Weekly Review 15 January 2016

Been away on my summer vacation the last few weeks, so not been able to blog much. Some interesting links that I Tweeted about since the last review a month ago:

1. Using machine learning in an NFL confidence pool http://theinstitute.ieee.org/ieee-roundup/opinions/ieee-roundup/how-to-use-machine-learning-to-beat-your-friends-in-an-nfl-confidence-pool
2. How eBay enterprise uses machine learning to detect fraudsters http://www.datanami.com/2015/12/21/tis-the-season-to-hunt-fraudsters-with-big-data/
3. Free data mining software:http://www.datasciencecentral.com/profiles/blogs/4-packages-for-data-analysis - R and Weka are there of course, hadn't heard of Orange before.
4. List of some real-world machine learning data sets: http://www.kdnuggets.com/2015/12/tour-real-world-machine-learning-problems.html The academic ones are all really well known (that is, old)
5. datasciencecentral.com/profiles/blogs/internet-of-things-selected-articles The Internet of Things is become really important in computational intelligence - so much data to model!
6. 15 words you shouldn't use if you want to sound smarter-I'd be happy if people stopped confusing "infer" and "imply" http://mashable.com/2015/05/03/words-eliminate-vocabulary/?utm_cid=p-disp-fb%23lHxBPJzBTRqW#kMQHWVmxvGqw
7. Big Data in agriculture - CI has a big role to play in agro/ecol data processing as well: http://www.datasciencecentral.com/profiles/blogs/big-data-in-agriculture-ddw2-1
8. Getting your paper noticed: http://blogs.nature.com/naturejobs/2016/01/06/five-top-tips-for-getting-your-paper-noticed … One of the five points is using social media: http://www.fasttrackimpact.com/#!Create-a-social-media-strategy-for-your-research-that-delivers-real-impact/hmlp3/564df9090cf20af044b924ca 
9. Bias is a potential problem in all data sets, not just Big Data: http://www.datanami.com/2016/01/08/beware-of-bias-in-big-data-feds-warn/ 
10. Applying ANN to proteomics: https://agenda.weforum.org/2015/12/how-machine-learning-helps-biologists-crack-lifes-secrets/ Something I was looking at about 15 years ago...
11. 5 papers on Deep Learning explained: http://www.kdnuggets.com/2016/01/more-arxiv-deep-learning-papers-explained.html 
12. Yahoo releases a 1.5 TB data set: https://thestack.com/cloud/2016/01/14/yahoo-news-dataset-artificial-intelligence-news-feed/
13. The differences between machine learning, machine intelligence, deep learning and AI: http://www.kdnuggets.com/2016/01/what-is-machine-intelligence-ml-deep-learning-ai.html
14. Finding whales in ocean photographs, a step-by-step tutorial: http://www.datasciencecentral.com/profiles/blogs/finding-whales-in-ocean-water-edge-detection-blob-processing-and
15. Deep learning projects on GitHub: http://www.kdnuggets.com/2016/01/top-10-deep-learning-github.html
16. Having kids is a disadvantage in a research career: http://www.sciencedaily.com/releases/2016/01/160111092607.htm - I'd rather have my daughter than a high-powered career
17. AI is set to wipe out a lot of casual and low-skilled jobs: http://www.spectator.co.uk/2016/01/i-robot-you-unemployed/
18. Machine Intelligence in the Real World-how companies go to market: http://techcrunch.com/2015/11/26/machine-intelligence-in-the-real-world/
19. Uploading a paper to http://academia.org  gives more citations over time: https://www.academia.edu/12297791/Open_Access_Meets_Discoverability_Citations_to_Articles_Posted_to_Academia.edu Can't cite a paper that can't be found