During the last decade, a considerable amount of effort has been made to classify variable stars using different machine learning techniques. Typically, light curves are represented as vectors of statistical descriptors or features that are used to train various algorithms. These features demand big computational powers that can last from hours to days, making impossible to create scalable and efficient ways of automatically classifying variable stars. Also, light curves from different surveys cannot be integrated and analyzed together when using features, because of observational differences. For example, having variations in cadence and filters, feature distributions become biased and require expensive data-calibration models. The vast amount of data that will be generated soon make necessary to develop scalable machine learning architectures without expensive integration techniques. Convolutional Neural Networks have shown impressing results in raw image classification and representation within the machine learning literature. In this work, we present a novel Deep Learning model for light curve classification, mainly based on convolutional units. Our architecture receives as input the differences between time and magnitude of light curves. It captures the essential classification patterns regardless of cadence and filter. In addition, we introduce a novel data augmentation schema for unevenly sampled time series. We test our method using three different surveys: OGLE-III; Corot; and VVV, which differ in filters, cadence, and area of the sky. We show that besides the benefit of scalability, our model obtains state of the art levels accuracy in light curve classification benchmarks.

The field of machine learning is becoming more and more mainstream every year. With this growth come many libraries and tools to abstract away some of the most difficult concepts to implement for people starting out. Most people will say you need a higher level degree in ML to work in the industry. If you love working with data and practical math, then I would say this is not true. I did not graduate college with a Machine Learning or data degree yet I am working with ML right now at a startup. I want to share what I used to learn and how I got here in hopes that it will help someone else.

MIT's move signals two trends in higher education: growing investment in sophisticated technology research and increased fundraising from the private sector. Last year, the university announced plans to partner with IBM on a 10-year, $240 million AI research effort. The resulting MIT-IBM Watson AI Lab is co-located with an IBM research facility in Boston and brings together faculty members and students as well as IBM and university researchers to enhance AI's impact across industries. More recently, IBM partnered with Columbia University to develop research competency in blockchain technology through the Columbia-IBM Center for Blockchain and Data Transparency. Public institutions are nabbing corporate funding, too.

ESILV engineering students, used to working on real-life cases, put their specific skills at the service of cross-disciplinary teams. Organised by ESILV, French business school IESEG and De Vinci FabLab, this one-day hackathon had over a hundred students from various higher education institutions work together: ESILV, IESEG, Epitech, Institut Mines Télécom and École Polytechnique. Organised in cross-disciplinary teams mixing business schools and engineering schools, the students had a few hours only to present a solution at the end of the day. Throughout the whole process, all teams were supported by professional coaches from Oracle, Accenture, Total, Orange, Cap Gemini to name but a few. In the late afternoon, teams pitched their solutions for each issue in front of jurys.

The Massachusetts Institute of Technology (MIT) has announced a $1 billion initiative to reshape how the college operates and make artificial intelligence a part of its curriculum for all students. The shakeup is being made, MIT president L. Rafael Reif said, to "prepare students of today for the world of the future" and represents the biggest change to curriculum at the school since the 1950s. The effort will be spearheaded by a $350 million donation from from Blackstone investment firm CEO Stephen Schwarzman. An additional $300 million has been raised for the $1 billion project. The Stephen A. Schwarzman College of Computing will work on incorporating computing and AI into all fields of study at MIT, encouraging cross-disciplinary endeavors, and exploring ways to create a shared structure between the university's five existing schools.

The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.

Earlier this week, Google announced that it was piloting a machine learning intensive for college students. Today, its broader Machine Learning Crash Course is adding a new training module on fairness when building AI. As adoption of machine learning continues, ethics and fairness are very important considerations. While AI can have the "potential to be fairer and more inclusive at a broader scale than decision-making processes based on ad hoc rules or human judgments," there might be underlying biases present in the data used to train these models. Other issues involve insuring that AI is fair in all situations, while more broadly there is "no standard definition of fairness."

Eventually we were spending just about all our free time messing around with any machine we could get our hands on. At an age when other high school kids were sneaking out of the house to go partying, Paul and I would sneak out at night to go use the computers in a lab at the University of Washington. It sounds geeky, and it was, but it was also a formative experience, and I'm not sure I would have had the courage to do it without Paul. I know it would have been a lot less fun. Later, when I was a student at Harvard, I got in trouble for letting Paul use the campus computer lab without permission.)

Another year has flown by and stores everywhere are yet again full of school supplies, one more useful (or eccentric) than others. The back-to-school season is a stressful season, for students, parents and teachers alike. But stress is a part of life and back-to-school stress is supposed to be worth it: educated kids will turn into smart adults who'll ensure everyone's future. We've only taken just a few steps into the 21st Century, after all. With smartphones in our hands, virtual assistants in our homes and various ed-tech tools in our classrooms, we all rely on technology to make our lives easier during this stressful period.

According to the International Federation of Robotics, in February 2018, the average global robot density was 74 robot units per 10,000 employees, up from 66 in 2015. As well as increasing in popularity, robots are also performing more complex and surprising tasks. To keep you in the loop, here are three robot updates from October so far. By Leah Elston-Thompson, senior account executive at Stone Junction Last week, the news broke that Pepper the robot will be giving evidence in Parliament, marking the first use of a non-human witness. The Commons Education Select Committee has invited Pepper to answer questions about artificial intelligence (AI) in the labour market.