As Software 2.0 takes shape, how are AI processes--and the jobs that go with them--changing? When it comes to new AI automation, the Software 2.0 movement can be seen as akin to the auto industry's recent evolution. Beginning in the 1990s and rapidly progressing into the 2000s, the auto industry saw a huge rise in AI automation such as digital vehicle diagnostics. Throughout this, people worried that digital transformation might take jobs away, when instead it led to "greater profits, productivity, and competitiveness," according to a 2008 study by the journal of Technological Forecasting and Social Change. In short, these innovations didn't replace jobs, per se; they simply changed the processes of jobs from the manufacturer's assembly line down to the mechanic's garage.

Understanding the complex: Robert Mueller did not ride into the sunset wielding a flaming sword while atop a unicorn, having saved the republic after all. Many people are upset that his much-anticipated report did not immediately "fix" everything, but Lili Loofbourow understands why they're frustrated--it's inherently American to place imaginary superheroes on pedestals, after all. As she writes, "Maybe this is inevitable. Instability makes people cling to their prophet of choice." At least we've got the unicorn illustration.)

When Brandon Araki arrived at MIT in 2015 as a master's candidate in mechanical engineering, he brought along the picobug, a tiny robot that can fly, crawl, and grasp small objects. Before Araki joined Daniela Rus's Distributed Robotics Lab (DRL), he'd been working with collaborators at several universities on the diminutive autonomous machine, which weighs 30 grams and fits in the palm of his hand. He wasn't quite sure what he might do next with the picobug, but when his new boss watched it in action, she was smitten. "I want a hundred of them!" Rus said. Rus, who doubles as the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), imagines a future packed with autonomous machines capable of flying, driving, performing simple surgeries, and more.

Let me ask you this: Do you trust other drivers? If you answered yes, then you've never been in a car, or used a crosswalk, or gone outside. Good news is, technology will eliminate horrible human decision-making with self-driving cars. But then you've got another beast to deal with: the flying car. Can you imagine a world where Mr. Bean is allowed to roam the sky? Well have no fear, because MIT researchers have crafted a beautifully orchestrated framework for how a swarm of driving and flying robots might share the streets and air without kickstarting a societal meltdown.

The problem is that robots that are good at one mode of transportation are usually bad at another. Airborne drones are fast and agile, but generally have too limited of a battery life to travel for long distances. Ground vehicles, on the other hand, are more energy efficient, but slower and less mobile. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are aiming to develop robots that can both maneuver around on land and take to the skies. In a new paper, the team presented a system of eight quadcopter drones that can fly and drive through a city-like setting with parking spots, no-fly zones, and landing pads.

Being able to both walk and take flight is typical in nature – many birds, insects and other animals can do both. If we could program robots with similar versatility, it would open up many possibilities: picture machines that could fly into construction areas or disaster zones that aren't near roads, and then be able to squeeze through tight spaces to transport objects or rescue people. The problem is that usually robots that are good at one mode of transportation are, by necessity, bad at another. Drones are fast and agile, but generally have too limited of a battery life to travel for long distances. Ground vehicles, meanwhile, are more energy efficient, but also slower and less mobile.

More than a week after his death, Prince is everywhere. Artists the world over counted him as an influence and mentor, and Japan is no exception. When news of Prince's death broke, Japanese musicians, from pop idols to rappers, tweeted their goodbyes. But what a lot of people may not realize is that Prince also had profoundly affected one of the most bizarre comic and anime franchises Japan has ever produced – a series called, appropriately enough, "Jojo's Bizarre Adventure." The series isn't quite as popular as titles like "One Piece" or "Dragon Ball Z," but it has been running since 1985, spawned video game and novel spinoffs and produced a serious cult following.

In this position paper we introduce our approach to positive computing by developing and integrating methods for future assistant and companion agents which could help us a) avoid making mistakes due to biases caused by insufficient knowledge, b) be more empathic and righteous, c) be more sensitive and thoughtful. We present text processing techniques for automatic discovery of possible reasoning errors and provide hints to make users doubt their beliefs when there is a possibility of harm. We present existing sources and methods, discuss on how natural language processing technologies could contribute to various aspects of well-being by giving examples of systems we develop, and describe the strengths and weaknesses of our approach.

An intelligent system of the future should make its user feel comfortable, which is impossible without understanding context they coexist in. However, our past research did not treat language information as a part of the context a robot works in, and data about reasons why the user had made his decisions was not obtained. Therefore, we decided to utilize the Web as a knowledge source to discover context information that could suggest a robot's behavior when it acquires verbal information from its user or users. By comparing user utterances (blogs, Twitter or Facebook entries, not direct orders) with other people's written experiences (mostly blogs), a system can judge whether it is a situation in which the robot can perform or improve its performance. In this paper we introduce several methods that can be applied to a simple floor-cleaning robot. We describe basic experiments showing that text processing is helpful when dealing with multiple users who are not willing to give rich feedback. For example, we describe a method for finding usual reasons for cleaning on the Web by using Okapi BM25 to extract feature words from sentences retrieved by the query word "cleaning". Then, we introduce our ideas for dealing with conflicts of interest in multiuser environments and possible methods for avoiding such conflicts by achieving better situation understanding. Also, an emotion recognizer for guessing user needs and moods and a method to calculate situation naturalness are described.

By this paper we would like to open a discussion on the need ofBy this paper we would like to open a discussion on the need of Emotional Intelligence as a feature in machines interacting with humans. However, we restrain from making a statement about the need of emotional experience in machines. We argue that providing machines computable means for processing emotions is a practical need requiring implementation of a set of abilities included in the Emotional Intelligence Framework. We introduce our methods and present the results of some of the first experiments we performed in this matter.