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: http://www.fastcompany.com/3060570/facebooks-formula-for-winning-at-ai
2. How AI is making inroads into the legal profession: http://www.thecollegefix.com/post/27773/
3. Google vs Baidu in speech recognition: http://techcrunch.com/2016/06/11/google-baidu-and-the-race-for-an-edge-in-the-global-speech-recognition-market/
4. A philosopher's views on the dangers of artificial intelligence: https://www.theguardian.com/technology/2016/jun/12/nick-bostrom-artificial-intelligence-machine
5. Five ways engineers can improve their writing: http://theinstitute.ieee.org/career-and-education/career-guidance/five-ways-engineers-can-improve-their-writing
6. Dango uses neural networks to recommend emojis: http://motherboard.vice.com/en_au/read/with-dango-app-ai-is-learning-to-meme
7. Watch Sunspring, a sci-fi movie written by an AI: http://techcrunch.com/2016/06/11/watch-this-short-sci-fi-movie-with-a-script-written-by-an-ai/
8. Using machine learning to fight ransomeware: http://www.datanami.com/2016/06/14/machine-learning-enlisted-fight-ransomware/
9. How to select the kernel of a support vector machine: http://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html
10. Next step for AI research is how they can learn on their own: http://theinstitute.ieee.org/technology-focus/technology-topic/the-next-step-for-artificial-intelligence-is-machines-that-get-smarter-on-their-own
11. Where machine learning is going to disrupt businesses next: http://tomtunguz.com/key-ingredient-machine-learning/?platform=hootsuite
12. AI have now passed the Turing test for sound: http://www.techrepublic.com/article/how-new-ai-fools-humans-into-thinking-artificial-sounds-are-real/
13. Springboard, Google's enterprise AI assistant: http://techcrunch.com/2016/06/14/google-launches-springboard-an-ai-powered-assistant-for-its-enterprise-customers/
14. Apple is opening-up Siri to third-party developers: http://www.computerworld.com/article/3083149/mac-os-x/apple-touts-a-i-in-ios-and-opens-crown-jewels-to-devs.html - Joining other companies with open AI platforms
15. How to construct parsimonious binary classification trees: http://www.kdnuggets.com/2016/06/breiman-stone-parsimonious-binary-classification-trees.html
16. I think every academic has come across a workplace bully at some time, academia attracts egotistical people: https://www.insidehighered.com/advice/2016/06/15/advice-dealing-bullying-behavior-essay
17. A neural network-based system that turns rough sketches into photorealistic portraits: https://www.technologyreview.com/s/601684/machine-vision-algorithm-learns-to-transform-hand-drawn-sketches-into-photorealistic-images/ Includes link to paper
18. Finding bugs with AI: http://motherboard.vice.com/en_au/read/cyber-grand-challenge The ultimate goal is to patch the bugs, too.
19. Is the future of smartphones a single AI? http://www.theverge.com/2016/6/14/11939310/andy-rubin-google-android-playground-ai-robotics
20. Developing an "ethical" AI that can make life-or-death decisions: http://www.techrepublic.com/article/building-ethical-machines-how-it-can-help-ai-make-life-or-death-decisions/
21. How is AI going to surprise us in the future? http://www.kdnuggets.com/2016/06/how-much-ai-surprise.html
22. Six lessons for getting the best out of machine learning: http://www.techrepublic.com/article/ibm-watson-six-lessons-from-an-early-adopter-on-how-to-do-machine-learning/
23. Using deep learning neural networks for drug discovery: http://scienmag.com/deep-learning-system-for-drug-discovery-to-be-presented-at-the-machine-intelligence-summit-in-berlin/
24. A smart car dashcam that rates everyone else's driving: http://spectrum.ieee.org/cars-that-think/transportation/sensors/the-ai-dashcam-app-that-wants-to-rate-every-driver-in-the-world?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook 
25. A concise history of data mining: http://dataconomy.com/history-data-mining/
26. How to get started with mining Twitter data with Python: http://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-1.html
27. A nice overview of the key concepts of machine learning for people who know nothing about it: http://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/
28. Using machine learning to buy advertising: http://www.datasciencecentral.com/profiles/blogs/when-milliseconds-count-using-ai-to-buy-advertising
29. Neural networks and the future of AI: https://techcrunch.com/2016/06/16/neural-networks-artificial-intelligence-and-our-future/
30. Using machine learning to improve performance of power plants: http://www.informationweek.com/iot/ge-uses-machine-learning-to-restore-italian-power-plant/d/d-id/1325918?
31. A basic explanation of how backpropagation works: http://www.kdnuggets.com/2016/06/visual-explanation-backpropagation-algorithm-neural-networks.html
32. Google has opened a dedicated machine learning research lab in Zurich: http://www.informationweek.com/big-data/big-data-analytics/google-launches-ai-machine-learning-research-center-/d/d-id/1325942
33. On the importance of open API for data science: http://www.kdnuggets.com/2016/06/open-api-economy-growth-big-data-analytics.html
34. Analysing sport teams play using machine learning - heading towards an AI coach? http://motherboard.vice.com/en_au/read/coach-bots-nba-ai
35. Student evaluations of lecturers are very blunt instruments, it's not surprising that there is bias in them: https://www.insidehighered.com/advice/2016/06/17/removing-bias-student-evaluations-faculty-members-essay
36. Machine learning for personalised advertising: http://www.pubexec.com/article/the-future-of-marketing-will-be-built-on-personalization-artificial-intelligence/
37. Machine learning libraries in Javascript: http://www.kdnuggets.com/2016/06/top-machine-learning-libraries-javascript.html
38. We're getting close to Sci-Fi levels of AI: http://www.huffingtonpost.com/entry/the-amazing-artificial-intelligence-we-were-promised-is-coming-finally_b_10592674.html?section=india
39. Future trends in AI: http://www.kdnuggets.com/2016/06/machine-learning-trends-future-ai.html
40. Machine learning with Python for complete beginners: http://pythonforengineers.com/machine-learning-for-complete-beginners/
41. A brief, point-by-point history of data mining: http://www.kdnuggets.com/2016/06/rayli-history-data-mining.html
42. A short FAQ on RankBrain, how Google applies deep learning to search: http://searchengineland.com/faq-all-about-the-new-google-rankbrain-algorithm-234440#.V2xDOlIYrKc.twitter
43. Review of deep learning models and applications: http://www.kdnuggets.com/2016/06/review-deep-learning-models.html
44. Generating sculptures with a deep neural network and an EA: http://www.popsci.com/creative-ai-learns-to-sculpt-3d-printable-objects
45. Five myths about machine learning: http://www.forbes.com/sites/teradata/2015/11/13/five-myths-about-machine-learning-you-need-to-know-today/#37831dd2275c
46. According to this article, compliance is the knowledge job most likely to be taken over by AI: https://hbr.org/2016/06/the-knowledge-jobs-most-likely-to-be-automated
47. Identifying NSFW images using machine learning: http://www.kdnuggets.com/2016/06/algorithmia-improving-nudity-detection-nsfw-image-recognition.html
48. How Google is putting machine learning into everything: https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70#.n1ai2xwao
49. A good argument in favour of all research publications being open-access: http://arstechnica.com/science/2016/06/what-is-open-access-free-sharing-of-all-human-knowledge/
50. The impact of machine-generated screenplays: http://motherboard.vice.com/en_au/read/how-machine-generated-screenplays-may-affect-artists
51. The AI lawyer named Ross has been hired by its first real law firm: http://futurism.com/artificially-intelligent-lawyer-ross-hired-first-official-law-firm/
52. An AI that predicts human actions after being trained on TV programmes: http://www.geekwire.com/2016/computer-binge-watches-tv-predict-ai/
53. Google's suggested rules for AI that prevent AI from becoming harmful: http://www.extremetech.com/extreme/230718-google-researchers-tackle-ai-and-robotics-safety-prevent-future-toasters-from-killing-us-in-our-sleep
54. A cheat-sheet on machine learning algorithms: http://www.datasciencecentral.com/profiles/blogs/the-making-of-a-cheatsheet-emoji-edition
55. Applying cloud-based intelligence to off-the-shelf robots: http://www.theverge.com/circuitbreaker/2016/6/24/12027808/tend-ai-cloud-machine-learning-co-working-robots
56. AI will create jobs as well as destroy jobs - it just won't create as many jobs as it destroys: http://www.informationweek.com/strategic-cio/it-strategy/robots-ai-wont-destroy-jobs-yet/d/d-id/1326056
57. A beginners experiences with deep learning: https://www.theguardian.com/technology/2016/jun/28/google-says-machine-learning-is-the-future-so-i-tried-it-myself
58. Predictions that AI will replace 16 % of white collar jobs by 2025, but create another 9 %: http://www.theregister.co.uk/2016/06/28/forrester_reports_ai_will_create_jobs/
59. An adaptive AI for air combat: http://www.newsmax.com/Newsfront/air-force-ai-top-gun-software/2016/06/27/id/735925/
60. Google has built an AI that picks out the most important parts of an image: https://techcrunch.com/2016/06/28/google-researchers-teach-ais-to-see-the-important-parts-of-images-and-tell-you-about-them/
61. According to the paper, the air combat AI is a genetic-fuzzy system: http://www.omicsgroup.org/journals/genetic-fuzzy-based-artificial-intelligence-for-unmanned-combat-aerialvehicle-control-in-simulated-air-combat-missions-2167-0374-1000144.php?aid=72227 
62. An overview of deep learning: http://www.datasciencecentral.com/profiles/blogs/guide-to-deep-learning
63. Why we need to stop worrying about AI: http://fortune.com/2016/06/28/artificial-intelligence-potential/
64. A list of deep learning libraries in different languages: http://www.datasciencecentral.com/profiles/blogs/deep-learning-libraries-by-language
65. Landing a job in artificial intelligence: http://theinstitute.ieee.org/technology-focus/technology-topic/how-to-land-a-job-in-artificial-intelligence
66. Infographic on the current state of artificial intelligence: http://www.datasciencecentral.com/profiles/blogs/the-state-of-artificial-intelligence-infographic
67. Looking inside convolutional neural networks: http://www.kdnuggets.com/2016/06/peeking-inside-convolutional-neural-networks.html
68. Are journal editors cheating the impact factor measure? https://www.insidehighered.com/views/2016/07/01/examination-whether-academic-journal-rankings-are-being-manipulated-essay
69. Predicting cancer metastasis - seems to be using machine learning of some description: http://www.digitaltrends.com/cool-tech/cancer-spread-prediction-algorithm/
70. I like #4, "don't multi-task". I have to keep reminding myself "one thing at a time!" https://elearningindustry.com/5-ways-survive-student-email-avalanche
71. Although to be honest, it's not an avalanche of email from students that usually takes up my time:   https://elearningindustry.com/5-ways-survive-student-email-avalanche
72. Brief introduction to text mining: http://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html
73. Experts' opinions on Satya Nadella's 10 rules for AI: http://www.techrepublic.com/article/ai-experts-weigh-in-on-microsoft-ceos-10-new-rules-for-artificial-intelligence/
74. The promise, and problems, of machine learning in cybersecurity: https://techcrunch.com/2016/07/01/exploiting-machine-learning-in-cybersecurity/
75. Intel is tuning its Xeon Phi chips to make them better suited to machine learning: http://www.computerworld.com/article/3090991/computer-hardware/intel-tunes-its-mega-chip-for-machine-learning.html
76. Satya Nadella calls for accountability in AI, biased systems already exist: https://www.technologyreview.com/s/601812/microsofts-ceo-calls-for-accountable-ai-ignores-the-algorithms-that-already-rule-our-lives/
77. Implementing recursive neural networks in TensorFlow: http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html
78. AI can see the world, but it doesn't see the world the same way we do: https://www.technologyreview.com/s/601819/ai-is-learning-to-see-the-world-but-not-the-way-humans-do/