User contributions
From RB Wiki
- 09:44, 4 February 2020 diff hist -24 m Bayesianism current
- 09:44, 4 February 2020 diff hist +93 m User:Lê Nguyên Hoang current
- 09:42, 4 February 2020 diff hist +2,889 m Laplace 1814 →Laplace's philosophy of probability current
- 21:37, 3 February 2020 diff hist 0 m Laplace 1814 →Laplace's philosophy of probability
- 18:03, 3 February 2020 diff hist +3 m Laplace 1814 →Laplace's philosophy of probability
- 16:32, 3 February 2020 diff hist +13 m Bayesianism
- 15:53, 3 February 2020 diff hist +99 m Laplace 1814
- 15:51, 3 February 2020 diff hist -5 m Bayesianism
- 15:50, 3 February 2020 diff hist +45 m Bayesianism
- 15:49, 3 February 2020 diff hist +16 m Turing 1950 →Final remarks current
- 15:32, 3 February 2020 diff hist -2 m Laplace 1814 →Laplace's philosophy of probability
- 15:32, 3 February 2020 diff hist +7,679 N Laplace 1814 Created page with "In 1814, Pierre-Simon Laplace published "An Philosophical Essay on Probabilities" [https://play.google.com/books/reader?id=rDUJAAAAIAAJ&hl=en&pg=GBS.PA3.w.1.0.0 Laplace1814] [..."
- 14:07, 3 February 2020 diff hist +18 m Welcome to the Robustly Beneficial Wiki →How today's (and probably tomorrow's) AIs work
- 10:25, 3 February 2020 diff hist +116 m Preference learning from comparisons →Gaussian process
- 10:03, 3 February 2020 diff hist +1,484 m Preference learning from comparisons →Gaussian process
- 09:46, 3 February 2020 diff hist +17 m Preference learning from comparisons →Classical models
- 09:45, 3 February 2020 diff hist +41 m Preference learning from comparisons →Classical models
- 09:45, 3 February 2020 diff hist +560 m Preference learning from comparisons →Classical models
- 09:45, 3 February 2020 diff hist +181 m Preference learning from comparisons →Markov chain for preference learning
- 09:32, 3 February 2020 diff hist +879 m Preference learning from comparisons →Markov chain for preference learning
- 09:22, 3 February 2020 diff hist +2 m Preference learning from comparisons →Classical models
- 09:08, 3 February 2020 diff hist +10 m Preference learning from comparisons →Classical models
- 09:07, 3 February 2020 diff hist +28 m Preference learning from comparisons →Classical models
- 09:06, 3 February 2020 diff hist +104 m Preference learning from comparisons →Classical models
- 09:05, 3 February 2020 diff hist +4,566 N Preference learning from comparisons Created page with "It has been argued that we humans are much more effective at comparing alternatives than at scoring them [https://infoscience.epfl.ch/record/255399/files/EPFL_TH8637.pdf Mayst..."
- 08:18, 3 February 2020 diff hist +66 m Welcome to the Robustly Beneficial Wiki →How to solve AI ethics (hopefully)
- 22:29, 2 February 2020 diff hist +6 m Robustly Beneficial group →Past papers
- 10:59, 2 February 2020 diff hist -1 m Robustly Beneficial group
- 10:40, 2 February 2020 diff hist +225 m Robustly Beneficial group →Candidate future papers
- 10:37, 2 February 2020 diff hist +458 m Robustly Beneficial group →Candidate future papers
- 10:36, 2 February 2020 diff hist +50 m YouTube →Effects current
- 10:35, 2 February 2020 diff hist +99 m AI opportunities →Healthcare current
- 10:34, 2 February 2020 diff hist +143 m Mental health →Impact
- 10:33, 2 February 2020 diff hist +85 m Robust statistics
- 10:32, 2 February 2020 diff hist +50 m Adversarial attacks →Poisoning attacks
- 10:32, 2 February 2020 diff hist +50 m Interpretability →Black box current
- 10:30, 2 February 2020 diff hist +2 m Robustly Beneficial group →Candidate future papers
- 10:30, 2 February 2020 diff hist +20 m Robustly Beneficial group →Candidate future papers
- 10:29, 2 February 2020 diff hist +28 m Robustly Beneficial group →Past papers
- 10:28, 2 February 2020 diff hist +2,291 m Robustly Beneficial group →Past papers
- 10:19, 2 February 2020 diff hist +3,494 N Robustly Beneficial group Created page with "The Robustly Beneficial group is an AI ethics group, started by Louis Faucon and Sergei Volodin, in Lausanne, Switzerland. The group is now managed by ..."
- 09:46, 2 February 2020 diff hist -56 m Welcome to the Robustly Beneficial Wiki →About the authors
- 13:38, 1 February 2020 diff hist -29 m Welcome to the Robustly Beneficial Wiki
- 21:30, 30 January 2020 diff hist +266 m Mental health →Impact
- 08:50, 30 January 2020 diff hist +179 m Welcome to the Robustly Beneficial Wiki
- 15:01, 29 January 2020 diff hist +383 N AI governance Created page with "AI governance is the problem of understanding what forces act on the development of AIs, and what ought to be done to aligns such efforts with ethical values. == Guidelines =..." current
- 17:58, 28 January 2020 diff hist +98 m Stochastic gradient descent current
- 17:56, 28 January 2020 diff hist +2,179 N Stochastic gradient descent Created page with "Stochastic gradient descent (SGD) is the most widely used learning algorithm. For a very general perspective, SGD consists in iterating (1) draw some data point and (2) slight..."
- 15:53, 28 January 2020 diff hist +46 m Human liabilities current
- 15:52, 28 January 2020 diff hist +172 N Human liabilities Created page with "The safety of algorithms can be limited by the liabilities of humans in charge of the algorithms. Below we list such possible liabilities. == Phishing == == Ransomware =="
- 15:51, 28 January 2020 diff hist +23 m Welcome to the Robustly Beneficial Wiki →Why AI safety and ethics is harder than meets the eye
- 15:26, 28 January 2020 diff hist +73 m Adversarial attacks →Astroturfing attacks
- 15:11, 28 January 2020 diff hist +154 m Interpretability →Neural network activations
- 15:09, 28 January 2020 diff hist +111 m Interpretability
- 15:09, 28 January 2020 diff hist +107 m Adversarial attacks →Evasion attacks
- 15:05, 28 January 2020 diff hist +62 m Interpretability →What to expect from interpretability?
- 22:08, 27 January 2020 diff hist +297 m Online polarization →Online radicalization current
- 22:07, 27 January 2020 diff hist +265 m YouTube →Effects
- 20:47, 27 January 2020 diff hist +261 m Impressive advances in AI →Image processing
- 20:44, 27 January 2020 diff hist +226 m Impressive advances in AI
- 15:08, 27 January 2020 diff hist +26 m Welcome to the Robustly Beneficial Wiki →How today's (and probably tomorrow's) AIs work
- 15:08, 27 January 2020 diff hist +228 N Specialized hardware Created page with "There is a lot of ongoing research on specialized hardware for machine learning, as well as algorithms optimized for the specialized hardware. GPU TPU Phase-change memory..." current
- 13:17, 27 January 2020 diff hist 0 m Machine learning →Empirical observations current
- 13:16, 27 January 2020 diff hist +1,097 m Machine learning →Turing's argument
- 13:09, 27 January 2020 diff hist +30 m Machine learning
- 13:08, 27 January 2020 diff hist +599 m Machine learning →Turing's argument
- 13:04, 27 January 2020 diff hist +373 m Machine learning
- 12:37, 27 January 2020 diff hist +2,121 N Adversarial attacks Created page with "Adversarial attacks encompass a large range of users' behaviors trying to hack an algorithm's vulnerabilities for their advantages. == Evasion attacks == An evasion attack i..."
- 10:11, 27 January 2020 diff hist +95 m Impressive advances in AI →Sound processing
- 22:52, 26 January 2020 diff hist +85 m Robust statistics →What if there are more outliers than inliers?
- 22:51, 26 January 2020 diff hist +88 m Robust statistics →What if there are more outliers than inliers?
- 18:07, 26 January 2020 diff hist +52 m Social choice →Bounds for limited communication complexity
- 15:44, 26 January 2020 diff hist +162 m YouTube →Key numbers
- 11:35, 26 January 2020 diff hist -6 m Online polarization →Online radicalization
- 11:34, 26 January 2020 diff hist -6 m AI risks →A list of risks current
- 11:34, 26 January 2020 diff hist -12 m Interpretability
- 11:33, 26 January 2020 diff hist -6 m YouTube →Effects
- 11:03, 26 January 2020 diff hist -124 m Robustly beneficial →Robustly beneficial to distributional shift current
- 11:02, 26 January 2020 diff hist +488 m Robustly beneficial
- 12:57, 25 January 2020 diff hist +472 N How physicists can contribute Created page with "Machine learning algorithms will likely have numerous applications in physics, for instance in material science, nuclear physics and photovoltaics. == Algorithms for cleaner..." current
- 12:54, 25 January 2020 diff hist +45 m Welcome to the Robustly Beneficial Wiki →How to solve AI ethics (hopefully)
- 10:00, 25 January 2020 diff hist -53 m Robust statistics
- 20:19, 24 January 2020 diff hist +42 m AI risks →Security mindset
- 20:18, 24 January 2020 diff hist +306 m AI risks →Security mindset
- 19:09, 24 January 2020 diff hist +169 m Robust statistics →Poisoning models
- 19:07, 24 January 2020 diff hist +531 m Robust statistics
- 16:22, 24 January 2020 diff hist +67 m How mathematicians can contribute
- 20:31, 23 January 2020 diff hist +39 m Robust statistics →Poisoning models
- 20:31, 23 January 2020 diff hist +17 m Robust statistics →Poisoning models
- 20:29, 23 January 2020 diff hist +10 m Robust statistics →Poisoning models
- 20:27, 23 January 2020 diff hist +649 m Robust statistics →Poisoning models
- 17:50, 23 January 2020 diff hist +161 m Algorithmic bias current
- 15:06, 23 January 2020 diff hist +241 m Robust statistics →Robust statistics for neural networks
- 15:04, 23 January 2020 diff hist -324 m Robust statistics →Robustness to strong poisoning
- 14:01, 23 January 2020 diff hist +1 m Robust statistics →Robustness to additive poisoning
- 10:03, 23 January 2020 diff hist -26 m Robust statistics
- 10:02, 23 January 2020 diff hist +516 m Robust statistics
- 09:59, 23 January 2020 diff hist +143 m Robust statistics
- 09:58, 23 January 2020 diff hist +2 m Robust statistics
- 09:57, 23 January 2020 diff hist +211 m Robust statistics