A history of AI in 10 landmarks by @lukedormehl via @DigitalTrends

Ten moments to remember in the development of AI.

Interesting to look back and think about where it has come from and how that progression happened.

How analytics and machine learning can aid transplant decisions by Dimitris Bertsimas and Nikolaos Trichakis via @infomgmt

A new tool looks at 10 years of data and millions of prior decisions to estimate a patient’s waiting time in the context of a current active organ offer.

Great idea and a good use of these techniques and the data that has been collected.

Selling data to feed hedge fund computers is one of the hottest areas of finance right now by @johndetrixhe via @qz

Kumesh Aroomoogan has come a long way from his days at Wall Street bank Citigroup, where his job was to stay on top of breaking news. He remembers getting yelled at by a trader because he stopped watching news feeds to go the bathroom and missed a market-moving headline. Now, he’s the co-founder and CEO of Accern, a company that automates his old job.

Interesting article - seems it's not only money but data that makes people wealthy.

Definition of a Quant Fund here.

80% of data scientists will have deep learning in their toolkits by 2018, predicts Gartner by Divina Paredes via @cio_nz

Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence(AI), reports Gartner.

Good well thought out article based on the Gartner report. Deep learning is definitely a step above machine learning so I can understand the usefulness.

Why AI and advanced analytics benefits still elude most organisations by Tony Fisher via @infomgmt

Enterprises are too often drowning in an over-abundance of data types and sources - many of which contradict each other.

I think he makes some good points - how can you work out what you have and what you can do from it?  How can you work out what you can do with it?  If you don't have data management you can't know what you have so that you can think about what you can do with it.  It's clear to me that you have to have clear benefits and understand your data in order to do any kind of project and actually achieve those benefits.

Data Security for Data Scientists by @therriaultphd via @Medium

Ten practical tips for protecting your data (and more importantly, everyone else’s!)

These tips are great and you should study them quite closely. I know in the past I've probably broken some of them although at that time I don't know if anyone was so conscious of those things.

Analytics maturity models and the control environment by Angel Serrano via @infomgmt

It is important to maintain information quality and a management framework to ensure that the data used for the analysis is fit for the purpose.

This was interesting and reminds me of all the other maturity models in existence like CMM and these in the list on Wikipedia.