Our children's education is vital. And we are on the cusp of a pedagogical revolution, an upending of traditional instruction. We must invest now to keep education lock-step with technological progress. Automation, machine learning, and artificial intelligence may be serving up the greatest challenge we have ever faced when it comes to education. As these technologies displace jobs at faster and faster rates, we'll increasingly need a workforce that's adaptable.

Imagine you are a parent whose child is applying to get into school. You'd want your child to have the best possible chance of being accepted at the school of your choice, right? Now, what if you knew that the decision of where to place your child was being made by artificial intelligence (AI). Would you trust an algorithm to have your child's best interests at heart? That's exactly the scenario parents in one of the major Dutch cities have encountered ever since the school system embraced AI to create a more equitable and evenly distributed student allocation system.

Airplanes fly over Xiamen City, southeast China's Fujian Province, July 18, 2018. After a short holiday, we have a lot to catch up on. AI made huge leaps and has made insurance claims 176,000 times more efficient than humans, China has opened their low-altitude airspace for the booming drone industry, and classrooms are getting quantified using AI and brain research. Let's get you the news. Yes, you read that correctly.

Cuban was in conversation with Charlie Kirk, the 24-year-old founder of Turning Point USA, a right-wing non-profit organization aimed at promoting conservative political ideas among high school students. Cuban does not identify as Republican or Democrat: "I don't belong to any political party and I never will," Cuban told Kirk. Amid a wide-ranging debate about what the scope of government should be, Cuban argued that the government should be involved in funding artificial intelligence research. In November, the billionaire warned that the United States should not allow countries like China and Russia to pull ahead in terms of developing artificial intelligence. China's government has said publicly it plans to be the global leader in artificial intelligence by 2030 and Russian President Vladimir Putin has said, "the one who becomes the leader in this sphere will be the ruler of the world."

We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option discovery methods and variational autoencoders, and introduce Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection. In VALOR, the policy encodes contexts from a noise distribution into trajectories, and the decoder recovers the contexts from the complete trajectories. Second: we propose a curriculum learning approach where the number of contexts seen by the agent increases whenever the agent's performance is strong enough (as measured by the decoder) on the current set of contexts. We show that this simple trick stabilizes training for VALOR and prior variational option discovery methods, allowing a single agent to learn many more modes of behavior than it could with a fixed context distribution. Finally, we investigate other topics related to variational option discovery, including fundamental limitations of the general approach and the applicability of learned options to downstream tasks.

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach, and decomposition approach, and then discuss the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, batch MTL models are difficult to handle this situation and online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing are reviewed to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works. Finally, we present theoretical analyses and discuss several future directions for MTL.

It seems like voice interfaces are going to be a big part of the future of computing; popping up in phones, smart speakers, and even household appliances. But how useful is this technology for people who don't communicate using speech? Are we creating a system that locks out certain users? These were the questions that inspired software developer Abhishek Singh to create a mod that lets Amazon's Alexa assistant understand some simple sign language commands. In a video, Singh demonstrates how the system works. An Amazon Echo is connected to a laptop, with a webcam (and some back-end machine learning software) decoding Singh's gestures in text and speech.

Big data and artificial intelligence (AI) are helping doctors make better treatment decisions and improve the delivery of care, say Imperial experts. Dr Aldo Faisal, Reader in Neurotechnology jointly at the Department of Bioengineering and the Department of Computing at the College, and Mr Erik Mayer, Clinical Senior Lecturer at the Department of Surgery & Cancer at the College and Honorary Consultant Surgeon at The Royal Marsden NHS Foundation Trust and Imperial College Healthcare NHS Trust, presented their work on big data and AI at the recent Imperial College Academic Health Science Centre (AHSC) seminar series. In a packed lecture theatre, Dr Faisal outlined his work on using artificial intelligence to help doctors make better treatment decisions for the condition Duchenne muscular dystrophy. Duchenne muscular dystrophy is a genetic muscle wasting disease that begins in childhood and mainly affects boys. It usually renders patients unable to walk by age 12 and carries an average life expectancy of 26 years.

How long have your employees been at your company? Do they possess the skills needed to lead you into the future and assist with your company transformation? Probably not, but that doesn't mean you should hire new employees. I've said it before and I'll say it again, it makes the most sense to teach your current employee base the new skills that are required instead of spending the and money to hire new ones. In fact, sixty-two percent of executives believe they will need to retrain or replace more than a quarter of their workforce between now and 2030 due to automation and digitization.

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