Swarm robotics has experienced a rapid expansion in recent years, primarily fueled by specialized multi-robot systems developed to achieve dedicated collective actions. These specialized platforms are in general designed with swarming considerations at the front and center. Key hardware and software elements required for swarming are often deeply embedded and integrated with the particular system. However, given the noticeable increase in the number of low-cost mobile robots readily available, practitioners and hobbyists may start considering to assemble full-fledged swarms by minimally retrofitting such mobile platforms with a swarm-enabling technology. Here, we report one possible embodiment of such a technology designed to enable the assembly and the study of swarming in a range of general-purpose robotic systems. This is achieved by combining a modular and transferable software toolbox with a hardware suite composed of a collection of low-cost and off-the-shelf components. The developed technology can be ported to a relatively vast range of robotic platforms with minimal changes and high levels of scalability. This swarm-enabling technology has successfully been implemented on two distinct distributed multi-robot systems, a swarm of mobile marine buoys and a team of commercial terrestrial robots. We have tested the effectiveness of both of these distributed robotic systems in performing collective exploration and search scenarios, as well as other classical cooperative behaviors. Experimental results on different swarm behaviors are reported for the two platforms in uncontrolled environments and without any supporting infrastructure. The design of the associated software library allows for a seamless switch to other cooperative behaviors, and also offers the possibility to simulate newly designed collective behaviors prior to their implementation onto the platforms.

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word's contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus — VKontakte collected during the Russia-Ukraine crisis in 2014 — 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.

Twitter represents a massively distributed information source over a kaleidoscope of topics ranging from social and political events to entertainment and sports news. While recent work has suggested that variations on standard classifiers can be effectively trained as topical filters (Lin, Snow, and Morgan 2011; Yang et al. 2014; Magdy and Elsayed 2014), there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? Furthermore, what features and feature classes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”. The results of this long-term study of topic classifier performance provide a number of important insights, among them that (1) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training and (2) simple terms and locations are the most informative feature classes (despite training on classes labeled via hashtags).

Research into artificial intelligence is going gangbusters, and the frenetic pace won't let up for about five years -- after which the industry will concentrate around a handful of core technologies and leaders, the head of Intel's new AI division predicts. Intel is keen to be among them. In March, it formed an Artificial Intelligence Products Group headed by Naveen Rao. He previously was CEO of Nervana Systems, a deep-learning startup Intel acquired in 2016. Rao sees the industry moving at breakneck speed.

For decades, artificial intelligence (AI) researchers have sought to enable computers to perform a wide range of tasks once thought to be reserved for humans. In recent years, the technology has moved from science fiction into real life: AI programs can play games, recognize faces and speech, learn, and make informed decisions. As striking as AI programs may be (and as potentially unsettling to filmgoers suffering periodic nightmares about robots becoming self-aware and malevolent), the cognitive technologies behind artificial intelligence are already having a real impact on many people's lives and work. AI-based technologies include machine learning, computer vision, speech recognition, natural language processing, and robotics;1 they are powerful, scalable, and improving at an exponential rate. Developers are working on implementing AI solutions in everything from self-driving cars to swarms of autonomous drones, from "intelligent" robots to stunningly accurate speech translation.2 And the public sector is seeking--and finding--applications to improve services; indeed, cognitive technologies could eventually revolutionize every facet of government operations. For instance, the Department of Homeland Security's Citizenship and Immigration and Services has created a virtual assistant, EMMA, that can respond accurately to human language. EMMA uses its intelligence simply, showing relevant answers to questions--almost a half-million questions per month at present. Learning from her own experiences, the virtual assistant gets smarter as she answers more questions. Customer feedback tells EMMA which answers helped, honing her grasp of the data in a process called "supervised learning."3 While EMMA is a relatively simple application, developers are thinking bigger as well: Today's cognitive technologies can track the course, speed, and destination of nearly 2,000 airliners at a time, allowing them to fly safely.4

Thousands of analysts, journalists and developers came to the Washington State Conference Center in Seattle today to see what Microsoft had to unveil at its three-hour-long Build conference. As it turns out, there wasn't a lot of interesting news for non-developers. In other words, if you had played a drinking game with the trigger words being "Azure," "Microsoft Graph" and "Visual Studio," you would have needed two kegs of liquor. To be fair though, Build is an event for developers. Still, there were updates around new Cortana skills, artificial intelligence for the workplace and a PowerPoint translator tool that may have useful applications for consumers.

The timing of the FBI director's dismissal, the Senate minority leader said, raises questions about the integrity of the Justice Department's look at potential collaboration between the Trump campaign and Russia.

In an important advance that takes us one step closer to the inevitable robopocalypse, MIT researchers have developed a system that teaches robots how to acquire new skills--and then teach those skills to different types of robots. The system is called C-LEARN, and it was developed by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Using C-LEARN, people who have no experience with computer programming can teach a robot how to perform a task--like dropping a flask into a bucket, or pulling a rod from a container--by providing it with some basic rules about the task, and allowing the robot to view a single demonstration of the task being completed. Incredibly, a robot can then transfer this newly-acquired knowledge to another robot, even if the robot learning is physically different than the robot teaching. Eventually, the C-LEARN system could allow factories to utilize a host of different robot types, and not have to worry about programming each and every one of them individually.

The first is an exciting new Volta-based GPU instance that we think will completely change the face of the AI developer world through a 3x speedup on LSTM training. Second, we are announcing plans to train 100,000 developers through the Deep Learning Institute (DLI) running on AWS. The third is the joint development of tools that enable large-scale deep learning for the broader developer community. AWS is also delivering sessions at GTC including using Apache MXNet training at scale on Amazon EC2 P2 instances and at the edge through the support of NVIDIA's Jetson TX2 platform. The Tesla V100, based on the Volta architecture and equipped with 640 Tensor Cores, provides breakthrough performance of 120 teraflops of mixed precision deep learning performance.

Deep learning--one of the most promising approaches to artificial intelligence--could change the way cancer is detected. The National Cancer Institute provided computer scientists with 2,000 low-dose CT scans of patients' torsos with the goal of improving the detection of lung cancer. While the imaging technique uses less radiation than other methods, when doctors interpret the images, they tend to make false positive diagnoses--they're not easy images for human eyes to decipher. It's no secret that humans routinely miss things in images, and particularly medical images. Four years ago, researchers asked 24 radiologists to pick out a nodule on a CT scan.