Tuesday, April 18, 2017

Review for 18 April 2017

Below are some of the interesting links I Tweeted about recently.
  1. Did an AI spam generator just get funded? https://techcrunch.com/2017/04/09/saleswhale-seed-funding/
  2. AI? Sounds more like a calendar! Maybe the dosage is set by an expert system: http://www.techrepublic.com/article/new-study-shows-how-ai-can-improve-recovery-in-stroke-patients/ 
  3. Distributing machine learning learning on smartphones: http://www.theverge.com/2017/4/10/15241492/google-ai-user-data-federated-learning Privacy win!
  4. Detecting cows with machine learning to make Indian roads safer: https://motherboard.vice.com/en_us/article/machine-learning-will-save-indias-cows-from-bad-drivers I've been to India, cows really are everywhere.
  5. AlphaGo is to take on multiple human opponents simultaneously: https://www.theguardian.com/technology/2017/apr/10/deepminds-alphago-to-take-on-five-human-players-at-once 
  6. Common mistakes in machine learning projects: https://www.datanami.com/2017/04/11/four-common-mistakes-machine-learning-projects/ #1 is similar to my bugbear: biased data sets!
  7. Google AutoDraw converts your doodles into proper drawings using machine learning: https://techcrunch.com/2017/04/11/googles-autodraw-uses-machine-learning-to-help-you-draw-like-a-pro/
  8. Using machine learning to detect anomalies in data streams: https://techcrunch.com/2017/04/12/sisense-pulse-uses-machine-learning-to-trigger-data-anomaly-alerts/
  9. Detecting fake news with a naive Bayesian classifier: http://www.kdnuggets.com/2017/04/machine-learning-fake-news-accuracy.html 
  10. A list of cheat-sheets for data science and machine learning: http://www.datasciencecentral.com/profiles/blogs/20-cheat-sheets-python-ml-data-science
  11. Fooling image recognition/classifier systems: http://www.theverge.com/2017/4/12/15271874/ai-adversarial-images-fooling-attacks-artificial-intelligence 
  12. A basic introduction to neural networks: https://techcrunch.com/2017/04/13/neural-networks-made-easy/ 
  13. Bias in data sets leads to bias in models, there is nothing surprising about that: https://www.theguardian.com/technology/2017/apr/13/ai-programs-exhibit-racist-and-sexist-biases-research-reveals 
  14. My citations have just topped 1000: https://scholar.google.com/citations?user=Z29KBKYAAAAJ