The launch of Google's app called Google Trips is, on the surface, just another travel app helping you organize your trip. But, really this is not what it is at all. Google's universe, these days, is being powered by machine learning algorithms everywhere. Being what we can best imagine as localized artificial intelligence programs they also inform each other and sync seamlessly from different regions of the Googleverse to provide a seamless experience. So Gmail (AI enhanced, these days), talks to Google Maps and Google Search, Google Now and even Chrome to better understand what the end user is doing it provides a helpful AI assistant in the guise of an app that aims to take the stress away from working out the details of travel so that you can focus instead on what is truly important: the content of your trip.

Much ink has been spilled on the subject of how the jobs market is being impacted by artificial intelligence (AI) and robotics. The well-known study by economists Frey and Osborne published in 2013, which predicts that 47% of all currently existing jobs in the United States will come under threat over the next twenty years, is regularly brought out of the cupboard as a terrifying spectre. Other far more optimistic studies, based on longer time-frames, have delivered a riposte to this – largely unfounded – scaremongering, which has in fact been repeated many times over throughout our history. However, the potential impact of AI on general education and vocational skills training – two means of preparing people for the labour market – is still being largely disregarded. The model whereby you learn during the first half of your life and spend the remaining years applying what you have learned in the world of work has held up pretty well.

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.