Magically seeing around corners to spot moving people or objects may not rank first in most people's superhero daydreams. But MIT researchers have shown how they could someday bestow that superpower upon anyone with a smartphone. Their secret to peeking around corners is detecting slight differences in light patterns reflected from moving objects or people. Those reflected light patterns form subtle variations in the shadowy area near the base of each corner. MIT's Computer Science and Artificial Intelligence Lab (CSAIL) created simple software that can detect fuzzy pattern variations in the pixels of a 2-D video--taken by a basic consumer camera or even a smartphone camera--and reconstruct the speed and trajectory of moving objects by stitching together multiple, distinct 1-D images.

General Motors just took another step to prepare itself for the future of driving, acquiring a startup that makes what could prove a key technology to unlock self-driving cars for use in fleets. Cruise, GM's self-driving car startup, will now source its lidar laser sensors from Strobe, a Pasadena-based startup that the Detroit automaker just acquired. GM did not disclose the terms of the deal, which it announced Monday morning, but it's a potentially crucial move in its plan to deploy large fleets of robocars, given the importance of the sensor, and the difficulty of making it not just robust and reliable, but cost effective. "Our mission is to remove the driver from the vehicle and ultimately deploy these vehicles at massive scale," says Cruise founder and CEO Kyle Vogt. "Lidar sensors have been one of the bottlenecks."

SonicWall works with 18,000 partners, and CEO Bill Conner believes collaboration is central in cybersecurity. The cybersecurity industry is currently abuzz with talk of automation, principally the central role it will play in handling the mass of threats besieging organisations daily. Automation might be a current trend, but this does not mean it is an entirely new phenomenon, as some organisations have been repelling attacks with machine learning for years. One such company is internet security provider, SonicWall, and CBR spoke to the CEO of the company, Bill Conner, to gain insight on how it is taking on the threat landscape and keeping customers safe. Mr Conner said: "We have focussed on how to use software to isolate segments in your network; our firewalls have been using machine learning for over ten years. A malware cocktail is the easiest way to think of it, these three different technical engines are looking at every file that is unknown, and it is going to block that file from going in until it can characterise it and give it a green light in real time, it is all automated."

The Office of Naval Research has announced plans to partner with the Walter Reed National Military Medical Center, the Naval Research Laboratory, and several universities to develop a new form of leg prostheses. As well as being more comfortable, these smart artificial limbs will help users avoid the risk of infection. The Monitoring OsseoIntegrated Prosthesis (MOIP) project hinges upon a titanium fixture that is surgically implanted into the recipient's femur. Bone generates around the point where it's inserted, so only the small connection point juts out. An artificial limb can be attached or detached from this connector at will.

Twenty-five years ago, Nikos Papanikolopoulos started his work in the University of Minnesota's Artificial Intelligence, Robotics and Vision lab. For the first five years, the lab researched and developed robotics that were largely unheard of. In 1997, his lab had its landmark moment, when the U.S. Military used his lab's Scout robot to survey dangerous war zones in lieu of humans. It built the lab's reputation into a national standout. Today, Papanikolopoulos and his lab are part of a rapidly emerging field of science fusing yesterday's unbelievable with today's uncharted -- artificial intelligence.

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