The role that adult education and colleges play in preparing the labour market for technological disruption will be explored by MPs. The World Economic Forum, which holds an annual conference at the Swiss ski resort of Davos, set the theme of its 2016 gathering of world leaders around the topic of the fourth industrial revolution. Since then, the term has entered common parlance and it is now widely characterised as concerning what impact that new technologies, including artificial intelligence and robotics, will have on the labour market with many low and medium skilled jobs believed to be at risk of automation. Now the education select committee is to explore the issue of preparing for the so-called fourth industrial revolution. Stressing the importance of the inquiry, committee chairman Robert Halfon said by the 2030s, as many as 28 per cent of the current jobs taken by 16- 24-year-olds are likely to be at risk of automation.
The increasing sophistication of automated systems will have far-reaching implications for work and employment, and governments should be ready for upheaval. "WHO IS READY FOR THE COMING WAVE OF AUTOMATION? The Automation Readiness Index", created by The Economist Intelligence Unit and sponsored by ABB, assesses how well-prepared 25 countries are for the challenges and opportunities of intelligent automation. The Automation Readiness Index compares countries on their preparedness for the age of intelligent automation. In assessing the existence of policy and strategy in the areas of innovation, education and the labour market, the study finds that little policy is in place today that specifically addresses the challenges of AI- and robotics-based automation.
In recent years, Convolutional Neural Networks (CNNs) have shown remarkable performance in many computer vision tasks such as object recognition and detection. However, complex training issues, such as "catastrophic forgetting" and hyper-parameter tuning, make incremental learning in CNNs a difficult challenge. In this paper, we propose a hierarchical deep neural network, with CNNs at multiple levels, and a corresponding training method for lifelong learning. The network grows in a tree-like manner to accommodate the new classes of data without losing the ability to identify the previously trained classes. The proposed network was tested on CIFAR-10 and CIFAR-100 datasets, and compared against the method of fine tuning specific layers of a conventional CNN. We obtained comparable accuracies and achieved 40% and 20% reduction in training effort in CIFAR-10 and CIFAR 100 respectively. The network was able to organize the incoming classes of data into feature-driven super-classes. Our model improves upon existing hierarchical CNN models by adding the capability of self-growth and also yields important observations on feature selective classification.
MIT President L. Rafael Reif announced today a significant expansion of the Institute's programs in learning research and online and digital education -- from pre-kindergarten through residential higher education and lifelong learning -- that fulfills a number of recommendations made in 2014 by the Institute-Wide Task Force on the Future of MIT Education. Most notably, Reif announced the creation of the MIT Integrated Learning Initiative (MITili), to be led by Professor John Gabrieli, and a new effort to increase MIT's ability to improve science, technology, engineering, and mathematics (STEM) learning by students from pre-kindergarten through high school (pK-12), to be led by Professor Angela Belcher. The announcement also included a program to support faculty innovations in MIT residential education and new work to enhance MIT's continuing education programs. In keeping with the high priority of these new efforts and of the entire field of digital learning, Professor Sanjay Sarma, now dean of digital learning, will oversee them in the newly created position of vice president for open learning, reporting directly to Reif. Chancellor Cynthia Barnhart, who will share responsibility with Sarma for several aspects of this work, predicts that the programs announced today will have "far-reaching and tremendous implications for education -- for MIT students as well as for students not at MIT."
There is a strange dichotomy at the moment surrounding the future of work. In public, political movements throughout the western world have seen populist campaigners railing against the threat to jobs from low-wage migrants entering a country, and outsourcing to low-cost regions by multinationals. What hasn't really been touched on is the impact automation might have on jobs in the future. What began with the famous study from Oxford University academics Carl Benedikt Frey and Michael Osborne back in 2013, which highlighted the huge number of white and blue collar jobs that could be disrupted by automation, has progressed to growing interest from governments around the world.. For instance, a report by the British government's Science & Technology Select Committee into AI examined the issue from a range of aspects, from ethics to employment.
In this talk, Prof. Iiyoshi goes head to head with an AI questioning the fate of education and lifelong learning! Toru Iiyoshi was previously a senior scholar and Director of the Knowledge Media Laboratory at the Carnegie Foundation for the Advancement of Teaching (1999-2008), and Senior Strategist in the Office of Educational Innovation and Technology at Massachusetts Institute of Technology (2009-2011). He is the co-editor of the Carnegie Foundation book, "Opening Up Education: The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge" (MIT Press, 2008) and co-author of three books including "The Art of Multimedia: Design and Development of The Multimedia Human Body" and numerous academic and commercial articles. He received the Outstanding Practice Award in Instructional Development and the Robert M. Gagne Award for Research in Instructional Design from the Association for Educational Communications and Technology. Currently, he is the director and a professor of the Center for the Promotion of Excellence in Higher Education (CPEHE) at Kyoto University.
On July 27, Department of Civil and Environmental Engineering Professor Philip Gschwend presented on environmental topics to approximately 75 MIT alumni in Mashpee, Massachusetts. The room was filled with red jackets, a signature of those who have been MIT alumni for at least 50 years. Attendees also included members of the Cardinal and Gray Society and the Emma Rogers Society, whose purpose is to gather together periodically to socialize and engage in intellectual conversations. The air was filled with comradery and laughter -- a clear sign that this group was not new to each others' company. Theodore (Ted) Korelitz '56, chair of the Cardinal and Gray Society, welcomed everyone and recognized those who made special efforts to attend, including the most senior alumnus present, Clifford Woods '46, who arrived with his wife, Patricia.
We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered family of kernels there exists one that yields low approximation error on all tasks, the overhead associated with learning such a kernel vanishes and the complexity converges to that of learning when this good kernel is given to the learner.