As we further consider how to train future lawyers for the Algorithmic Society and develop the quality of thinking, listening, relating, collaborating, and learning that will define smartness in this new age, law schools must reach beyond their storied walls. In law, we must got beyond talking about algorithmic implications to actually help shape algorithmic performance. We need lawyers and programmers to work together to create a sound "machine learning corpus." There's potential for an entirely new subfield to emerge if given the right support. With many law school attached to major research universities, it's a great place to start this cross-pollination and interdisciplinary work.

Google Doodles are often challenging, fun and help illuminate the ideas and people that have changed the world. But today's homepage celebrates something even more fundamental than normal. The Doodle centres on coding – helping children to learn the languages that power the site and its homepage itself. And it even lets people do some of that themselves, helping teach some coding basics. It is just one of the various projects by Google and its competitors like Apple that is intended to allow children to code.

Hyderabad: ICFAI Foundation for Higher Education (IFHE), deemed to be University of ICFAI organised a lecture on'Big Data and Artificial Intelligence'. Dr Kalyan Veeramachaneni, Principal Research Scientist at the Laboratory for Information and Decision System (LIDS), Massachusetts Institute of Technology delivered the lecture. He shared his experiences about Big Data, Machine Learning, and Artificial Intelligence with the students. Dr. Kalyan said Machine Learning and predictive models are traditionally created by human scientists by generating feature metrics and the generated models are deployed to address various business requirements. He stated that automating the process of creating the models, proved a time-saving initiative.

A linguistics company is using AI to shorten the time it takes to learn a new language. It takes about 200 hours, using traditional methods, to gain basic proficiency in a new language. This AI-powered platform claims it can teach from beginner to fluency in just a few months – through once-daily 20 minute lessons. Learning a new language is hard. Some people seem to pick up new dialects with ease, but for the rest of us it's a trudge through rote memorization.

Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Specifically, we consider the setting where there is a very large set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration. At test time, a demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task. The use of soft attention allows the model to generalize to conditions and tasks unseen in the training data. We anticipate that by training this model on a much greater variety of tasks and settings, we will obtain a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks. Videos available at https://bit.ly/nips2017-oneshot .

In an era marked by rapid advances in automation and artificial intelligence, new research assesses the jobs lost and jobs gained under different scenarios through 2030. The technology-driven world in which we live is a world filled with promise but also challenges. Cars that drive themselves, machines that read X-rays, and algorithms that respond to customer-service inquiries are all manifestations of powerful new forms of automation. Yet even as these technologies increase productivity and improve our lives, their use will substitute for some work activities humans currently perform--a development that has sparked much public concern. Building on our January 2017 report on automation, McKinsey Global Institute's latest report, Jobs lost, jobs gained: Workforce transitions in a time of automation (PDF–5MB), assesses the number and types of jobs that might be created under different scenarios through 2030 and compares that to the jobs that could be lost to automation. The results reveal a rich mosaic of potential shifts in occupations in the years ahead, with important implications for workforce skills and wages. Our key finding is that while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging--matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.

Few weeks after announcing a partnership with Cambridge Judge Business School, and earmarking Rs 200 crore for foraying into Southeast Asian Markets and the Middle East, UpGrad, the online higher education platform, has now announced a partnership with IIIT-Bangalore to launch PG Diploma programme in machine learning and artificial intelligence. The 11-month Post Graduate Diploma programme in machine learning and artificial intelligence (AI) is a rigorous and selective PG Diploma Programme which will enable learners in mastering concepts in Machine Learning and AI like classification algorithms, deep learning, natural language processing (NLP), reinforcement learning and graph models amongst others. "Colleges and Universities have in-depth knowledge of technical domains and the latest research happening in these domains. IIIT Bangalore brings a strong understanding of evolving areas like machine learning and AI. Hence such a collaboration will help us bring to the learners, the most recent developments in the field." The curriculum is developed by the IIIT-B faculty and leading industry professionals in Indian technology sector.

New robot to help online students learn better - Scientists have developed an innovative robot that can help online students more engaged and connected to the instructor and students in the classroom. Stationed around the class, each robot has a mounted video screen controlled by the remote user that lets the student pan around the room to see and talk with the instructor and fellow students participating in-person....

I suppose that its in Amazon's best interest to not have people hacking accounts and spinning up the maximum amount of EC2s to mine Bitcoins.

Want to understand string theory in 20 minutes? The theoretical physicist has the handy knack of explaining the seemingly unexplainable, taking science out of its academic comfort-zone and into the general public. As co-founder of the yearly World Science Festival, Greene is passionate about increasing public awareness of not just the important of science but also its power to inspire us. He speaks to WIRED about how AI might replace biological life on Earth, the post-truth twilight zone and the challenge of computing consciousness. And we used to do all calculations with a pencil and paper, before that we did it scratching it out on tablets, but as technology progresses we have ever more powerful tools. I would say the same thing about AI, it's something that we can harness in order that we can do our jobs better.