Monday, August 8, 2016

Weekly Review 8 August 2016

It's been a while since my last review post. This is because I have been away at the ITx conference in Wellington, followed by the WCCI 2016 conference in Vancouver, B.C. Below are some of the interesting links I Tweeted about in the last few weeks.

  1. A more and more common story, this is why I went into the private tertiary education sector, better job security: http://www.abc.net.au/radionational/programs/scienceshow/catherine-osborne-how-australia-fails-mid-career-scientists/7588644
  2. An overview of Bayesian machine learning: http://www.kdnuggets.com/2016/07/bayesian-machine-learning-explained.html
  3. An AI-based VC fund: https://techcrunch.com/2016/07/13/non-artificial-intelligence-please/
  4. An improved Turing Test shows how dumb chatbots really are: https://www.technologyreview.com/s/601897/tougher-turing-test-exposes-chatbots-stupidity/
  5. A tutorial on machine learning in Python: http://www.datasciencecentral.com/profiles/blogs/would-you-survive-the-titanic-a-guide-to-machine-learning-in
  6. An AI that detects hints of depression in speech: http://motherboard.vice.com/en_au/read/machine-learning-algorithm-spots-depression-based-on-speech-patterns
  7. The kinds of problems that AI still can't do: http://www.kdnuggets.com/2016/07/hard-problems-ai-cant-yet-touch.html
  8. How machine learning is driving artificial intelligence: http://www.datanami.com/2016/07/11/report-machine-learning-driving-ai/
  9. Five open-source deep learning projects: http://www.kdnuggets.com/2016/07/five-deep-learning-projects-cant-overlook.html
  10. The coming clash between EU regulations and artificial intelligence: http://www.wired.com/2016/07/artificial-intelligence-setting-internet-huge-clash-europe/?utm_content=buffer08177&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer
  11. How AI-driven companies like Google depend on public data: https://techcrunch.com/2016/07/09/we-need-to-talk-about-ai-and-access-to-publicly-funded-data-sets/
  12. The problems with current chatbots: https://techcrunch.com/2016/07/16/bursting-the-chatbot-bubble/
  13. The application of supercomputers in deep learning: http://nextbigfuture.com/2016/07/supercomputers-can-accelerate-machine.html
  14. Zoom.ai is launching an AI executive assistant: https://techcrunch.com/2016/07/14/zoom-ai/
  15. A list of resources for learning about deep learning: http://www.kdnuggets.com/2016/07/start-learning-deep-learning.html
  16. A machine learning based email autoresponder: https://techcrunch.com/2016/07/13/zendesks-automatic-answers-taps-machine-learning-ai-to-generate-bot-style-email-responses/
  17. Predicting Game of Thrones betrayals using machine learning: http://dataconomy.com/machine-learning-can-predict-game-of-thrones-betrayals/
  18. Ten categories for machine learning and AI algorithms: http://www.kdnuggets.com/2016/07/10-algorithm-categories-data-science.html
  19. Helping AI better understand what we are saying to them: https://techcrunch.com/2016/07/15/pat-launches-private-beta-to-help-ai-understand-what-you-say/
  20. The AI boom in Silicon Valley: http://www.nytimes.com/2016/07/18/technology/on-wheels-and-wings-artificial-intelligence-swarms-silicon-valley.html?partner=IFTTT&_r=1
  21. Good news, 9 mill. people will be liberated from sweatshops by robots-Bad news, 9 mill. people without jobs: https://www.theguardian.com/sustainable-business/2016/jul/16/robot-factories-threaten-jobs-millions-garment-workers-south-east-asia-women
  22. Google's using deep learning to optimise the energy efficiency of cooling its server farms: http://www.bloomberg.com/news/articles/2016-07-19/google-cuts-its-giant-electricity-bill-with-deepmind-powered-ai
  23. A list of more than 50 machine learning API: http://www.datasciencecentral.com/profiles/blogs/list-of-50-machine-learning-apis
  24. How deep learning networks scale: http://www.kdnuggets.com/2016/07/deep-learning-networks-scale.html
  25. Using machine learning to manage virtual servers: http://www.datanami.com/2016/08/02/machine-learning-brings-real-insight-jordans-virtual-environment/
  26. Google, Microsoft, IBM, Amazon, Facebook are all renting-out access to their AI systems: https://www.technologyreview.com/s/602037/google-and-microsoft-want-every-company-to-scrutinize-you-with-ai/
  27. Current developments in deep learning: http://www.datasciencecentral.com/profiles/blogs/on-going-developments-and-outlook-for-deep-learning
  28. Yes, AI is just as biased as people, because AI are made by people. That has been obvious for a long time: https://www.theguardian.com/technology/2016/aug/03/algorithm-racist-human-employers-work
  29. Diagnosing autism using machine learning: https://www.sciencedaily.com/releases/2016/07/160712142403.htm
  30. Why Open Source programming languages are winning over proprietary languages: http://www.techrepublic.com/article/why-open-source-programming-languages-are-crushing-proprietary-peers/ Better to learn R than Matlab?
  31. An overview of deep learning neural networks applied to machine translation: https://kv-emptypages.blogspot.co.nz/2016/06/the-emerging-world-of-neural-net-driven.html
  32. A commented list of resources explaining NoSQL: http://www.kdnuggets.com/2016/07/seven-steps-understanding-nosql-databases.html
  33. A new version of PMML - Predictive Modelling Markup Language - has been released: http://www.kdnuggets.com/2016/08/data-mining-group-pmml-v43.html 
  34. Ten simple rules for using statistics properly and effectively: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004961
  35. How to use machine learning for face recognition: https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.wlicrwx4j
  36. Using machine learning to predict the genetic basis of autism: http://www.natureworldnews.com/articles/26110/20160802/predict-autism-machine-learning.htm
  37. Why Harvard Business School is teaching its MBA students about AI: http://www.businessbecause.com/news/full-time-mba/4100/harvard-business-school-is-teaching-mbas-about-ai
  38. Two more Google machine learning API are now in open beta: https://www.sdxcentral.com/articles/news/google-clouds-machine-learning-apis-hit-beta/2016/07/
  39. Top programming languages for 2016 - Python & R are now numbers 3 & 5, respectively. http://spectrum.ieee.org/static/interactive-the-top-programming-languages-2016?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook
  40. Detecting sarcasm using a neural network: https://techcrunch.com/2016/08/04/this-neural-network-tries-to-tell-if-youre-being-sarcastic-online/ A lot of people still struggle to detect sarcasm...
  41. Developing chatbots for HR: https://www.technologyreview.com/s/602068/the-hr-person-at-your-next-job-may-actually-be-a-bot/ 
  42. Will artificial intelligence's ever get common sense? http://www.kdnuggets.com/2016/08/common-sense-artificial-intelligence-2026.html
  43. How investors feel about artificial intelligence: http://techemergence.com/how-investors-feel-about-artificial-intelligence-from-29-ai-founders-and-executives/
  44. Intelligent security and surveillance systems: http://www.extremetech.com/extreme/232728-when-you-look-at-the-camera-and-it-looks-back-how-artificial-intelligence-is-revolutionizing-home-security
  45. OpenAI is calling for an "AI Police" http://www.wired.com/2016/08/openai-calling-techie-cops-battle-code-gone-rogue/?mbid=social_twitter - I seem to remember the "Turing Police" in Neuromancer...
  46. Using machine learning to predict crop-yield from satellite images: http://www.theverge.com/2016/8/4/12369494/descartes-artificial-intelligence-crop-predictions-usda 
  47. IBM is arguing that AI should be assisting people rather than replacing them: http://www.informationweek.com/government/leadership/ibm-ai-should-stand-for-augmented-intelligence/d/d-id/1326496?
  48. Arthur C. Clarke was writing about IA - Intelligence Amplifiers - in 1986: http://www.informationweek.com/government/leadership/ibm-ai-should-stand-for-augmented-intelligence/d/d-id/1326496?
  49. Using machine learning to find zero-day exploits on the dark web: https://www.technologyreview.com/s/602115/machine-learning-algorithm-combs-the-darknet-for-zero-day-exploits-and-finds-them/
  50. Yahoo has used machine learning to develop a troll-detecting algorithm: http://www.wired.co.uk/article/yahoo-online-abuse-algorithm
  51. The paper describing Yahoo's troll-detector: http://www2016.net/proceedings/proceedings/p145.pdf
  52. A paper on estimating crop yield from images, this time in China: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7524771&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7524771