brain emulation

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pages: 574 words: 164,509

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, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk,, endogenous growth, 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 Markoff, John von Neumann, knowledge worker, longitudinal study, 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, zero-sum game

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.

pages: 589 words: 147,053

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 cycle, business process, Clayton Christensen, cloud computing, correlation does not imply causation, creative destruction, demographic transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, information asymmetry, job automation, job satisfaction, John Markoff, 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. Sandberg, Anders, and Nick Bostrom. 2008. “Whole Brain Emulation: A Roadmap.” Technical Report #2008–2003, Future of Humanity Institute, Oxford University. 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.

pages: 144 words: 43,356

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, basic income, 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,, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, hedonic treadmill, 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, peer-to-peer, peer-to-peer model, 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, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

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.

pages: 252 words: 79,452

To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death by Mark O'Connell

3D printing, Ada Lovelace, AI winter, Airbnb, Albert Einstein, artificial general intelligence, brain emulation, clean water, cognitive dissonance, computer age, cosmological principle, dark matter, disruptive innovation, double helix, Edward Snowden, effective altruism, Elon Musk, Extropian, friendly AI, global pandemic, impulse control, income inequality, invention of the wheel, Jacques de Vaucanson, John von Neumann, knowledge economy, Law of Accelerating Returns, life extension, lifelogging, Lyft, Mars Rover, means of production, Norbert Wiener, Peter Thiel, profit motive, Ray Kurzweil, RFID, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Singularitarianism, Skype, Stephen Hawking, Steve Wozniak, superintelligent machines, technological singularity, technoutopianism, The Coming Technological Singularity, Travis Kalanick, trickle-down economics, Turing machine, uber lyft, Vernor Vinge

He had gained considerable fame in recent years for his role in the creation of optogenetics, a neuromodulation technique whereby individual neurons in the brains of living animals could be switched on and off by the application of directed light photons. Randal had mentioned his name on several occasions during our discussions—both as someone broadly supportive of whole brain emulation and whose work was of significant relevance to that project—and Boyden had been a speaker at the Global Future 2045 event in New York the previous year. It was Boyden’s belief, he told me, that it would eventually be possible to build neuroprosthetic replacements for brain parts—which, if you take the Ship of Theseus view of things, is essentially the same as believing that whole brain emulation is possible. “Our goal is to solve the brain,” he said. He was referring here to the ultimate goal of neuroscience, which was to understand how the brain did what it did, how its billions of neurons, and the trillions of connections between them, organized themselves in such a way as to produce specific phenomena of consciousness.

“At which point you’ll what? Be able to translate this worm’s neural activity into code? Into a computable form?” “Yes,” said Boyden. “That’s the hope.” I felt that he was holding back from telling me he believed that whole brain emulations would at some point become a reality, but it was clear that he felt the principle to be sound, in a way that Nicolelis did not. And what he was telling me, ultimately, was that whether or not it led to it in the end, and whether or not it was his own ultimate goal, the kind of research that was necessary for the achievement of whole brain emulation was precisely the kind of research he himself was doing at MIT. This was all clearly a very long way from where Randal wanted to get to, a very long way from his mind, or mine, or yours, on a laptop screen, with its hundred billion firing neurons glimmering with the light of purified consciousness.

Anders and the attractive Frenchwoman to my right were engaged in what seemed to me an impenetrably technical discussion about the progress of research into mind uploading. The conversation had turned to Ray Kurzweil, the inventor and entrepreneur and director of engineering at Google who had popularized the idea of the Technological Singularity, an eschatological prophecy about how the advent of AI will usher in a new human dispensation, a merger of people and machines, and a final eradication of death. Anders was saying that Kurzweil’s view of brain emulation, among other things, was too crude, that it totally ignored what he called the “subcortical mess of motivations.” “Emotions!” said the Frenchwoman, emotionally. “He doesn’t need emotions! That is why!” “That might be true,” said Alberto. “He wants to become a machine!” she said. “That is what he really wants to be!” “Well,” said Anders, poking thoughtfully among the bowl of empty shells, searching in vain for an uneaten pistachio.

pages: 798 words: 240,182

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, Douglas Engelbart, Drosophila,, endogenous growth, 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, lifelogging, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, pattern recognition, Pepto Bismol, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Ronald Reagan, scientific worldview, silicon-based life, Singularitarianism, social intelligence, 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, zero-sum game

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, 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.

pages: 377 words: 97,144

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

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,, 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, Norman Macrae, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, 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, twin studies, 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.

pages: 372 words: 101,174

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,, epigenetics, George Gilder, Google Earth, Isaac Newton, iterative process, 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, selective serotonin reuptake inhibitor (SSRI), 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, 5. Jonah Lehrer, “Can a Thinking, Remembering, Decision-Making Biologically Accurate Brain Be Built from a Supercomputer?” Seed, 6. Fildes, “Artificial Brain ‘10 Years Away.’” 7. See 8. Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap, Technical Report #2008–3 (2008), Future of Humanity Institute, Oxford University,‐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.

When Computers Can Think: The Artificial Intelligence Singularity by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann, Michelle Estes

3D printing, AI winter, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, blue-collar work, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, create, read, update, delete, cuban missile crisis, David Attenborough, Elon Musk,, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

The egg might remain balanced for a considerable period time, but eventually gravity will win. To be fair, Yudkowsky has never said that building a friendly AGI would be easy, nor has he said that he knows how to do it. He merely states that if it is not done then humanity will be at grave risk from the AGIs that will almost certainly be built in the not-too-distant future. That is a premise with which this author is in full agreement. Whole brain emulation An alternative approach to producing an AGI is to focus on whole brain emulation because such an intelligence would be fundamentally human, and so would share our values. Doing this would only require an understanding of the physics of brains, not how cognition actually arises. This means that the AGI would have limited ability to recursively self-improve, so there would be more opportunity to understand and control it. If one ignores the enormous technical difficulty of building an aeroplane out of feathers, there is still the issue as to whose brain should be emulated.

Non-silicon intelligence 16. Premature destruction 7. Proposed Solutions 1. Just turn it off 2. Lock it up 3. Freeze it 4. Show AGIs the light 5. Virtuous machines 6. Ethics 7. Infanticide 8. Three laws of robotics 9. Friendly AGI 10. Friendly AGI research 11. Fast take off 12. Single AGI 13. Goal consistency 14. Unpredictable algorithms 15. Ethics 16. Defeating natural selection 17. Wishful thinking 18. Whole brain emulation 19. Chain of AGIs 20. Running away 21. Just do not build an AGI 8. Political Will 1. Atom bombs 2. Iran's atomic ambitions 3. Stuxnet 4. Glass houses 5. Zero day exploits 6. Practicalities of abstinence 7. Restrict computer hardware 8. Asilomar conference 9. Patent trolls 10. Does it really matter? 9. Conclusion 1. Geological history 2. History of science 3. Natural selection 4. Human instincts 5.

It critiques the impressive early results in AI research, and then reviews various approaches to modelling the world formally using logic, and the difficulty of reasoning with uncertain knowledge. Building robots that can function in the real world introduces additional problems of vision and movement. Both artificial and biological neural networks are also described in some detail together with the practical difficulties involved with brain emulation. This part provides sufficient technical details to understand how the technologies actually work, but without using heavy mathematics. It should help raise the level of discussion about artificial intelligence. What will computers think about? Public, NASA supercomputer. The third part of the book considers what the true nature of an intelligent machine might be. It takes a novel approach by first considering what forces made people the way we are.

pages: 48 words: 12,437

Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong

artificial general intelligence, brain emulation, effective altruism, Flash crash, friendly AI, shareholder value, Turing test

Yampolskiy, “Leakproofing the Singularity: Artificial Intelligence Confinement Problem,” Journal of Consciousness Studies 2012, nos. 1–2 (2012): 194–214, 4. David John Chalmers, “The Singularity: A Philosophical Analysis,” Journal of Consciousness Studies 17, nos. 9–10 (2010): 7–65, 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,

Accessed December 31, 2012. 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. 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. King, Ross D. “Rise of the Robo Scientists.” Scientific American 304, no. 1 (2011): 72–77. doi:10.1038/scientificamerican0111-72.

pages: 688 words: 147,571

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Basel III, bitcoin, blockchain, brain emulation, Clapham omnibus, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, distributed ledger, don't be evil, Donald Trump, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, medical malpractice, Nate Silver, natural language processing, nudge unit, obamacare, off grid, pattern recognition, Peace of Westphalia, race to the bottom, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge

Artificial neural networks are computer systems made up of large number of interconnected units, each of which can usually compute only one thing.65 Whereas conventional networks fix the architecture before training starts, artificial neural networks use “weights” in order to determine the connectivity between inputs and outputs.66 Artificial neural networks can be designed to alter themselves by changing the weights on the connections which makes activity in one unit more or less likely to excite activity in another unit.67 In “machine learning” systems, the weights can be re-calibrated by the system over time—often using a process called backpropagation—in order to optimise outcomes.68 Broadly, symbolic programs are not AI under this book’s functional definition, whereas neural networks and machine learning systems are AI.69 Like Russell and Norvig’s clock, any intelligence reflected in a symbolic system is that of the programmer and not the system itself.70 By contrast, the independent ability of neural networks to determine weights between connections is an evaluative function characteristic of intelligence. Neural networks and machine learning are techniques which fall within this book’s definition of AI, but they are not the only technologies capable of doing so. This book’s definition of AI is intended to cover neural networks but also to be sufficiently flexible to encompass also other technologies which may become more prevalent in the future—one example being whole brain emulation (the science of attempting to map and then reproduce the entire structure of an animal brain). This functional definition may be under-inclusive from the perspective of those seeking a universal measure of intelligence. Unlike most other definitions, it does not attempt to encompass all the technologies which have traditionally been described as “intelligent”. However, as noted above, the intention is only to cover those aspects of technology which are salient from a legal perspective.

It may be the case in the coming decades that the same becomes true of integrated technology. The precise boundaries between what is “human” and what is “artificial” for the purposes of ascribing rights are a matter beyond the scope of this chapter. The main point is that the distinction between human and technology may become increasingly fluid.122 A further biology-based route to AI through whole brain emulation does not aim to augment or update human brains, but rather to create an entirely new brain capable of intelligent thoughts, feelings and consciousness , using a combination of technology and bioengineering.123 As noted above, owing to her status as the first cloned mammal, throughout her life Dolly the sheep was monitored and cared for by teams of scientists and veterinarians, receiving state of the art care.124 In the same way that we treated this quasi-artificial sheep with equal or greater respect to a natural sheep, would we not do the same for an artificial human brain?

It has never been suggested that an attempt to regain lost brain functions by using artificial parts would entail a greater cost to his legal recognition”. 121“The World’s Most Famous Real-Life Cyborgs”, The Medical Futurist, http://​medicalfuturist.​com/​the-worlds-most-famous-real-life-cyborgs/​, accessed 1 June 2018. 122For further argument as to why technological advances should not lead to a reduction in rights, see also Nick Bostrom, “In Defence of Posthuman Dignity”, Journal of Value Inquiry, Vol. 37, No. 4 (2005), 493–506: “From the Transhumanist standpoint, there is no need to behave as if there were a deep moral difference between technological and other means of enhancing human lives. By defending posthuman dignity we promote a more inclusive and humane ethics, one that will embrace future technologically modified people as well as humans of the contemporary kind”. 123Anders Sandberg and Nicholas Bostrom, “Whole Brain Emulation: A Roadmap”, Technical Report #2008–3, Future of Humanity Institute, Oxford University, www.​fhi.​ox.​ac.​uk/​reports/​2008‐3.​pdf, accessed 1 June 2018. 124“Dolly the Sheep”, Website of National Museums Scotland, https://​www.​nms.​ac.​uk/​explore-our-collections/​stories/​natural-world/​dolly-the-sheep/​, accessed 1 June 2018. 125See, for instance, John Harris, “‘Goodbye Dolly?’ The Ethics of Human Cloning”, Journal of Medical Ethics, (2007), 23(6), 353–360. 126“The Immortalist: Uploading the Mind to a Computer”, BBC Magazine, 14 March 2016, http://​www.​bbc.​co.​uk/​news/​magazine-35786771, accessed 1 June 2018. 127Roger Penrose, The Emperor’s New Mind (Oxford: Oxford University Press, 1998).

pages: 345 words: 104,404

Pandora's Brain by Calum Chace

AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, lateral thinking, mega-rich, 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?

pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk,, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

One way to achieve this goal might be whole-brain emulation, currently being pursued in the Blue Brain project in Switzerland. This involves trying to map, simulate, and replicate the activity of the (more than) 80 billion neurons and tens of trillions of synapses in the human brain, together with the workings of the central nervous system.25 Whole-brain emulation remains a remote prospect but is not thought to be technically impossible.26 As Murray Shanahan argues, our own brains are proof that it’s physically possible to assemble ‘billions of ultra-low-power, nano-scale components into a device capable of human-level intelligence’.27 Most contemporary AI research, however, is not concerned with artificial general intelligence or whole-brain emulation. Rather, it is geared toward creating machines capable of performing specific, often quite narrow, tasks with an extraordinary degree of efficacy.

E. 398 Schechner, Sam 419 Schmidt, Eric 374, 376, 384, 385, 398, 435 Schneider, Nathan 430 Schneier, Bruce 388 Scholz, Trebor 430 Schrems, Max 64–5 Schultz, Jason 325, 394, 418, 429, 431 Schumpeter, Joseph 218, 219, 221, 242, 409 Schwab, Klaus 319, 379, 381, 382, 417, 428 Scoble, Robert 381, 404, 405 Scott, Clare 385 Scott, James C. 127–8, 130–1, 133–4, 395 scrutable nature of society 127–34, 337 scrutiny 89, 122–41 auxiliary function 124, 125 cryptography 182 digital liberation 170 in digital lifeworld 127–40 disciplinary function 124, 125–7 implications 141 nature of 123–4 power of 124–7 public and private power 154, 155, 156–7, 160 separation of powers 358–9 of staff 267 search engines perception-control 147–8, 150, 151, 152 totalitarianism 177 see also Google Sedol, Lee 31 self-driving vehicles 30 communication between 48 ‘cyber’ and ‘real’ distinction, disappearance of 97 democracy 359 digital liberation 169 embedded rules 73–4 force, digitization of 103–4 harm principle 198, 204 liberty and private power 192 lidar 49 machine learning 35 privatization of force 116, 117–18 robotics 54–5 technological unemployment 299 totalitarianism 178 utility analogy 158 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Index self-enforcing law 101–3 Semayne’s Case 396 Sen, Amartya 408 Sensifall 407 sensitivity of technology 49–53 separation of powers 358 sexism 273 neutrality fallacy 291 rule-based injustice 283, 285 sexuality Apple’s homosexuality ‘cure’ apps 235–6 Hart–Devlin debate 202 justice in recognition 272–3 neutrality fallacy 289 political agenda 72 Shakespeare, William 308, 310, 331, 426 Shanahan, Murray 373, 374, 375, 376, 379, 436 whole-brain emulation 33 shared values principle 353–4, 355, 357 sharing economy 335–6 Shead, Sam 424 Shel, Israel 381, 404, 405 Shiller, Benjamin Reed 419–20 Shin, Laura 379 shopping platforms, and distributive justice 269 Siedentop, Larry 389, 418, 429 Siegel, Eric 397, 398, 419 Silicon Valley automation of morality 177 brain–computer interfaces 48 employee characteristics 8, 294 employee numbers 319 ‘Google Doctrine’ 15 Moore’s Law 39, 41 philosophical engineers 8 startups, claims of 6–7 Silva, Shiroma 413 Silver, David 372, 374 Silverman, Craig 412 Simon, Julie 410, 414, 416 Simonite, Tom 375, 386, 404 Singer-Vine, Jeremy 419 Singh Grewal, David 428 511 Skinner, Quentin 167, 401 Skunk Riot Control Copter 179 Skype 148 Slee, Tom 290–1, 422, 423 Sloan Digital Sky Survey 65 smart assets 47 smart cities 44, 50, 66 fairness principle 353 scrutiny 130 smart contracts 47, 106–7 automation of force 119 smart devices 43 automation of force 119 code’s empire 97 connectivity 45, 48 dolls 182 glasses 58 guns 106 hacking 182, 183 harm principle 197–8 pervasiveness 43–4 pills 51 privatization of force 116 productive technologies 316 scrutiny 134, 135–6 toilet paper dispensers 51 toilets 182 vibrators 135–6 smart dust technology 50 smartphones Android 318 checking 42 Direct Democracy 240 Freedom app 166 GPS 64 implantable 52 interfaces 51 location prediction 139 NeuroSky headsets 48 perception-control 146 Pokémon Go 58 processing power 38 scrutiny 135 sensors 50–1 smart stores 299 Smith, Adam 264, 301, 325, 326, 429 Smith, Bryant Walker 383 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 512 Index Smith, Cooper 387 Smith, Mat 377 Snapchat 44, 58 Snowden, Edward 156 social justice 3, 10, 24–5, 346 algorithmic injustice 279–94 in distribution 257–70, 274, 278 future 340–1 nature of 258–9 property 313–41 in recognition 271–8 sharing economy 336 technological unemployment 295–312 Socrates 136, 217, 226 Solon, Olivia 419 Solove, Daniel J. 194, 406 Soltani, Ashkan 419 sousveillance 63 South Africa 179 Spain 50, 58 Special Air Service (SAS) 287–8 speech freedom of 190–1, 235–7 recognition 30 Spence, Michael 425, 427, 431 Spencer, Herbert 308, 426 spinach, bomb-detecting 51 Spinoza, Baruch 224, 411 spintronic materials 41 Spotify 236 Sprat, Thomas 79 Squires, Judith 72, 389, 420 Srnicek, Nick 426 stability, and democracy 225, 234 standards digital law 107–8, 109–10, 113 network effect 320, 321 Staples 269 state and distributive justice 264, 265 ownership of capital 329–30 supercharged see supercharged state Statista 378 statistics 17–18 status, and work paradigm 301, 307–9 Steele, Billy 396 Steiner, Christopher 391, 421 Stephen, James Fitzjames 201–2, 407 Stoics 323 Straitens, Iman 416 structural regulation 356–9 Sudha, L.

Mitchell 376 Wallach, Wendell 374, 381, 383, 384, 393, 394, 435 airport security system 120 Walzer, Michael 154, 310, 400, 420, 426 war 71–2 Wassom, Brian D. 402 Watkins, Alan 416 Waymo 54 Wealth Cyclone 322–3, 327 Data Deal 337 sharing economy 336 usufructuary rights 331 wearable technologies 44, 135 Weber, Max 369–70, 394, 432 bureaucracy 18 modernity 69 state and force 114 task of politics 346 WeChat 148, 319 Weigend, Andreas 338, 431 Weinstein, James 414 wellbeing, and the work paradigm 302–3, 309–11 Wells, H. G. 12 WhatsApp 97, 183, 318, 320 Whipple, Tom 384 515 Whitby, Andrew 423 White, Stuart 390 Whitehead, Alfred North 177, 403 whole-brain emulation 33 Wholefoods 319 Wiesel, Elie 292 Wiki Democracy 212, 243–6, 254, 348 Wikipedia bots 233–4 commons 333 constant change 245 contributors 45, 243 goal 245 Iran 183 Turkey 183 Wilde, Oscar 310, 426 Wile, Rob 372 will, limited understanding and strength of 365 William of Moerbeke 215 William the Conqueror 16 Williams, Alex 426 Winfield, Alan 382, 383 Winner, Langdon 14, 368 Wisconsin v Loomis 174 wisdom of crowds 224–5, 226 Direct Democracy 240 Wiki Democracy 244 wise restraints 348 Deliberative Democracy 237 Digital Liberalism 205 liberty 184–6, 187 Wittgenstein, Ludwig 25, 368, 370, 382, 390 clarity of expression 82 face as soul of the body 52 language, limits of 11 thinking 345 Wolfenden Report 202 Wolfgang, Meldon 383 women’s liberation movement 73 Wong, Gillian 397, 422 Wong, Joon Ian 379 Wong, Julia 367, 423 Woolley, Samuel C. 413 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 516 Index Wootson Jr, Cleve R. 396 work distributive justice 266–7, 268 resistance to automation 305 right to 304–5, 307 as scarce resource 303–4 see also technological unemployment work ethic 262, 308–9 work paradigm 300–11 after the 305–11 income 301 responses 303–5 status 301 wellbeing 302–3 World Economic Forum 51 Wozniak, Steve 314 Wright, Aaron 392, 394 Wright, Orville 21 Wright, Ronald 367 writing AI systems 30 importance to politics 16–17, 133 law 111, 112 and memory 136 perception-control 149 Wu, Tim 189, 313–14, 385, 406, 426 Wu,Yonghui 371 Wyatt, Sally 370 Wyss Institute 383 xenophobia 273 Xiong, Wei 371 Xqbot 233–4 Yahoo 156 Yonck, Richard 404, 420, 423 Young, Angelo 428 Young, Iris Marion 273, 291, 417, 420, 423 YouTube acquisition 319 for Press 378 Global Internet Forum to Counter Terrorism 191 Iran’s version 184 machine learning 36 user-generated content 315 user numbers 45 Zenbo 55 Zeng, Xiao-Jun 416 Zittrain, Jonathan 152, 399

pages: 336 words: 93,672

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, global pandemic, Google Glasses, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, Turing machine, twin studies, 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?

pages: 390 words: 109,870

Radicals Chasing Utopia: Inside the Rogue Movements Trying to Change the World by Jamie Bartlett

Andrew Keen, back-to-the-land, Bernie Sanders, bitcoin, blockchain, blue-collar work, Boris Johnson, brain emulation, centre right, clean water, cryptocurrency, Donald Trump, drone strike, Elon Musk, energy security, Ethereum, ethereum blockchain, failed state, gig economy, hydraulic fracturing, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jaron Lanier, job automation, John Markoff, Joseph Schumpeter, Kickstarter, life extension, Occupy movement, off grid, Peter Thiel, post-industrial society, postnationalism / post nation state, precariat, QR code, Ray Kurzweil, RFID, Rosa Parks, Ross Ulbricht, Satoshi Nakamoto, self-driving car, Silicon Valley, Silicon Valley startup, Skype, smart contracts, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, technoutopianism

‘About us’, Cryonics Institute, 19. Ibid.; ‘Frozen body: Can we return from the dead?’, BBC, 2013,; Michael Hendricks, ‘The false science of cryonics’, MIT Technology Review, 2015, 20. Anders Sandberg and Nick Bostrom, ‘Whole brain emulation: A road map’, Technical Report #2008-3, Future of Humanity Institute, Oxford University, 2008, 21. In 2013, a research group from MIT prompted the Obama administration to launch the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, which sponsors researchers to map the brain’s neurons. A similar initiative is taking place in Europe, called the Human Brain Project, which aims to create super-computer simulations to show everything we know about how the human brain operates. 22.

The effects of freezing the body, and especially the brain, are wildly uncertain and even the Cryonic Institute concedes that it is ‘unknown’ whether future societies will be able to revive cryonic subjects successfully.19 But they received a boost in 2015 when one test showed that the roundworm Caenorhabditis elegans retains its memories after being cryopreserved at liquid-nitrogen temperatures. The final, and most speculative, of all the transhumanist technologies is called ‘mind uploading’. Each person’s brain has a unique set of neurons and neural pathways, shaped by all the things that person has seen, heard, felt and done. Whole-brain emulation involves creating a faithful ‘map’ of the brain using advanced scanning techniques. Advocates hope that an identical copy of a person’s brain would be indistinguishable from the original, and so would replicate that person’s mind, which could be stored on a (presumably very large) memory stick. If you die you can always re-upload a back-up file into a synthetic human body.20 Several respected academic institutes are mapping the brain to better understand how it works, mainly to help combat various neurological diseases.

pages: 291 words: 81,703

Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen

Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, Drosophila,, 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, Johannes Kepler, John Markoff, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, P = NP, 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.

pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, 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, Thomas Bayes, 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.”

pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk,, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

For an overview of current abilities and limitations in scene interpretation, see JASON, “Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD,” 10. 232 Brain imaging: “Human Connectome Project | Mapping the Human Brain Connectivity,” accessed June 15, 2017, “Meet the World’s Most Advanced Brain Scanner,” Discover Magazine, accessed June 15, 2017, 232 whole brain emulations: Anders Sandburg and Nick Bostrom, “Whole Brain Emulation: A Roadmap,” Technical Report #2008-3, Oxford, UK, 2008, 232 “When people say a technology”: Andrew Herr, email to the author, October 22, 2016. 232 “last invention”: Irving J. Good, “Speculations Concerning the First Ultraintelligent Machine”, May 1964,

What it would take to build such a machine is a matter of pure speculation, but there is at least one existence proof that general intelligence is possible: us. Even if recent advances in deep neural networks and machine learning come up short, eventually an improved understanding of the human brain should allow for a detailed neuron-by-neuron simulation. Brain imaging is improving quickly and some researchers believe whole brain emulations could be possible with supercomputers as early as the 2040s. Experts disagree wildly on when AGI might be created, with estimates ranging from within the next decade to never. A majority of AI experts predict AGI could be possible by 2040 and likely by the end of the century, but no one really knows. Andrew Herr, who studies emerging technologies for the Pentagon, observed, “When people say a technology is 50 years away, they don’t really believe it’s possible.

pages: 360 words: 100,991

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence by Richard Yonck

3D printing, AI winter, artificial general intelligence, Asperger Syndrome, augmented reality, Berlin Wall, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, crowdsourcing, Elon Musk,, epigenetics, friendly AI, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta analysis, meta-analysis, Metcalfe’s law, neurotypical, Oculus Rift, old age dependency ratio, pattern recognition, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, software as a service, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

In many ways, they look to this as the next stage in human development. As we’ll explore in the next two chapters, this may be the single best option available to us. 17 FOR BETTER AND FOR WORSE Guangzhou, Guangdong, China—March 30, 2045 The Tianhe-14 supercomputer, nestled deep inside the National Supercomputing Center in Guangzhou, China, has been running the latest version of the People’s Human Brain Emulation for thirty-seven days straight. Throughout that time, processing demands have remained constant. Textbook constant. Power requirements likewise. Oddly, though, the cognition test suites that run continuously against the system’s deep learning algorithms report less than optimal gains across their many analytics modules. Suddenly and without warning, the system’s energy consumption begins to climb steeply.

See massive open online courses (MOOCs) Moodies, 72 Moore, Gordon, 38 Moore’s law, 38–40, 147 Mor, Yuval, 72, 76–77 Mori, Masahiro, 96–98 Moss, Frank, 62 MP3, 210 Mukai, Toshiharu, 152 Musk, Elon, 263–264 My Real Baby, 200 Myriad Genetics, 75 N Nadine, 87 Nanyang Technological University, 87 NAO, 112–113, 152 Napster, 210 NASA Ames Research Center, 256 NASA Space Technology 5 (ST5) antennas, 256 Nass, Clifford, 28, 50 National Center on Elder Abuse, 155–156 National Science Foundation, 60 NDR-113 (Andrew), 232 Negroponte, Nicholas, 52 Nemesysco, 73 neural networks, 67–68 NeuroSky, 213 neurotransmitters, 186, 187, 190, 216, 220, 221 “New Strategy for Robots,” 151 Nexi - MDS(mobile-dexterous-social) robot, 85 Next-Generation Identification, 144 1984, 229 Noldus, 72 nonverbal communication, 25–26 North Carolina State University study (2013), 114 Norvig, Peter, 39 O objectophilia, 186–188 Objectùm-Sexuality Internationale, 187 Oculus Rift, 189 Office Assistant, 51–52 Office for Windows, 51 Official Secrets Act, 37 online training systems, 120–121 Ono, 88 “Onslaught (Dove),” 69–70 open source EEG projects, 126 opsins, 213–214 optogenetics, 213–214, 218 On the Origin of Species (Darwin), 228 Orwell, George, 229 OS ONE, 195 “otherness,” 106 oxytocin, 16, 186, 196 P P-consciousness. See Phenomenal-consciousness Pan paniscus, 14 Pan troglodytes, 14 Paranthropus boisei, 12, 14–15 paraphilia, 187 Parkinson’s disease and DBS systems, 126–127 PARO, 148–149 patent and intellectual property (IP) law, 75 pattern recognition, 53–54 Pearson, Ian, 168–169 peer-to-peer file sharing, 210 People’s Human Brain Emulation, 240 Pepper, 82–83 Perceptio, 75 personal biometrics, 5 personal identity, 206 Personal Robotics Group, MIT, 85, 118–119 personalized education, 117–118 Peter Sager Wallenberg Charitable Trust, 66 Phenomenal-consciousness, 243–249, 258, 270 Physiio, 70, 74 physio-emotional self-knowledge, 34 Picard, Rosalind, 42–48, 51, 53, 57, 60–62, 77 Pinker, Steven, 13, 267 Pinocchio, 233–234 Plato, 29–30 Pleo, 89–90 Plutarch, 206 The Polar Express (Allsburg/Zemeckis), 95–96 The Positronic Man (Asimov and Silverberg), 232 Post-traumatic stress disorder (PTSD), 221–222 Michael’s nightmares, 122–123 and the military, 123–125 prosthetics, 103–104 Psychological Operations (PSYOP), 134 Psychology Today, 141 PSYOP.

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Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk,, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

Pinker, “Thinking does not imply subjugating.” 33. For an optimistic view arguing that AI safety problems will necessarily be resolved in our favor: Steven Pinker, “Tech prophecy.” 34. On the unsuspected alignment between “skeptics” and “believers” in AI risk: Alexander, “AI researchers on AI risk.” CHAPTER 7 1. For a guide to detailed brain modeling, now slightly outdated, see Anders Sandberg and Nick Bostrom, “Whole brain emulation: A roadmap,” technical report 2008-3, Future of Humanity Institute, Oxford University, 2008. 2. For an introduction to genetic programming from a leading exponent, see John Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, 1992). 3. The parallel to Asimov’s Three Laws of Robotics is entirely coincidental. 4. The same point is made by Eliezer Yudkowsky, “Coherent extrapolated volition,” technical report, Singularity Institute, 2004.

See work, elimination of Tegmark, Max, 4, 114, 138 Tellex, Stephanie, 73 Tencent, 250 tensor processing units (TPUs), 35 Terminator (film), 112, 113 Tesauro, Gerry, 55 Thaler, Richard, 244 Theory of the Leisure Class, The (Veblen), 230 Thinking, Fast and Slow (Kahneman), 238 thinking, learning from, 293–95 Thornton, Richard, 133 Times, 7, 8 tool (narrow) artificial intelligence, 46, 47, 136 TPUs (tensor processing units), 35 tragedy of the commons, 31 Transcendence (film), 3–4, 141–42 transitivity of preferences, 23–24 Treatise of Human Nature, A (Hume), 167 tribalism, 150, 159–60 truck drivers, 119 TrueSkill system, 279 Tucker, Albert, 30 Turing, Alan, 32, 33, 37–38, 40–41, 124–25, 134–35, 140–41, 144, 149, 153, 160–61 Turing test, 40–41 tutoring, 100–101 tutoring systems, 70 2001: A Space Odyssey (film), 141 Uber, 57, 182 UBI (universal basic income), 121 uncertainty AI uncertainty as to human preferences, principle of, 53, 175–76 human uncertainty as to own preferences, 235–37 probability theory and, 273–84 United Nations (UN), 250 universal basic income (UBI), 121 Universal Declaration of Human Rights (1948), 107 universality, 32–33 universal Turing machine, 33, 40–41 unpredictability, 29 utilitarian AI, 217–27 Utilitarianism ((Mill), 217–18 utilitarianism/utilitarian AI, 214 challenges to, 221–27 consequentialist AI, 217–19 ideal utilitarianism, 219 interpersonal comparison of utilities, debate over, 222–24 multiple people, maximizing sum of utilities of, 219–26 preference utilitarianism, 220 social aggregation theorem and, 220 Somalia problem and, 226–27 utility comparison across populations of different sizes, debate over, 224–25 utility function, 53–54 utility monster, 223–24 utility theory, 22–26 axiomatic basis for, 23–24 objections to, 24–26 value alignment, 137–38 Vardi, Moshe, 202–3 Veblen, Thorstein, 230 video games, 45 virtual reality authoring, 101 virtue ethics, 217 visual object recognition, 6 von Neumann, John, 23 W3C Credible Web group, 109 WALL-E (film), 255 Watson, 80 wave function, 35–36 “we’re the experts” argument, 152–54 white-collar jobs, 119 Whitehead, Alfred North, 88 whole-brain emulation, 171 Wiener, Norbert, 10, 136–38, 153, 203 Wilczek, Frank, 4 Wiles, Andrew, 185 wireheading, 205–8 work, elimination of, 113–24 caring professions and, 122 compensation effects and, 114–17 historical warnings about, 113–14 income distribution and, 123 occupations at risk with adoption of AI technology, 118–20 reworking education and research institutions to focus on human world, 123–24 striving and enjoying, relation between, 121–22 universal basic income (UBI) proposals and, 121 wage stagnation and productivity increases, since 1973, 117 “work in human–machine teams” argument, 163 World Economic Forum, 250 World Wide Web, 64 Worshipful Company of Scriveners, 109 Zuckerberg, Mark, 157 ABCDEFGHIJKLMNOPQRSTUVWXYZ About the Author Stuart Russell is a professor of Computer Science and holder of the Smith-Zadeh Chair in Engineering at the University of California, Berkeley.

pages: 331 words: 47,993

Artificial You: AI and the Future of Your Mind by Susan Schneider

artificial general intelligence, brain emulation, Elon Musk, Extropian, hive mind, life extension, megastructure, pattern recognition, Ray Kurzweil, Search for Extraterrestrial Intelligence, silicon-based life, Stephen Hawking, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Turing machine, Turing test, Whole Earth Review, wikimedia commons

Roco, M. C., and W. S. Bainbridge, eds. 2002. Converging Technologies for Improved Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Science. Arlington, VA: National Science Foundation and Department of Commerce. Sacks, O. 1985. The Man Who Mistook His Wife for a Hat and Other Clinical Tales. New York: Summit Books. Sandberg, A., and N. Bostrom. 2008. “Whole Brain Emulation: A Roadmap.” Technical Report 2008–3. Oxford: Future of Humanity Institute, Oxford University. Sawyer, R. 2005. Mindscan. New York: Tor. Schipp, Debbie. 2016. “Boyfriend’s Delivery of Love for the Woman Whose Brain Is Frozen,”, June 19,

pages: 267 words: 82,580

The Dark Net by Jamie Bartlett

3D printing, 4chan, bitcoin, blockchain, brain emulation, carbon footprint, creative destruction, 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, longitudinal study, Mark Zuckerberg, Marshall McLuhan, moral hazard, moral panic, Occupy movement, pre–internet, Ray Kurzweil, Ross Ulbricht, 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’.

pages: 404 words: 113,514

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, MITM: man-in-the-middle, 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.

pages: 303 words: 67,891

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006 by Ben Goertzel, Pei Wang

AI winter, artificial general intelligence, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, G4S, 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.

pages: 761 words: 231,902

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, Asilomar, augmented reality, autonomous vehicles, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence,, call centre, carbon-based life, cellular automata, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, disintermediation, double helix, Douglas Hofstadter,, 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, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, 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, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, 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, Thomas Bayes, 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.