Sunday, July 3, 2016

Review 12 June - 3 July

I was travelling on business, and got behind on the weekly review posts. Here is a review of the links that I tweeted about over the last three weeks:

  1. Facebook's race to catch-up in AI:
  2. How AI is making inroads into the legal profession:
  3. Google vs Baidu in speech recognition:
  4. A philosopher's views on the dangers of artificial intelligence:
  5. Five ways engineers can improve their writing:
  6. Dango uses neural networks to recommend emojis:
  7. Watch Sunspring, a sci-fi movie written by an AI:
  8. Using machine learning to fight ransomeware:
  9. How to select the kernel of a support vector machine:
  10. Next step for AI research is how they can learn on their own:
  11. Where machine learning is going to disrupt businesses next:
  12. AI have now passed the Turing test for sound:
  13. Springboard, Google's enterprise AI assistant:
  14. Apple is opening-up Siri to third-party developers: - Joining other companies with open AI platforms
  15. How to construct parsimonious binary classification trees:
  16. I think every academic has come across a workplace bully at some time, academia attracts egotistical people:
  17. A neural network-based system that turns rough sketches into photorealistic portraits: Includes link to paper
  18. Finding bugs with AI: The ultimate goal is to patch the bugs, too.
  19. Is the future of smartphones a single AI?
  20. Developing an "ethical" AI that can make life-or-death decisions:
  21. How is AI going to surprise us in the future?
  22. Six lessons for getting the best out of machine learning:
  23. Using deep learning neural networks for drug discovery:
  24. A smart car dashcam that rates everyone else's driving: 
  25. A concise history of data mining:
  26. How to get started with mining Twitter data with Python:
  27. A nice overview of the key concepts of machine learning for people who know nothing about it:
  28. Using machine learning to buy advertising:
  29. Neural networks and the future of AI:
  30. Using machine learning to improve performance of power plants:
  31. A basic explanation of how backpropagation works:
  32. Google has opened a dedicated machine learning research lab in Zurich:
  33. On the importance of open API for data science:
  34. Analysing sport teams play using machine learning - heading towards an AI coach?
  35. Student evaluations of lecturers are very blunt instruments, it's not surprising that there is bias in them:
  36. Machine learning for personalised advertising:
  37. Machine learning libraries in Javascript:
  38. We're getting close to Sci-Fi levels of AI:
  39. Future trends in AI:
  40. Machine learning with Python for complete beginners:
  41. A brief, point-by-point history of data mining:
  42. A short FAQ on RankBrain, how Google applies deep learning to search:
  43. Review of deep learning models and applications:
  44. Generating sculptures with a deep neural network and an EA:
  45. Five myths about machine learning:
  46. According to this article, compliance is the knowledge job most likely to be taken over by AI:
  47. Identifying NSFW images using machine learning:
  48. How Google is putting machine learning into everything:
  49. A good argument in favour of all research publications being open-access:
  50. The impact of machine-generated screenplays:
  51. The AI lawyer named Ross has been hired by its first real law firm:
  52. An AI that predicts human actions after being trained on TV programmes:
  53. Google's suggested rules for AI that prevent AI from becoming harmful:
  54. A cheat-sheet on machine learning algorithms:
  55. Applying cloud-based intelligence to off-the-shelf robots:
  56. AI will create jobs as well as destroy jobs - it just won't create as many jobs as it destroys:
  57. A beginners experiences with deep learning:
  58. Predictions that AI will replace 16 % of white collar jobs by 2025, but create another 9 %:
  59. An adaptive AI for air combat:
  60. Google has built an AI that picks out the most important parts of an image:
  61. According to the paper, the air combat AI is a genetic-fuzzy system: 
  62. An overview of deep learning:
  63. Why we need to stop worrying about AI:
  64. A list of deep learning libraries in different languages:
  65. Landing a job in artificial intelligence:
  66. Infographic on the current state of artificial intelligence:
  67. Looking inside convolutional neural networks:
  68. Are journal editors cheating the impact factor measure?
  69. Predicting cancer metastasis - seems to be using machine learning of some description:
  70. I like #4, "don't multi-task". I have to keep reminding myself "one thing at a time!"
  71. Although to be honest, it's not an avalanche of email from students that usually takes up my time:
  72. Brief introduction to text mining:
  73. Experts' opinions on Satya Nadella's 10 rules for AI:
  74. The promise, and problems, of machine learning in cybersecurity:
  75. Intel is tuning its Xeon Phi chips to make them better suited to machine learning:
  76. Satya Nadella calls for accountability in AI, biased systems already exist:
  77. Implementing recursive neural networks in TensorFlow:
  78. AI can see the world, but it doesn't see the world the same way we do:

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