Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule,which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting $<$subject, predicate, object$>$ triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.

Nando de Freitas is a "machine learning professor at Oxford University, a lead research scientist at Google DeepMind, and a Fellow of the Canadian Institute For Advanced Research (CIFAR) in the Neural Computation and Adaptive Perception program." Above, you can watch him teach an Oxford course on Deep Learning, a hot subfield of machine learning and artificial intelligence which creates neural networks--essentially complex algorithms modeled loosely after the human brain--that can recognize patterns and learn to perform tasks. To complement the 16 lectures you can also find lecture slides, practicals, and problems sets on this Oxford web site. If you'd like to learn about Deep Learning in a MOOC format, be sure to check out the new series of courses created by Andrew Ng on Coursera. Oxford's Deep Learning course will be added to our list of Free Online Computer Science Courses, part of our meta collection, 1,300 Free Online Courses from Top Universities.

We're taught from an early age to take notes. I believe the first time I recall starting to take notes was when I entered junior high. In fact, I can even recall being required to take a class on how to take notes – Roman numbers, indentation, etc. The act of taking notes is one of the few activities that has stood the test of time as I continued to do this not only in junior high, but throughout my education and it is something that I continue to do regularly today in my professional career. The reason why notes continue to be so prevalent in all of our lives is not because our teachers were so effective at influencing our young impressionable selves, but it's because taking notes is an extremely useful practice. It allows us to capture the highlights of a class, meeting, or event and serves as a tool to help us recall information or re-enforce learning.

The invention of artificial intelligence has been hotly debated over the years. Some view this tool as the first step toward a world where human professions are no longer necessary. Others see artificial intelligence as a cost-effective means of being more productive during the day. The truth may fall somewhere in between these extremes, particularly when it comes to education. Artificial intelligence has already transformed the face of learning in a major way.

In 2016, DeepMind's AlphaGo famously defeated Lee Sedol, an international Go champion, becoming the first computer program to beat a human world champion. In 2018, LawGeex, an AI contract review platform, pulled the same stunt on human lawyers. The AI system achieved a 94 percent accuracy rate at surfacing risks in non-disclosure agreements (NDAs). Experienced human lawyers average out at 85 percent accuracy for the same task. The study, conducted in collaboration with Duke and Stanford Law Schools, pitted AI against 20 top U.S.-trained lawyers with decades of experience specifically in reviewing NDAs, one of the most common agreements in law.

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.

Google has started the Intelligent Taiwan initiative to help promote digital transformation of the country's economy and strengthen AI (artificial intelligence) capability of its companies, according to Google's Taiwan managing director Chien Lee-feng. With Intelligent Taiwan, Google hopes to realize its "AI first" concept in Taiwan, Chien indicated, adding Taiwan meets all Google's requirements for developing AI, including those for software, hardware and cloud computing infrastructure. In the initiative, Google will expand its manpower in Taiwan by recruiting over 300 software/hardware engineers and R&D staff, provide digital marketing training for over 50,000 local small- to medium-size businesses and students, and train over 5,000 local students in AI programming in 2018, Chien said. According to Chien, for digital marketing training, Google will provide a free and convenient online platform and facilities in Taichung and Tainan to cater to those who prefer face-to-face training. For AI training for students, Google will first give in-depth instruction to university and senior high school teachers, and seed instructors with government-sponsored Institute for Information Industry (III) using Google-developed Machine Learning Crash Course.

Artificial intelligence and robotics are disrupting every aspect of work and redefining productivity. The old ways of not just working, but also assessing capabilities, hiring and compensation, are undergoing a massive change. In a conversation with Knowledge@Wharton, Srikanth Karra, chief human resource officer at Indian IT services firm Mphasis, discusses what this means for individuals, organizations and countries. Karra said managerial jobs and tasks that are repetitive in nature will be displaced and the ability to learn new skills will be critical for individuals who want to stay relevant. Companies will need to devise new ways of training and assessing the skills of employees while countries must develop a learning ecosystem. "Work will be more contractual in nature and deep technical skills, creativity and learnability will be at a premium," he noted.

SOUTHBOROUGH, Mass., – (March 08, 2018)–Virtusa Corporation (NASDAQ GS: VRTU), a global business consulting and IT outsourcing company that accelerates business outcomes for its clients,has been included in Gartner'sMarket Guide for Data Science and Machine Learning Service Providers. The report, published on October 31, 2017, states: "Data and analytics leaders looking for support for their data science and machine learning projects should use this research to identify and engage with candidate service providers to fill the analytics deficit and augment their existing data scientists with specific skills." According to Gartner, "the growing demand for DS&ML as a competitive differentiator is forcing organizations to acquire an even wider portfolio of skilled resources – from statisticians and data scientists, to chief analytics officers. However, there is a shortage of data science skills in the market, making it difficult to source the right skills." "We feel that we are one of the most visible DS&ML service partners worldwide and are proud to be recognized in Gartner's Market Guide," said Kumar Ramamurthy, senior vice president and global head, Data & Analytics, Virtusa.

Artificial intelligence (AI) is changing the landscape of higher education. According to Dr. Keng Siau, artificial intelligence will "perform an array of general tasks with consciousness, sentience and intelligence." That could mean that higher education may no longer be the path to a professional career. University degrees have always led to professional careers; AI may change that path and offer new forms of learning. Ultimately, AI will change the way colleges have approached education.