Before it makes sense to answer this question, one needs to think a bit about the relationship between data science and machine learning. To me personally, data science includes machine learning. Machine learning by definition is the ability of a machine to generalize knowledge from data - call it learning or induction if you like. Without data, there is little machines can learn. So if anything, the increase in machine learning usage more broadly in many different industries will be a catalyst to push data science to increasing relevance.

Machine learning developers who want to use Google's Go language as their development platform have a small but growing number of projects to choose from. Rather than call out to libraries written in other languages, chiefly C/C, developers can work with machine learning libraries written directly in Go. Existing machine learning libraries in other languages have a far larger culture of users, but there's clearly an interest in having Go toolkits that take advantage of the language's conveniences. GoLearn, described as a "batteries included" machine learning library, is one of the most prominent. "Simplicity, paired with customisability, is the goal," the developers write in their introduction to the project.

This is the pattern where intelligence lives with the data in the database. Imagine a core transactional enterprise application built with a database such as SQL Server. What if you could embed intelligence, i.e. advanced analytics algorithms plus data transformations, within the database itself, to make every transaction intelligent in real time? That's now possible for the first time with R and ML built into SQL Server 2016. At the Summit, we'll illustrate this with a fascinating demo of real-time predictive fraud detection and scoring in SQL Server.

Scientists at SONY CSL Research Laboratory have created the first-ever entire songs composed by Artificial Intelligence: "Daddy's Car" and "Mister Shadow". The researchers have developed FlowMachines, a system that learns music styles from a huge database of songs. "Daddy's Car" is composed in the style of The Beatles. French composer Benoît Carré arranged and produced the songs, and wrote the lyrics. The two songs are excerpts of albums composed by Artificial Intelligence to be released in 2017.

Scientists at SONY CSL Research Laboratory have created the first-ever entire songs composed by Artificial Intelligence: "Daddy's Car" and "Mister Shadow". The researchers have developed FlowMachines, a system that learns music styles from a huge database of songs. "Mister Shadow" is composed in the style of American songwriters such as Irving Berlin, Duke Ellington, George Gershwin and Cole Porter. French composer Benoît Carré arranged and produced the songs, and wrote the lyrics. The two songs are excerpts of albums composed by Artificial Intelligence to be released in 2017.

If you have not been living under a rock for the last year or so, you would not have missed all the excitement about how Artificial Intelligence enabled solutions are taking over the world, at least the IT world. AI has been around since the 60s and has had at least couple of cycles of peaks and troughs (poetically called'AI winters'). Earlier AI approaches had still a large human component to get the deeper insights out of data which the machines processed in an'intelligent' way. With advances in machine learning algorithms, increased machine power and cloud computing, now AI systems have become capable of getting deeper insights out of data compared to human experts. AI poses unique challenges for the established SI players.

You are statistically incredibly more likely to die in a car crash than on an airplane, but people still fear flying more than driving. Why? Partly, psychologists say, we blow the risks out of proportion because we don't like feeling out of control of our fate. Our life is in the pilot's hands. Self-driving cars will inevitably give people a similar feeling, even if they are much safer than today's vehicles. Computers are expected to be vastly better drivers than humans, but that requires people to turn over the wheel--knowing they won't have control over the computer's split-second decisions if there is an accident.

I've had this book on pre-order since spring and it finally arrived on Friday. I subsequently devoured it over the weekend. The book lays out a clear and compelling case for how data-driven algorithms can become -- in contrast to their promise of amoral objectivism -- efficient means for reproducing and even exacerbating social inequalities and injustices. From predictive policing and recidivism risk models to targeted marketing for predatory loans and for-profit universities, O'Neil explains how to recognize WMDs by 3 distinct features: The taxonomy provides a simple framework for identifying WMDs in the wild. However, importantly for data scientists and other data practitioners, it forms a checklist (or rather an anti-checklist) to keep in mind when developing models that will be deployed into the real world.

The last couple of years have seen a large number of organizations and developers rush towards getting familiar with Machine Learning fundamentals and coming to grips with what it takes to integrate it into their applications. While you can definitely build out your own Machine Learning platform, it is not for everyone and companies like Google are now releasing fully managed API platforms where they expose the Machine Learning platform that they have built over the years. The main value to potential users is that these companies have likely trained their Machine Learning models for years and now the best of these services can be had with a single API call. The latest offering from Google is the Cloud Natural Language API which gives developers insights into unstructured text. A REST API is available to invoke the above functionality and we are going to deep dive into the Sentiment Analysis part of the API to first understand how it works and then build out a Slack Team helper that decodes the sentiment of the text provided to it.

Business tech companies are going to get an overdose of AI marketing. Your friendly neighborhood enterprise software provider has a window into much of your corporate data. And now it wants to provide you with artificial intelligence-fueled insights in what'll equate to a barrage of characters -- Watson, Einstein, Alexa, Siri, Cortana -- a lot of jargon and with any luck some actual automated digital processes. Rest assured you may wind up with the former before getting to the latter destination. There are some things that machines are simply better at doing than humans, but humans still have plenty going for them.