artificial general intelligence
description: theoretical class of AI able to perform any intelligence-based task a human can
122 results
Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006
by Ben Goertzel and Pei Wang · 1 Jan 2007 · 303pp · 67,891 words
The publisher is not responsible for the use which might be made of the following information. PRINTED IN THE NETHERLANDS ADVANCES IN ARTIFICIAL GENERAL INTELLIGENCE: CONCEPTS, ARCHITECTURES AND ALGORITHMS Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights
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in May 20-21, 2006 at Washington DC. The theme of the workshop is “Transitioning from Narrow AI to Artificial General Intelligence.” In this introductory chapter, we will clarify the notion of “Artificial General Intelligence”, briefly survey the past and present situation of the field, analyze and refute some common objections and doubts regarding this
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extent of producing publications and preliminary results. More or less coincidentally, several books have appeared within 4 P. Wang and B. Goertzel / Introduction: Aspects of Artificial General Intelligence the last few years, presenting several AGI projects, with theoretical and technical designs with various levels of detail [2]; [16]; [3]; [17]; [5]; [6
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– but rather that attention should be paid to resolving the outstanding issues through concerted research. 8 P. Wang and B. Goertzel / Introduction: Aspects of Artificial General Intelligence 3.7. “AGI research is not fruitful” Some oppositions to AGI research come mainly from practical considerations. Given the nature of the problem, research results
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contemporary AI work, computational language learning is one thing, and learning about physical objects and 12 P. Wang and B. Goertzel / Introduction: Aspects of Artificial General Intelligence their interrelationships is something else entirely. In an integrated intelligent mind, however, language and physical reality are closely interrelated. AGI research, to be effective, must
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control (the concrete operational stage); inference-based inference control (the formal stage); and inference-based modification P. Wang and B. Goertzel / Introduction: Aspects of Artificial General Intelligence 15 of inference rules (the post-formal stage). The pragmatic implications of this view of cognitive development are discussed in the context of classic Piagetan
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Intelligence: Sequential Decisions based on Algorithmic Probability, Springer, 2005. [4] B. Goertzel. The Structure of Intelligence, Springer, 1993. [5] B. Goertzel and C. Pennachin (editors), Artificial General Intelligence, Springer, 2007. [6] B. Goertzel. The Hidden Pattern, BrownWalker, 2006. [7] J. Searle, Minds, Brains, and Programs, Behavioral and Brain Sciences 3 (1980), 417
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2002. [26] J. Schmidhuber, Goedel machines: self-referential universal problem solvers making provably optimal self-improvements. In B. Goertzel and C. Pennachin (editors), Artificial General Intelligence, 2006. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 17
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] 18.“...the essential, domain-independent skills necessary for acquiring a wide range of domain-specific knowledge – the ability to learn anything. Achieving this with `artificial general intelligence' (AGI) requires a highly adaptive, generalpurpose system that can autonomously acquire an extremely wide range of specific knowledge and skills and can improve its own
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October 14, 2003. [41] P. Voss. Essentials of general intelligence: The direct path to AGI. In B. Goertzel and C. Pennachin, editors, Artificial General Intelligence. Springer-Verlag, 2005. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 25
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mostly based on the conceptual framework presented in Stan Franklin’s workshop presentation (and represented in this volume by his article “A Foundational Architecture for Artificial General Intelligence”) with a couple additions and variations. All individuals who presented talks on AGI architectures at the workshop were invited to respond to the questionnaire,
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Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 36 A Foundational Architecture for Artificial General Intelligence Stan FRANKLIN Computer Science Department & Institute for Intelligent Systems, The University of Memphis Abstract. Implementing and fleshing out a number of psychological and neuroscience theories
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this direction was the May 2006 AGIRI Workshop, of which this volume is essentially a proceedings. The term AGI, artificial general intelligence, was introduced as a modern successor to the earlier strong AI. Artificial General Intelligence What is artificial general intelligence? The AGIRI website lists several features, describing machines • • • • with human-level, and even superhuman, intelligence. that generalize
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memory includes autobiographical memory, the memory of events as described above, and semantic memory, the memory for facts. S. Franklin / A Foundational Architecture for Artificial General Intelligence 41 Figure 6. Attention and Action Selection. Attention & Action Selection In this section the gray “rest of cognition box” has disappeared, to be replaced by
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another filtering process that decides what part of the recent percepts and episodic recall to bring to conscious- 42 S. Franklin / A Foundational Architecture for Artificial General Intelligence ness. Again the criteria for this filtering include relevance, importance, urgency, and insistence. Procedural memory then uses the contents of consciousness, what comes to
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is to dive right in and attempt to build a Perceptual Learning Encode Procedural Learning Figure 7. Learning. S. Franklin / A Foundational Architecture for Artificial General Intelligence 43 full-blown AGI directly. This strategy, while surely ambitious, may well succeed. A second possible strategy might be to construct a sequence of increasingly
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perceptual memory. Their instantiations as sequences of actions contribute to perceptual learning, including conceptualization, leading to further understanding. S. Franklin / A Foundational Architecture for Artificial General Intelligence 45 Figure 9. Sloman’s Architecture. The long-term working memory of Ericsson and Kinstch [42] is incorporated into LIDA’s workspace (see below), in
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a continuously iterating cognitive cycle. Higher-level cognitive processes are composed of sequences of several or many of 46 S. Franklin / A Foundational Architecture for Artificial General Intelligence these cognitive cycles. Such higher-level cognitive processes might include deliberation, volition, problem solving, and metacognition. Let’s take a quick, guided tour through
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TEM encodings decay in humans within hours or a day. DM encodings Figure 10. The LIDA Cognitive Cycle. S. Franklin / A Foundational Architecture for Artificial General Intelligence 47 only occur through offline consolidation from TEM. Though they can decay away, when sufficiently reinforced DM encodings can last a lifetime. Both episodic memories
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Cambridge: Cambridge University Press. [19] Baars, B. J. 1997. In the Theater of Consciousness. Oxford: Oxford University Press. S. Franklin / A Foundational Architecture for Artificial General Intelligence 53 [20] Baars, B. J. 2002. The conscious access hypothesis: origins and recent evidence. Trends in Cognitive Science 6:47–52. [21] Franklin, S. 2003
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Columbia, Canada. Vancouver, British Columbia, Canada. [51] Edelman, G. M. 1987. Neural Darwinism. New York: Basic Books. 54 S. Franklin / A Foundational Architecture for Artificial General Intelligence [52] Lehmann, D., H. Ozaki, and I. Pal. 1987. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr. Clin. Neurophysiol. 67
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sides of the same coin. In Proceedings of the Sixth International Workshop on Epigenetic Robotics, vol. 128. Paris, France: Lund University Cognitive Studies. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 55 A
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, Eric. (2004) What is Thought? MIT Press, Cambridge, MA. [3] Hutter, M. (2006) Universal Algorithmic Intelligence: A Mathematical Top->Down Approach,pp 228291 in Artificial General Intelligence (Cognitive Technologies) (Hardcover) by Ben Goertzel and Cassio Pennachin (eds), Springer 74 E. Baum / A Working Hypothesis for General Intelligence [4] Schmidhuber, J.(2004) Optimal
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Norton, 1963. [12] L. Birnbaum, Rigor mortis: a response to Nilsson's “Logic and artificial intelligence”, Artificial Intelligence 47 (1991), 57-77. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 94 Adaptive Algorithmic
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C. Schultz, “Integrating Cognition, Perception, and Action through Mental Simulation in Robots,” Robotics and Autonomous Systems, vol. 49, pp. 13–23, 2004. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 111 Cognitive Map
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of Computer Science and Science Applications International Corporation (SAIC), Integrated Intelligence Solutions Operation and Novamente LLC Abstract. A program evolution component is proposed for integrative artificial general intelligence. The system’s deployment is intended to be comparable, on Marr’s level of computational theory, to evolutionary mechanisms in human thought. The challenges
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celoxica.com [5] Peter N. Martin, Genetic Programming in Hardware, PhD thesis, University of Essex, 2003, http://homepage.ntlworld.com/petemartin/HardwareGeneticProgramming.pdf Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 159 Complex
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of Experimental Child Psychology, 1994, 58, 184-199. [5] B. Goertzel, C. Pennachin, A. Senna, T. Maia, G. Lamacie. “Novamente: an integrative architecture for Artificial General Intelligence.” Proceedings of IJCAI 2003 Workshop on Cognitive Modeling of Agents. Acapulco, Mexico, 2004. [6] M. Looks, B. Goertzel and C. Pennachin. “Novamente: an integrative architecture
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for Artificial General Intelligence.” Proceedings of AAAI 2004 Symposium on Achieving Human-Level AI via Integrated Systems and Research, Washington DC, 2004. [7] B. Goertzel and Cassio Pennachin. “The
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[59] T. Gilovich, D. Griffin & D. Kahneman, (Eds.). Heuristics and biases: The psychology of intuitive judgment. Cambridge, UK: Cambridge University Press, 2002 Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 195 Indefinite Probabilities
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College, Alamosa, Colorado and Novamente LLC b Novamente LLC Abstract. The creation of robust mechanisms for uncertain inference is central to the development of Artificial General Intelligence systems. While probability theory provides a principled foundation for uncertain inference, the mathematics of probability theory has not yet been developed to the point where
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heuristic ones), we argue that this mode of quantifying uncertainty may be adequate to serve as an ingredient of powerful artificial general intelligence. Introduction As part of our ongoing work on the Novamente artificial general intelligence (AGI) system, we have developed a logical inference system called Probabilistic Logic Networks (PLN), designed to handle the
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9, 2004, Pages 837–857. [15] Ben Goertzel, Matthew Iklé, “Revision of Indefinite Probabilities via Entropy Minimization”, in preparation, expected publication 2007. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 217 Virtual Easter
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Social Learning, Cognitive Process Integration, and the Dynamic Emergence of the Self Ben GOERTZEL Novamente LLC Abstract. The Novamente Cognition Engine (NCE) architecture for Artificial General Intelligence is briefly reviewed, with a focus on exploring how the various cognitive processes involved in the architecture are intended to cooperate in carrying out moderately
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The Growth of Logical Thinking from Childhood to Adolescence. New York: Basic Books, 1958. [4] Goertzel, Ben and Cassio Pennachin (2006). The Novamente Design for Artificial General Intelligence. In Artificial General Intelligence, Springer-Verlag. [5] Goertzel, Ben (2006). Patterns, Hypergraphs and General Intelligence. Proceedings of International Joint Conference on Neural Networks, IJCNN 2006, Vancouver CA, to
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appear. [6] Goertzel, Ben, C. Pennachin, A. Senna, T. Maia, G. Lamacie. (2003) “Novamente: an integrative architecture for Artificial General Intelligence.” Proceedings of IJCAI 2003 Workshop on Cognitive Modeling of Agents. Acapulco, Mexico, 2003. [7] Goertzel, Ben, C. Pennachin, A. Senna, M. Looks. (2004) “The Novamente
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29] Yudkowsky, Eliezer (2007). Artificial Intelligence and Global Risk, in Global Catastrophic Risks, Ed. by Nick Bostrom and Milan Cirkovic, Oxford University Press. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. 253 Probabilistic Logic
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these aspects, which draws on probability theory and algorithmic information theory, among other areas. Unlike most contemporary AI projects, it is specifically oriented towards artificial general intelligence (AGI), rather than being restricted by design to one narrow domain or range of cognitive functions. The NAIE integrates aspects of prior AI projects and
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the first International Conference on Simulation of Adaptive Behavior, Meyer J.A. and Wilson S. (eds.). MIT Press. Franklin, S. (2006). A Foundational Architecture for Artificial General Intelligence, this volume. Ikle’, M., Goertzel, B. and Goertzel, I. (2006). Quantifying Weight of Evidence in Uncertain Inference via Hybridizing Confidence Intervals and Imprecise Probabilities,
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Goertzel, B. and Goertzel, I. (2006). Quantifying Weight of Evidence in Uncertain Inference via Hybridizing Confidence Intervals and Imprecise Probabilities, this volume 276 Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms B. Goertzel and P. Wang (Eds.) IOS Press, 2007 © 2007 The authors and IOS Press. All rights reserved. How Do
We Are as Gods: A Survival Guide for the Age of Abundance
by Peter H. Diamandis and Steven Kotler · 13 Apr 2026 · 225pp · 76,418 words
world’s greatest technologists and thought leaders are saying about We Are as Gods… “We Are as Gods is the critical reading for the coming Artificial General Intelligence and Singularity. Peter Diamandis and Steven Kotler show that while exponential technologies deliver the capability for radical abundance, the real challenge lies in upgrading our
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the time you’re reading this book? It could be 200. It could be 500. In other words, even if you don’t believe that artificial general intelligence is around the corner, super-genius computing is most definitely our next horizon. Or have we already crossed that threshold? What do we know for
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capital, network, and expertise toward solving challenges that matter at civilizational scale. We’re living through the most consequential technological inflection point in human history. Artificial general intelligence is effectively here. Artificial superintelligence arrives within years. Historically, successful leaders optimize existing companies: better margins, incremental growth. But linear thinking will fail during the
Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI
by Carissa Véliz · 21 Apr 2026 · 503pp · 129,255 words
] Ilya Sutskever, co-founder of OpenAI, told a group of researchers, “We’re definitely going to build a bunker before we release AGI,” so-called artificial general intelligence. “Of course,” he added, “it’s going to be optional whether you want to get into the bunker.”[76] * * * — In their mindset, winning involves doing
The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
by Sebastian Mallaby; · 30 Mar 2026 · 607pp · 161,998 words
therefore puny or phony—might be impolitic. To protect Goertzel’s standing in the research community, Legg persuaded him to go with an alternative title: Artificial General Intelligence. Around the time he coined that term, Legg read The Age of Spiritual Machines by the inventor and futurist Ray Kurzweil. The book’s central
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studied with Marcus Hutter. He’d done this theoretical proof of intelligence. He was already going to the Singularity Summit. He had coined the term artificial general intelligence. He had all these contacts in the nascent AGI world that I didn’t even know existed. “Here was a guy who’d dedicated, independently
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past several months, he had been plotting a stealth AI company, whose mission was not just to invent AI, but rather to go after AGI, artificial general intelligence. Hassabis already had a plan, a brilliant cofounder, and a list of potential collaborators. He just needed an investor. Playing off what Suleyman had told
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their lunches—that he wanted to leverage technology to drive social change—Hassabis stated the obvious. If Suleyman was truly interested in improving the world, artificial general intelligence was the best possible vehicle for him. An infinity machine would have infinite potential. “We saw technology as a force multiplier,” Hassabis recalled of this
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to some thirty pages, ranging from high-concept futurism to the specific milestones that DeepMind would reach before its next funding round. It explained why artificial general intelligence was necessary; why it would prove possible to build; and why DeepMind’s approach to the challenge was superior to that of its rivals. The
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2012, Mnih presented his PhD findings at an AI conference in Scotland. The conference was dominated by sober projects like his; futuristic schemes to build artificial general intelligence were nowhere on the agenda. But at the reception the first evening, the tone suddenly shifted. Two conference participants showed up at the party and
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deep learning. After all, the reward signals in reinforcement learning resembled the dopamine signals in the human brain. If the brain was the template for artificial general intelligence, RL would be indispensable to building it.[7] Mnih began to think that these crazy guys might know something. He had bounced between Toronto and
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or elsewhere—was calling upon the full congregation of the faithful to buy into a vision: a vision that usually included a theory of how artificial general intelligence might be built, coupled with a call to safeguard it. And although these visions might vary subtly from one person to the next, believers shared
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a single lab was going to take the technology forward, the most obvious contender was DeepMind. Moreover, when it came to the final steps to artificial general intelligence, Hassabis’s fascination with science fiction and scientific history fused into a heroic vision: He imagined convening a band of elite scientists in a secluded
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; what’s more, they would outperform humans not only at the jobs that existed today, but also at the ones that might be invented tomorrow. Artificial general intelligence was a general technology, with scarily generalized effects on human relevance. At a minimum, Suleyman insisted, AI would be good only if Google acted to
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need autonomous machine intelligence.[11] * * * • • • The question was what AlphaZero meant, not just for humans and their cognitive limits, but rather for the road to artificial general intelligence. For Silver, this breakthrough for reinforcement learning marked a revolution. AlphaZero had mastered three different complex games from scratch, without human instruction or human data
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experience. Hassabis shared Silver’s enthusiasm for reinforcement learning, albeit for his own reasons. Thanks to his PhD in neuroscience, he had always thought that artificial general intelligence would depend on integrating multiple components: perceptual systems built on convolutional neural nets; various kinds of memory, both long-term and short-term; search algorithms
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the best RL researchers chose to work at DeepMind. All of which meant that Hassabis’s strategic calculation was logical. So long as progress toward artificial general intelligence involved reinforcement learning, DeepMind could maintain its lead by cornering the market in this part of the AI supply chain. But Hassabis’s strategy involved
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be superhuman in its generality. In a counterfactual version of history, this second vision might have appealed powerfully to DeepMind, whose mission was to create artificial general intelligence. But just as everything in Sutskever’s training had prepared him to build language models, so Hassabis and Silver had been primed to underestimate them
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Halcyon Molecular.” BACK TO NOTE REFERENCE 19 Luke Nosek, author interview, January 4, 2023. BACK TO NOTE REFERENCE 20 “DeepMind: Building the World’s First Artificial General Intelligence,” copy provided to the author by DeepMind, September 30, 2010. BACK TO NOTE REFERENCE 21 According to the September 23, 2011, Companies House filing, Founders
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, 289, 301, 369, 430n4 An, James, 438n27 Andreessen, Marc, 321–22 Anfinsen, Christian, 258–59 Anthropic, 190, 301, 303, 306, 374, 433n24 Apollo 11, 88 artificial general intelligence (AGI), 62 believers and skeptics in, 88 definition of, 78 God and, xix–xx, 73, 114–15 international body for, 376 introspection and, 144 intuition
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in, 149 singleton scenario of, 167–69, 172, 228 for societal problem-solving, 76 space travel compared with, 111 Thiel scouting companies in, 73–74 Artificial General Intelligence (Goertzel), 57 artificial intelligence (AI) academic compared with gaming, 403n31 biology and, 404n14 community, 167 consciousness and, 439n19 Dartmouth pioneers of, 23–24 doctors, 378
Nexus
by Ramez Naam · 16 Dec 2012 · 502pp · 124,794 words
yielded up a plethora of fascinating talks: Neural Substrates of Symbolic Reasoning, Intelligence and Prospects for Increasing It, Emotive-Loop Programming: A New Path to Artificial General Intelligence. How could they even hold these talks? In the US the topics of half of them would be classified as Emerging Technological Threats. No wonder
Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World
by Mo Gawdat · 29 Sep 2021 · 259pp · 84,261 words
unlikely. I think you AGreeP. If You Can’t Beat Them . . . Some of those who recognize that we will not be able to control an artificial general intelligence that is smarter than us, suggest that we plug them directly into our bodies instead. A sort of ‘if you can’t beat them, join
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Turing predicted that ‘once the machine thinking method had started, it would not take long to outstrip our feeble powers.’ As AI transitions to AGI, artificial general intelligence, and beyond the confines of the programmable tasks the machine was invented to carry out, the concerns heighten. Irving Good, who was a consultant on
Smarter Than Us: The Rise of Machine Intelligence
by Stuart Armstrong · 1 Feb 2014 · 48pp · 12,437 words
Evolution and Technology 22, no. 1 (2012): 116–131, http://jetpress.org/v22/goertzel-pitt.htm. 4. Ben Goertzel, “CogPrime: An Integrative Architecture for Embodied Artificial General Intelligence,” OpenCog Foundation, October 2, 2012, accessed December 31, 2012, http://wiki.opencog.org/w/CogPrime_Overview. Chapter 10 A Summary There are no convincing reasons
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,” Minds and Machines 22, no. 4 (2012): 299–324, doi:10.1007/s11023-012-9282-2. 2. Stephen M. Omohundro, “The Basic AI Drives,” in Artificial General Intelligence 2008: Proceedings of the First AGI Conference, Frontiers in Artificial Intelligence and Applications 171 (Amsterdam: IOS, 2008), 483–492. 3. Roman V. Yampolskiy, “Leakproofing the
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, and Eric Steinhart, eds. Singularity Hypotheses: A Scientific and Philosophical Assessment. The Frontiers Collection. Berlin: Springer, 2012. Goertzel, Ben. “CogPrime: An Integrative Architecture for Embodied Artificial General Intelligence.” OpenCog Foundation. October 2, 2012. Accessed December 31, 2012. http://wiki.opencog.org/w/CogPrime_Overview. Goertzel, Ben, and Joel Pitt. “Nine Ways to Bias
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(blog), April 8, 2007. http://amartester.blogspot.co.uk/2007/04/bugs-per-lines-of-code.html. Omohundro, Stephen M. “The Basic AI Drives.” In Artificial General Intelligence 2008: Proceedings of the First AGI Conference, 483–492. Frontiers in Artificial Intelligence and Applications 171. Amsterdam: IOS, 2008. Parameswaran, Ashwin. “People Make Poor Monitors
World Without Mind: The Existential Threat of Big Tech
by Franklin Foer · 31 Aug 2017 · 281pp · 71,242 words
acquire anything approximating human consciousness. Then there are the revolutionaries who gravitate toward Kurzweil and the singularitarian view. They aim to build computers with either “artificial general intelligence” or “strong AI.” For most of Google’s history, it trained its efforts on incremental improvements. During that earlier era, the company was run by
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to Page talk to his employees, he returns time and again to the metaphor of the moonshot. The company has an Apollolike program for reaching artificial general intelligence: a project called Google Brain, a moniker with creepy implications. (“The Google policy on a lot of things is to get right up to the
Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz · 15 Mar 2021 · 414pp · 109,622 words
around the same time as Google Brain, DeepMind was a start-up dedicated to an outrageously lofty goal. It aimed to build what it called “artificial general intelligence”—AGI—technology that could do anything the human brain could do, only better. That endgame was still years, decades, or perhaps even centuries away, but
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the dangers of this technology in both the present and the future. Their stated aim—contained in the first line of their business plan—was artificial general intelligence. But at the same time, they told anyone who would listen, including potential investors, that this research could be dangerous. They said they would never
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’t understand its growing importance. More to the point: Facebook was a social networking company. It built Internet technology for the here and now, not “artificial general intelligence” or any other technology unlikely to reach the real world for years to come. The company motto was “Move Fast and Break Things,” a slogan
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Shane Legg near Russell Square, and as the three men talked, Hassabis and Legg explained what they were trying to do. They were building AGI—artificial general intelligence—and they were beginning with systems that played games. As he listened, Sutskever thought they’d lost touch with reality. AGI was not something serious
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Jeff Dean, Google Brain was intent on building technologies with practical and immediate impact: speech recognition, image recognition, translation, healthcare. DeepMind’s stated mission was artificial general intelligence, and it was chasing this North Star by teaching systems to play games. Google Brain was an integral part of Google that delivered revenue. DeepMind
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had interviewed with Hassabis and Legg while still a grad student at the University of Toronto and the two DeepMind founders said they were building artificial general intelligence, he thought they had lost touch with reality. But after his personal success with image recognition and machine translation at Google—and after he spent
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the same beliefs and ambitions—but he worried their conversations would come back to haunt him. If others heard he was discussing the rise of artificial general intelligence, he would be branded a pariah across the wider community of researchers. When OpenAI was unveiled, the official announcement did not mention AGI. It only
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the current technologies, but their goal was a machine that could do anything the human brain could do. “OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly
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and his researchers released a new charter for the lab: “OpenAI Charter,” OpenAI blog, https://openai.com/charter/. “OpenAI’s mission is to ensure that artificial general intelligence”: Ibid. DeepMind trained a machine to play capture the flag: Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning et al., “Human-level Performance in 3D Multiplayer
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–38 Android smartphones and speech recognition, 77–79 Angelova, Anelia, 136–37 ANNA microchip, 52–53 antitrust concerns, 255 Aravind Eye Hospital, 179–80, 184 artificial general intelligence (AGI), 100, 109–10, 143, 289–90, 295, 299–300, 309–10 artificial intelligence (AI). See also intelligence ability to remove flagged content, 253 AI
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winter, 34–35, 288 AlphaGo competition as a milestone event, 176–78, 198 artificial general intelligence (AGI), 100, 109–10, 143, 289–90, 295, 299–300, 309–10 the black-box problem, 184–85 British government funding of, 34–35 China
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, 156, 210, 325 NYU Center for Mind, Brain, and Consciousness debate between Yann LeCun and Gary Marcus, 268–72 On Intelligence (Hawkins), 82 OpenAI and artificial general intelligence (AGI), 289–90, 295, 299 counteroffers made to researchers leaving for, 164 creation of a machine to win at Dota/Dota 2, 281, 297 and
Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl · 10 Aug 2016 · 252pp · 74,167 words
yet-undiscovered novel approach to simulating an AI that any chatbot technology employed today could ever fool an experienced chatbot creator into believing they possess [artificial] general intelligence.’ Turing wasn’t particularly concerned with the metaphysical question of whether a machine can actually think. In his famous 1950 essay, ‘Computing Machinery and Intelligence
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of AI weapons has been a goal since virtually the field’s earliest days. What he and Musk were specifically pointing towards was something called Artificial General Intelligence, or AGI. So far, all of the applications of Artificial Intelligence described in this book have come under the broad umbrella heading of ‘Narrow AI
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scientists are now trying to build more biologically brain-like algorithms like the ones described in the last chapter. So what is so ‘general’ about Artificial General Intelligence, then? In contrast to narrow, single-domain AI applications, a general intelligence would show a more wide-ranging, humanlike intelligence. It would be, as Herbert
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matter collapses in on itself. Like a black hole, the technological Singularity is wholly unfathomable to the human mind. For this reason, speculating about where Artificial General Intelligence could potentially take us is interesting, but ultimately the stuff of science fiction for now. It’s a little like automobile pioneer Henry Ford’s
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A lifetime of sci-fi movies and books have ingrained in us the expectation that there will be some Singularity-style ‘tipping point’ at which Artificial General Intelligence will take place. Devices will get gradually smarter and smarter until, somewhere in a secret research lab deep in Silicon Valley, a message pops up
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on Mark Zuckerberg or Sergey Brin’s computer monitor, saying that AGI has been achieved. Like Ernest Hemingway once wrote about bankruptcy, Artificial General Intelligence will take place ‘gradually, then suddenly’. This is the narrative played out in films like James Cameron’s seminal Terminator 2: Judgment Day. In that
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involved. For instance, should the nervous system of C. elegans, as described in the last chapter, be satisfactorily replicated inside a computer, would that represent Artificial General Intelligence? Although such a breakthrough may lead to insights that could improve our existing machine learning tools, the answer is that perhaps it may not. C
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any American dead, then that’s what we’ll get.’ But it’s not only the far future applications of AI – or the development of Artificial General Intelligence – that poses challenges. Artificial Stupidity In April 2012, Rocco DiGiorgio got home from work to find that his house smelled terrible. Dog faeces were virtually
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as coming up with geeky home automation projects. Others work toward goals like bringing about AGI. ‘No challenge today is more important than creating beneficial artificial general intelligence (AGI), with broad capabilities at the human level and ultimately beyond,’ reads the website of OpenCog, an open-source software initiative which describes itself as
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, 236, 238–9, 242 Apple iPhone 108, 113, 181 Apple Music 158–9 Apple Watch 66, 199 architecture 186 Artificial Artificial Intelligence (AAI) 153, 157 Artificial General Intelligence (AGI) 226, 230–4, 239–40, 254 Artificial Intelligence (AI) 2 authentic 31 development problems 23–9, 32–3 Good Old-Fashioned (Symbolic) 22, 27
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a strange house and make a cup of coffee. Exploring this hypothesis, some researchers now suggest the ‘coffee test’ as a potential measure for AGI, Artificial General Intelligence. I will discuss AGI later on in this book. Chapter 4 fn1 To be fair to Mitsuku, very few of us would have a good
The Simulation Hypothesis
by Rizwan Virk · 31 Mar 2019 · 315pp · 89,861 words
Elon Musk: A Mission to Save the World
by Anna Crowley Redding · 1 Jul 2019 · 190pp · 46,977 words
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by Thomas H. Davenport and Julia Kirby · 23 May 2016 · 347pp · 97,721 words
The Economic Singularity: Artificial Intelligence and the Death of Capitalism
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Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside
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