Laparoscopy is a surgical technique in which a fiber-optic camera is inserted into a patient's abdominal cavity to provide a video feed that guides the surgeon through a minimally invasive procedure. Laparoscopic surgeries can take hours, and the video generated by the camera -- the laparoscope -- is often recorded. Those recordings contain a wealth of information that could be useful for training both medical providers and computer systems that would aid with surgery, but because reviewing them is so time consuming, they mostly sit idle. Researchers at MIT and Massachusetts General Hospital hope to change that, with a new system that can efficiently search through hundreds of hours of video for events and visual features that correspond to a few training examples. In work they presented at the International Conference on Robotics and Automation this month, the researchers trained their system to recognize different stages of an operation, such as biopsy, tissue removal, stapling, and wound cleansing.

When scholars explore new ideas, it seems instinctive to share their thinking. Last year computer science professors Joseph Halpern and Bart Selman became co-principal investigators for a nationwide project to ensure that robots and artificial intelligences (AIs) will act in ways beneficial to humans. "We thought we should get undergrads involved in thinking about these ideas," Selman said. Thus was born a new course, CS 4732, Ethical and Social Issues in AI, about how robots and artificial intelligences may change our world, and what we ought to be doing about it. "These undergrads may be directly involved in developing the software behind these systems," Selman added, noting that many may go on to jobs with Google, Tesla and other major players.

One of the very hot topics so far in 2017 is artificial intelligence (AI) and its potential disruptive impact on the legal profession. Questions ranging from, "Will AI replace lawyers?" to "Does it make sense to attend law school with the rise of AI?" to "How will AI impact the delivery, cost, and quality of legal services?" IN FACT, THE INTERSECTION OF AI AND THE LAW HAS recently captured the attention of major media outlets including The New York Times ("A.I. is Doing Legal Work. In addition, nowadays you would be hardpressed to attend a legal conference without a session, panel, or presentation on AI. This article reviews the basics of AI, key use cases for AI in the legal profession, some primary AI-related legal issues, and steps that your law firm or in-house legal department may want to take to become AI-ready. In his book "The Fourth Industrial Revolution,"3 Klaus Schwab, executive chairman and founder of The World Economic Forum, begins by briefly reviewing the three earlier industrial revolutions that transformed our society and then devotes the remainder of the book to describing how our world recently entered a whole new era in which we will witness unprecedented major and rapid technological innovations. AI has the potential to be a disruptive force in our "Fourth Industrial Revolution." Like many newer and transformational technologies, there is no uniform definition for AI. An October 2016 report issued by the White House called "Preparing for the Future of Artificial Intelligence" states the following: "Some define AI loosely as a computerized system that exhibits behavior that is commonly thought of as requiring intelligence.

Previously, I had started this conversation with the saying "I am not a Geek, but I need a job too…". Here is why: Technological anxiety (oh yes, it is a thing). I don't want to be a victim of the inevitable wave of "robots taking over our jobs" which is a simplistic explanation for the impact of advancements in technology in the workplace. The idea that half of today's jobs may vanish has changed my view of my children's future. Quincy Larson, Teacher at FreeCodeCamp (an open-source community that helps you learn to code, build pro bono projects for nonprofits, and get a job as a developer) has not stopped in his attempt to get more people coding.

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are looking to learn. But we wanted to develop a more data-driven approach to what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity. The ranking is based on average rank of CRAN (The Comprehensive R Archive Network) downloads and Stack Overflow activity (full ranking here [CSV]).

Prof. Pierre Dillenbourg and the team from the Computer-Human Interaction in Learning and Instruction (CHILI) Lab, explain how they are building robots to use in the classrooms of tomorrow. It is CHILI's goal to deeply integrate Human-Computer Interaction (HCI) and learning sciences, especially in addressing practical problems in learning, teaching, and instruction. If you enjoyed this'meet the lab' video, you can also watch another in the NCCR Robotics series below:

Let's do a quick exercise. Did you start at the top or bottom? New data show that the way you draw a circle holds clues about where you come from. In November, Google released an online game called Quick, Draw!, in which users have 20 seconds to draw prompts like "camel" and "washing machine." It's fun, but the game's real aim is to use those sketches to teach algorithms how humans draw.

Summary: What do you need to do to get an entry level job in data science? This article is written for anyone who is considering becoming a data scientist. That includes young people just starting their bachelor's degrees and folks in the first two or three years of their careers who want to make the switch. It's not for folks who know they are going to pursue one of the new Master's in Data Science or Ph.D. candidates. It's for folks looking for entry level jobs that are specifically on the data science career ladder.

Measuring the relationship between any pair of variables is a rich and active area of research that is central to scientific practice. In contrast, characterizing the common information among any group of variables is typically a theoretical exercise with few practical methods for high-dimensional data. A promising solution would be a multivariate generalization of the famous Wyner common information, but this approach relies on solving an apparently intractable optimization problem. We leverage the recently introduced information sieve decomposition to formulate an incremental version of the common information problem that admits a simple fixed point solution, fast convergence, and complexity that is linear in the number of variables. This scalable approach allows us to demonstrate the usefulness of common information in high-dimensional learning problems. The sieve outperforms standard methods on dimensionality reduction tasks, solves a blind source separation problem that cannot be solved with ICA, and accurately recovers structure in brain imaging data.

Generative models have long been the dominant approach for speech recognition. The success of these models however relies on the use of sophisticated recipes and complicated machinery that is not easily accessible to non-practitioners. Recent innovations in Deep Learning have given rise to an alternative - discriminative models called Sequence-to-Sequence models, that can almost match the accuracy of state of the art generative models. While these models are easy to train as they can be trained end-to-end in a single step, they have a practical limitation that they can only be used for offline recognition. This is because the models require that the entirety of the input sequence be available at the beginning of inference, an assumption that is not valid for instantaneous speech recognition. To address this problem, online sequence-to-sequence models were recently introduced. These models are able to start producing outputs as data arrives, and the model feels confident enough to output partial transcripts. These models, like sequence-to-sequence are causal - the output produced by the model until any time, $t$, affects the features that are computed subsequently. This makes the model inherently more powerful than generative models that are unable to change features that are computed from the data. This paper highlights two main contributions - an improvement to online sequence-to-sequence model training, and its application to noisy settings with mixed speech from two speakers.