very deep core capability of recognizing patterns.
• To do logical thinking, we need to use the neocortex, which is basically a large pattern recognizer. It is not an ideal mechanism for performing logical transformations, but it is the only facility we have for the job.
• We have established that a human master in a particular field has mastered about 100,000 chunks of knowledge.
• At the highest conceptual level, we are continually making predictions--who is going to walk through the door next, what someone is likely to say next, what we expect to see when we turn the corner, the likely results of our own actions, and so on. These predictions are constantly occurring at every level of the neocortex hierarchy.
• We often miss recognize people and things and words because our threshold for confirming an expected pattern is too low.
• The lists of patterns that constitute a memory are in forward order, and we are able to remember our memories only in that order, hence the difficulty we have in reversing our memories.
• A memory needs to be triggered by another thought/memory. We experience this mechanism of triggering when we are perceiving a pattern.
• Learning is critical to human intelligence. If we were to perfectly model and simulate the human neocortex and all of the other brain regions that it requires to function, it would not be able to do very much--in the same way that a newborn infant cannot do much.
• Learning and recognition take place simultaneously. We start learning immediately, and as soon as we’ve learned a pattern, we immediately start recognizing it. The neocortex is continually trying to make sense of the input presented to it.
• Relaxing professional taboos turn out to be useful for creative problem solving.
• As we learn professional skills, we learn the ways of thinking that are recognized and rewarded in our professions, and thereby avoid patterns of thought that might betray the methods and norms of that profession. Many of these taboos are worthwhile, as they enforce social order and consolidate progress. However, they can also prevent progress by enforcing an unproductive orthodoxy.
• Cultural rules are enforced in the neocortex with help from the old brain, especially the amygdala. Every thought we have triggers other thoughts, and some of them will relate to associated dangers.
• Natural selection does nothing even close to striving for intelligence. The process is driven by differences in the survival and reproduction rates of replicating organisms in a particular environment.
• Once biological evolution stumbled on a neural mechanism capable of hierarchical learning, it found it to be immensely useful for evolution's one objective, which is survival. The benefit of having a neocortex became acute when quickly changing circumstances favored rapid learning.
• Species of all kinds--plants and animals--can learn to adapt to changing circumstances over time, but without a neocortex they must use the process of genetic evolution.
• The salient survival advantage of the neocortex was that it could learn in a matter of days. If a specific encounters dramatically changed circumstances and one member of that species invents or discovers or just stumbled upon (these three methods all being variations of innovation) a way to adapt to that change, other individuals will notice, learn, and copy that method, and it will quickly spread virally to the entire population.
• Learning takes place in the creation of connections between these units [neuronal assemblies], not within them, and probably in the synaptic strengths of those interunit connections.
• 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.
• Animals used to live and survive without a neocortex, and indeed all non mammalian animals continue to do so today. We can view the human neocortex as the great sublimator--thus our primitive motivation to avoid a large predator may be transformed by the neocortex today into completing an assignment to impress our boss; the great hunt may become writing a book on, say, the mind; and pursuing reproduction may become gaining public recognition or decorating your apartment.
• The neocortex often deals with the problems it is assigned by redefining them.
• We are apparently able to keep up to about four items in our working memory at a time, two per hemisphere according to recent research by neuroscientists at the MIT Picower Institute for Learning and Memory.
• 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.
• There is a continual struggle in the human brain as to whether the old or the new brain is in charge.
• Clearly part of what we regard as aptitude is the product of nurture, that is to say, the influences of environment and other people.
• If particular regions can be optimized for different types of patterns, then it follows that individual brains will also vary in their ability to learn, recognize, and create certain types of patterns.
• A very important skill I noted earlier is the courage to pursue ideas that go against the grain of orthodoxy. Invariably, people we regard as geniuses pursued their own mental experiments in ways that were not initially understood or appreciated by their peers.
• 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.
• Invariably we find metaphors from one field that solve problems in another.
• 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.
• 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.
• One approach to building a digital brain is to simulate precisely a biological one.
• People often fail to appreciate how powerful mathematics can be--keep in mind that our ability to search much of human knowledge in a fraction of a second with search engines is based on a mathematical technique.
• 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.
• Not surprisingly, the hierarchical nature of language closely mirrors the hierarchical nature of our thinking. Spoken language was our first technology, with written language as the second.
• Mastering language is a powerfully leveraged capability.
• Ultimately machines will be able to master all of the knowledge on the Web--which is essentially all of the knowledge of our human-machine civilization.
• Professional knowledge tends to be more highly organized, structured, and less ambiguous than its common sense counterpart, so it is highly amenable to accurate natural language understanding using these techniques.
• With regard to our biological intelligence, although our neocortex has significant plasticity, its basic architecture is limited by its physical constraints.
• The human brain appears to be able to handle only four simultaneous lists at a time (without the aid of tools such as computers), but there is no reason for an artificial neocortex to have such a limitation.
• A computer can become a brain if it is running brain software.
• Simply increase the redundancy until you get the reliability you need.
• 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.
• A primary reason for the redundancy in the brain is the inherent unreliability of neural circuits.
• The brain is extremely slow but massively parallel. Today’s digital circuits are at least 10 million times faster than the brain’s electrochemical switches. Conversely, all 300 million of the brain’s neocortical pattern recognizers process simultaneously, and all quadrillion of its interneuronal connections are potentially computing at the same time.
• The key issue for providing the requisite hardware to successfully model a human brain, though, is the overall memory and computational throughput required. We do not need to directly copy the brain’s architecture, which would be a very inefficient and inflexible approach.
• Models often get simpler at a higher level.
• Minds are simply what brains do.
• Redundancy, as we have learned, is a key strategy deployed by the neocortex. But there is another level of redundancy in the brain, in that its left and right hemispheres, while not identical, are largely the same.
• The quintessential example of the law of accelerating returns is the perfectly smooth, doubly exponential growth of the price/performance of computation, which has held steady for 110 years through two world wars, the Great Depression, the Cold War, the collapse of the Soviet Union, the reemergence of China, the recent financial crisis, and all of the other notable events of the late nineteenth, twentieth, and early twenty-first centuries.
• 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 law of accelerating returns.
• 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.
• The reason why information technologies are able to consistently transcend the limitations of any particular paradigm is that the resources required to computer or remember or transmit a bit of information are vanishingly small.
• Essentially, we always use the latest technology to create the next. Technologies build on themselves in an exponential manner, and this phenomenon is readily measurable if it involves an information technology.
• Intelligence may be defined as the ability to solve problems with limited resources, in which a key such resource is time. Thus the ability to more quickly solve a problem like finding food for avoiding a predator reflects greater power of intellect.
• Intelligence evolved because it was useful for survival--a fact that may seem obvious, but one with which not everyone agrees.
• A primary reason that people believe that life is getting worse is because our information about the problems of the world has steadily improved.
• The intelligence we will create from the reverse-engineering of the brain will have access to its own source code and will be able to rapidly improve itself in an accelerating iterative design cycle.
• Although there is considerable plasticity in the biological human brain, as we have seen, it does have a relatively fixed architecture, which cannot be significantly modified, as well as a limited capacity.
• From quantitative improvement comes qualitative advance.
• Biological evolution is continuing but technological evolution is moving a million times faster than the former.