Sunday, November 13, 2016

Review August - November 2016

 It's been a while since my last review post. Below are some of the interesting links I Tweeted about in the last few weeks.
  1. 7 ways AI could be a good thing:
  2. A fairly detailed overview of IBM's Watson AI:
  3. Using deep learning to automate spearphishing attacks:
  4. And publishers are still being dicks about Sci-Hub:
  5. Getting started understanding machine vision:
  6. AI applied to marketing and advertising:
  7. The Nervana chip optimised for deep learning:
  8. AI and machine learning in finance:
  9. A guide to Google deepmind:
  10. Tutorial on neural networks in R:
  11. Brief step-by-step guide to building an expert system:
  12. Recent advances in quantum computers are promising for AI:
  13. 3 thoughts from Yann LeCunn on why deep learning works so well:
  14. AI in healthcare-where it is, and where it's going:
  15. Why movies like The Terminator are not to blame for the bad journalism around AI:
  16. Recognising hand gestures using IBM's neuromorphic chips:
  17. I've said it time and again-biased data produces biased models:
  18. 10 signs of a bad place to work:
  19. The dangers of using biased data in training a model - with Donald Trump as the cautionary example:
  20. Why we're experiencing an AI boom:
  21. Using ensembles of models to boost performance:
  22. AI in medical apps:
  23. Creating an invisible user interface with AI:
  24. Machine learning in finance-where it is, and where it's going:
  25. No matter how good the model, if it isn't interpreted or applied properly, it is useless:
  26. Samsung demos its camera based on IBM's neuromorphic chip:
  27. An AI-based assistant for firefighters:
  28. Deep learning is now being used in the Dragon speech recognition system:
  29. Companies continue to invest big in AI:
  30. OpenAI is set to receive the first "deep learning in a box" system:
  31. Facebook open sources its fast text processing system:
  32. How to train an AI doctor:
  33. Boosting your competitive advantage using machine learning:
  34. Mapping poverty using machine learning:
  35. Machine learning systems now out-perform humans in diagnosing cancer biopsies:
  36. An Android malware detection system using machine learning:
  37. A chatbot to help homeless people get government housing:
  38. Tracking Beijing pickpockets with machine learning:
  39. Demystifying deep reinforcement learning:
  40. Getting up to speed on deep learning:
  41. So, which professions fit these criteria the most? 6 signs your job is going to be automated:
  42. "Academic clickbait" - the right choice of title for a paper can massively increase its reach:
  43. Baidu has open sourced their deep learning toolkit:
  44. Predicting air quality in South Africa with machine learning:
  45. Samsung is embedding neural networks in the chips for their new phones:
  46. Identifying regions of poverty from satelite images using machine learning:
  47. Yandex is using machine learning to target users with less-annoying ads:
  48. On how important sleep is for resetting the brain:
  49. Research begins on using deep learning to segment cancerous tissue in scans:
  50. Comparison of deep learning and AI:
  51. Part 1 of a gentle introduction to TensorFlow:
  52. Part 2 of a gentle introduction to TensorFlow:
  53. Using GPU to turn a PC into a supercomputer:
  54. 10 programming career tips:
  55. 5 requirements for a successful mobile app:
  56. 10 ways to make your mobile app fail:
  57. 9 ways bad managers drive good employees away:
  58. Microsoft is putting deep neural networks in a fridge:
  59. Some basic stuff - Why neural networks need activation functions:
  60. 10 Java machine learning libraries:
  61. How is growing up around AI going to affect the next generation?
  62. How convolutional neural networks work:
  63. How search at eBay got a boost from machine learning:
  64. What one university did to graduate more women from computer science:
  65. Sturgeon's law applies to AI as well - 90 % of them are crap:
  66. Skin colour is not the only attribute distinguishing different populations - think the programmers forgot that:
  67. PayPal is using machine learning to detect fraud. All based on open-source tools, too:
  68. Microsoft open sources its deep learning toolkit:
  69. Classifying cucumbers using deep neural networks:
  70. A neural network based app to find local food:
  71. Machine checking of statistics in published papers:
  72. 82 free data science e-books from O'Reilly:
  73. Classifying urban sounds using deep neural networks:
  74. Why you should stop trying to multi-task:
  75. Text-to-speech using deep ANN to directly generate waveforms rather than sequences of syllables:
  76. Some thoughts on how AI could manage your money for you:
  77. Some military uses of machine learning:
  78. Computer vision as a service:
  79. Overview of reinforcement learning:
  80. How Apple's wireless earbuds could lead to always-on AI:
  81. The dangers of relying on a supervised learning model when making decisions:
  82. The future of AI and machine learning:
  83. Nvidia's low-power AI optimised computer:
  84. Glad this kind of thing doesn't happen much in NZ. Since I'm in a mixed-race marriage myself, it's very troubling:
  85. Music mastering with machine learning:
  86. AI and automation will eliminate 6% of jobs in the US by 2021:
  87. An overview of decision trees:
  88. Description of the bagging approach used in random forests:
  89. Career opportunities in AI:
  90. Learning machine learning in a year:
  91. Microsoft wants to use AI to "solve" cancer:
  92. Number of jobs replaced by AI will be smaller than expected:
  93. An AI that learned how to play deathmatch Doom from pixel data:
  94. Paper on the Doom deathmatch AI:
  95. Overview of 9 key papers in deep learning:
  96. Machine-generated peer reviews pass for the real thing:
  97. How publish or perish selects for bad research:
  98. FuzzyML is the first IEEE standard to come out of the Computational Intelligence Society:
  99. Apparently it's possible to use TensorFlow on D-Wave's quantum computers:
  100. Sounds a lot like an ecosystem of Darwinian bots:
  101. Microsoft claims their ANN-based speech recognition system is the most accurate:
  102. Why AI is booming now:
  103. Looks like it isn't easy to make a living with open source AI:
  104. Deep learning leads to more accurate processing of mammograms:
  105. A neural network zoo:
  106. Tried to build a system to identify sepsis, ended up with a system that predicted deaths:
  107. Google open sources its image auto-captioning system, based on TensorFlow:
  108. I regularly tell my daughter to not do a PhD-instead, choose a career with better security than academia:
  109. The limits of machine learning:
  110. Building a robot with object recognition using TensorFlow:
  111. Of course our AI are going to be racist/sexist. Biased data leads to biased models.
  112. Why is this still surprising to people? I was teaching my undergrad students this 16 years ago:
  113. k-Means vs Expectation-maximization clustering:
  114. A pretty weak argument from the IEEE on why people shouldn't use Sci-Hub:
  115. Deep learning-based language translation:
  116. Paper on deep learning-based translation:
  117. Description of what machine learning actually is:
  118. Microsoft's move towards AI:
  119. What companies get wrong about machine learning:
  120. How Microsoft is integrating AI into Office 365:
  121. The points made in this article are even more important now:
  122. You'd be hard-pressed to find a research paper author opposed to Sci-Hub, because more downloads==more citations:
  123. Don't think authors-content creators-are opposed to Sci-Hub. Publishers are,it threatens their extortionate profits:
  124. We're doomed:
  125. List of 15 tutorials on deep learning:
  126. Is a Java deep learning library worth $3M? Would have thought an open source project could do it.
  127. Google's cloud-based deep learning API is in beta:
  128. More opportunities than risks when investing in AI:
  129. Investment advisers are betting big on machine learning:
  130. Five safety problems with AI: Most of which seem concerned with the safety of the AI...
  131. I don't think any human-created AI can be unbiased, all humans have some biases:
  132. Machine learning is seen as a savior for security:
  133. Reverse engineering cloud-based machine learning models:
  134. Paper on reverse-engineering machine learning models:
  135. Predicting future human behaviour with deep learning:
  136. Yahoo has open sourced its porn-detecting convolutional neural network:
  137. Reading people's facial expressions using Google's Cloud Vision API:
  138. Why human curation still has a place among algorithmic organisation of information:
  139. Researchers claim that there is no inborn aptitude for programming, it can all be taught: Only studied 1 university
  140. Looks like a primitive version of the personality constructs from Neuromancer:
  141. The next generation of neural networks will use spiking neurons:
  142. We need a Data Mining Code of Ethics, to prevent this kind of sloppy work affecting the public:
  143. Review of six cloud-based machine learning services:
  144. What happens if you are not careful with building your model? You get a "weapon of math destruction"
  145. Yes, machine learning models can be sexist, classist, racist, otherwise biased if data used to train them is biased:
  146. What will people do when AI replaces all of the jobs?
  147. AI-written poetry. It's really bad. But I expect it will get better:
  148. Google investigates a framework to deal with bias in machine learning:
  149. Paper on equal opportunity in machine learning: 
  150. Paper on external memory for deep neural networks:
  151. How to compete in the age of AI:
  152. Simple advice for getting an academic job:
  153. Collective learning in a deep neural network:
  154. Are schools preparing students for the age of AI?
  155. Smart traffic lights make commuting more efficient:
  156. Myself, I'd prefer Sir Patrick Stewart:
  157. If a monkey can't own the copyright on a selfie it took, a computer shouldn't be able to own a patent:
  158. AI makes us better at what we are already good at:
  159. Overview of ANN and deep learning:
  160. Reducing patient no-shows with machine learning:
  161. Promoting collaboration in software development using machine learning:
  162. Paper on using deep reinforcement learning to play first-person shooter games:
  163. Microsoft claims human-level speech recognition performance:
  164. The Royal Navy is investigating the use of AI in threat assessment:
  165. More on spiking ANN:
  166. An AI corporate executive:
  167. Using machine learning to detect expenses fraud:
  168. Is Facebook slipping behind in AI?
  169. A role for machine learning in predictive analytics for marketing:
  170. 3 things to consider when deciding if a business needs AI:
  171. Paper: The Mythos of Model Interpretability:
  172. How Airbnb makes use of machine learning:
  173. How will AI deal with moral dilemmas?
  174. Intro to using neural networks in Python:
  175. How Uber uses machine learning in most of what it does:
  176. I remember seeing nonsensical conference papers well before we had autocorrect:
  177. I suspect that this is a symptom of us approaching peak-hype for machine learning:
  178. Brief summary of Stephen Hawking's thoughts on AI:
  179. Assessing Clinton & Trumps emotional intelligence with AI:
  180. Why analog computing is a good match with AI:
  181. Deep Fried Data: … How machine learning can make anything "taste" good
  182. I always preferred the character of Chandler myself:
  183. The blind spot in AI research:
  184. List of five free ebooks on machine learning:
  185. Universities helping their staff with online profiles, but could it lead to unwanted uniformity?
  186. A rather inspiring post by my old school-mate LenaRobinson:
  187. Why we need more diversity in AI:
  188. AI is not out to get us:
  189. An AI "judge" that predicts the outcome of cases: - I wonder if they called it Dredd?
  190. Awesome falsehoods - should be required reading for every programmer:
  191. Why advances in AI should be kept in the public eye:
  192. TechCrunch explains WTF machine learning is: But article describes ANN rather than machine learning
  193. No, I don't think that AI is going to end humanity:
  194. Overview of recurrent neural networks:
  195. Some sociological issues of intelligent machines:
  196. Overview of deep learning on GPU:
  197. IBM has made Watson AI available as a cloud service:
  198. An AI for digesting research papers: Poor AI
  199. Tutorial on implementing classification measures in Python:
  200. Microsoft has released the next version of its Cognitive Toolkit to beta:
  201. Embedding AI in airport security scanners: Badder than a bad thing that's very, very bad.
  202. Even a flower-order business is betting on AI to increase it's business:
  203. The impact of machine learning / AI on privacy:
  204. Is AI going to create more jobs than it destroys?
  205. Pick your pastiche with deep learning:
  206. AI-generated encryption:
  207. Paper on AI-generated encryption:
  208. Identifying malicious URLs using machine learning:
  209. IBM says that in 5 years Watson will be behind every business & personal decision:
  210. Humans and machines together give better language understanding:
  211. Microsoft's Cognitive Toolkit is intended to bring machine learning to the masses:
  212. What I look for when examining a postgraduate thesis: …
  213. An argument for why academics should not spend time on social media:
  214. A fairly comprehensive introduction to using neural networks in TensorFlow:
  215. An argument that current AI is not good enough to justify a universal basic income:
  216. Generative adversarial neural networks:
  217. Human intelligence + artificial intelligence is the future, not AI alone:
  218. CCTV operators will be the next group made redundant by AI:
  219. How to get good at R:
  220. A Bot-builder for non-programmers:
  221. 8 pitfalls for developers moving from R to Python:
  222. Automating customer complaints with machine learning:
  223. How Bayesian inference works:
  224. Microsoft Concept Graph: giving machines and AI common sense:
  225. Why AI and machine learning is hard:
  226. A piece on my home town:
  227. How machine learning is used in higher education:
  228. How AI will transform business:
  229. Even with AI entering the workforce, people are still needed:
  230. DeepMind is planning on taking on Starcraft 2 next:
  231. Paper on the good and the bad of using machine learning in higher education:
  232. There's more and more competition for fewer and fewer full-time permanent academic positions, so people are leaving:
  233. An advance in automated lip reading accuracy, using deep learning over a fairly restricted data set:
  234. Creating an unbiased model is hard as long as data sets are unbalanced:
  235. When AI have "erotic" dreams (NSFW):
  236. Cleaning podcasts with deep learning:
  237. A brief and basic introduction to neural networks:
  238. Using machine learning to count dugongs in drone images:
  239. Classifying books by genre from their cover art, using deep learning:

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