The Massachusetts Institute of Technology (MIT) has announced a $1 billion initiative to reshape how the college operates and make artificial intelligence a part of its curriculum for all students. The shakeup is being made, MIT president L. Rafael Reif said, to "prepare students of today for the world of the future" and represents the biggest change to curriculum at the school since the 1950s. The effort will be spearheaded by a $350 million donation from from Blackstone investment firm CEO Stephen Schwarzman. An additional $300 million has been raised for the $1 billion project. The Stephen A. Schwarzman College of Computing will work on incorporating computing and AI into all fields of study at MIT, encouraging cross-disciplinary endeavors, and exploring ways to create a shared structure between the university's five existing schools.
The Massachusetts Institute of Technology (MIT) today announced a $1 billion initiative to reshape how the college operates and make artificial intelligence a part of its curriculum for all students. The shakeup is being made, MIT president L. Rafael Reif said, to "prepare students of today for the world of the future" and represents the biggest change to curriculum at the school since the 1950s. The effort will be spearheaded by a $350 million donation from from Blackstone investment firm CEO Stephen Schwarzman. An additional $300 million has been raised for the $1 billion project. The Stephen A. Schwarzman College of Computing will work on incorporating computing and AI into all fields of study at MIT, encouraging cross-disciplinary endeavors, and exploring ways to create a shared structure between the university's five existing schools.
Successful candidates will have a Doctoral degree (Ph.D.), publications, and demonstrated research competencies and capabilities commensurate with appointment levels in the department(s) of interest, as well as demonstrated interest in and experience with collaborative teaming and/or transdisciplinary efforts Successful candidates will be expected to develop and maintain externally funded research programs (individual and collaborative), engage in both undergraduate and graduate education, and contribute their leadership, partnering and innovative thinking towards global prominence in their respective discipline. Teaching opportunities will vary by department and teaching qualifications will be considered for fit within respective department(s).
Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.
A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."
There are over 950 active startups utilizing or developing AI technologies, of which 445 startups have raised one or more funding rounds. Over the last five years, an average of 140 startups are established annually. AI startups raised $1.94 billion funds in 2017, up 70% from 2016, and have already raised $1.5 billion for the 2018 year to date, tracking almost identically from the previous period. Deal volume peaked at 207 investments in 2017, up 19% from 2016, but is tracking lower as of the 2018 year to date at 133 deals, down from 158 over the previous period. Deal volume for B and A rounds exhibited the most growth, up 64% and 32% respectively from 2016 to 2017.
This ebook, based on the latest ZDNet/TechRepublic special feature, looks at the rise of e-commerce and the digital transformation of retail companies. It takes a lot of machine learning and computer vision to ensure that a pair of high-end sneakers is authentic. GOAT is the largest sneaker marketplace and specializes in selling authentic goods. Specifically, GOAT provides buyers and sellers of sneakers an authenticity guarantee with a "ship to verify" model. GOAT, which has both e-commerce and physical retail locations, has 400 employees and 60 of them are engineers with 7 data scientists.
When it comes to the breakthroughs that brilliant scientists and engineers are working on in 2018, artificial intelligence technology somehow manages to be both the most promising and most polarizing development of these times. As a collective, Big Tech is throwing billions of dollars at artificial intelligence, which those involved would rather we all call machine learning. The notion that we can teach computers to learn -- to absorb data, recognize patterns, and take action -- could have an enormous impact on nearly everything we do with a computer, and pave the way for computers to move into new and game-changing places, such as the self-driving car. This technology still has a long way to go, despite the fact we've been talking about it for decades. But it's starting to become real, and alongside that progress has come perhaps one of the biggest backlashes against an aspect of the evolution of information technology.
Aki Fujimura, chief executive of D2S, sat down with Semiconductor Engineering to discuss Moore's Law and photomask technology. Fujimura also explained how artificial intelligence and machine learning are impacting the IC industry. What follows are excerpts of that conversation. SE: For some time, you've said we need more compute power. So we need faster chips at advanced nodes, but cost and complexity are skyrocketing. Fujimura: Moore's Law is definitely slowing down, but I'm confident there will be continued innovation everywhere to keep it going for a while. There's a lot that every discipline of the eco-system is working on to make incremental and breakthrough improvements.
The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, has selected 10 data science and machine learning projects for its Aurora Early Science Program (ESP). Set to be the nation's first exascale system upon its expected 2021 arrival, Aurora will be capable of performing a quintillion calculations per second, making it 10 times more powerful than the fastest computer that currently exists. The Aurora ESP, which commenced with 10 simulation-based projects in 2017, is designed to prepare key applications, libraries, and infrastructure for the architecture and scale of the exascale supercomputer. Researchers in the Laboratory for Nuclear Science's Center for Theoretical Physics have been awarded funding for one of the projects under the ESP. Associate professor of physics William Detmold, assistant professor of physics Phiala Shanahan, and principal research scientist Andrew Pochinsky will use new techniques developed by the group, coupling novel machine learning approaches and state-of-the-art nuclear physics tools, to study the structure of nuclei.