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: http://www.nextbigfuture.com/2016/10/ai-market-is-projected-to-grow-from-8.html 
2. Making music with neural networks: http://www.theregister.co.uk/2016/11/11/ai_pop_music_maker/ 
3. Point and click chatbot builder: https://techcrunch.com/2016/11/10/kitt-ais-chartflow-helps-you-build-better-chatbots/ 
4. Implementing machine learning algorithms in parallel using GPU: http://www.kdnuggets.com/2016/11/parallelism-machine-learning-gpu-cuda-threading.html 
5. Spotting insider trading with data mining: https://www.datanami.com/2016/11/08/sec-mines-data-spot-insider-trading/ 
6. A descriptive overview of convolutional neural networks: http://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html 
7. The current state of machine intelligence: http://www.datasciencecentral.com/profiles/blogs/the-current-state-of-machine-intelligence-3-0 
8. Overview of computer vision: https://techcrunch.com/2016/11/13/wtf-is-computer-vision/ 
9. Examining the relationships we have with present primitive AI: https://techcrunch.com/2016/11/13/defining-our-relationship-with-early-ai/ 
10. An argument that it makes more economic sense for AI to replace highly-paid workers first: http://www.theregister.co.uk/2016/11/14/the_sharks_of_ai_will_attack_expensive_and_scarce_workers_faster_than_they_eat_drivers/ 
11. Adobe is developing Sensei, its own intelligent assisstant: https://techcrunch.com/2016/11/14/adobe-makes-big-bets-on-ai-and-the-public-cloud/ 
12. Has Microsoft made a break-through in machine language comprehension? http://www.techrepublic.com/article/microsoft-has-found-a-way-to-bring-human-language-intelligence-to-our-dumb-computers/ 
13. It seems that Facebook uses machine learning to identify fake news content: https://techcrunch.com/2016/11/14/facebook-fake-news/ 
14. The shortcomings of deep learning: http://www.kdnuggets.com/2016/11/shortcomings-deep-learning.html 
15. Getting to grips with neural networks with Google's AI Experiments showcase: https://techcrunch.com/2016/11/15/googles-ai-experiments-help-you-understand-neural-networks-by-playing-with-them 
16. Some predictions on the future of artificial intelligence: http://www.kdnuggets.com/2016/11/13-forecasts-on-artificial-intelligence.html 
17. An AI-based task manager: https://techcrunch.com/2016/11/15/gluru/ But is it better than my textfile named ToDo.txt?
18. Semantic Scholar, an AI-based search engine for research papers: https://techcrunch.com/2016/11/11/scientists-gain-a-versatile-modern-search-engine-with-the-ai-powered-semantic-scholar/ 
19. Can AI replace HR? https://www.linkedin.com/pulse/can-robots-replace-hr-michael-gretczko?trk=hp-feed-article-title-comment 
20. Machine learning based upsampling of images: http://www.theverge.com/2016/11/16/13649016/google-machine-learning-low-res-image-raisr 
21. The crucial elements missing from chatbot AI: http://www.techrepublic.com/article/mobile-ai-chatbot-intelligence-masquerading-as-the-real-deal/ 
22. Google's ANN-based doodle classifier: http://www.theverge.com/2016/11/15/13641876/google-ai-experiments-quick-draw-image-recognition-game 
23. OpenAI has chosen Microsoft Azure as its cloud platform of choice: http://www.techrepublic.com/article/microsoft-partners-with-openai-to-advance-ai-research-with-azure/ 
24. Google is expanding its cloud-based AI services: http://www.theverge.com/2016/11/15/13640420/google-cloud-service-machine-learning-ai-translation-computer-vision 
25. Nexar is using machine learning in car dash cams to predict collisions: https://techcrunch.com/2016/11/15/nexars-vehicle-to-vehicle-network-will-use-dash-cam-ai-to-prevent-accidents/ 
26. The future of AI is inseparable from humans: http://www.theverge.com/a/verge-2021/humanity-and-ai-will-be-inseparable 
27. List of and commentaries on useful tools for building chatbots: https://chatbotsmagazine.com/the-tools-every-bot-creator-must-know-c0e9dd685094#.2fclmqz8w 
28. Colour me skeptical about the claim that the system recognises handwriting better than humans: https://techcrunch.com/2016/11/17/searchink-unlocking-the-handwritten-past-and-present-with-machine-learning/ 
29. Someone who spent more time talking to bots than their spouse wouldn't have a spouse for long: https://www.datanami.com/2016/11/16/ai-powered-bots-gearing-up-serve-you/ 
30. Overview of opinion mining: http://dataconomy.com/opinion-mining-extracting-opinions/ 
31. Classifying porn with ANN: http://www.theregister.co.uk/2016/1/18/ai_gives_smut_peddlers_helping_hand/ 
32. I suspect this is a case of either seriously biased data or outright fraud: http://www.theregister.co.uk/2016/11/18/ai_can_tell_if_youre_a_criminal/ 
33. Where to apply machine learning first in your business: http://techemergence.com/where-to-apply-machine-learning-first/ 
34. Bias in machine learning models and how to prevent it: http://www.techrepublic.com/article/bias-in-machine-learning-and-how-to-stop-it/ 
35. Automated medical diagnostic tools are still not as good as human doctors: http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/doctors-still-struggle-to-make-the-most-of-computer-aided-diagnosis/?utm_source=humanosalert&utm_medium=email&utm_campaign=111616 
36. British government report on the future implications of AI: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/566075/gs-16-19-artificial-intelligence-ai-report.pdf 
37. Howto: Deep learning-based object recognition in Microsoft Cognitive Toolkit: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/ 
38. Why "Reply All" is not a good idea: http://www.businessinsider.com.au/reply-all-email-chain-1-2-million-nhs-employees-2016-11?r=US&IR=T