In Phoenix, cars are self-navigating the streets. In many homes, people are barking commands at tiny machines, with the machines responding. On our smartphones, apps can now recognize faces in photos and translate from one language to another. Artificial intelligence is here -- and it's bringing new possibilities, while also raising questions. Do these gadgets and services really behave as advertised? How will they evolve in the years ahead?

From smarter electric grids to automated monitoring of at-risk environments, there are many areas where technology could have exponential effects on sustainability. If the world's natural resources are increasingly stressed and depleted, the silver lining may be that we're becoming better equipped at tracking that destruction and potentially doing something about it. Cheap, widespread sensor networks, the internet of things, magnitude-improvements in computing power, open source algorithms–these all allow us to manage oceans and forests more effectively, if we want the opportunity. Artificial intelligence systems that can sense, think, learn, and act on their own could allow a major upgrade in conservation efforts, in dealing with climate change, and living in a more energy-efficient manner. A report released during the recent Davos World Economic Forum meeting laid more than 80 potential environmental applications for AI, ranging from the mundane to the futuristic.

It's 2035, the Second American Civil War has been won by the other side, and you find yourself in a heap of trouble with Attorney General Logan Paul. He has dispatched an all-seeing eye-in-the-sky to tail you, an agile flying machine equipped with 13 cameras and a top speed of 25 miles per hour. The drone knows your face, your gait and your clothing. It hovers persistently behind your back, moving when you move, stopping when you stop, resisting every effort to shake it. You run into the woods, but you still can't lose it.

In the Black Mirror episode Nosedive, the protagonist, Lacie, lives in a saccharine world of pleasantries in which every personal or business interaction is scored. Everything depends on the social score, and everyone is desperate to move up in the rankings. But the omnipresent rating game has one big catch: ranking up is incredibly hard, while ranking down is rapid and easy, like a free-fall. Welcome to the reputation economy, where the individual social graph -- the social data set about each person -- determines one's value in society, access to services, and employability. In this economy, reputation becomes currency.

Precision medicine and artificial intelligence (AI) are complicated by design: Both scientific fields rely on extreme specificity, complex equations, and forces that can't be seen. As both fields begin to alter the healthcare landscape, they could plant a number of legal landmines. Can algorithms or biomarkers be patented? Will centers be able to access the large data sets they need to perform accurate AI? What control over their data should patients have?

AI has become one of the great, meaningless buzzwords of our time. In this video, the Chief Data Scientist of Dun and Bradstreet explains AI in clear business terms. How do you improve the way people work and live? Accenture's Technology Vision 2018 report tackles this question by highlighting trends and rapid advancements in technologies that are improving the way people work and live. The report highlights a need for a fundamental shift in leadership that is required to cultivate partnerships with customers and business partners, and to further accelerate the adoption of artificial intelligence as the fuel for enterprises to grow and deliver social impact.

We study the question of fair clustering under the {\em disparate impact} doctrine, where each protected class must have approximately equal representation in every cluster. We formulate the fair clustering problem under both the $k$-center and the $k$-median objectives, and show that even with two protected classes the problem is challenging, as the optimum solution can violate common conventions---for instance a point may no longer be assigned to its nearest cluster center! En route we introduce the concept of fairlets, which are minimal sets that satisfy fair representation while approximately preserving the clustering objective. We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms. While finding good fairlets can be NP-hard, we proceed to obtain efficient approximation algorithms based on minimum cost flow. We empirically quantify the value of fair clustering on real-world datasets with sensitive attributes.

A wave of obsession for all-things machine learning (ML) has washed over the technology and business communities -- and society more broadly -- in the last several years, and understandably so; machine learning-enabled products and services can present myriad benefits to an organization -- not least the ability to harness large swaths of data to make previously tedious tasks more easy and efficient. Having a solid foundation for real-world ML is a major determinant of success for new initiatives, and is an exciting area of research and engineering in its own right, but the implementation of ML can even be challenging for organizations with mature engineering strength, and it goes without saying that there can be pitfalls and misconceptions in attempts to make the jump between machine learning research and ML in production environments. A frequently overshadowed and often under-appreciated aspect of getting it right is the infrastructure that enables robust, well-managed research and serves customers in production applications. A key lever in setting the foundation for a successful ML program is building a culture and an atmosphere that allows you to trial these efforts at scale: accelerating the rate of scientific experimentation on the road to production and, ultimately, to business value. The cloud is an integral part of these efforts, and it can enable teams to develop and deploy well-governed, accurate ML models to high-volume production environments.

Better Life Lab is a partnership of Slate and New America. "I see you keep winning Elizabeth also you have an awesome smile." These messages would have been innocent enough, hardly a blip in the often-crass landscape of direct messages women have come to expect on dating apps. But these were messages I received from a man I've never met and who is twice my age on LinkedIn. In the thick of the #MeToo movement that has brought so much attention to the unsavory abuses of workplace relationships for sexual and romantic gratification, I was surprised to see this blatant flirtation creep into my professional space.

A 56-percent rise in applications linked to artificial intelligence and the fourth industrial revolution, Industry 4.0, has been seen in three years at the European Patent Office (EPO), according to Turkish Patent and Trademark Office head Monday. The fourth industrial revolution, known as Industry 4.0, offers technology and digital-based working conditions, which may have negative effects on the workforce in the near future. The Industry 4.0 approach is the new production concept of the future and reflects the current trend of automation and data exchange in manufacturing technologies. Speaking at the Artificial Intelligence, Industry 4.0 and Intellectual Property Law Summit held in capital Ankara, Turkpatent President Habip Asan said: "There is a 56-percent rise in the applications about the artificial intelligence and Industry 4.0 in the recent three years to European Patent Office. "In 2016, at least 5,000 patent applications about the Industry 4.0 were made." He also said a record had been set in industrial property rights applications in 2017 in Turkey. "Patent applications rose nearly 35 percent.