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2. Tool AI

3. 2011-05-10-givewell-holdenkarnofskyjaantallinn.doc

4. Thoughts on the Singularity Institute (SI)

5. Tools versus Agents

6. Learning to summarize from human feedback

7. ​ GPT-3 Creative Fiction

8. Decision Transformer: Reinforcement Learning via Sequence Modeling

9. Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers

10. Deep Neural Networks for YouTube Recommendations

11. Top-K Off-Policy Correction for a REINFORCE Recommender System

12. Reinforcement Learning for Recommender Systems: A Case Study on Youtube

13. ​ note#advanced-chess-obituary

    [Transclude the forward-link's context]

14. The Season of Burning Trucks

15. What If the City Ran Waze and You Had to Obey It? Could This Cure Congestion?

16. We Traveled to Holloman Air Force Base for a Glimpse of the Future of War-And the Future of Work

17. Developers Predict That Pilotless Devices Will Join Planes in Civilian Airspace—And Dream of Electric Robots Counting Sheep

18. Sam Altman’s Manifest Destiny: Is the head of Y Combinator fixing the world, or trying to take over Silicon Valley?

19. The United States Has Put Artificial Intelligence at the Center of Its Defense Strategy, With Weapons That Can Identify Targets and Make Decisions.

20. Forget about Drones, Forget about Dystopian Sci-Fi—A Terrifying New Generation of Autonomous Weapons Is Already Here. Meet the Small Band of Dedicated Optimists Battling Nefarious Governments and Bureaucratic Tedium to Stop the Proliferation of Killer Robots And, Just Maybe, save Humanity from Itself.

21. https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF#page=8

22. https://www.gutenberg.org/cache/epub/16550/pg16550-images.html#Cpage445

23. Traffic-Weary Homeowners and Waze Are at War, Again. Guess Who’s Winning?

24. ‘Cut-Through’ Traffic Caused by Waze App Must Stop, L.A. Councilman Says

25. LA Residents Complain about ‘Waze Craze’

26. ​ How Should We Critique Research?

27. Probabilistic Integration: A Role in Statistical Computation?

28. Pure Exploration for Multi-Armed Bandit Problems

29. Best Arm Identification in Multi-Armed Bandits

30. Multi-Bandit Best Arm Identification

31. Decision Making Using Thompson Sampling

32. Best-Arm Identification Algorithms for Multi-Armed Bandits in the Fixed Confidence Setting

33. On the Complexity of Best Arm Identification in Multi-Armed Bandit Models

34. Optimal Design in Psychological Research

35. Rerandomization to improve covariate balance in experiments

36. The Power of Two Random Choices: A Survey of Techniques and Results

37. Attention and Augmented Recurrent Neural Networks

38. Modeling Human Reading with Neural Attention

39. Hierarchical Object Detection With Deep Reinforcement Learning

40. Generating Images from Captions with Attention

41. DRAW: A Recurrent Neural Network For Image Generation

42. Show, Attend and Tell: Neural Image Caption Generation With Visual Attention

43. Learning to Combine Foveal Glimpses With a Third-Order Boltzmann Machine

44. Neural Machine Translation by Jointly Learning to Align and Translate

45. On Learning Where To Look

46. Recurrent Models of Visual Attention

47. Iterative Alternating Neural Attention for Machine Reading

48. Can Active Memory Replace Attention?

49. Attention Is All You Need

50. Foveation-based Mechanisms Alleviate Adversarial Examples

51. Character-Level Language Modeling with Deeper Self-Attention

52. Adaptive Computation Time for Recurrent Neural Networks

53. Spatially Adaptive Computation Time for Residual Networks

54. Feedback Networks

55. Multi-Scale Dense Networks for Resource Efficient Image Classification

56. RAM: Dynamic Computational Time for Visual Attention

57. IDK Cascades: Fast Deep Learning by Learning not to Overthink

58. BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks

59. Learning Policies for Adaptive Tracking with Deep Feature Cascades

60. Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback

61. Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks

62. Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

63. Adaptive Neural Networks for Efficient Inference

64. BlockDrop: Dynamic Inference Paths in Residual Networks

65. Neural Speed Reading via Skim-RNN

66. Learning to select computations

67. Universal Transformers

68. Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

69. PonderNet: Learning to Ponder

70. Neural Ordinary Differential Equations

71. Differentiable Neural Computers

72. Reinforcement Learning Neural Turing Machines—Revised

73. Hybrid computing using a neural network with dynamic external memory

74. Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

75. Simple statistical gradient-following algorithms for connectionist reinforcement learning

76. Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

77. Bidirectional Attention Flow for Machine Comprehension

78. Towards Information-Seeking Agents

79. Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

80. Learning to Organize Knowledge and Answer Questions with N-Gram Machines

81. Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications

82. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

83. Machine Learning for Systems and Systems for Machine Learning

84. A View on Deep Reinforcement Learning in System Optimization

85. Device Placement Optimization with Reinforcement Learning

86. A Hierarchical Model for Device Placement

87. The Case for Learned Index Structures

88. Meta-Learning Neural Bloom Filters

89. SageDB: A Learned Database System

90. GAP: Generalizable Approximate Graph Partitioning Framework

91. Optimizing Query Evaluations using Reinforcement Learning for Web Search

92. Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation

93. AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

94. MLGO: a Machine Learning Guided Compiler Optimizations Framework

95. Universal quantum control through deep reinforcement learning

96. MuZero with Self-competition for Rate Control in VP9 Video Compression

97. Reinforcement Learning for Datacenter Congestion Control

98. Software-Defined Far Memory in Warehouse-Scale Computers

99. Learning Memory Access Patterns

100. Learning-based Memory Allocation for C++ Server Workloads

101. Learning to Perform Local Rewriting for Combinatorial Optimization

102. Solving Mixed Integer Programs Using Neural Networks

103. Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs

104. Placement Optimization with Deep Reinforcement Learning

105. Chip Placement with Deep Reinforcement Learning

106. A Full-stack Accelerator Search Technique for Vision Applications

107. The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

108. Tuning Recurrent Neural Networks with Reinforcement Learning

109. Reward Augmented Maximum Likelihood for Neural Structured Prediction

110. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

111. Sequence Level Training with Recurrent Neural Networks

112. Deep Reinforcement Learning for Dialogue Generation

113. Mastering the game of Go with deep neural networks and tree search

114. AlphaGo Zero: Mastering the game of Go without human knowledge

115. The Predictron: End-To-End Learning and Planning

116. Deep Reinforcement Learning for Mention-Ranking Coreference Models

117. Language as a Latent Variable: Discrete Generative Models for Sentence Compression

118. Self-critical Sequence Training for Image Captioning

119. Dual Learning for Machine Translation

120. Neural Combinatorial Optimization with Reinforcement Learning

121. Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

122. End-to-end optimization of goal-driven and visually grounded dialogue systems

123. Adversarial Neural Machine Translation

124. Zero-Shot Dual Machine Translation

125. Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

126. Stochastic Constraint Programming as Reinforcement Learning

127. A Deep Reinforced Model for Abstractive Summarization

128. Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

129. Deal or No Deal? End-To-End Learning for Negotiation Dialogues

130. Grammatical Error Correction with Neural Reinforcement Learning

131. Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

132. Reinforced Video Captioning with Entailment Rewards

133. Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

134. Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection

135. Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive Summarization

136. Automatically Composing Representation Transformations as a Means for Generalization

137. Improving Abstraction in Text Summarization

138. A Study of Reinforcement Learning for Neural Machine Translation

139. Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction

140. Learning to Optimize Join Queries With Deep Reinforcement Learning

141. OCD: Optimal Completion Distillation for Sequence Learning

142. Better Rewards Yield Better Summaries: Learning to Summarise Without References

143. Fine-Tuning Language Models from Human Preferences

144. A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

145. Generative Adversarial Imitation Learning

146. Connecting Generative Adversarial Networks and Actor-Critic Methods

147. Generative Adversarial Parallelization

148. NIPS 2016 Tutorial: Generative Adversarial Networks

149. 6.6 Actor-Critic Methods

150. Asynchronous Network Architecture for Semi-Supervised Learning

151. Decoupled Neural Interfaces using Synthetic Gradients

152. Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

153. Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks

154. Unsupervised Machine Translation Using Monolingual Corpora Only

155. Learning to Optimize

156. Learning to Optimize Neural Nets

157. Learning to learn by gradient descent by gradient descent

158. Neural Optimizer Search With Reinforcement Learning

159. Deep Reinforcement Learning for Accelerating the Convergence Rate

160. An Actor-critic Algorithm for Learning Rate Learning

161. Learned Optimizers that Scale and Generalize

162. Metacontrol for Adaptive Imagination-Based Optimization

163. Reinforcement Learning for Learning Rate Control

164. Online Learning of a Memory for Learning Rates

165. Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces

166. Backprop Evolution

167. Understanding and correcting pathologies in the training of learned optimizers

168. LHOPT: A Generalizable Approach to Learning Optimizers

169. Biased Importance Sampling for Deep Neural Network Training

170. Online Batch Selection for Faster Training of Neural Networks

171. Neural Data Filter for Bootstrapping Stochastic Gradient Descent

172. Stochastic Optimization with Bandit Sampling

173. ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

174. Differentiable Learning-to-Normalize via Switchable Normalization

175. AutoAugment: Learning Augmentation Policies from Data

176. Bayesian Active Learning for Classification and Preference Learning

177. Active Learning for High Dimensional Inputs Using Bayesian Convolutional Neural Networks

178. Uncertainty in Deep Learning

179. Teaching Machines to Describe Images via Natural Language Feedback

180. Deep reinforcement learning from human preferences

181. Active Learning for Convolutional Neural Networks: A Core-Set Approach

182. Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification

183. Classification with Costly Features using Deep Reinforcement Learning

184. Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning

185. Learning to Learn from Noisy Web Videos

186. Brief Summary of the Panel Discussion at DL Workshop @ICML 2015

187. Active Learning Literature Survey

188. Machine Teaching for Bayesian Learners in the Exponential Family

189. Dataset Distillation

190. "Less Than One"-Shot Learning: Learning n Classes From M < N Samples

191. A Solvable Model of Neural Scaling Laws

192. https://x.com/fchollet/status/1082347142830743552

193. Freeze-Thaw Bayesian Optimization

194. Neural Architecture Search with Reinforcement Learning

195. Designing Neural Network Architectures using Reinforcement Learning

196. Learning to Learn without Gradient Descent by Gradient Descent

197. RL²: Fast Reinforcement Learning via Slow Reinforcement Learning

198. Learning to reinforcement learn

199. Approximate Bayes Optimal Policy Search Using Neural Networks

200. HyperNetworks

201. PathNet: Evolution Channels Gradient Descent in Super Neural Networks

202. Optimization as a Model for Few-Shot Learning

203. Efficient K-shot Learning with Regularized Deep Networks

204. DeepArchitect: Automatically Designing and Training Deep Architectures

205. CoDeepNEAT: Evolving Deep Neural Networks

206. Large-Scale Evolution of Image Classifiers

207. Learning to Reason: End-to-End Module Networks for Visual Question Answering

208. Inferring and Executing Programs for Visual Reasoning

209. Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks

210. Meta Networks

211. Efficient Architecture Search by Network Transformation

212. Learning Transferable Architectures for Scalable Image Recognition

213. SMASH: One-Shot Model Architecture Search through HyperNetworks

214. Practical Block-wise Neural Network Architecture Generation

215. N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning

216. Gradient-free Policy Architecture Search and Adaptation

217. Swish: Searching for Activation Functions

218. Intriguing Properties of Adversarial Examples

219. Finding Competitive Network Architectures Within a Day Using UCT

220. A Flexible Approach to Automated RNN Architecture Generation

221. Learning to Prune Filters in Convolutional Neural Networks

222. Regularized Evolution for Image Classifier Architecture Search

223. Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions

224. Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution

225. Learning to Optimize Tensor Programs

226. Resource-Efficient Neural Architect

227. Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

228. MnasNet: Platform-Aware Neural Architecture Search for Mobile

229. Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

230. Searching Toward Pareto-Optimal Device-Aware Neural Architectures

231. Evolutionary-Neural Hybrid Agents for Architecture Search

232. InstaNAS: Instance-aware Neural Architecture Search

233. IRLAS: Inverse Reinforcement Learning for Architecture Search

234. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

235. The Evolved Transformer

236. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

237. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

238. StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks

239. EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks With AutoML

240. Evolving Space-Time Neural Architectures for Videos

241. Google Vizier: A Service for Black-Box Optimization

242. Introducing FBLearner Flow: Facebook's AI Backbone

243. Efficient Reductions for Imitation Learning

244. Unifying Count-Based Exploration and Intrinsic Motivation

245. ​ Complexity no Bar to AI

246. ​ Candy Japan’s new box A/B test

247. https://news.ycombinator.com/item?id=13231808

248. https://www.reddit.com/r/ControlProblem/comments/5jlkgi/why_tool_ais_want_to_be_agent_ais/

249. Risks from Learned Optimization: Introduction

250. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models

251. One Big Net For Everything

252. Sutton & Barto Book: Reinforcement Learning: An Introduction

253. Reddit: Reinforcement Learning subreddit

254. https://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/

255. https://hci.iwr.uni-heidelberg.de/system/files/private/downloads/1848175122/schmitt_kunstliche-motivation-report.pdf

256. https://philarchive.org/archive/TURMAA-6v2

257. Deep Reinforcement Learning Doesn’t Work Yet

258. The Ethics of Reward Shaping

259. 2018-07-26-synced-googleaichiefjeffdeansmlsystemarchitectureblueprint.html

260. Solving the Mystery of Link Imbalance: A Metastable Failure State at Scale

261. Reflective Oracles: A Foundation for Classical Game Theory

262. https://www.fhi.ox.ac.uk/wp-content/uploads/Reframing_Superintelligence_FHI-TR-2019-1.1-1.pdf

263. The Bitter Lesson

264. AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

265. ​ ‘end-to-end’ directory

266. There’s plenty of room at the Top: What will drive computer performance after Moore’s law?

267. ​ ‘tech economics’ directory

268. Modeling the Human Trajectory

269. Superexponential [Modeling the Human Trajectory]

270. Wikipedia Bibliography:

     1. Reinforcement learning

     2. Google Maps

     3. Waze  :

        https://en.wikipedia.org/wiki/Waze

     4. Nick Bostrom

     5. Superintelligence: Paths, Dangers, Strategies  :

        https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies

     6. Amdahl’s law

     7. High-frequency trading

     8. Knight Capital Group § 2012 stock trading disruption  :

        https://en.wikipedia.org/wiki/Knight_Capital_Group#2012_stock_trading_disruption

     9. Autopilot  :

        https://en.wikipedia.org/wiki/Autopilot

     10. Death by GPS  :

         https://en.wikipedia.org/wiki/Death_by_GPS

     11. William Wordsworth

     12. Samuel Taylor Coleridge  :

         https://en.wikipedia.org/wiki/Samuel_Taylor_Coleridge

     13. Extended mind thesis

     14. A/B testing

     15. Partially observable Markov decision process

     16. Braess's paradox  :

         https://en.wikipedia.org/wiki/Braess%27s_paradox

     17. Design of experiments

     18. Sequential analysis

     19. Response surface methodology

     20. Adaptive design (medicine)

     21. Multi-armed bandit

     22. Optimal experimental design

     23. Variance

     24. Latin square

     25. Blocking (statistics)

     26. Queueing theory

     27. Deep learning

     28. Long short-term memory

     29. Committee machine  :

         https://en.wikipedia.org/wiki/Committee_machine

     30. Jeff Dean

     31. Virtual memory compression  :

         https://en.wikipedia.org/wiki/Virtual_memory_compression

     32. Linear programming § Integer unknowns  :

         https://en.wikipedia.org/wiki/Linear_programming#Integer_unknowns

     33. Greedy algorithm

     34. Beam search

     35. Monte Carlo tree search

     36. Stochastic gradient descent

     37. Markov decision process

     38. Importance sampling  :

         https://en.wikipedia.org/wiki/Importance_sampling

     39. Oversampling and undersampling in data analysis

     40. Active learning (machine learning)

     41. Decision boundary  :

         https://en.wikipedia.org/wiki/Decision_boundary

     42. Hyperparameter optimization

     43. Gaussian process

     44. Dilution (neural networks)

     45. Type system

     46. AlphaGo

     47. KGS Go Server

     48. Weak supervision § Semi-supervised learning

     49. Montezuma’s Revenge (video game)