Fast-food workers, cashiers, cooks, delivery people and their supporters held a rally outside New York City Hall on May 24, 2017.Erik Mcgregor/Pacific Press/Zuma From the window of his university office in Louvain-la-Neuve, Belgium, philosophy professor Philippe Van Parijs--considered by many to be Europe's most prominent advocate for the idea that the state should provide a regular income to every citizen--can see the mailbox where he sent off invitations to the first "basic income" conference more than 30 years ago. "I'm quite amazed by the seed we threw on the ground now," he says. After decades of obscurity, the idea is suddenly in fashion. Politicians around the world are interested and a handful of governments, such as Finland and the Canadian province of Ontario, are planning or considering basic-income pilot projects. But the idea of basic income has been around for more than 200 years, rising on waves of political and economic turmoil only to disappear in calmer times.

Roundup Welcome to our roundup of major AI news from the past two weeks. Machine learning is so hyped right now, it doesn't help when companies such as Intel and Nvidia announce new chips and reveal little information about the specs, but make lofty claims of increased speed and precision. It's also difficult to keep track of all the different software frameworks and hardware options. Outfits like ARM, AMD, Amazon and Facebook are aware of this and are trying to make it easier to transfer models written in one language to another and optimize the models across various chips. Google's'surprise' Pixel 2 chip It's the first smartphone chip Google has ever designed, and it wasn't announced during the launch of the Pixel 2, which features the new silicon, because, er, it isn't enabled nor supported by applications yet.

Deals and dollars to cybersecurity startups are on track to break records in 2017, according to research firm CB Insights, and those that use artificial intelligence to fight cyber crime are among them. Darktrace, a global machine learning company specialized in cyber defense, raised $75 million in July of this year in a series D round of venture capital funding led by Insight Venture Partners, with participation from Summit Partners, KKR, and TenEleven Ventures. Darktrace has raised $180 million in total since its inception, and the company now claims a post-funding valuation of $825 million. Read on to find out more about The Innovator's picks for AI cybersecurity startups to watch. What it does: Applies AI algorithms to predict, identify and stop malware and mitigate damage from zero-day attacks.

Some people are afraid that heavily armed artificially intelligent robots might take over the world, enslaving humanity -- or perhaps exterminating us. These people, including tech-industry billionaire Elon Musk and eminent physicist Stephen Hawking, say artificial intelligence technology needs to be regulated to manage the risks. But Microsoft founder Bill Gates and Facebook's Mark Zuckerberg disagree, saying the technology is not nearly advanced enough for those worries to be realistic. As someone who researches how AI works in robotic decision-making, drones and self-driving vehicles, I've seen how beneficial it can be. I've developed AI software that lets robots working in teams make individual decisions, as part of collective efforts to explore and solve problems.

The idea of smart cities – infrastructure interlinked by software – isn't new, but it's undeniably cool. Who wouldn't want to live somewhere where programs use data and evidence, not intuition, to actively improve their day-to-day lives? Now imagine that an entire smart city actually exists, but it's even more advanced than you could possibly imagine, where infrastructural systems are altered on the fly by an artificial intelligence (AI). This may sound futuristic, but one such place can already be found in China. As reported back in October 2016, the government of the city of Hangzhou – home to over 9 million people – collaborated with Alibaba and Foxconn to build the "City Brain" project.

Other than simplifying jobs, what can ML do to improve functions in data-intensive industries? Given the rise of machine learning (ML), the disruptive potential on every industry has become commonly accepted knowledge. What's been referred to as the merging of humans and machines is poised to transform society, politics, industrial processes, medicine, business and even war. In fact, IDC projects that 75 percent of future business software will include some form of artificial intelligence (ML being a form of AI) features within the year. The benefits of this type of technology are most significant within data-intensive, algorithm-based industries.

Octavia, a humanoid robot designed to fight fires on Navy ships, has mastered an impressive range of facial expressions. When she's turned off, she looks like a human-size doll. She has a smooth white face with a snub nose. Her plastic eyebrows sit evenly on her forehead like two little capsized canoes. When she's on, however, her eyelids fly open and she begins to display emotion.

The U.S. has so far been relatively permissive toward AI technologies--and we should keep it that way. It's the reason so much innovation happens here rather than in the more prohibitory European nations. The main reason the government hasn't hampered the industry with regulation is that there's no overbearing federal agency dedicated strictly to AI. Instead, we have a patchwork of federal and state authorities scrutinizing these technologies. The Federal Trade Commission and the National Highway Traffic Safety Administration, for example, recently hosted a workshop to determine how to oversee automated-car technologies.

From sales funnel acceleration to network management automation, artificial intelligence (AI) applications have rapidly emerged as key drivers of business advantage. Gartner ranked "AI and Advanced Machine Learning" as of one of its 10 strategic technology initiatives for 2017, citing a wide range of potential use cases including ones in autonomous vehicles, mesh devices and virtual assistants/advisors. AI in networking is key to a future of automation, since WAN connectivity will need to keep pace if all of these AI-enabled innovations are to reach their full potential. Consider the case of chatbots: These AI-powered programs made headlines when Facebook, among other tech titans, highlighted their utility in streamlining basic online activities such as customer support and ticket purchases. AI and machine learning in networking have become more useful as WAN requirements have evolved.

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. We proposed the Multiscale Generalized Correlation (MGC) in Shen et al. 2017 as a novel correlation measure, which worked well empirically and helped a number of real data discoveries. But there is a wide gap with respect to the theoretical side, e.g., the population statistic, the convergence from sample to population, how well does the algorithmic Sample MGC perform, etc. To better understand its underlying mechanism, in this paper we formalize the population version of local distance correlations, MGC, and the optimal local scale between the underlying random variables, by utilizing the characteristic functions and incorporating the nearest-neighbor machinery. The population version enables a seamless connection with, and significant improvement to, the algorithmic Sample MGC, both theoretically and in practice, which further allows a number of desirable asymptotic and finite-sample properties to be proved and explored for MGC. The advantages of MGC are further illustrated via a comprehensive set of simulations with linear, nonlinear, univariate, multivariate, and noisy dependencies, where it loses almost no power against monotone dependencies while achieving superior performance against general dependencies.