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Research finds some AI advances are over-hyped

#artificialintelligence

Is it possible some instances of artificial intelligence are not as intelligent as we thought? Call it artificial artificial intelligence. A team of computer graduate students reports that a closer examination of several dozen information retrieval algorithms hailed as milestones in artificial research were in fact nowhere near as revolutionary as claimed. In fact, AI used in those algorithms were often merely minor tweaks of previously established routines. According to graduate student researcher Davis Blalock at the Massachusetts Institute of Technology, after his team examined 81 approaches to developing neural networks commonly believed to be superior to earlier efforts, the team could not confirm that any improvement, in fact, was ever achieved.


Eye-catching advances in some AI fields are not real

#artificialintelligence

Artificial intelligence (AI) just seems to get smarter and smarter. Each iPhone learns your face, voice, and habits better than the last, and the threats AI poses to privacy and jobs continue to grow. The surge reflects faster chips, more data, and better algorithms. But some of the improvement comes from tweaks rather than the core innovations their inventors claim--and some of the gains may not exist at all, says Davis Blalock, a computer science graduate student at the Massachusetts Institute of Technology (MIT). Blalock and his colleagues compared dozens of approaches to improving neural networks--software architectures that loosely mimic the brain.


Eye-catching advances in some AI fields are not real

#artificialintelligence

Artificial intelligence (AI) just seems to get smarter and smarter. Each iPhone learns your face, voice, and habits better than the last, and the threats AI poses to privacy and jobs continue to grow. The surge reflects faster chips, more data, and better algorithms. But some of the improvement comes from tweaks rather than the core innovations their inventors claim--and some of the gains may not exist at all, says Davis Blalock, a computer science graduate student at the Massachusetts Institute of Technology (MIT). Blalock and his colleagues compared dozens of approaches to improving neural networks--software architectures that loosely mimic the brain.


Working from home amid coronavirus crisis welcome new normal in Massachusetts: survey

Boston Herald

This working from home routine is growing on people. The Pioneer Institute surveyed 700 people -- most in Greater Boston -- during the coronavirus pandemic and nearly 63% said they want to stick at home at least one day a week permanently. That, says the think tank, will be a major factor on how companies invest in commercial real estate and how the state should deliver public transportation where -- and when -- it's needed. "The survey results suggest that the pandemic may lead to significant shifts in attitudes toward commuting, with potentially large impacts on the demand for commercial real estate in major job centers, internet connectivity, and transit and transportation planning and budgeting," said Andrew Mikula, who authored the analysis. The survey hits just weeks after the MBTA announced it will likely need to use about a quarter of the $827 million emergency federal funding it received to close a major pandemic-caused revenue gap in this year's budget.


Robot sheep dog herds animals in New Zealand

The Independent - Tech

Farmers in New Zealand have used a four-legged robot to herd sheep, patrol fields and perform other agricultural tasks. The feats were carried out as part of a demonstration of Spot – a robotic dog developed by Massachusetts-based engineering firm Boston Dynamics. Equipped with software developed by robotics company Rocos, Spot was controlled remotely to shepherd sheep across a mountainside. "The age of autonomous robots is upon us," claimed Rocos chief executive David Inggs. "Our customers are augmenting their human workforces to automate physical processes that are often dull, dirty, or dangerous. Organisations can now design, schedule and manage inspection missions remotely."


MIT moves toward greener, more sustainable artificial intelligence

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While current artificial intelligence (AI) technology holds strategic and transformative potential, it isn't always environmentally-friendly due to high energy consumption. To the rescue are researchers from Massachusetts Institute of Technology (MIT), who have devised a solution that not only lowers costs but, more importantly, reduces the AI model training's carbon footprint. Back in June 2019, the University of Massachusetts at Amherst revealed that the amount of energy utilized in AI model training equaled 626,000 pounds of carbon dioxide. Contemporary AI isn't just run on a personal laptop or simple server. Rather, deep neural networks are deployed on diverse arrays of specialized hardware platforms.


A Foolproof Way to Shrink Deep Learning Models

#artificialintelligence

Researchers have proposed a technique for shrinking deep learning models that they say is simpler and produces more accurate results than state-of-the-art methods. Massachusetts Institute of Technology (MIT) researchers have proposed a technique for compressing deep learning models, by retraining a smaller model whose weakest connections have been "pruned," at its faster, initial rate of learning. The technique's groundwork was partly laid by the AutoML for model compression (AMC) algorithm from MIT's Song Han, which automatically removes redundant neurons and connections, and retrains the model to reinstate its initial accuracy. MIT's Jonathan Frankle and Michael Carbin determined that the model could simply be rewound to its early training rate without tinkering with any parameters. Although greater shrinkage is accompanied by reduced model accuracy, in comparing their method to AMC or earlier work by Frankle on weight-rewinding techniques, Frankle and Carbin found that it performed better regardless of the amount of compression.


MBZUAI announces academic year to start in January 2021 - Biz Today

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ABU DHABI: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the world's first graduate-level, research-based artificial intelligence (AI) university, has announced that the start of its first academic year has been rescheduled for January 2021. The decision was made in light of safety measures taken on campus due to the disruption caused by the COVID-19 pandemic, during the Board of Trustees meeting that took place over video conference earlier today. Chaired by His Excellency Dr. Sultan Ahmed Al Jaber, Chairman of the Board of Trustees, UAE Minister of State, the meeting was attended by MBZUAI Interim President, Professor Sir Michael Brady, professor of Oncological Imaging at the University of Oxford, UK; Professor Anil K. Jain, a University Distinguished Professor at Michigan State University, USA; Dr. Kai-Fu Lee, a technology executive and venture capitalist based in Beijing, China; Professor Daniela Rus, Director of Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL), USA, and Peng Xiao, CEO of Group 42. During the meeting, the Board of Trustees also discussed the status of the nearly completed Masdar City campus and facilities, student onboarding and engagement plan, faculty and leadership appointments, and potential industry partnerships. Regarding the decision to postpone the first intake of students, His Excellency Dr Sultan Ahmed Al Jaber said: "The University is eager and ready to welcome our first cohort of students from around the world, however, given the ongoing global coronavirus pandemic, the decision to start the academic year in 2021 been made in the best interest of the prospective students, faculty, and staff, whose health and wellbeing is our top priority. We want our students to be able to focus on their studies and research, and take full advantage of the world-class education that they will receive at the MBZUAI campus."


How Artificial Intelligence can help fight COVID-19

#artificialintelligence

The pandemic caused by COVID-19 is the first global public health crisis of the 21st century. And today, multiple AI-powered projects based on data science, 'machine learning' or'big data', are being used across a broad range of fields to predict, explain and manage the different scenarios caused by the health crisis. AI is being used to support and help those making decisions. "No decisions, at any step, are fully and exclusive delegated on the algorithm," explains Nuria Oliver, data scientist, who holds a Ph.D. from the Media Lab at Massachusetts Institute of Technology (MIT) and is the Regional Government of Valencia's commissioner on AI matters. In the context of the pandemic, AI is being applied and delivering results in three fields: in virus research and the development of drugs and vaccines; in the management of services and resources at healthcare centers; and in the analysis of data to support public policy decisions aimed at managing the crisis, such as the confinement measures.


New MIT Neural Network Architecture May Reduce Carbon Footprint by AI

#artificialintelligence

Artificial Intelligence may seem transient, yet it always managed to have a controversial presence. Recently it raised concerns about its sustainability. In June 2019, the University of Massachusetts at Amherst study discovered that a single large (213 million parameters) Transformer-based neural network built using NAS (commonly used in machine translation) has produced around 626,000 pounds of carbon dioxide. This amount is equivalent to five times more than an average car produces in its lifespan. These massive consumption numbers are because of the energy needed to run specialized hardware like GPUs and TPUs for AI training and development.