Created by Sebastian Schmieg and launched in October 2016 on the website of The Photographers' Gallery, Decision Space marked the beginning of a series of works raising questions around photography, big-data, surveillance, the hidden manual labor behind artificial intelligence and the biases embedded in algorithmic systems. In Decision Space, visitors were invited to assign all the images available on the gallery's website to one of four categories: Problem, Solution, Past and Future. The project resulted in a new conceptual dataset for machine learning and machine vision which can be now be browsed and downloaded at "This is the Problem, the Solution, the Past and the Future". This new dataset makes possible experiments in teaching computers how to understand images within a set of meaningful and complex categories. It consists of around 2.500 photos and includes artists like Cindy Sherman, Jacques-Henri Lartigue, Elliott Erwitt, Sebastião Salgado, Weegee, Valie Export, Francesca Woodman, Simon Fujiwara, Trevor Paglen and many more.

Disruptive technologies are dictating a new future for humankind. Almost every day we hear of new advances that blur the lines between the realms of the physical, the digital and the biological. Robots are now in our operating rooms and fast-food restaurants. It's possible, using 3D imaging and stem cell extraction, to grow human bone from a patient's own cells. This tsunami of technological change is clearly challenging the ways in which we operate as a society.

FROM avoiding jaywalkers to emergency braking to eventually, perhaps, chauffeuring the vehicle itself, it is clear that artificial intelligence (AI) will be an important part of the cars of the future. But it is not only the driving of them that will benefit. AI will also permit such cars to use energy more sparingly. Cars have long had computerised engine-management that responds on the fly to changes in driving conditions. The introduction of electric power has, however, complicated matters.

With advances in information technology, robotics, and artificial intelligence developing at a rapid rate, workforce dislocations are happening now and are here to stay. As existing trends accelerate and irreversibly change the workforce as we know it, the question to be answered is–what will we do to broadly share the gains and alleviate the challenges? Cities often lead trend cycles for mass adoption of new technologies. We all know that technological unemployment is as old as technological innovation. However, American cities are entering a period where the maturation of certain technologies will serve as a force multiplier–affecting every sector of the local economy, every worker, and every job. Automation helps innovation flourish and brings cost savings for businesses, but also displaces jobs.

Apple is pushing past its famous secrecy for the sake of artificial intelligence. In December, the Cupertino tech giant quietly published its first AI research paper. Now, it's joining the Partnership on AI to Benefit People and Society, an industry nonprofit group founded by some of its biggest rivals, including Microsoft, Google and Amazon. On Friday, the partnership announced that Apple's head of advanced development for Siri, Tom Gruber, is joining its board. Gruber has been at Apple since 2010 when the iPhone maker bought Siri, the company he cofounded and where he served as CTO.

The less constrained model indeed performs better (judging by AIC, which drops from to 3696 to 3278). Autocorrelation of errors also is reduced overall. Now, with the improved models, let's finally get forecasting!

Tax season is around the corner, and for most Americans, it involves dealing with the complex tax code, an accountant, and maybe friends who claim to be tax experts. According to IRS statistics, there were 507 million visitors to the irs.gov website in 2016, a 3 percent increase when compared to 2015. Furthermore, Americans spend 6.1 billion hours and $233.8 billion complying with the tax code. With complexity comes confusion and frustration, leading many taxpayers to turn to tax preparers or tax preparation software. Those who don't are at a disadvantage because most Americans are unaware of which deductions to consider, dependents to claims, student loan amounts to deduct, or where to file.

This series of talks is part of the Foundations of Machine Learning Boot Camp. Videos for each talk area will be available through the links above.

The big data paradigm has changed how we make decisions. Armed with sophisticated machine learning and deep learning algorithms that can identify correlations hidden within huge data sets, big data has given us a powerful new tool to predict the future with uncanny accuracy and disrupt entire industries. What if some decisions can't be based just on data? That may sound like a heresy to people who have devoted themselves to the religion of data, to the business leaders who have declared their allegiance to making data-driven decisions. Sure, there may be obstacles to overcome, such as data cleanliness, governance, and security concerns.

Nokia has announced two new services for operators to boost their efficiency with the use of AI and predictive analytics. Jumping on the digital assistant trend, MIKA (Multi-purpose Intuitive Knowledge Assistant) is a specialist helper for telecoms operators which provides quick access to detailed information. Based on Nokia's AVA platform, MIKA will improve the efficiency of engineers by connecting them with best practice solutions to problems. Nokia claims MIKA is the first assistant'trained' for the telecommunications industry and provides more concise help over the general information provided by other assistants. The automation it provides helps to save time spent looking for solutions on the internet or waiting to speak with someone who has knowledge of how to solve the issue.