Where are the robots, exactly? One answer--if you read the steady flow of doomy articles online -- is that automation is everywhere, not just all over the media but (you would have to conclude) thoroughly infiltrating the economy. In that sense, the trend seems omnipresent even as it spawns a kind of free-floating dread amongst the chattering class. Yet, that can't be right. Almost nothing in today's economy is evenly distributed, whether it be technology, productivity, output, or inclusive prosperity.

But the two men have cemented one of the stranger relationships of the Trump reign. Based on a half dozen sources with front row seats to the odd couple, the enemy (Paul) of a bigger enemy (McConnell) can become one of Trump's few Senate friends. That, by the way, is a big problem for GOP leadership. Top Hill Republicans -- as well as senior administration officials -- are frustrated and concerned. On the campaign trail, Trump tweeted that Paul was "truly weird" and "without a properly functioning brain."

Over 100 CEOs of artificial intelligence and robotics firms recently signed an open letter warning that their work could be repurposed to build lethal autonomous weapons -- "killer robots." They argued that to build such weapons would be to open a "Pandora's Box." This could forever alter war. Over 30 countries have or are developing armed drones, and with each successive generation, drones have more autonomy. Automation has long been used in weapons to help identify targets and maneuver missiles.

Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim of this article is to survey how fairness is formalized in the machine learning literature for the task of prediction and present these formalizations with their corresponding notions of distributive justice from the social sciences literature. We provide theoretical as well as empirical critiques of these notions from the social sciences literature and explain how these critiques limit the suitability of the corresponding fairness formalizations to certain domains. We also suggest two notions of distributive justice which address some of these critiques and discuss avenues for prospective fairness formalizations.

This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix

In recent years, optimization theory has been greatly impacted by the advent of sum of squares (SOS) optimization. The reliance of this technique on large-scale semidefinite programs however, has limited the scale of problems to which it can be applied. In this paper, we introduce DSOS and SDSOS optimization as more tractable alternatives to sum of squares optimization that rely instead on linear and second order cone programs respectively. These are optimization problems over certain subsets of sum of squares polynomials (or equivalently subsets of positive semidefinite matrices), which can be of interest in general applications of semidefinite programming where scalability is a limitation. We show that some basic theorems from SOS optimization which rely on results from real algebraic geometry are still valid for DSOS and SDSOS optimization. Furthermore, we show with numerical experiments from diverse application areas---polynomial optimization, statistics and machine learning, derivative pricing, and control theory---that with reasonable tradeoffs in accuracy, we can handle problems at scales that are currently far beyond the reach of sum of squares approaches. Finally, we provide a review of recent techniques that bridge the gap between our DSOS/SDSOS approach and the SOS approach at the expense of additional running time. The appendix of the paper introduces an accompanying MATLAB package for DSOS and SDSOS optimization.

When you read or hear news stories about the imminent takeover of robots and algorithms that will eliminate jobs for human workers, many times the first examples given are blue-collar jobs like factory workers and taxi drivers. And you may have mentally congratulated yourself because your "professional" job is safe from the threat of being outsourced to computers. But don't feel so safe just yet. More and more, sophisticated algorithms and machine learning are proving that jobs previously thought to be the sole purview of humans can be done -- as well or better -- by machines. Boston Consulting Group has predicted that by 2025 as much as a quarter of jobs currently available will be replaced by either smart software or robots.

Researchers from McGill University, Montreal, have developed an artificial intelligence program (AI) that they believe is one way to put an end to online hate speech. Haji Mohammad Saleem and his team have developed a system that learns hate speech from a data dump that they collected from Reddit between 2006 and 2016, that is 10 years of hate speech from support and abuse groups on the platform, reports Futurism. They were also able to use such data dumps from other sites. Their focus was three major groups who tend to receive a lot of hate – women, African Americans and overweight people. "We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data," wrote the researchers in a release.

Spacewalking astronauts worked at giving the International Space Station's big robot arm a new hand Thursday. Commander Randy Bresnik and Mark Vande Hei tackled the job on the first of three NASA spacewalks planned over the next two weeks. It needs to be replaced before an Orbital ATK supply ship launches in November. Spacewalking astronauts worked at giving the International Space Station's big robot arm a new hand Thursday. The Deep Space Gateway will orbit Earth and the moon and will open up opportunities for future exploration of deep space, as well as a return to the moon and missions to Mars.

Many leading AI researchers think that in a matter of decades, artificial intelligence will be able to do not merely some of our jobs, but all our jobs, forever transforming life on Earth. The reason that many dismiss this as science fiction is that we've traditionally thought of intelligence as something mysterious that can only exist in biological organisms, especially humans. But such carbon chauvinism is unscientific. From my perspective as a physicist and AI researcher, intelligence is simply a certain kind of information processing performed by elementary particles moving around, and there's no law of physics that says one can't build machines more intelligent than us in all ways. This suggests that we've only seen the tip of the intelligence iceberg, and that there's an amazing potential to unlock the full intelligence that's latent in nature and use it to help humanity flourish -- or flounder. If we get it right, the upside is huge: Since everything we love about civilization is the product of intelligence, amplifying our own intelligence with AI has the potential solving tomorrow's thorniest problems.