brain emulation Concepts

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World

by James D. Miller · 14 Jun 2012 · 377pp · 97,144 words

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

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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

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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

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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

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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)

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or anywhere else in the galaxy. Reasons to Believe in the Likely Development of AI through Either a Kurzweil Merger, Clues from the Human Brain, Brain Emulation, or Creating an AI from Scratch: •The human brain exists, showing that it’s possible to create intelligent, flexible entities that can study their own

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are right in their criticisms of each other, then a Singularity is much less likely to occur. Eliezer’s primary objection to the feasibility of brain emulations is that similar technologies haven’t been built by reverse-engineering biology—when humans learned to fly, we did it by building airplanes, not by

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.html. Segal, Nancy L. 1999. Entwined Lives: Twins and What They Tell Us About Human Behavior. New York: Dutton. Shulman, Carl. November 23, 2008. “‘Evicting’ Brain Emulations.” Overcoming Bias (blog). http://www.overcomingbias.com/2008/11/suppose-that-ro.html. Shulman, Carl, and Nick Bostrom. 2012. “How Hard is Artificial Intelligence? The

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, 24 Gottfredson, Linda, 64 Gould, Eric, 194 Grace, Katja, 201–2 gratification, postponed, 80 Greek mythology, 41 Greely, Henry, 109–10 H Hanson, Robin, xviii brain emulation as conditional on civilization not collapsing, 10 bullet-eater, 141, 179 Cryonics Institute, 214 emulation of human brain, 139–41 emulation scenario, 181, 188–89

The Age of Em: Work, Love and Life When Robots Rule the Earth

by Robin Hanson · 31 Mar 2016 · 589pp · 147,053 words

tried to prove that conventional wisdom wrong, by analyzing in unprecedented breadth and detail the social implications of minds “uploaded” into computers, a.k.a. “brain emulations,” or “ems” for short. While ems are hardly sure to appear, their chances seem high enough to justify substantial analysis. My priority in this book

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1. Start Overview; Summary 2. Modes Precedents; Prior Eras; Our Era; Era Values; Dreamtime; Limits 3. Framing Motivation; Forecasting; Scenarios; Consensus; Scope; Biases 4. Assumptions Brains; Emulations; Complexity; Artificial Intelligence 5. Implementation Mindreading; Hardware; Security; Parallelism II. Physics 6. Scales Speeds; Bodies; Lilliput; Meetings; Entropy; Miserly Minds 7. Infrastructure Climate; Cooling; Air

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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

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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

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of “artificial intelligence,” that is, robots smart enough to substitute wholesale for human workers. Second, I guess that the first such robots will be whole brain emulations, or “ems,” within roughly a century or so. DEFINITION: An em results from taking a particular human brain, scanning it to record its particular cell

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territory, so you can plan your search of the dark. I will mainly present a single baseline scenario, centered on the appearance of cheap whole brain emulation. It will help that this main assumption is relatively discrete, without many complicating “almost” scenarios to consider. That is, mostly you either have a fully

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coordinate in many ways at many scales on many topics, even so relatively little global coordination is achieved to change the price or quantity of brain emulations. Note that this doesn’t mean that I predict or recommend zero regulation, that I deny the possibility of strategically restricted supply and demand, or

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we do see a modest tendency toward economic efficiency in law and politics (Cooter and Ulen 2011; Weingast and Wittman 2008). I thus assume that brain emulation is available relatively competitively, with many competing organizations able to profit from its application. I also assume that the world I describe is past a

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appear in my tone and overall evaluation, readers should rely more on my estimates of specific consequences. Chapter 4 Assumptions Brains The concept of whole brain emulation has been widely discussed in futurism (Martin 1971; Moravec 1988; Hanson 1994b, 2008b; Shulman 2010; Alstott 2013; Eth et al. 2013; Bostrom 2014) and in

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brain cells, an ability to emulate brain cell signal processing implies an ability to emulate whole brain signal processing, although at a proportionally larger cost. Brain emulations require three supporting technologies: brain scanners, brain cell models, and signal-processing hardware (e.g., computers). Brain scans will be feasible when all three of

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vast irrelevant complexity there. Biology may use that extra complexity to keep the whole system working, but emulations could use much simpler methods. The Whole Brain Emulation Roadmap (Sandberg and Bostrom 2008) considers in detail the technical feasibility of this scenario, that is, of “the possible future one-to-one modeling of

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the function of the human brain.” It concludes: [Whole brain emulation] on the neuronal/synaptic level requires relatively modest increases in microscopy resolution, a less trivial development of automation for scanning and image processing, a research

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would have commanded had emulations not existed. An enormous amount has been written, both careful and sloppy, on the possibility, feasibility, identity, and consciousness of brain emulations. However, the concepts of “identity” and “consciousness” that so animate many of those debates play little role in the physical, engineering, social, and human sciences

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two. That is, I consider a point in time when it is still not yet possible to make much economic use of small parts of brain emulations, to usefully combine substantially different emulations, to design new brains from scratch, or to substantially redesign human brains. Yes, in this scenario small-scale brain

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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

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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

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forecast of 37 years until there is a 50% chance of human level AI (Müller and Bostrom 2014). Incidentally, none of those 29 thought that brain emulation “might contribute the most” to human level AI. It turns out that ordinary AI experts tend to be much less optimistic when asked about the

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time. The duration of the em era before AI software is discussed more in Chapter 27, Intelligence Explosion section. The ability to experiment directly with brain emulations might speed the development of other forms of human level AI. Even so, the opacity of ems as complex systems should limit this progress. Thus

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can read each other’s minds, they may pretend that they cannot. Hardware What sort of physical devices are required to make an em? As brain emulations would be implemented in artificial signal-processing hardware, our long engineering experience with the realized costs and features of such hardware gives us a basis

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other computing tasks do not; they are rare, intermittent, or have only modest gains from specialized hardware. Brain emulations are run nearly continuously, and special hardware could probably achieve large efficiency gains. So if brain emulation became a very common computing task, this would probably be done on hardware specialized to the task of

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software is an active area of research today (Bogdan et al. 2007). As human brains are large, parallel, and have an intrinsically fault-tolerant design, brain emulation software is likely to need less special adaptation to run on fault-prone hardware. Such hardware is usually cheaper to design and construct, occupies less

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hardware may be developed to support them. Those trying to compute other things would then sometimes try to reframe their tasks to look more like brain emulation tasks, just as today some reframe other computing tasks to look like graphic-processing tasks. Signal-processing hardware costs, such as cost per operation and

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. But even if one must emulate the finest of these levels of detail to make a functioning brain emulation, at rates given by Moore’s law it would take only another half-century for a brain emulation device to cost about a million dollars (Sandberg and Bostrom 2008). After that, the cost of an

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second or in an objective day. This includes the cost to make hardware, to protect and support it, and to power and cool it. As brain emulation is a very parallel computing task, an em might run efficiently at a steady but very slow rate just by using one single slow processor

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hardware is determined by other considerations. Miserly Minds The use of energy-efficient hardware can change em behavior in many ways. For example, when a brain emulation is run on a reversible computer, then once per reversing period it must pay to erase a single maximally compressed copy of its period-ending

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brain itself. However, humans today are routinely comfortable and moderately productive interacting with video game environments that require vastly less computing power than human-speed brain emulations will require. Also, instead of sending very fine-grain low-level signals of very particular sights and sounds, it may become possible to send cheaper

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might inspire widespread outrage and a vigorous investigation. Of course this switch can’t function correctly without appropriate support from the computer hardware that manages brain emulation processes. A right for one em copy to immediately end itself is the simplest suicide right to implement and justify. It is also possible to

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languages, tools, and habits matched to particular performance tradeoff choices. After an initial period of large rapid gains, the software and hardware designs for implementing brain emulations probably reach diminishing returns, after which there are only minor improvements. This is what we see for software compiler and emulation programs today. In contrast

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humans. Ems may treat humans more with sympathy, and ancestral gratitude, but less with respect. They may even routinely mock humans. For example, just as brain emulations may be called “ems” for short, humans may be called “ums” for short, as this is part of the word “human” and also insultingly describes

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forms of general artificial intelligence are feasible when ems are first realized, there might be trillions of dollars to be gained from selling access to brain emulations. Local profit incentives should thus drive most local choices that cause change. The introduction of many technologies induces changes that are relatively gradual and anticipated

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fall as abilities gradually rise. In contrast, the introduction of other technologies induces more sudden and unanticipated jumps in abilities and costs. The technology of brain emulation is of this second more sudden sort, because partial or nearly accurate emulations are of little use. The early em economy creates a burst of

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and then. I am more skeptical about our ability to foresee endpoints without at least outlining the paths between here and there. Many doubt that brain emulations will be our next huge technology change, and aren’t interested in analyses of the consequences of any big change except the one they personally

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consider most likely or interesting. Many of these people expect traditional artificial intelligence, that is, hand-coded software, to achieve broad human level abilities before brain emulations appear. I think that past rates of progress in coding smart software suggest that at previous rates it will take two to four centuries to

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.S. Public Agricultural Research.” American Journal of Agricultural Economics 93(5): 1257–1277. Alstott, Jeff. 2013. “Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development.” Journal of Artificial General Intelligence 4(3): 153–163. Alvanchi, Amin, SangHyun Lee, and Simaan AbouRizk. 2012. “Dynamics of Working Hours in Construction.” Journal

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by the Extent of the Market? Evidence from French Cities.” Journal of Urban Economics 69(1): 56–71. Eckersley, Peter, and Anders Sandberg. 2014. “Is Brain Emulation Dangerous?” Journal of Artificial General Intelligence 4(3): 170–194. Edmond, Mark. 2015. “Democratic vs. Republican occupations.” Verdant Labs Blog, June 2. http://verdantlabs.com

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Challenge the Future of Multicore.” Transactions on Computer Systems 30(3): 11. Eth, Daniel, Juan-Carlos Foust, and Brandon Whale. 2013. “The Prospects of Whole Brain Emulation within the next Half-Century.” Journal of Artificial General Intelligence 4(3): 130–152. Evstigneev, Igor, Thorsten Hens, and Klaus Schenk-Hoppé. 2009. “Evolutionary Finance

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, 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

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. 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

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/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

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of Law. Harvard University Press. Shea, John. 1993. “Do Supply Curves Slope Up?” Quarterly Journal of Economics 108(1): 1–32. Shulman, Carl. 2010. “Whole Brain Emulation and the Evolution of Superorganisms.” Machine Intelligence Research Institute working paper. http://intelligence.org/files/WBE-Superorgs.pdf. Shulman, Carl, and Nick Bostrom. 2013. “Embryo

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, 325 bits 78, 80 blackmail 274 bodies 72–4, 103, 106, 167 quality of 74 bosses 172, 176, 200 bots 113–14 brain cells 46 brain emulations see emulations brain scanners 46 brain, the 6, 45–7, 341, 342, 346, 349 aging of 128 cell models 46 complexity of 49 emulating brain

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, 250–2, 327, 354, 361 water 87, 90–2 Watkins, John 33 wealth 23, 26, 245–6, 321–2, 325, 336–8 weapons 251 Whole Brain Emulation Roadmap (Sandberg and Bostrom) 47 Wiener, Anthony 33 wind pressures 92, 93 work 167–77, 327, 328, 331 conditions 169 culture 321, 322, 323, 324

How to Create a Mind: The Secret of Human Thought Revealed

by Ray Kurzweil · 13 Nov 2012 · 372pp · 101,174 words

from this project. 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

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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 Whole Brain Emulation: A Roadmap by Anders Sandberg and Nick Bostrom. Neural Nets In 1964, at the age of sixteen, I

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/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

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–3n thinking compared with to, 26–27 universality of, 26, 181–82, 185, 188, 192, 207 Computer and the Brain, The (von Neumann), 191 computers: brain emulated by, see brain, human, computer emulation of consciousness and, 209–11, 213–15, 223 intelligent algorithms employed by, 6–7 knowledge base expanded by, 4

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function, collapse of, 218–19, 235–36 Wedeen, Van J., 82–83, 90, 129, 262 Werblin, Frank S., 94–95 Whitehead, Alfred North, 181 Whole Brain Emulation: A Roadmap (Sandberg and Bostrom), 129–30, 130, 131 Wiener, Norbert, 115, 143 Wikipedia, 6, 156, 166, 170, 176, 232, 270, 279 Wittgenstein, Ludwig, 219

The Transhumanist Reader

by Max More and Natasha Vita-More · 4 Mar 2013 · 798pp · 240,182 words

Distant Future Terminological Exercises: ER < - > EP - > …- > IE - > ?! - > . Social Implications 14 Uploading to Substrate-Independent Minds Your Mind, but not Constrained to the Biological Brain Whole Brain Emulation Main Developments toward SIM Structural Connectomics and Functional Connectomics Structure-Function and Questions of Resolution and Scope SIM within our Life-Spans What is the

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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

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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

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of Humanity Institute, Oxford University. He co-authored with Nick Bostrom “Converging Cognitive Enhancements” (Annals of the New York Academy of Science, 2006); and “Whole Brain Emulation: A Roadmap” (Technological Report, 2008). Wrye Sententia, PhD, is Postdoctoral Lecturer, University of California Davis. She authored “Neuroethical Considerations: Cognitive Liberty & Converging Technologies for Improving

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tangentially as suggested in How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics (Hayles 1999). 7 As understood by Randal Koene as “whole brain emulation” and more recently “substrate-independent minds.” http://www.kurzweilai.net/pattern-survival-versus-gene-survival. 8 Life expansion means increasing the length of time a

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and regenerative cells growing organs), nanotechnology (nanomedicine, nanorobotics, and molecular manufacturing) and human–computer interaction, including artificial intelligence (artificial general intelligence), and processes for whole brain emulation. The quintessence of being alive – that element of you, the psyche according to Aristotle, form the biotechnogenesis of matter as they repeatedly collapse and expand

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(the science of mapping the brain) is one way in which life expansion media could emerge. 3 In relation to uploads, otherwise known as whole brain emulation or substrate-independent minds. 4 In relation to non-biological computational platforms. 5 In relation to Teilhard de Chardin’s “noosphere” or what is suggested

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experiences. Of six technology paths through which we may gain substrate-independence for our minds, the most conservative and well-supported by research is Whole Brain Emulation (WBE). Koene notes that, back in 2000, WBE was considered science fiction, since it was beyond what was then considered feasible science and engineering. That

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created. Of course this isn’t the only possible path to creating advanced AGI. As Kurzweil and others have noted, it’s possible that detailed brain emulation will get there first. I’m focusing on the integrative cognitive and computer science-based approach here, because it’s the one I’m following

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.” http://strategicphilosophy.blogspot.com/2009/06/how-fast-will-future-arrive-how-will.html (accessed October 30, 2011). Sandberg, Anders and Bostrom, Nick (2008) Whole Brain Emulation: A Roadmap. Technical Report #2008-3. Future of Humanity Institute, Oxford University. Vinge, Vernor (1993) “The Coming Technological Singularity: How to Survive in the Post

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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

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. 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. That activity is comprised of the interactions, the

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(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

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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

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(e.g., an Android cell phone) to an emulator of the same processing operations on a different hardware platform (e.g., a Macintosh computer). Whole brain emulation differs from modeling and simulation in computational neuroscience and neuroinformatics in that the functions and parameters used for the emulation come from an individual brain

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, of many possible forms allowed by physical properties. By now, you can probably tell that a whole brain emulation takes a very specific tack in its approach to achieving substrate-independent minds. We consider whole brain emulation the most conservative approach. If we understood a lot more about the way the mind works and

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details about the range of those fundamental components and to identify and re-implement that neuro-anatomy and neuro-physiology in another computational substrate. Whole brain emulation resembles carefully copying each tiny speck on the canvas of a masterpiece instead of attempting to redraw the masterpiece using broad strokes carried out by

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(indicated by the white arrow) filled with neurotransmitters within the body of a synaptic terminal (within the red square). The steps involved in achieving whole brain emulation will involve the development of new tools such as the ATLUM and will undoubtedly teach us many things about the brain. Still, creating a whole

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brain emulation does not automatically guarantee a full understanding of brain and mind – or require it! What it does give us are the essential requirements of a

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with developments, truly experience that which our tools are capable of, and intimately benefit from those advances. Main Developments toward SIM Since the term whole brain emulation was introduced in 2000 several important developments have made substrate-independent minds a feasible project in the foreseeable future. The transistor density and storage available

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with a sequential Von Neumann architecture, parallel computing platforms, and in particular neuromorphic platforms, are a much better target for the implementation of a whole brain emulation. An example of neuromorphic processor hardware is the chip developed at IBM as an outcome of research in the DARPA SyNAPSE program led by Dharmendra

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the International Neuroinformatics Coordinating Facility (INCF) have made a concerted effort to drive neuroinformatics toward shared resources and shared collections of data. Projects in whole brain emulation will require methods of representation and implementation that are well suited to the necessary scale and resolution (e.g., see Figure 14.2). Those requirements

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specific steps in a program to develop WBE. The first hypothesis being tested by Dalrymple is “Recording of membrane potential is sufficient to enable whole brain emulation of C. Elegans.” Through emulation of the nematode C. Elegans, he will determine cases when information at the molecular level is not needed and when

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with integrated circuit technology, applied at the dimensions of red blood cells. The past decade also marked an essential shift in the perception of whole brain emulation and the possibility of substrate-independent minds. In my personal role, seeking the accomplishment of SIM, I was also dealing with the essential tasks of

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was particularly true when speaking with leaders in neuroscience, computer science, and related fields such as the burgeoning fields of neural engineering and nanotechnology. Whole brain emulation was science fiction, beyond the horizon of feasible science and engineering. That is not true any more. Now, leading scientists and principal investigators, including Ed

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Boyden, Sebastian Seung, Ted Berger, and George Church, consider high resolution connectomics and efforts towards whole brain emulation to be serious and relevant research and technology development goals addressed in their laboratories. Structural Connectomics and Functional Connectomics In the brain, processing and memory

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be better obtained from structural data acquisition. Even if purely structural or purely functional data acquisition could provide all the necessary information for a whole brain emulation, then such a constraint would still carry a burden of risk that is better avoided from the perspective of sensible engineering. It seems unwise to

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not entirely independent either. There are at present a few projects that are specifically investigating hypotheses that address the main two research questions for whole brain emulation, namely: (1) the question of the scope and resolution that are needed to acquire data and then produce an emulation that satisfies the objective of

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ATLUM to acquire and reconstruct in accordance with the structure and function in an individual brain, the Blue Brain Project will not develop a whole brain emulation in the truest sense. Even so, the large-scale model building methods, verification protocols, simulations, and hypothesis testing that result from Blue Brain will be

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work forces researchers to confront challenges of functional interfacing within core circuitry of the brain that are close analogues of the challenges involved in whole brain emulation. SIM within our Life-Spans The problem of achieving substrate-independent minds is one of solving several specific issues: Determining the scope and resolution of

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.net. August 24, 2011. http://www.kurzweilai.net/achieving-substrate-independent-minds-no-we-cannot-copy-brains. Koene, Randal A. (2012) “Experimental Research in Whole Brain Emulation: The Need for Innovative In-Vivo Measurement Techniques.” International Journal of Machine Consciousness special issue 4/1. Koene, Randal A., Tijms, B., van Hees, P

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Circuits in a Column of Rat Vibrissal Cortex.” Cerebral Cortex doi:10.1093/cercor/bhr317, pp. 1–17. Sandberg, Anders and Bostrom, N. (2008) Whole Brain Emulation: A Roadmap. Oxford: Future of Humanity Institute, Oxford University. Zador, Anthony (2011) “Sequencing the Connectome: A Fundamentally New Way of Determining the Brain’s Wiring

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to enhancement as a “spiritual obligation,” he only fuels opponents’ misapprehensions of what constitutes a collective “good.”9 Likewise, unreflective prophecies of something like whole brain emulation can do more harm than “good.” Opponents to cognitive enhancement, reading the same technological tea leaves as Singularitarians, fear that virtually every aspect of “human

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that, generically, even small increasing returns tend to produce radical growth. If mental capital becomes copyable (such as would be the case for AI or brain emulation) extremely rapid growth would also become likely. Introduction The set of concepts today commonly referred to as “technological singularity” has a long history in the

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progress to speed up as the economy grows faster, which, if included, would speed up the transition. Similar economic effects appear likely to occur if brain emulations (simulations of human brains with sufficient resolution to produce human-equivalent behavior and problem-solving [Sandberg and Bostrom 2008]) could be created (Hanson 1994, 2008b

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capital (embodied in humans, artificial intelligence or posthumans) becomes relatively cheaply copyable, extremely rapid growth is likely to follow. Hence observing progress towards artificial intelligence, brain emulation, or other ways of increasing human capital might provide evidence for or against type A singularities. There is a notable lack of models of how

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/fastgrow.html. Hanson, Robin (1998c) “Long-Term Growth as a Sequence of Exponential Modes.” http://hanson.gmu.edu/longgrow.pdf. Hanson, Robin (2008a) “Economics of Brain Emulations.” In Peter Healey and Steve Rayner, eds., Unnatural Selection: The Challenges of Engineering Tomorrow’s People. London: EarthScan, pp, 150–158. Hanson, Robin (2008b) “Economics

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Exponents and Log-Periodic Corrections in Frustrated Systems.” J. Phys. I France 6/3 (March), pp. 327–355. Sandberg, Anders and Bostrom, Nick (2008) Whole Brain Emulation: A Roadmap. Technical Report #2008-3. Future of Humanity Institute, Oxford University. http://www.fhi.ox.ac.uk/reports/2008-3.pdf. Schmidhuber, Juergen (2006

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virtual reality virtue Vita-More, Natasha Vital Progress Summit vitrification Von Neumann, John VR, see virtual reality Walford, Roy Walker, Mark wearable West, Michael whole brain emulation Wiener, Norbert World Transhumanist Association worst-case scenario Wowk, Brian Yudkowsky, Eliezer

Superintelligence: Paths, Dangers, Strategies

by Nick Bostrom · 3 Jun 2014 · 574pp · 164,509 words

Great expectations Seasons of hope and despair State of the art Opinions about the future of machine intelligence 2. Paths to superintelligence Artificial intelligence Whole brain emulation Biological cognition Brain–computer interfaces Networks and organizations Summary 3. Forms of superintelligence Speed superintelligence Collective superintelligence Quality superintelligence Direct and indirect reach Sources of

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technological development Preferred order of arrival Rates of change and cognitive enhancement Technology couplings Second-guessing Pathways and enablers Effects of hardware progress Should whole brain emulation research be promoted? The person-affecting perspective favors speed Collaboration The race dynamic and its perils On the benefits of collaboration Working together 15.

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term history of world GDP. 2. Overall long-term impact of HLMI. 3. Supercomputer performance. 4. Reconstructing 3D neuroanatomy from electron microscope images. 5. Whole brain emulation roadmap. 6. Composite faces as a metaphor for spell-checked genomes. 7. Shape of the takeoff. 8. A less anthropomorphic scale? 9. One simple

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takeover scenario. 11. Schematic illustration of some possible trajectories for a hypothetical wise singleton. 12. Results of anthropomorphizing alien motivation. 13. Artificial intelligence or whole brain emulation first? 14. Risk levels in AI technology races. List of Tables 1. Game-playing AI 2. When will human-level machine intelligence be attained? 3

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. How long from human level to superintelligence? 4. Capabilities needed for whole brain emulation 5. Maximum IQ gains from selecting among a set of embryos 6. Possible impacts from genetic selection in different scenarios 7. Some strategically significant technology

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they will be superintelligent. How do we get from here to there? This chapter explores several conceivable technological paths. We look at artificial intelligence, whole brain emulation, biological cognition, and human–machine interfaces, as well as networks and organizations. We evaluate their different degrees of plausibility as pathways to superintelligence. The existence

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motivation in later chapters, but it is so central to the argument in this book that it is worth bearing in mind throughout. Whole brain emulation In whole brain emulation (also known as “uploading”), intelligent software would be produced by scanning and closely modeling the computational structure of a biological brain. This approach

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thus represents a limiting case of drawing inspiration from nature: barefaced plagiarism. Achieving whole brain emulation requires the accomplishment of the following steps. First, a sufficiently detailed scan of a particular human brain is created. This might involve stabilizing the brain

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understand the low-level functional characteristics of the basic computational elements of the brain. No fundamental conceptual or theoretical breakthrough is needed for whole brain emulation to succeed. Whole brain emulation does, however, require some rather advanced enabling technologies. There are three key prerequisites: (1) scanning: high-throughput microscopy with sufficient resolution and

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development of the requisite enabling technologies, it is clear that a very great deal of incremental technical progress would be needed to bring human whole brain emulation within reach.24 For example, microscopy technology would need not just sufficient resolution but also sufficient throughput. Using an atomic-resolution scanning tunneling microscope

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great expansion of neurocomputational libraries and major improvements in automated image processing and scan interpretation would also be needed. Table 4 Capabilities needed for whole brain emulation In general, whole brain emulation relies less on theoretical insight and more on technological capability than artificial intelligence. Just how much technology is required for whole

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Neurotransmitter molecules would not be simulated individually, but their fluctuating concentrations would be modeled in a coarse-grained manner. To assess the feasibility of whole brain emulation, one must understand the criterion for success. The aim is not to create a brain simulation so detailed and accurate that one could use it

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neurocomputational principles discovered during emulation efforts and hybridize them with synthetic methods, and that this would happen before the completion of a fully functional whole brain emulation. The possibility of such a spillover into neuromorphic AI, as we shall see in a later chapter, complicates the strategic assessment of the desirability

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of seeking to expedite emulation technology. How far are we currently from achieving a human whole brain emulation? One recent assessment presented a technical roadmap and concluded that the prerequisite capabilities might be available around mid-century, though with a large uncertainty

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of boxes represents a final sequence of advances which can commence after preliminary hurdles have been cleared. The stages in this sequence correspond to whole brain emulations of successively more neurologically sophisticated model organisms—for example, C. elegans → honeybee → mouse → rhesus monkey → human. Because the gaps between these rungs—at least

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enhanced biological or organizational intelligence would accelerate scientific and technological developments, potentially hastening the arrival of more radical forms of intelligence amplification such as whole brain emulation and AI. This is not to say that it is a matter of indifference how we get to machine superintelligence. The path taken to get

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cognitive content would therefore not be possible between just any pair of machine intelligences. In particular, it would not be possible among first-generation whole brain emulations.) • New modules, modalities, and algorithms. Visual perception seems to us easy and effortless, quite unlike solving textbook geometry problems—this despite the fact that

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and so forth, managers of the first emulation cohort would find plenty of room for innovation in managerial practices. After initially plummeting when human whole brain emulation becomes possible, recalcitrance may rise again. Sooner or later, the most glaring implementational inefficiencies will have been optimized away, the most promising algorithmic variations

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expansion once a working software mind has been attained; the possibility of algorithmic improvements; the possibility of scanning additional brains (in the case of whole brain emulation); and the possibility of rapidly incorporating vast amounts of content by digesting the Internet (in the case of artificial intelligence).24 * * * Box 4 On

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the successful project be? Some paths to superintelligence require great resources and are therefore likely to be the preserve of large well-funded projects. Whole brain emulation, for instance, requires many different kinds of expertise and lots of equipment. Biological intelligence enhancements and brain–computer interfaces would also have a large

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least some progress. It would be easier to monitor projects that require significant amounts of physical capital, as would be the case with a whole brain emulation project. Artificial intelligence research, by contrast, requires only a personal computer, and would therefore be more difficult to monitor. Some of the theoretical work

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, is unavailing in the case of a newly created seed AI. But augmentation is a potential motivation selection method for other paths to superintelligence, including brain emulation, biological enhancement, brain–computer interfaces, and networks and organizations, where there is a possibility of building out the system from a normative nucleus (regular

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system, like that of a human being, will not get corrupted when its cognitive engine blasts into the stratosphere. As discussed earlier, an imperfect brain emulation procedure that preserves intellectual functioning may not preserve all facets of personality. The same is true (though perhaps to a lesser degree) for biological enhancements

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most salient with respect to AI, which might be structured very differently than human intelligence. But even if machine intelligence were initially achieved though whole brain emulation, resulting in conscious digital minds, the competitive forces unleashed in a post-transition economy could easily lead to the emergence of progressively less neuromorphic

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be one such copy for every other resident. This could be used to prohibit the development of weapons of mass destruction, to enforce regulations on brain emulation experimentation or reproduction, to enforce a liberal democratic constitution, or to create an appalling and permanent totalitarianism39 The first-order effect of such a

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recapitulate. Moreover, the mechanism is presumably closely tailored to the human neurocognitive architecture and therefore not applicable in machine intelligences other than whole brain emulations. And if whole brain emulations of sufficient fidelity were available, it would seem easier to start with an adult brain that comes with full representations of some human values

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variant of the Kolmogorov complexity, which we encountered in Box 1, Chapter 1).27 * * * Emulation modulation The value-loading problem looks somewhat different for whole brain emulation than it does for artificial intelligence. Methods that presuppose a fine-grained understanding and control of algorithms and architecture are not applicable to emulations. On

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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

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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

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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

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in the backstabbing night of political bogeys. Pathways and enablers Should we celebrate advances in computer hardware? What about advances on the path toward whole brain emulation? We will look at these two questions in turn. Effects of hardware progress Faster computers make it easier to create machine intelligence. One effect

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distribution) then the WBE-first scenario would have advantages paralleling those of human cognitive enhancement, which we discussed above. Figure 13 Artificial intelligence or whole brain emulation first? In an AI-first scenario, there is one transition that creates an existential risk. In a WBE-first scenario, there are two risky

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nature or civilization might have improved by the time we confront this challenge—should the WBE-first path seem attractive. To figure out whether whole brain emulation technology should be promoted, there are some further important points to place in the balance. Most significantly, there is the technology coupling mentioned earlier:

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a reasonably good understanding of how the system will work. Such understanding may not be necessary to merely copy features of an existing system. Whole brain emulation relies on wholesale copying of biology, which may not require a comprehensive computational systems-level understanding of cognition (though a large amount of component-

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level understanding would undoubtedly be needed). Neuromorphic AI may be like whole brain emulation in this regard: it would be achieved by cobbling together pieces plagiarized from biology without the engineers necessarily having a deep mathematical understanding of how

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due to the intervention of some defeater, such as an existential catastrophe. If strong superintelligence arrived not in the shape of artificial intelligence or whole brain emulation but through one of other paths we considered above, then a slower takeoff would be more likely. CHAPTER 5: DECISIVE STRATEGIC ADVANTAGE 1. A

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the possibility that a singleton might emerge though the direct or indirect effects of a nanotechnology breakthrough; and the greater feasibility of neuromorphic and whole brain emulation approaches to machine intelligence. It is beyond the scope of our investigation to consider all these issues (or the parallel issues that might arise for

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ubiquitous access to smart phones, attractive virtual reality environments for social intercourse, and so forth. 21. Investment in emulation technology could speed progress toward whole brain emulation not only directly (through any technical deliverables produced) but also indirectly by creating a constituency that will push for more funding and boost the visibility

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45–50. Hanson, Robin. 2009. “Tiptoe or Dash to Future?” Overcoming Bias (blog), December 23. Hanson, Robin. 2012. “Envisioning the Economy, and Society, of Whole Brain Emulations.” Paper presented at the AGI Impacts conference 2012. Hart, Oliver. 2008. “Economica Coase Lecture Reference Points and the Theory of the Firm.” Economica 75 (299

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Singularity.” Paper presented at the Roadmaps to AGI and the Future of AGI Workshop, Lugano, Switzerland, March 8. Sandberg, Anders. 2013. “Feasibility of Whole Brain Emulation.” In Philosophy and Theory of Artificial Intelligence, edited by Vincent C. Müller, 5: 251–64. Studies in Applied Philosophy, Epistemology and Rational Ethics. New York

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and Bostrom, Nick. 2006. “Converging Cognitive Enhancements.” Annals of the New York Academy of Sciences 1093: 201–27. Sandberg, Anders, and Bostrom, Nick. 2008. Whole Brain Emulation: A Roadmap. Technical Report 2008-3. Future of Humanity Institute, University of Oxford. Sandberg, Anders, and Bostrom, Nick. 2011. Machine Intelligence Survey. Technical Report 2011

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von Neumann, John 44, 87, 114, 261, 277, 281 W wages 65, 69, 160–169 Watson (IBM) 13, 71 WBE, see whole brain emulation (WBE) Whitehead, Alfred N.6 whole brain emulation (WBE) 28–36, 50, 60, 68–73, 77, 84–85, 108, 172, 198, 201–202, 236–245, 252, 266, 267, 274,

To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death

by Mark O'Connell · 28 Feb 2017 · 252pp · 79,452 words

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

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known as much as anything for his advocation and theorizing of the idea of mind uploading, of what was known among the initiates as “whole brain emulation.” It wasn’t, he insisted, that he wanted this right away; even if such a thing might be possible in the near future—and he

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website of Carboncopies, which I learned was a “nonprofit organization with a goal of advancing the reverse engineering of neural tissue and complete brains, Whole Brain Emulation and development of neuroprostheses that reproduce functions of mind, creating what we call Substrate Independent Minds.” This latter term, I read, was the “objective to

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area, like the encoding of memory, for instance, with a view to figuring out how that might fit into an overall road map for whole brain emulation.” Having worked for a while at Halcyon Molecular, a Silicon Valley gene-sequencing and nanotechnology start-up funded by Peter Thiel, he decided to stay

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in which a PC’s operating system could be emulated on a Mac, as what he called “platform independent code.” The relevant science for whole brain emulation is, as you’d expect, hideously complicated, and its interpretation deeply ambiguous, but if I can risk a gross oversimplification here, I will say that

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at the Future of Humanity Institute. The event resulted in the publication of a technical report, coauthored by Anders Sandberg and Nick Bostrom, entitled “Whole Brain Emulation: A Roadmap.” The report began with the statement that mind uploading, though still a remote prospect, was nonetheless theoretically achievable through the development of technologies

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beard, plumage of pink hair, Birkenstocks, black-painted toenails. The people who worked for him, he said, knew of his long-term interest in whole brain emulation, but it was not something that drove the company in its day-to-day dealings. It just so happened that the sort of technology that

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at these brain slices, I understood that, even if a greatly scaled-up version of this scanning technology eventually made it possible to perform whole brain emulation, it would be impossible to emulate the brain of an animal without killing that animal—or at least killing the original, embodied version. This was

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processes to a more suitable computational substrate. Then our minds won’t have to stay so small.” At the root of this concept of whole brain emulation, and of transhumanism itself as a movement or an ideology or a theory, was, I realized, the sense of ourselves as trapped in the wrong

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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

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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

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? 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

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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

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of the bounds of the thinkable. — In my first couple of conversations with Randal, my questions tended to focus on the technical aspects of whole brain emulation—on the means by which it might be achieved, and on the overall feasibility of the project. This was useful insofar as it confirmed for

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exist independently of the substrate on which I operated because the self was the substrate, and the substrate was the self. The idea of whole brain emulation—which was, in effect, the liberation from matter, from the physical world—seemed to me an extreme example of the way in which science, or

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redemption would come in the form of liberation from that body. And a technological version of this liberation seemed to me to be what whole brain emulation was ultimately all about. This techno-dualistic account of ourselves, as software running on the hardware of our bodies, had grown out of an immemorial

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of weed, Nietzsche’s mad animals. — In the weeks and months after I returned from San Francisco, I thought obsessively about the idea of whole brain emulation. I would be taking a break from work and walking to a coffee shop, and a car would drive past me a little too fast

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was true, it seemed like it would be a potential propaganda victory for transhumanism, for substrate independence, for the Ship of Theseus view of whole brain emulation. It was a vertiginous thought: that the person who had first read about transhumanism in Dublin ten years ago had no material connection to the

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my wife and me smiling less indulgently at our son’s persistent questions about death. I felt no increased attraction toward cryonic suspension, or whole brain emulation, or radical life extension; I felt no greater urge toward becoming a machine. But I was by no means unflinching, either, in the face of

The Singularity Is Near: When Humans Transcend Biology

by Ray Kurzweil · 14 Jul 2005 · 761pp · 231,902 words

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

The Singularity Is Nearer: When We Merge with AI

by Ray Kurzweil · 25 Jun 2024

. As another point of perspective, children born today will likely see the Turing test passed while they are in elementary school and see even richer brain emulation achieved when they are of college age. One final comparison is that I am completing this book in 2023, which, even under pessimistic assumptions, is

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probably closer to full brain emulation being feasible than it is to 1999, when I first made many of these predictions in The Age of Spiritual Machines. Passing the Turing Test

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the contents of living brains to nonbiological mediums, we transition from the merely simulated replicants I describe to actual mind uploading, also known as whole-brain emulation, or WBE. Simulating a mind on a nonbiological medium can mean vastly different things in computational terms. In 2008, John Fiala, Anders Sandberg, and Nick

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Bostrom identified eleven different levels of possible brain emulation.[89] But to simplify here, brain emulations fall into roughly five categories, proceeding from most abstract to most exhaustive: functional, connectomic, cellular, biomolecular, and quantum. Functional emulations are those

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be available until the next century.[90] One of the major research projects of the next two decades will be figuring out what level of brain emulation is sufficient. Many who think quantum-level emulation is necessary take this position because they believe subjective consciousness rests on (as yet unknown) quantum effects

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the brain, and crack the code of how the brain represents information. (For a deeper dive into progress toward mind uploading, the computational dimensions of brain emulation, and even a proposed technology called Matrioshka brains that might one day allow humanity to harness massive amounts of energy for computation, see this endnote

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raw computing speed that vary irregularly between machines. Additional RESOURCES Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap, technical report 2008-3, Future of Humanity Institute, Oxford University (2008), https://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf. William D. Nordhaus, “The Progress of Computing,” discussion paper 1324, Cowles Foundation

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for the sources used for all the cost-of-computation calculations in this book. BACK TO NOTE REFERENCE 149 Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap, technical report 2008-3, Future of Humanity Institute, Oxford University (2008), 80–81, https://www.fhi.ox.ac.uk

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/brain-emulation-roadmap-report.pdf. BACK TO NOTE REFERENCE 150 Sandberg and Bostrom, Whole Brain Emulation. BACK TO NOTE REFERENCE 151 Mitch Kapor and Ray Kurzweil, “A Wager on the Turing Test: The Rules,” KurzweilAI

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,” Engineered Arts, YouTube video, March 31, 2023, https://www.youtube.com/watch?v=yUszJyS3d7A. BACK TO NOTE REFERENCE 88 Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap, technical report 2008-3, Future of Humanity Institute, Oxford University (2008), 13, https://www.fhi.ox.ac.uk

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/brain-emulation-roadmap-report.pdf. BACK TO NOTE REFERENCE 89 Sandberg and Bostrom, Whole Brain Emulation, 80–81. BACK TO NOTE REFERENCE 90 For further resources related to mind uploading and brain emulation, see S. A. Graziano, “How Close Are We to Uploading Our

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,000-Atom Nano Device,” Phys.org, January 14, 2014, https://phys.org/news/2014-01-supercomputer-simulate-atom-nano-device.html; Anders Sandberg, “Ethics of Brain Emulations,” Journal of Experimental & Theoretical Artificial Intelligence 26, no. 3 (April 14, 2014): 439–57, https://doi.org/10.1080/0952813X.2014.895113; Ray Kurzweil, How

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: A Primer in Abnormal Development (New York: Plenum Press, 1989), 113, https://books.google.co.uk/books?id=gV0rBgAAQBAJ; Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A Roadmap, technical report 2008-3, Future of Humanity Institute, Oxford University (2008), 80, https://www.fhi.ox.ac.uk

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/brain-emulation-roadmap-report.pdf; see the appendix for the sources used for all the cost-of-computation calculations in this book. BACK TO NOTE REFERENCE 130

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to Spare?”; “Firing Behavior and Network Activity of Single Neurons in Human Epileptic Hypothalamic Hamartoma”; Abel, Behavioral Teratogenesis and Behavioral Mutagenesis; Sandberg and Bostrom, Whole Brain Emulation, 80; see the appendix for the sources used for all the cost-of-computation calculations in this book. BACK TO NOTE REFERENCE 131 Chapter 7

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also Google weak nuclear force, 96–97 weapons, 274–76, 280–82. See also nuclear weapons Weibel, Peter, 245 well-formed formulas (WFFs), 15 whole-brain emulation (WBE). See mind uploading whole genome sequencing, 2, 135, 189, 261 “Why the Future Doesn’t Need Us” (Joy), 277–78 Wikipedia, 38, 68, 168

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

by Richard Yonck · 7 Mar 2017 · 360pp · 100,991 words

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

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–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

Surviving AI: The Promise and Peril of Artificial Intelligence

by Calum Chace · 28 Jul 2015 · 144pp · 43,356 words

– 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

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of detail. 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

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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. They consist of a cell body, an axon to

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as MRI (Magnetic Resonance Imaging) are too blunt, resolving images in micro-metres, which means one metre divided by a million. The resolution required for brain emulation is a thousand times greater, at the nano-metre level. (Atoms and molecules live at an even smaller scale, the pico-metre scale, which is

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computing will be the preserve of very large, well-funded organisations. But assuming the processing power of computers continues to grow, large organisations interested in brain emulation may be able to afford several such systems, and eventually even wealthy hobbyists will come into the market. This will certainly happen if Moore’s

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, the BRAIN project is funding the development of tools and methodologies, and the HBP is building the actual model of a brain. Reasons why whole brain emulation might not work The more detailed a model has to be, the harder it is to build. If a brain’s functions can be replicated

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described above it is likely that it would be significantly different from a human brain, both in operation and in behaviour. While a successful whole brain emulation could be expected to produce something which thought somewhat like a human, an AGI based on traditional AI might think in an entirely alien way

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, yet we can now fly further and faster that they can. AI may be the same. The first AGI may be the result of whole brain emulation, backed up by only a partial understanding of exactly how all the neurons and other cells in any particular human brain fit together and work

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about AGI because computer processing power enables many of the processes which in turn could enable AGI. In the last chapter we saw how whole brain emulation requires continued improvements in scanning, computational capacity, and modelling. Each of these requires faster and more powerful computers. Similarly, narrow AI techniques are improving all

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that an AGI could have its intelligence enhanced. Its mind could be faster, bigger, or have better architecture. 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

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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. It is interesting to speculate whether this AGI, if conscious, would experience life at 2

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and promotes connectivity. The ratio between neocortex and other brain areas in humans is twice that of chimpanzees. Whether the first AGI is developed by brain emulation or by building on narrow AI, once it has been developed, its creators can run experiments, varying parts or all of its architecture. Controlled tests

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comes into being. We saw in chapter 4 that the two most plausible ways to create an AGI and then a superintelligence are a) whole brain emulation, and b) improvements on existing narrow AI systems. It is plausible, although by no means certain, that a superintelligence whose software architecture is based on