Monday, November 21, 2016

Weeky Review 21 November 2016

Below are some of the interesting links I Tweeted about in the last week.

  1. The AI market is predicted to keep growing: 
  2. Making music with neural networks: 
  3. Point and click chatbot builder: 
  4. Implementing machine learning algorithms in parallel using GPU: 
  5. Spotting insider trading with data mining: 
  6. A descriptive overview of convolutional neural networks: 
  7. The current state of machine intelligence: 
  8. Overview of computer vision: 
  9. Examining the relationships we have with present primitive AI: 
  10. An argument that it makes more economic sense for AI to replace highly-paid workers first: 
  11. Adobe is developing Sensei, its own intelligent assisstant: 
  12. Has Microsoft made a break-through in machine language comprehension? 
  13. It seems that Facebook uses machine learning to identify fake news content: 
  14. The shortcomings of deep learning: 
  15. Getting to grips with neural networks with Google's AI Experiments showcase: 
  16. Some predictions on the future of artificial intelligence: 
  17. An AI-based task manager: But is it better than my textfile named ToDo.txt?
  18. Semantic Scholar, an AI-based search engine for research papers: 
  19. Can AI replace HR? 
  20. Machine learning based upsampling of images: 
  21. The crucial elements missing from chatbot AI: 
  22. Google's ANN-based doodle classifier: 
  23. OpenAI has chosen Microsoft Azure as its cloud platform of choice: 
  24. Google is expanding its cloud-based AI services: 
  25. Nexar is using machine learning in car dash cams to predict collisions: 
  26. The future of AI is inseparable from humans: 
  27. List of and commentaries on useful tools for building chatbots: 
  28. Colour me skeptical about the claim that the system recognises handwriting better than humans: 
  29. Someone who spent more time talking to bots than their spouse wouldn't have a spouse for long: 
  30. Overview of opinion mining: 
  31. Classifying porn with ANN: 
  32. I suspect this is a case of either seriously biased data or outright fraud: 
  33. Where to apply machine learning first in your business: 
  34. Bias in machine learning models and how to prevent it: 
  35. Automated medical diagnostic tools are still not as good as human doctors: 
  36. British government report on the future implications of AI: 
  37. Howto: Deep learning-based object recognition in Microsoft Cognitive Toolkit: 
  38. Why "Reply All" is not a good idea:

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