Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people's everyday narratives, asking such questions as "what might be the possible reason of ...?", or "what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at https://wilburone.github.io/cosmos.

There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, to ask a few questions on this topic from their point of view and I found this concept of guided automation quite interesting as well, since it is directly involved in the practice of automated machine learning. Rosaria Silipo: What is automated machine learning? Christian Dietz: Automated machine learning is about building a system, process or application able to automatically create, train and test machine learning models with as little human input as possible.

Link: Machine Learning Basics - SQL Server 2017, R, Python & T-SQL This article explains the basics of SQL Server Machine Learning Services. Also you get to compare the functional equivalent of both languages with reference manuals available in this course. These examples range from basics to advanced complex visualizations. Machine Learning Basics with SQL Server 2017, R and Python is a course in which a student having no experience / awareness of Machine Learning / R / Python / SQL Server 2017 Machine Learning Services would be trained step by step to a level where the student is confident to independently work independently with each of them. Course includes practical hands-on queries with explanation and analysis, and theoretical coverage of key concepts.

Link: Deep Learning with TensorFlow 2.0 [2019] Data Science Deep Learning Machine-Learning Scientific Libraries ... Learn about the updates being made to TensorFlow in its 2.0 version. We'll give an ... 8,767 students enrolled Created by 365 Careers, 365 Careers Team Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Some basic Python programming skills You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course. All software and data used in the course are free. Data scientists, machine learning engineers, and AI researchers all have their own skillsets.

They were given six weeks to accomplish the task, and that is what they did. A couple of weeks ago, members of the Red River Rage, which is what the students from Fargo North, Fargo South and Davies High School call themselves, were among the winners of a regional tournament held in Grand Forks. Now, they are heading to the 2019 FIRST Robotics Championship tournament in Detroit, which will be held April 24-27. FIRST Robotics is an international high school robotics competition that gives students real-world engineering experience. There are about 4,800 teams around the world and about 600 will compete in Detroit, according to Ellen Shafer, one of several mentors of the local high school team, which includes her son, Ethan Shafer, a junior at North High.

Automation is not likely to eliminate millions of jobs any time soon--but the U.S. still needs vastly improved policies if Americans are to build better careers and share prosperity as technological changes occur, according to a new MIT report about the workplace. The report, which represents the initial findings of MIT's Task Force on the Work of the Future, punctures some conventional wisdom and builds a nuanced picture of the evolution of technology and jobs, the subject of much fraught public discussion. The likelihood of robots, automation, and artificial intelligence (AI) wiping out huge sectors of the workforce in the near future is exaggerated, the task force concludes--but there is reason for concern about the impact of new technology on the labor market. In recent decades, technology has contributed to the polarization of employment, disproportionately helping high-skilled professionals while reducing opportunities for many other workers, and new technologies could exacerbate this trend. Moreover, the report emphasizes, at a time of historic income inequality, a critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.

Raytheon has deployed artificial intelligence technology to support training programs for the U.S. Army and envisions AI and machine learning tools integrated with virtual and augmented reality to help improve the skills of warfighters. The company said Wednesday it is developing intelligent bots designed to handle lower-level logistics functions at the Army's Multinational Readiness Center in Germany and intends to use data from the bots to provide training aids, gear equipment for the Army's virtual training programs. Malachi Lawson, technical director for global training solutions at Raytheon, said that VR and AR-driven environments integrated with AI result in less dangerous mission exercises compared to live training. Corey Hendricks, the company's senior solutions architect, noted that AI and ML capabilities can also be used to develop hyper-engagement tools that work to inform warfighters' decision-making by analyzing data such as routines and daily habits. He added that such emerging technologies may support efforts to improve soldiers' cultural awareness and help commanders in battlefield operations through the implementation of combat doctrine data.

Data sets and benchmarks have been some of the most effective tools to speed progress in AI. Our current renaissance in deep learning has been fueled in part by the ImageNet benchmark. Recent advances in natural language processing have been hastened by the GLUE and SuperGLUE benchmarks. "Deepfake" techniques, which present realistic AI-generated videos of real people doing and saying fictional things, have significant implications for determining the legitimacy of information presented online. Yet the industry doesn't have a great data set or benchmark for detecting them.

Thieves used voice-mimicking software to imitate a company executive's speech and dupe his subordinate into sending hundreds of thousands of dollars to a secret account, the company's insurer said, in a remarkable case that some researchers are calling one of the world's first publicly reported artificial-intelligence heists. The managing director of a British energy company, believing his boss was on the phone, followed orders one Friday afternoon in March to wire more than $240,000 to an account in Hungary, said representatives from the French insurance giant Euler Hermes, which declined to name the company. The request was "rather strange," the director noted later in an email, but the voice was so lifelike that he felt he had no choice but to comply. The insurer, whose case was first reported by the Wall Street Journal, provided new details on the theft to The Washington Post on Wednesday, including an email from the employee tricked by what the insurer is referring to internally as "the false Johannes." Now being developed by a wide range of Silicon Valley titans and AI start-ups, such voice-synthesis software can copy the rhythms and intonations of a person's voice and be used to produce convincing speech.

The Central Board of Secondary Education (CBSE) has partnered with technology giant Microsoft India to train 1,000 teachers in Information and Communication Technology (ICT) tools. The capacity building programme, according to a CBSE circular, will include aspects of Artificial Intelligence, gamification through Minecraft, collaborative tools such as Teams, Flipgrid, OneNote. Each selected school is asked to nominate two teachers for the programme for which registrations will close on September 9. The programme will train teachers from CBSE-affiliated schools across India. It will begin on September 11 and will conclude on September 28.