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Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John von Neumann, knowledge worker, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey
We will shortly have more to say about the relative danger of whole brain emulation, neuromorphic AI, and synthetic AI, but we can already flag another important technology coupling: that between whole brain emulation and AI. Even if a push toward whole brain emulation actually resulted in whole brain emulation (as opposed to neuromorphic AI), and even if the arrival of whole brain emulation could be safely handled, a further risk would still remain: the risk associated with a second transition, a transition from whole brain emulation to AI, which is an ultimately more powerful form of machine intelligence. There are many other technology couplings, which could be considered in a more comprehensive analysis. For instance, a push toward whole brain emulation would boost neuroscience progress more generally.13 That might produce various effects, such as faster progress toward lie detection, neuropsychological manipulation techniques, cognitive enhancement, and assorted medical advances.
One reason, discussed earlier, is that a later arrival of superintelligence may be preferable, in order to allow more time for progress on the control problem and for other favorable background trends to culminate—and thus, if one were confident that whole brain emulation would precede AI anyway, it would be counterproductive to further hasten the arrival of whole brain emulation. But even if it were the case that it would be best for whole brain emulation to arrive as soon as possible, it still would not follow that we ought to favor progress toward whole brain emulation. For it is possible that progress toward whole brain emulation will not yield whole brain emulation. It may instead yield neuromorphic artificial intelligence—forms of AI that mimic some aspects of cortical organization but do not replicate neuronal functionality with sufficient fidelity to constitute a proper emulation. If—as there is reason to believe—such neuromorphic AI is worse than the kind of AI that would otherwise have been built, and if by promoting whole brain emulation we would make neuromorphic AI arrive first, then our pursuit of the supposed best outcome (whole brain emulation) would lead to the worst outcome (neuromorphic AI); whereas if we had pursued the second-best outcome (synthetic AI) we might actually have attained the second-best (synthetic AI).
Technology couplings Suppose that one thinks that solving the control problem for artificial intelligence is very difficult, that solving it for whole brain emulations is much easier, and that it would therefore be preferable that machine intelligence be reached via the whole brain emulation path. We will return later to the question of whether whole brain emulation would be safer than artificial intelligence. But for now we want to make the point that even if we accept this premiss, it would not follow that we ought to promote whole brain emulation technology. One reason, discussed earlier, is that a later arrival of superintelligence may be preferable, in order to allow more time for progress on the control problem and for other favorable background trends to culminate—and thus, if one were confident that whole brain emulation would precede AI anyway, it would be counterproductive to further hasten the arrival of whole brain emulation.
The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson
8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business process, Clayton Christensen, cloud computing, correlation does not imply causation, demographic transition, Erik Brynjolfsson, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, job automation, job satisfaction, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge
After a modest time period (perhaps seconds, perhaps hours), these copies cannot be usefully merged again, although they may interact a lot. In addition, any em can be tweaked in a limited number of ways. Artificial Intelligence Brain emulation is not the only possible way to make machines that can do almost all human jobs. For over a half-century, researchers in “artificial intelligence” (AI) have tried to directly and explicitly design and write software to accomplish many of the impressive functions performed by the human brain. This AI approach to creating intelligent machines is very different from the direct brain emulation approach that is the focus of this book. Brain emulation is more like porting software from one machine to another machine. To port software, one need only write software for the new machine that allows that machine to emulate the machine language of the old machine.
Salvador, Fabrizio, Martin de Holan, and Frank Piller. 2009. “Cracking the Code of Mass Customization.” MIT Sloan Management Review 50(3): 70–79. Sandberg, Anders. 2014. “Monte Carlo model of brain emulation development.” Working Paper 2014–1 (version 1.2), Future of Humanity Institute. http://www.aleph.se/papers/Monte%20Carlo%20model%20of%20brain%20emulation%20development.pdf. Sandberg, Anders, and Nick Bostrom. 2008. “Whole Brain Emulation: A Roadmap.” Technical Report #2008–2003, Future of Humanity Institute, Oxford University. http://www.fhi.ox.ac.uk/__data/assets/pdf_file/0019/3853/brain-emulation-roadmap-report.pdf. Sandstrom, Gillian, and Elizabeth Dunn. 2014. “Social Interactions and Well-Being: The Surprising Power of Weak Ties.” Personality and Social Psychology Bulletin 40(7): 910–922.
Perhaps you were told that fictional scenarios are the best we can do. If so, I aim to show that you were told wrong. My method is simple. I will start with a particular very disruptive technology often foreseen in futurism and science fiction: brain emulations, in which brains are recorded, copied, and used to make artificial “robot” minds. I will then use standard theories from many physical, human, and social sciences to describe in detail what a world with that future technology would look like. I may be wrong about some consequences of brain emulations, and I may misapply some science. Even so, the view I offer will still show just how troublingly strange the future can be. So let us begin. Part I Basics Chapter 1 Start Overview You should expect the next great era after ours to be as different from our era as ours is from past eras.
3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, Peter Thiel, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E
However, it is not hard to imagine that if and when the prospect of conscious machines comes closer, the research may come under fire from particularly ardent worshippers. In the next three sections we will look at three ways to build a mind – an artificial system which can perform all the intellectual activities that an adult human can. They are: Whole brain emulation Building on artificial narrow intelligence A comprehensive theory of mind 4.2 – Whole brain emulation Whole brain emulation is the process of modelling (copying or replicating) the structures of a brain in very fine detail such that the model produces the same output as the original. So if a brain produces a mind, then the emulation (the model) produces a mind also. A replicated mind which is indistinguishable from the original is called an emulation.
The wiring diagram is called the connectome, by analogy with the genome, which is the map of an organism’s genetic material. Whole brain emulation is a mammoth undertaking. A human brain contains around 85 billion neurons (brain cells) and each neuron may have a thousand connections to other neurons. Imagine you could give every inhabitant of New York City a thousand pieces of string and tell them to hand the other end of each piece of string to a thousand other inhabitants, and have each piece of string send two hundred signals per second. Now multiply the city by a factor of ten thousand. That is a model of a human brain. It is often said to be the most complicated thing that we know of in the whole universe. To make the job of brain emulation more complex, individual neurons – the cells which brains are made up of – are not simple beasts.
Faster If the first AGI is a brain emulation it might well start out running at the same speed as the human brain it was modelled on. The fastest speed that signals travel within neurons is around 100 metres per second. Signals travel between neurons at junctions called synapses, where the axon (the longest part of a neuron) of one neuron meets the dendrite of another one. This crossing takes the form of chemicals jumping across the gap, which is why neuron signalling is described as an electro-chemical process. The synapse jumping part is much slower than the electrical part. Signals within computers typically travel at 200 million metres per second – well over half the speed of light. So by using the faster signalling speeds available to computers than to brains, a brain emulation AGI could operate 2 million times faster than a human.
The Transhumanist Reader by Max More, Natasha Vita-More
23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Drosophila, en.wikipedia.org, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, P = NP, pattern recognition, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Richard Feynman, Ronald Reagan, silicon-based life, Singularitarianism, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce
He authored Citizen Cyborg: Why Democratic Societies Must Respond to the Redesigned Human of the Future (Basic Books, 2004); and “Embracing Change with All Four Arms: A Post-Humanist Defense of Genetic Engineering” (Eubios Journal of Asian and International Bioethics 6, 1996). Randal A. Koene, PhD, is Founder and CEO, Carboncopies.org. He authored “Fundamentals of Whole Brain Emulation: State, Transition and Update Representations” (International Journal on Machine Consciousness 4, 2012); and “Embracing Competitive Balance: The Case for Substrate-Independent Minds and Whole Brain Emulation” (The Singularity Hypothesis: A Scientific and Philosophical Assessment, Springer, 2012). Ray Kurzweil, PhD, is Founder, Kurzweil Technologies, Inc., Co-Founder and Chancellor, Singularity University. He authored How to Create a Mind: The Secret of Human Thought Revealed (Viking Adult, 2012); The Singularity if Near: When Humans Transcend Biology (Penguin Books, 2006); and The Age of Spiritual Machines: When Computers Exceed Human Intelligence (Penguin Books, 2000).
There are on the present roadmap at least six technology paths (Koene 2012) through which we may enable functions of the mind to move from substrate to substrate (i.e. gaining substrate-independence). Of those six, the path known as Whole Brain Emulation (WBE) is the most conservative one and is receiving the most attention in terms of ongoing projects and researchers directly involved (Sandberg and Bostrom 2008). WBE proposes that we: 1. Identify the scope and the resolution at which mechanistic operations within the brain implement the functions of mind that we experience. 2. Build tools that are able to acquire structural and functional information at that scope in an individual brain. 3. Re-implement the whole structure and the functions in another suitable operational substrate, just as they were implemented in the original cerebral substrate. Whole Brain Emulation The biological substrate that is responsible for our present thinking supports all the activity of our experience.
But we have reached a point where for purposes of data acquisition these objects are now considered fairly large (e.g. 200 nm to 2,000 nm for synaptic spines and 4,000 nm to 100,000 nm for the neural soma), at least by the standards of the current nanotechnology industry (working with precision at 10s to 100s of nanometers). And in terms of their activity those components are mostly quiet. I coined the term whole brain emulation around February/March of 2000 during a discussion on the old “mind uploading research group” (MURG) mailing list, in an effort to remove confusion stemming from the use of the term “mind uploading”, which better refers to a process of transfer of a mind from a biological brain to another substrate. It has since found a home in mainstream neuroscience, although the less specific term “brain emulation” is also frequently used when a project does not take on the scope of whole brains. The concept of emulation, as opposed to simulation (a term in common use where models in computational neuroscience are involved), refers to the running of an exact copy of the functions of mind on another processing platform.
23andMe, affirmative action, Albert Einstein, artificial general intelligence, Asperger Syndrome, barriers to entry, brain emulation, cloud computing, cognitive bias, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Brooks, David Ricardo: comparative advantage, Deng Xiaoping, en.wikipedia.org, feminist movement, Flynn Effect, friendly AI, hive mind, impulse control, indoor plumbing, invention of agriculture, Isaac Newton, John von Neumann, knowledge worker, Long Term Capital Management, low skilled workers, Netflix Prize, neurotypical, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, Vernor Vinge, Von Neumann architecture
Kurzweil foresees mankind colonizing the universe at almost the maximum speed allowed by the laws of physics.59 I’m uncertain whether bioengineers will ever be able to figure out how to make extremely smart people by integrating computers into our brains. But the possibility that this could happen is a path to the Singularity that mankind has a reasonable chance of following. 2.Whole Brain Emulation An argument against using the brain as the basis for AI is that our brains are so complex it might take centuries for us to understand them well enough to merge them with machines. But even if we don’t completely understand how the brain works, we still might be able to create machine emulations of it. Brain emulation would essentially be an “upload” of a human brain into a computer. Assuming sufficiently high fidelity in both simulation and brain scanning, the emulation would think just as the original, biological human did. For any given input, the silicon brain and the biological brain would produce the same output.
Consequently, there’s an excellent chance that the software “essences” of our brains are robust enough that they could survive being ported to a machine. Of course, porting might introduce alterations that evolution never had a chance to protect us against, so the changes might make our brains nonfunctional. But whole brain emulation is still a path to the Singularity that could work, even if a Kurzweilian merger proves beyond the capacity of bioengineers. If we had whole brain emulations, Moore’s Law would eventually give us some kind of Singularity. Imagine we just simulated the brain of John von Neumann. If the (software adjusted) speed of computers doubled every year, then in twenty years we could run this software on computers that were a million times faster and in forty years on computers that were a trillion times faster.
If, say, the software code X32 caused a blue dot to appear on the Atari, while the code for the same action is Y78 on my current computer, then the emulator would translate X32 to Y78 so that the original Atari commands would work on my Intel machine. Doing this wouldn’t require me to understand why a game had blue dots. Once my computer had the emulator, it could run any Atari game without my having to understand how the software worked. An emulator for the human brain, similarly, could allow the uploading of a brain by someone ignorant of most of the brain’s biochemistry. The success of whole brain emulations would, in large part, come down to how well our brains can handle small changes because the emulations would never be perfect. Human brains, however, are extremely robust to environmental stress. You could hit someone, infect his brain with parasites, raise or lower the temperature of his environment, and feed him lots of strange information, and he’d probably still be able to think pretty much the way he did before.68 Evolution designed our brains to not go crazy when they encounter an unfamiliar environment.
How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Claude Shannon: information theory, cloud computing, computer age, Dean Kamen, discovery of DNA, double helix, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Isaac Newton, iterative process, Jacquard loom, Jacquard loom, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Norbert Wiener, optical character recognition, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize
Oxford University computational neuroscientist Anders Sandberg (born in 1972) and Swedish philosopher Nick Bostrom (born in 1973) have written the comprehensive Whole Brain Emulation: A Roadmap, which details the requirements for simulating the human brain (and other types of brains) at different levels of specificity from high-level functional models to simulating molecules.8 The report does not provide a timeline, but it does describe the requirements to simulate different types of brains at varying levels of precision in terms of brain scanning, modeling, storage, and computation. The report projects ongoing exponential gains in all of these areas of capability and argues that the requirements to simulate the human brain at a high level of detail are coming into place. An outline of the technological capabilities needed for whole brain emulation, in Whole Brain Emulation: A Roadmap by Anders Sandberg and Nick Bostrom.
An outline of the technological capabilities needed for whole brain emulation, in Whole Brain Emulation: A Roadmap by Anders Sandberg and Nick Bostrom. An outline of Whole Brain Emulation: A Roadmap by Anders Sandberg and Nick Bostrom. Neural Nets In 1964, at the age of sixteen, I wrote to Frank Rosenblatt (1928–1971), a professor at Cornell University, inquiring about a machine called the Mark 1 Perceptron. He had created it four years earlier, and it was described as having brainlike properties. He invited me to visit him and try the machine out. The Perceptron was built from what he claimed were electronic models of neurons. Input consisted of values arranged in two dimensions. For speech, one dimension represented frequency and the other time, so each value represented the intensity of a frequency at a given point in time. For images, each point was a pixel in a two-dimensional image. Each point of a given input was randomly connected to the inputs of the first layer of simulated neurons.
Mitchell Waldrop, “Computer Modelling: Brain in a Box,” Nature News, February 22, 2012, http://www.nature.com/news/computer-modelling-brain-in-a-box-1.10066. 5. Jonah Lehrer, “Can a Thinking, Remembering, Decision-Making Biologically Accurate Brain Be Built from a Supercomputer?” Seed, http://seedmagazine.com/content/article/out_of_the_blue/. 6. Fildes, “Artificial Brain ‘10 Years Away.’” 7. See http://www.humanconnectomeproject.org/. 8. Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap, Technical Report #2008–3 (2008), Future of Humanity Institute, Oxford University, www.fhi.ox.ac.uk/reports/2008‐3.pdf. 9. Here is the basic schema for a neural net algorithm. Many variations are possible, and the designer of the system needs to provide certain critical parameters and methods, detailed on the following pages. Creating a neural net solution to a problem involves the following steps: Define the input.
Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong
Yampolskiy, “Leakproofing the Singularity: Artificial Intelligence Confinement Problem,” Journal of Consciousness Studies 2012, nos. 1–2 (2012): 194–214, http://www.ingentaconnect.com/content/imp/jcs/2012/00000019/F0020001/art00014. 4. David John Chalmers, “The Singularity: A Philosophical Analysis,” Journal of Consciousness Studies 17, nos. 9–10 (2010): 7–65, http://www.ingentaconnect.com/content/imp/jcs/2010/00000017/f0020009/art00001. 5. Robin Hanson, “Economics of the Singularity,” IEEE Spectrum 45, no. 6 (2008): 45–50, doi:10.1109/MSPEC.2008.4531461; Robin Hanson, “The Economics of Brain Emulations,” in Unnatural Selection: The Challenges of Engineering Tomorrow’s People, ed. Peter Healey and Steve Rayner, Science in Society (Sterling, VA: Earthscan, 2009). 6. James Barrat, Our Final Invention: Artificial Intelligence and the End of the Human Era (New York: Thomas Dunne Books, 2013). 7. $750 million to develop the Mach3 alone (and another $300 million to market it). Naomi Aoki, “The War of the Razors: Gillette–Schick Fight over Patent Shows the Cutthroat World of Consumer Products,” Boston Globe, August 31, 2003, http://www.boston.com/business/globe/articles/2003/08/31/the_war_of_the_razors.
Accessed December 31, 2012. http://wiki.opencog.org/w/CogPrime_Overview. Goertzel, Ben, and Joel Pitt. “Nine Ways to Bias Open-Source AGI Toward Friendliness.” Journal of Evolution and Technology 22, no. 1 (2012): 116–131. http://jetpress.org/v22/goertzel-pitt.htm. Hanson, Robin. “Economics of the Singularity.” IEEE Spectrum 45, no. 6 (2008): 45–50. doi:10.1109/MSPEC.2008.4531461. ———. “The Economics of Brain Emulations.” In Unnatrual Selection: The Challenges of Engineering Tomorrow’s People, edited by Peter Healey and Steve Rayner. Science in Society. Sterling, VA: Earthscan, 2009. Hibbard, Bill. “Super-Intelligent Machines.” ACM SIGGRAPH Computer Graphics 35, no. 1 (2001): 13–15. http://www.siggraph.org/publications/newsletter/issues/v35/v35n1.pdf. King, Ross D. “Rise of the Robo Scientists.” Scientific American 304, no. 1 (2011): 72–77. doi:10.1038/scientificamerican0111-72.
Pandora's Brain by Calum Chace
3D printing, AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, Skype, speech recognition, stealth mode startup, Stephen Hawking, strong AI, technological singularity, theory of mind, Turing test, Wall-E
His un-combed mousy-brown hair and lived-in white shirt and linen jacket, faded chinos and brown hiking shoes suggested that he cared far more about ideas than about appearances. After a brilliant academic career at Imperial College London, where he was a professor of neuroscience, he had surprised his peers by moving to India to establish a brain emulation project for the Indian government. He ran the project for ten years, before retiring to research and write about the ethics of transhumanism and brain emulation. He was extremely intelligent, and highly focused and logical, but he sometimes failed to acknowledge contrary lines of thought. As a result, some of his thinking appeared not only outlandish to his peers, but worse, naive. Christensen looked too young to be a professor at Oxford. He was dressed in recognisably academic clothes: more formal than casual, but not new, and not smart.
People started to refer to Matt’s upload and subsequent disappearance as the ‘Sputnik moment’ for artificial intelligence: the day the balloon went up, the day people and governments began to take the prospect of machine intelligence seriously. Very seriously. Laws were passed in all major countries forbidding the initiation of a brain emulation or simulation, and international treaties were signed to underpin and help enforce these laws. Funding was withdrawn from several major research programmes around the world which were developing brain models for medical diagnostic purposes rather than mind emulation. Some of this funding was diverted to programmes designed to work out how a brain emulation could be guaranteed to be human-friendly, but it was obvious that the problem was immense. How do you pre-determine the goals and actions of a mind which is much smarter than its controllers, and getting even smarter all the time?
The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman
23andMe, Albert Einstein, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, Drosophila, epigenetics, Google Glasses, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, Turing machine, web application
If the neural basis of association has been entirely unraveled, the neural basis of higher-level cognition has not. Ethically, as full-scale human brain emulations have neared, the political battles have been heated. Some see modeled rodents as ethically equal to real rodents and argue that complete human-brain emulations merit rights equal to human beings. Some scholars see emotional distress in the rudimentary human brain simulants. Yet most (chose to) believe that a simulation is an imitation rather than the real thing, just like a computer simulating the aerodynamics of flight will never actually lift off. Politicians avoid the issue, but time is clearly running out. Will it be legal to employ a whole-brain emulation for intellectual work, much as one might employ a human? Would it be ethical? Does all income accrue to the owner of the simulation, or might those whose brains contributed to the simulation also deserve royalty fees, in addition to the hourly fees they were paid for their original participation in extended brain scans?
Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, income inequality, industrial robot, informal economy, Isaac Newton, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Narrative Science, Netflix Prize, Nicholas Carr, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra
For instance, scientists are learning how much our brain relies on our stomach (“thinking with your gut” is closer to the truth than we used to believe) and how much our brain relies on the more general interactions with our bodies and the external environment for its processing capabilities. Moving, and interacting with the environment, is needed to set in motion, sustain, and enrich our thoughts. That means “brain emulation” requires building a whole working body (or significant parts thereof), not just an abstract, digitalized “brain in a vat.” At that point, anyone might wonder whether it isn’t easier to start with the bodies and brains we already have and make them more effective by allying them with machines, or using machines as add-ons. The Freestyle model seems a lot more economical, and to most people a lot more palatable, than Kurzweil’s utopian project of brain uploads.
., 37, 164 Babbage, Charles, 6 Banerjee, Abhijit, 222 BBC, 144 Becker, Gary, 226–27 behavioral economics, 75–76, 99, 105, 110, 149, 227 Belle (chess program), 46 benefit costs, 36, 59, 113 Benjamin, Joel, 47 Berlin, Germany, 246 Berra, Yogi, 229 biases, cognitive, 99–100 Bierce, Ambrose, 134 “Big Data,” 185, 221 Black, Fischer, 203 blogs, 180–81 Bonaparte, Napoleon, 148 Borjas, George, 162 “bots,” 144–45 “brain emulation,” 137–38. See also artificial intelligence (AI) branes, 214 Brazil, 20 Breedlove, Philip M., 20 Bresnahan, Timothy F., 33 Brookings Institution, 53 Brooklyn, New York, 172, 240 Brownian motion, 203 Brynjolfsson, Erik, 6, 33 Burks, John, 62 business cycles, 45 business negotiations, 73, 158 California, 8, 241 Campbell, Howard, 246 Canada, 20, 171, 177 Candidates Match, 156 Capablanca, Jose Raoul, 150 capital flows, 166 capitalism, 258 careers, 41–44, 119–25, 126, 202 Carlsen, Magnus, 104, 156, 189 Carr, Nicholas, 153–54 Caterpillar, 38 cell phone service, 118 CEOs, 100 Chen, Yingheng, 79 chess and cheating, 146–51 Chess Olympiad, 147, 189 computer’s influence on quality of play, 106–8 and decision making, 98–99, 101–2, 104–5, 129 early computer chess, 7, 46–47, 67–70 and face-to-face instruction, 195 and gender issues, 31, 106–8 and globalization of competition, 168 and intuition, 68–70, 72, 97, 99, 101, 105–6, 109–10, 114–15 machine and human styles contrasted, 75–76, 77–86 machine vs. machine matches, 70–75 as model for education, 185–88, 191–92, 202–3 and opening books, 83–85, 86–87, 107, 135, 203 and player ratings, 120 simplicity of rules, 48–49 spectator interest in, 156–57 See also Freestyle chess Chess Tiger (chess program), 78 children and wealth inequality, 249 China chess players from, 108, 189 and demographic trends, 230 and geographic trends, 177 and global competition, 171 and labor competition, 5, 163–64, 167, 169–70 and political trends, 252 and scientific specialization, 216 choice.
3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day
That’s another route to AGI and beyond, sometimes confused with reverse engineering the brain. Reverse engineering seeks to first complete fine-grained learning about the human brain, then represent what the brain does in hardware and software. At the end of the process you have a computer with human-level intelligence. IBM’s Blue Brain project intends to accomplish this by the early 2020s. On the other hand, mind-uploading, also called whole brain emulation, is the theory of modeling a human mind, like yours, in a computer. At the end of the process you still have your brain (unless, as experts warn, the scanning and transfer process destroys it) but another thinking, feeling “you” exists in the machine. “If you had a superintelligence that started out as a human upload and began improving itself and became more and more alien over time, that might turn against humanity for reasons roughly analogous to the ones that you are thinking of,” Yudkowsky said.
Since most AI researchers agree that we can solve the mysteries of how a brain works, why not just build a brain? That’s the argument for “reverse engineering the brain,” the pursuit of creating a model of a brain with computers and then teaching it what it needs to know. As we discussed, it may be the solution for attaining AGI if software complexity turns out to be too hard. But then again, what if whole-brain emulation also turns out to be too hard? What if the brain is actually performing tasks we cannot engineer? In a recent article criticizing Kurzweil’s understanding of neuroscience, Microsoft cofounder Paul Allen and his colleague Mark Greaves wrote, “The complexity of the brain is simply awesome. Every structure has been precisely shaped by millions of years of evolution to do a particular thing, whatever it might be.… In the brain every individual structure and neural circuit has been individually refined by evolution and environmental factors.”
The Dark Net by Jamie Bartlett
3D printing, 4chan, bitcoin, blockchain, brain emulation, carbon footprint, crowdsourcing, cryptocurrency, deindustrialization, Edward Snowden, Filter Bubble, Francis Fukuyama: the end of history, global village, Google Chrome, Howard Rheingold, Internet of things, invention of writing, Johann Wolfgang von Goethe, Julian Assange, Kuwabatake Sanjuro: assassination market, life extension, litecoin, Mark Zuckerberg, Marshall McLuhan, moral hazard, Occupy movement, pre–internet, Ray Kurzweil, Satoshi Nakamoto, Skype, slashdot, technological singularity, technoutopianism, Ted Kaczynski, The Coming Technological Singularity, Turing test, Vernor Vinge, WikiLeaks, Zimmermann PGP
My first impression of Anders is of a genius but slightly madcap nineteenth-century scientist (an impression that is helped by his soft Swedish accent and precise, clipped sentences). He recently experimented with the cognitive enhancing drug modafinil, an experience that he claims was positive, and tells me he also plans to have magnets surgically inserted into his fingers so he can feel electromagnetic waves. But his main area of interest is mind uploading (what he calls ‘whole brain emulation’). In 2008, Anders published a 130-page instruction manual setting out exactly how the brain’s content, its precise structure, pathways and electric signals, could be transferred on to a computer chip. If it was perfectly copied, it would, thinks Anders, be indistinguishable from the real thing. Once you’ve got a file, you needn’t fear death – you can always be re-uploaded into a synthetic human body, or, he says, ‘some kind of robot’.
Atrocity Archives by Stross, Charles
airport security, anthropic principle, Berlin Wall, brain emulation, British Empire, Buckminster Fuller, defense in depth, disintermediation, experimental subject, glass ceiling, haute cuisine, hypertext link, Khyber Pass, mandelbrot fractal, Menlo Park, NP-complete, the medium is the message, Y2K, yield curve
But that's not the point, is it?" "Indeed not. When are you going to get to it?" "As soon as my hands stop shaking. Let's see. Rather than do this openly and risk frightening the sheeple by stationing a death ray on every street corner, our lords and masters decided they'd do it bottom-up, by legislating that all public cameras be networked, and having back doors installed in them to allow the hunter-killer basilisk brain emulators to be uploaded when the time comes. Which, let's face it, makes excellent fiscal strength in this age of outsourcing, public-private partnerships, service charters, and the like. I mean, you can't get business insurance if you don't install antitheft cameras, someone's got to watch them so you might as well outsource the service to a security company with a network operations centre, and the brain-dead music industry copyright nazis are campaigning for a law to make it mandatory to install secret government spookware in every Walkman--or camera--to prevent home taping from killing Michael Jackson.
AI winter, artificial general intelligence, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, information retrieval, Isaac Newton, John Conway, Loebner Prize, Menlo Park, natural language processing, Occam's razor, p-value, pattern recognition, performance metric, Ray Kurzweil, Rodney Brooks, semantic web, statistical model, strong AI, theory of mind, traveling salesman, Turing machine, Turing test, Von Neumann architecture, Y2K
There is some fairly strong biological evidence that our quest for power has evolutionary reasons, which means that I don’t think it’s a good assumption to make that an AI will have the same lust for power. [Hugo de Garis]: How can you be sure? [Cassio Pennachin ]: I’m not sure of anything, I’m just saying that lots of people seem to be assuming that its going to take over the world, that it’s a weapon, and I’m challenging that assumption. I’m not going to assume that evolutionary bias is carried over into AI’s, even if the AI is achieved through brain emulation. [Bill Redeen]: I do think we have to assume this is inevitable… the evolution and emergence of AGI. [Josh S. Hall]: I think it’s worth thinking about what happens if a group the size of Novamente can create an AGI and it works. Or, what if Hugo de Garis creates an AGI that works. Or, what if Sam S. Adams creates an AGI that works. If that is the case, there are going to be a billion of them in 10 years.
The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil
additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, George Gilder, Gödel, Escher, Bach, informal economy, information retrieval, invention of the telephone, invention of the telescope, invention of writing, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Mikhail Gorbachev, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Norbert Wiener, oil shale / tar sands, optical character recognition, pattern recognition, phenotype, premature optimization, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Richard Feynman, Rodney Brooks, Search for Extraterrestrial Intelligence, semantic web, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, transaction costs, Turing machine, Turing test, Vernor Vinge, Y2K, Yogi Berra
IBM's Blue Gene/L supercomputer, now being built and scheduled to be completed around the time of the publication of this book, is projected to provide 360 trillion calculations per second (3.6 Î 1014 cps).42 This figure is already greater than the lower estimates described above. Blue Gene/L will also have around one hundred terabytes (about 1015 bits) of main storage, more than our memory estimate for functional emulation of the human brain (see below). In line with my earlier predictions, supercomputers will achieve my more conservative estimate of 1016 cps for functional human-brain emulation by early in the next decade (see the "Supercomputer Power" figure on p. 71). Accelerating the Availability of Human-Level Personal Computing. Personal computers today provide more than 109 cps. According to the projections in the "Exponential Growth of Computing" chart (p. 70), we will achieve 1016cps by 2025. However, there are several ways this timeline can be accelerated. Rather than using general-purpose processors, one can use application-specific integrated circuits (ASICs) to provide greater price-performance for very repetitive calculations.