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ep.352: Robotics Grasping and Manipulation Competition Spotlight, with Yu Sun

Robohub

Yu Sun, Professor of Computer Science and Engineering at the University of South Florida, created and organized the Robotic Grasping and Manipulation Competition. Yu talks about the impact robots will have in domestic environments, the disparity between industry and academia showcased by competitions, and the commercialization of research. Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of South Florida (Assistant Professor 2009-2015, Associate Professor 2015-2020, Associate Chair of Graduate Affairs 2018-2020). He was a Visiting Associate Professor at Stanford University from 2016 to 2017, and received his Ph.D. degree in Computer Science from the University of Utah in 2007. Then he had his Postdoctoral training at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA (2007-2008) and the University of Utah (2008-2009).


Can artificial intelligence overcome the challenges of the health care system?

#artificialintelligence

Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills. The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations. Once virtual, the AI Cures Conference returned to in-person attendance at MIT's Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB). MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the "transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise."


Are There a Lot of Artificial Intelligence (A.I.) Jobs Right Now?

#artificialintelligence

A new breakdown shows that A.I. remains a highly specialized field with relatively few job openings--but that will almost certainly change in coming years. CompTIA's monthly Tech Jobs Report reveals that states with the largest tech hubs--including California, Texas, Washington, and Massachusetts--lead when it comes to A.I.-related job postings. It's true that companies don't need nearly as many machine-learning experts as, say, software developers or data scientists. Smaller organizations might not even have the budget to fill out an A.I. division. But CompTIA's job numbers keep growing month after month, indicating a sustained appetite for A.I. talent, especially among larger companies with the money to actually afford researchers and specialists.


AI solves complex physics problems by looking for signs of symmetry

New Scientist

A machine learning model can solve physics problems by simplifying them to be more symmetric. "There are many, many cases in the history of science where people thought things were more complicated than they actually were because they hadn't found the most simple description of it," says Max Tegmark at the Massachusetts Institute of Technology (MIT).


5 digital transformation and talent retention ideas from MIT Sloan Management Review

#artificialintelligence

Many of today's business challenges revolve around two core topics: navigating digital transformation and retaining talent. The latest insights from MIT Sloan Management Review focus on looking past common misconceptions about digital initiatives, setting the right KPIs for digital transformation success, and changing corporate culture and business operations so employees are more likely to stay. Just as today's business leaders should rethink common assumptions about the world of work and re-examine customer expectations, they may also need a new mindset about driving change. MIT Sloan senior lecturer George Westerman identifies four managerial assumptions about digital transformation that prevent enterprises from reaching their true potential. This emphasizes digital but not transformation -- the more difficult (and more important) element to address.


Technique protects privacy when making online recommendations

#artificialintelligence

Algorithms recommend products while we shop online or suggest songs we might like as we listen to music on streaming apps. These algorithms work by using personal information like our past purchases and browsing history to generate tailored recommendations. The sensitive nature of such data makes preserving privacy extremely important, but existing methods for solving this problem rely on heavy cryptographic tools requiring enormous amounts of computation and bandwidth. MIT researchers may have a better solution. They developed a privacy-preserving protocol that is so efficient it can run on a smartphone over a very slow network.


How AI Can Help Address The Global Shortage of Radiologists

#artificialintelligence

Today, over 2/3 of the people on earth do not have access to radiologists. The are big disparities between counties and within countries. Some countries like the US have tens of thousands of radiologists whereas 14 African countries have no radiologists at all. In India there is approximately one radiologist for every 100,000 people whereas in the US there is one radiologist for every 10,000 people. There are also disparities within countries.


Unpacking black-box models

#artificialintelligence

Modern machine-learning models, such as neural networks, are often referred to as "black boxes" because they are so complex that even the researchers who design them can't fully understand how they make predictions. To provide some insights, researchers use explanation methods that seek to describe individual model decisions. For example, they may highlight words in a movie review that influenced the model's decision that the review was positive. But these explanation methods don't do any good if humans can't easily understand them, or even misunderstand them. So, MIT researchers created a mathematical framework to formally quantify and evaluate the understandability of explanations for machine-learning models.


How robots are learning new skills from humans

#artificialintelligence

As robots begin to dominate our everyday lives, Massachusetts Institute of Technology(MIT) researchers have taught a robot how to learn a new pick-and-place task with the help of human demonstrations. The human demonstrations help to "reprogramme" the robot in random poses which it has never encountered earlier. The new technology allows the robot to quickly learn a new skill in 10 to 15 minutes. According to the researchers, the new technology uses a "neural network" to reconstruct 3D images, the system quickly grasps what the neural network has learned and then executes it. The new technology is set to be a gamechanger in e-commerce warehouse storage where robots have to perform a variety of tasks like storing mugs upside down and in various places.


AI on the Front Lines

#artificialintelligence

It's 10 a.m. on a Monday, and Aman, one of the developers of a new artificial intelligence tool, is excited about the technology launching that day. Leaders of Duke University Hospital's intensive care unit had asked Aman and his colleagues to develop an AI tool to help prevent overcrowding in their unit. Research had shown that patients coming to the hospital with a particular type of heart attack did not require hospitalization in the ICU, and its leaders hoped that an AI tool would help emergency room clinicians identify these patients and refer them to noncritical care. This would both improve quality of care for patients and reduce unnecessary costs. Aman and his team of cardiologists, data scientists, computer scientists, and project managers had developed an AI tool that made it easy for clinicians to identify these patients.