71 skills found · Page 1 of 3
djgagne / Ams Ml Python CourseMachine Learning in Python for Environmental Science Problems AMS Short Course Material
earth-env-data-science / Earth Env Data Science BookSource repo for Earth and Environmental Data Science book
eds-book / Eds BookA computational notebook community for open environmental data science 🌎
nicolasfauchereau / NIWA Python SeminarsA series of IPython notebooks on Python for data analysis geared towards environmental sciences
GreenNovaEmma / Migraine Risk CardA science-backed environmental migraine risk monitoring card for Home Assistant. Tracks 9 weather and air quality factors known to trigger migraines and displays a composite risk score with a visual gauge.
lter / LterdatasamplerLTER data samples to teach environmental data science
adeverse / Ade4Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences
beatrizmilz / Environmental Data ScienceA list of books, events, articles, journals, courses, etc related to Environmental Data Science.
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They work, but they work by brute force." (p. 198.) Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65. Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press. Koch, Christof, "Proust among the Machines", Scientific American, vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because "[e]ven the most sophisticated brain simulations are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient [is important] for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)
kristineccles / Introduction To RThis repository contains the documentation, background material, scripts, and data for a tutorial on the Introduction to R. This introductory workshop is greared towards people with a interest/ background in geography and environmental science.
iroreilly / Ams 2020 Ml Python CourseMaterials for the Machine Learning in Python for Environmental Science Problems AMS 2020 Short Course
ai2es / Tai4es Trustathon 2022Trust-A-Thon materials for the Trustworthy AI for Environmental Science Summer School
NERC-CEH / Data Science ToolboxCollection of data science methods/pipelines to support the UK's national capability in delivering world-leading environmental science.
TsaiRongFu / AI AquaculturingThis project is developed for smart aquaculture. We use a camera to capture pictures of fishes in the tank. A deep neural network (MobileNet) is applied to recognize the images to identify the situation of fishes in the tank. EdgeTPU is used for edge computing. We also monitor the water quality by some environmental sensors. All the information is integrated into a website constructed by Laravel, and a Linebot is used to deliver information on mobile phones.Four undergraduate students develop this project from the Department of Management Information Systems (MIS), National Pingtung University of Science and Technology (NPUST), Taiwan.
kierenAW / Conda Environments For Data ScienceEnvironmental configuration files used to aid in committing Data Science and Machine Learning.
Open-Source-Agriculture / Soil LogrThe soil mate app provides a simple and convenient way to collect soil data at sample locations in the field. The Soil Mate app is targeted across multiple industries, including agriculture, environmental science, geology, and mining. The current version of the app collects soil texture data.
Orpheus-AI / ZeusZeus Subnet leverages AI to forecast environmental variables using real-time global data. It incentivizes innovation in climate science by enabling miners and validators to develop and evolve efficient, decentralized prediction models.
ahamilton144 / Python For Environmental ResearchTeaching materials for an introduction to using Python for environmental research (UNC Department of Environmental Sciences and Engineering)
sustentarea / Logoclim⛅ WorldClim in NetLogo
yufree / EnviGCMSGC/LC-MS data analysis for environmental science
