Kurzweil has a solid reputation as an inventor of technically-advanced products that have very practical use. He is also a famed a futurists, and a shrewd businessman who has without a doubt learned how to capitalize, popularize, and monetize his own and other’s ideas and visions: some brilliant, some not so much according to skeptics.
As the New Yorker recognized, Kurzweil’s critics have not always been kind; PZ Myers, a renowned biologist once indicated that he is a genius… and one of the greatest hucksters of our time. The author of “Gödel, Escher, Bach,” Pulitzer Prize winner Doug Hofstadter said reading one of Kurzweil’s books was like mixing together good food with dog excrement: ultimately you can’t tell the good from the bad.
The astute reader will be aware of commercialization and hyperbole but not be dissuaded by it. Rather, I suggest you read to enjoy the broad strokes and general principles behind the ideas presented and use them as a catalyst to explore the various aspects he put together in an attempt to explain one of many possible approaches to achieving human-like artificial intelligence- that particular goal only one of several possible paths to self-directed thinking, perhaps consciousness, and sentience in a machine. See our Introduction to Artificial Intelligence for a brief overview of the various AI perspectives.
May Kurzweil’s collection of ideas inspire your imagination.
Kurzweil subscribes to the theory that Artificial Intelligence machines will soon be equaling the power of human thought-with all of its complexities and richness- and perhaps even outstripping it.
The rather broadly held theory is lent credence by some two major turning points;- In 1997, Gary Kasparov was beaten at Chess by Deep Blue of IBM, and in 2011, Watson an Artificial Intelligence machine also of IBM beat Brad Rutter and Ken Jennings in the Jeopardy Chess matches. He uses these two events to support the argument that the neuro-networks responsible for higher level/ hierarchical thinking (known as the Neocortex) actually have simple principles that can be well replicated, and that some of the more advanced AI machines such Siri- iPhone’s voice recognition software- and the aforementioned Watson already have the pattern recognition scheme used in their installed “brain”.
Kurzweil explains that this pattern recognition scheme is naturally hierarchical, meaning that lower-level patterns that pick minute inputs from the surroundings combine, triggering higher-level patterns picking more abstract categories that must be taught. Also, information moves upwards and downwards, causing feedback between higher and lower order patterns in a theory called the Pattern Recognition Theory of the Mind (PRTM), similar to the design of our best AI machines, and with a little tweaking- Kurzweil continues- will make it possible to design computers that match human thought, with such features as Identity, consciousness, and free will by 2029, eventually outstripping even human capabilities since they don’t have such biological incapacities as will be explained later. This advance, though, will allow us to use technology to update our neurochemistry in a merger Kurzweil calls the “singularity”.
It should be pointed out to the reader of this review, that the Singularity has morphed into several definitions. Originally conceived it simply meant the point at which machine intelligence surpasses human intelligence. Machines have concepts and thought beyond our comprehension, developing even faster and smarter machines further separating us from the new masterminds of the universe. See more on our treatment of that in the Human Extinction: Risks to Humanity section.
HOW THE NEOCORTEX WORKS
The ability to reason, analyze and prioritize enables mammals to think abstractly, as well as be predictive so we can processes, manipulate and store information from which we can adapt to or change a surrounding based on what we have learned about it. This intelligence comes from the Neocortex, which was added to previously existing sections of the brain by evolution.
The Neocortex gives mammals like humans the ability to think hierarchically and to understand singular parts of larger groups, groups that also belong to much bigger groups, and so on, helping us survive and thrive in two ways; It gives us a detailed and precise likeness of our surroundings and allows us to understand and adjust to the surroundings as our thoughts climb the levels of hierarchies, becoming more abstract and complex. The lack of the Neocortex- some scientists believe- contributed to the extinction of dinosaurs. Mammal Neocortex differ in size and development and account for 80% of the weight of human brain.
Neuroscientist Henry Markram of Switzerland deduced that the Neocortex can be reduced to a single thought process- hierarchical thinking- because of its uniform structure, as found out in a study where he scanned mammalian Neocortexes in search of neural assemblies. He indicated that the Neocortex appeared to be constructed of Lego-like collections of several dozen neurons in layers, connected to similarly structured super-assemblies connected to yet a higher layer of neuronal collections, and so on until the highest level represented the entire brain. He is now a Director at the Blue Brain project, intent on recreating the complexities of the human brain, beginning with a trial on rats.
The Pattern Recognition Theory of Mind (PRTM)
The author, borrowing from others before him, says that each layer of neural assemblies stands for a pattern recognizer that finds hierarchically organized information in the surroundings whether auditory, linguistic or any other information. Neural assemblies are pre-organized and innate, but are taught at each level of the neural assembly, incorporated with exact information. Human higher level thinking uses some 30 million recognizers and writes all information into different levels of neural assemblies in our brains. For example, on a human face the mouth and nose are recorded at a different neural assembly from the entire face such that even if some facial parts are absent, a face can still be recognized especially if enough parts of it are available to trigger a recognizer and send the information to the next upward level.
Before a pattern recognizer at one hierarchical level triggers another one higher, they prime it before sending signals back to recognizers at the next-lowest level, to prime and prepare their senses for firing. In this instance, if a person’s eye is detected, the recognizers for the face will be primed before signaling to those representing other parts of the face to detect given features. The author considers this predictive.
Pattern recognizers communicate with positive or negative signals to encourage or hinder firing depending on the possibility of a given pattern to exist and whether they come from lower or higher conceptual levels.
Every new or change in a sensory scenario is detected by the brain and is saved given a new pattern recognizer. Some, like different expressions of a relative are saved multiple times while redundant ones, like a face not seen for ages are eventually replaced to save storage space. This replacement causes memory to fade away slowly to the extent that a face seen before is no longer remembered. Pattern recognizers have a redundancy factor of about 100 to 1 depending on importance (like between relatives and first sighting).
This example is exclusive of the great abstraction levels that we reach with alarming regularity and means. According to the author we might not, for example, remember a reason for laughing yet remember that we did laugh. We must also note that these signals are sent at very high speeds and pattern recognizers fire across many given faculties at any given time.
The reach and presence of the Pattern Recognition Scheme
As can be seen below, different mental capabilities from the Neocortex are found in multiple brain parts, and other parts of the Neocortex are available to perform tasks that are assigned to any other parts should it be found that the said parts are damaged or missing from birth (brain cells in various locations can be “taught”, or rather learn to be multifunctional if necessary for survival. This is known as neural plasticity and has even been found in people having congenital defects.
Introducing Speech Recognition to Artificial Intelligence
As Kurzweil shows, advanced artificial intelligence machines and software programs already use the processes described of the Neocortex above.
When the author and other computer scientists first moved into the uncharted territory of artificial intelligence, they sought to solve problems using predefined intelligent solutions and programmed these problem types and solutions into a computer to be applied to arising problems as they came. Speech to text conversion (1980’s) was first tackled in this way- recording digital patterns which the program would try to match against human voice inputs. But since enunciation and pronunciation differ between people of different nationalities or races, or even with one person as they age, this method quickly became impracticable- too many variations would be needed in the “answer” databank. Kurzweil then tried another technique known as vector quantization: to summarize or reduce human speech into 1,024 points/ iterations.
He then recreated what goes on in a person’s brain while they spoke and simulated this so that the computer could identify new units of speech, as well as variations in enunciation and pronunciation using a technique very mathematical in nature known as the Hidden Markov Model which could “infer a hierarchy of states with connections and probabilities.”
With this done, he sought to set parameters of unknown data points and their organizational hierarchies, using the biological evolution and cross-bred multiple ‘solution organisms’ (genetic codes of multiple parameters) which even had mutations that were not definite, or properly defined in their parameter values. Multiple cross-breeding tests were conducted, where in the best resultant designs were set aside and used for setting parameters for the Hierarchical Hidden Markov Model (HHMM). This HHMM was trained with speech samples from people of different nationalities and races, and who had unique accents to learn “the likelihood that specific patterns of sound are found in each phoneme, how the phonemes influence one another, and the likely orders of phonemes.” At the end of the day, the HHMM discovered/ learned that there were different rules, which were very different yet delicate, but more importantly were much more useful than the previous hand-coded rules used. In short, as Kurzweil and team combined HHMMs to simulate the cortical organization that accompanies human learning and a genetic algorithm to simulate the biological evolution that gave rise to a particular cortical design. Both of these are self-organizing procedures. This became the cornerstone of subsequent speech recognition works and research, and is being used in other areas of AI like speech simulation and knowledge of natural languages.
The need for both self-organizing and pre-programmed systems
While self-organizing systems are generally more advanced than pre-programmed ones, Kurzweil says artificial intelligence machines are incorporated with both, especially because the pre-programmed systems are much faster when handling familiar information and present a good basis for lower conceptual levels of hierarchy. These two advantages over the otherwise more advanced self-organizing systems enable the self-organizing system to learn much quicker than it would do on its own, and be ready for practical use much faster. Combining both optimizes an effective AI machine. After the self-organizing system has fully learned, it’s expected that the pre-programmed system will be discontinued.
Watson; The Most Advanced Machine in AI
According to Kurzweil, Watson is an AI machine which uses an ‘expert manager’ called UIMA (Unstructured Information Management Architecture) to choose the correct sub systems for use in different situations and then with “intelligence” combines the outcomes (answers) of these systems. This method allows Watson to contribute to a resolution even though it may not deliver an actual answer to a given problem. This multi-processing also helps to gauge and build Watson’s confidence in its answers by use of a probability percentage. This example of probability percentages was witnessed at the Jeopardy matches. Kurzweil says the human brain also uses this method when statistical inference is used to resolve multiple hypotheses.
According to the author, Watson was designed around the complexities and richness of the Neocortex, although admittedly it’s still some way from posing as an actual human. For example, it could not ace the famed Turing test because it was never designed to pass it nor engage in intelligent conversation, rather it was designed to compete at Jeopardy and answer brief and not so complex questions. Kurzweil, though, believes with a little tweaking, Watson will perform those tasks considering that many AI advances occurred before the complexities of the Neocortex were well researched.
Simulating the Human Brain
Multiple attempts with varying degrees of success have been made to accurately simulate the human brain, ably assisted by technologies including the scanning technology used to uncover the grid-like patterns of the Neocortex’s connections. There a number of such technologies including the latest MRI techniques which are noninvasive scanning technologies.
The National Institutes of Health, through their Human Connectome project have chosen to use this technology and expect to build a complete 3-D map of the human brain complete with all its connections by 2014.
The Blue Brain Project
The Blue Brain Project, on the other hand aims to model and “simulate the human brain, including the entire Neocortex as well as the old-brain regions such as the cerebellum, amygdala, and hippocampus, and by recording the measurements of ion channels, neurotransmitters, and enzymes that generate and regulate every neuron’s electrochemical activity. They will be using a patch-clamp robot, another scanning technology, in a system that is automatic and able to scan neural tissue at one micro-meter of accuracy, avoiding the destruction of delicate membranes. In 2005, participants simulated one neuron, and in 2011 did a neural mesocircuit of 100 neocortical columns. They target 10,000 neurons and a rat brain by 2014. Their current goal is 2023 for fully-simulated Human Brain.
Educating the simulated brain
According to Kurzweil, the simulated brain cannot achieve human-level thinking unless it has the necessary content and he describes multiple potential methods to fulfill this requirement. The most likely, he surmises, is one that can simplify molecular models by creating functional equivalents at different levels of detail, starting with his personal functional algorithmic method to simulations that are closer to full molecular simulations. His book goes into greater detail, but he guestimates that it could speed the learning process 1000 fold or more.
Kurzweil explains that his Law of Accelerating returns (LOAR) is doubted by many because they don’t understand the concept of linear vs. exponential progressions where if forty linear steps is equal to 40 years, the same 40 steps on an exponential scale would equal a whopping trillion years. Based on the historical evidence of exponential advancement, he predicts more complex advances are coming, merging biological and technical evolution techniques. He confidently speculates on the possibility of a machine having human consciousness, identity and free will, purporting that any complex physical system will inevitably develop it. He cites man’s best friend, the canine, as an example of a non-human consciousness.
Consciousness, Free Will and Identity?
He also argues- concerning free will- that there’s a likelihood that we humans actually don’t have it, but just feel that we do, or alternately, like consciousness, perhaps it’s also an emergent property that evolves at high, complex levels. If these are true then it’s likely possible that a machine of human-level thinking would also have the same, or feel (have the perception) that it does. Kurzweil holds that identity is borne of our sense of free-will and experience. He extrapolates that a self-aware machine would naturally possess the same belief.
Beyond Human Intelligence
Kurzweil is also a proponent of the more advanced applications of AI. Synthetically producing a Neocortex and replacing our own biological one would enable the functioning of more than 300 million processors- or more. A billion? He considers the fact that digital neurons can be made to link up wirelessly- a big advantage over human ones which are linked physically.
He also considered the possibility of adding bug cleaning features to our brains, to remove/ reduce instances such as multiple thinking and inconsistent but colliding ideas in our brains. A module for detailed thinking could be designed to continually do background scans for inconsistencies in all existing ideas or patterns and update their compatibilities with each other. Inconsistent ideas would then be reviewed or eliminated. With this and other such implants, we would alleviate the risk of AI machines ever outstripping us in intelligence. We could then take advantage of the singularity by incorporating the exponential advances into our own biology. By doing so we could dispel some fears of losing our identity or changing the continuity of our body cells any more than nature replenishes them for us currently.
It’s only fair to say we are in a race with technology which is ever advancing. His far future vision is the spread of our non-biological intelligence to the four corners of the universe, infusing our deliberate will directly upon its fate. If we are able to break the speed of light barrier we could have a universal omnipresence within a few centuries. It is our destiny.
Certainly on that last conclusion this reviewer and this site agree. Science fiction writers and far futurists have been coming to that conclusion for years as well. See our own 2003 essay on the distant future. It is in fact the only logical conclusion to an assumed eternal existence in the known universe (although we disagree with the assumed ubiquitous non-biological entity).
In any case, let us all hope the boundaries of reality continue to expand the unknown at least as fast as our ability to consume and understand it, lest we be caught in the forever loop of The End is Just the Beginning.