Growing an organism is not like booting up a computer…A society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.
Carl Woese, quoted by Segall and Damer, 2024, The cosmological context of the origin of life: process Philosophy and the hot spring hypothesis, in: Astrophilosophy, Exotheology, and Cosmic Religion: Extraterrestrial Life in a Process Universe, p. 91
Latest
UN System Chief Executives Board for Coordination, 2024, Summary of deliberations: United Nations system white paper on artificial intelligence governance: an analysis of current institutional models and related functions and existing international normative frameworks within the United Nations system that are applicable to artificial intelligence governance. “Under the ILO governance model, both trade unions and organizations of employers are involved in standard -setting and meetings of executive committees for shared decision-making and governance on an equal footing with Governments” (p. 39).
Zhang, et al., 2024, Parallel molecular data storage by printing epigenetic bits on DNA, in: Nature. “An automatic liquid handling platform was used to typeset large-scale data at a speed of approximately 40 bits s−1”.
WIPO, 2024, Patent Landscape Report – Generative Artificial Intelligence (GenAI)
Esposito and Baravalle, 2023, The machine-organism relation revisited, in: History and Philosophy of the Life Sciences
Park, et al., 2023, Papers and patents are becoming less disruptive over time, in: Nature
OECD, 2023, Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research
Rindzevičiūtė, 2023, The Will to Predict: Orchestrating the Future Through Science
Stepney, 2023, Life as a Cyber-Bio-Physical System, in: Genetic Programming Theory and Practice XIX
Emmerich, 2021, Die Auswirkungen künstlicher Intelligenz auf die erfinderische Tätigkeit und das Erfinderprinzip
Kitano, 2021, Nobel Turing Challenge: creating the engine for scientific discovery, in: npj Systems Biology and Applications (Nobel Turing Challenge)
Undheim, 2021, Future Tech: How to Capture Value from Disruptive Industry Trends
Dally, et al., 2021, Evolution of the Graphics Processing Unit (GPU), in: IEEE Micro [Dally is a significant figure in NVIDIA]
Dixon, et al., 2021, Sensing the future of bio-informational engineering, in: Nature Communications
Burger, et al., 2020, A mobile robotic chemist, in: Nature
UN Inter-Agency Working Group on AI (2020-2025) – AI for Good
2010-2020
Nicholson, 2019, Is the cell really a machine?, in: Journal of Theoretical Biology
Veale and Cardoso, 2019, Computational Creativity: The Philosophy and Engineering of Autonomously Creative Systems
Stuart, 2019, The role of imagination in social scientific discovery: why machine discoverers will need imagination algorithms, in: Scientific Discovery in the Social Sciences
Charlton McIlwain, 2019, Black Software: The Internet and Racial Justice, From the Afronet to Black Lives Matter
Sybrandt, et al., 2017, MOLIERE: Automatic Biomedical Hypothesis Generation System, in: KDD
Mody, 2017, The Long Arm of Moore’s Law: Microelectronics and American Science
Jasanoff, 2016, The Ethics of Invention: Technology and the Human Future
Stephens, et al., 2015, Big data: astronomical or genomical?, in: PLoS Biology
LeCun, et al., 2015, Deep learning, in: Nature
Chen, 2013, Mapping Scientific Frontiers: The Quest for Knowledge Visualization
Rabinow and Dan-Cohen, 2013, A Machine to Make a Future: Biotech Chronicles
Dunbar and Klahr, 2013, Developmental differences in scientific discovery processes, in: Complex Information Processing: The Impact of Herbert A. Simon
Stengers, 2013, Une autre science est possible ! Manifeste pour un ralentissement des sciences (reviewed by Dufaud, 2015, in: Artefact)
Dutfield, 2012, Did Kary Mullis really invent PCR? (video)
Swanson, 2011, Literature-based Resurrection of Neglected Medical Discoveries, in: Journal of Biomedical Discovery and Collaboration (see also: Arrowsmith)
Vapnik, 2010, Introduction: four periods in the research of the learning problem, in: The Nature of Statistical Learning Theory, 2nd edition
2000-2010
Mitchell, 2009, Complexity: A Guided Tour
Engelbrecht, 2007, Computational Intelligence: an Introduction
Searls, 2007, A view from the dark side, in: PLoS Computational Biology
Dzeroski and Todorovski, 2007, Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences
Montrym and Moreton, 2005, The GeForce 6800, in: IEEE Micro
Thomas P. Hughes, 2004, Origins of the information revolution, in: Human-Built World: How to Think about Technology and Culture, pp. 96-109
McCorduck, 2004, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence (see also: McCorduck, 2019, This Could Be Important: My Life and Times with the Artificial Intelligentsia)
Carl R. Woese, 2004, A new biology for a new century, in: Microbiology and Molecular Biology Reviews
Menzies, 2003, 21st-century AI: proud, not smug, in: IEEE Intelligent Systems
Aviv Regev and Ehud Shapiro, 2002, Cellular abstractions: cells as computation, in: Nature
1990-2000
Langley, 1999, The Computational Support of Scientific Discovery, in: Advanced Course on Artificial Intelligence ACAI 1999: Machine Learning and Its Applications (Pat Langley, Adaptive Systems Group, Daimler Chrysler Research and Technology Center, Palo Alto)
Qin and Norton (eds.), 1999, Knowledge Discovery in Bibliographic Databases (University of Illinois)
Swanson and Smalheiser, 1997, An interactive system for finding complementary literatures: a stimulus to scientific discovery, in: Artificial Intelligence
Landauer, 1996, The physical nature of information, in: Physics Letters A
Carver Mead, 1990, Neuromorphic electronic systems, in: Proceedings of the IEEE
Institute for the Study of Learning and Expertise (ISLE) [website]
Before 1990
Forester, 1987, High-Tech Society
Bradshaw, et al., 1983, Studying scientific discovery by computer simulation, in: Science
Kraus and Bar-Cohen, 1983, Thermal Analysis and Control of Electronic Equipment
Langley, 1981, Data-driven discovery of physical laws, in: Cognitive Science
Yoxen, 1981, Life as a productive force: capitalizing the science and technology of molecular biology, in: Science, Technology, and the Labour Process: Marxist Studies, vol. 1 (eds. Levidow and Young), pp. 66-122
Duncan, 1981, Microelectronics: five areas of subordination, in: Science, Technology, and the Labour Process: Marxist Studies, vol. 1 (eds. Levidow and Young), pp. 172-207
Simon, 1977, Theory of scientific discovery, in: Models of Discovery and Other Topics in the Methods of Science. The argument is that the key to automated scientific discovery would be in understanding how human brains discover (the ‘psychology of problem solving’) and then seeking to replicate this process with a machine.
Buchholz (ed.), 1962, Planning a Computer System: Project Stretch
Herbert Simon was a notable Cold War academic personality who won the Nobel Prize in Economic Sciences (1978). He has been dubbed the ‘founding father’ of AI (UBS Nobel Perspectives). Besides Simon’s own autobiography, a biography, Crowther-Heyck, 2005, Herbert A. Simon: The Bounds of Reason in Modern America, and Hickey, 2016, Twentieth-Century Philosophy of Science: A History, are informative.
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