Nine years ago, the University of California at Irvine Medical School became the first medical school in the country to equip each of its 104 incoming students with their own iPads.
This month, at the same White Coat Ceremony where that announcement was made back in 2010, Dr. Michael J. Stamos, the school’s dean, surprised the class of 2023 with another gift: Butterfly handheld ultrasound devices.
The devices are the students’ to keep, and it’s no small investment on the school’s part — each device retails for just under $2,000.
“When our faculty director caught wind of Butterfly coming into existence, we had talked about this being a big game-changer for us,” Dr. Warren Wiechmann, UCI’s associate dean, told MobiHealthNews. “Historically, we had been using a lot of laptop and cart-based ultrasounds, which are technically portable but they’re not handheld and they are still a little bit limiting for our students. So when we heard about Butterflies, that really opened up the possibility that we could move toward this idea of having every student with an ultrasound machine in their pocket.”
It’s often tempting to hold Donald Trump and his administration uniquely responsible for the proliferation of facial recognition and other dystopian biometric tech. But the history of these programs shows that the attitudes which motivated this kind of AI-driven crackdown—and the legal authority to make it possible—are a lot older. In truth, the desire for massive biometric surveillance networks has been shared by politicians of all stripes for decades.
Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founding father of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast. [video, 1:15:58]
Alphabet’s life science branch Verily is teaming up with digital health startup iRhythm on a new initiative focused on creating screening, diagnosis and management tools for patients with atrial fibrillation.
The collaboration is specifically targeted at alerting patients with asymptomatic or silent atrial fibrillation about their condition. The goal is to develop products that not only identify the symptoms but also help patients manage the condition.
Are you just a computer made of meat? Are all your thoughts, feelings and experiences nothing more than circuits made from neurons in your head?
If you’re like a lot of people, your answer to this question will be a definitive “No!” From science to philosophy, there are lots of good reasons to hold that human beings are more than just computing machines. Unfortunately, many of the technologists bringing versions of artificial intelligence to the market are already sure they know that we are.
For them people are, indeed, just biological computers. And, based on this flawed perspective, powerful new machines are being built and pushed out into the world right now. If we’re not careful, we may all find ourselves living in a world of unconscious machines destined to make us less human by boxing us into an existence that fits their algorithms.
The brilliance of Kagan’s dissent lay in its clarity: She laid out the precise harms inflicted by partisan gerrymandering and explained how they can be measured and remedied. Kagan identified two distinct but intertwined constitutional violations: Warped maps “reduce the weight of certain citizens’ votes,” depriving them of the ability to participate equally in elections; they also punish voters for their political expression and association. These dual injuries, Kagan concluded, implicate fundamental principles of both equal protection and freedom of speech.
After castigating her conservative colleagues for minimizing these harms, Kagan illustrated the ease with which courts can address them. In his Rucho opinion, Chief Justice John Roberts insisted that federal courts were unable to determine when a partisan gerrymander goes “too far.” Kagan pointed out that, in fact, plenty of lower courts have already done exactly that. These courts deployed a three-part test. First, they ask whether mapmakers intended to entrench their party’s power by diluting votes for their opponents. Second, they ask whether the scheme succeeded. Third, they ask if mapmakers have any legitimate, nonpartisan explanation for their machinations. If they do not, the gerrymander must be tossed out.
University of Notre Dame, College of Science, News
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A multi-university collaboration led by the University of Notre Dame will use data-driven approaches to make the synthesis of complex organic molecules more predictable and efficient.
Olaf Wiest, professor in the Department of Chemistry and Biochemistry, will direct The Center for Computer-Assisted Synthesis (C-CAS). “This will significantly accelerate progress in drug discovery and materials science where such molecules are critical to fundamental research,” Wiest said.
The goal of C-CAS is to transform how the synthesis of complex organic molecules is planned and executed through applying principles of data science and machine learning to chemistry. C-CAS also trains new “data chemists” who are able to bridge the divide between data science and chemical synthesis by using quantitative, data-driven approaches to chemistry.
This fall, students at the Illinois Institute of Technology will be among the first in the country to have the option of pursuing an undergraduate degree in AI.
“AI is the future. We want to train a workforce that can tackle the challenges and opportunities of the future, which includes AI and machine learning,” said Aron Culotta, associate professor of computer science and director of Illinois Tech’s Bachelor of Science in Artificial Intelligence program.
A new joint project in the north-east of Scotland is looking to unlock a “treasure trove” of big data in the North Sea oil and gas sector.
The project, involving researchers from Aberdeen University, is using artificial intelligence (AI) to unlock the raw data collected by the oil and gas industry in order to help maximise the economic recovery.
Our vision is that computer scientists can and should play a key role in helping address societal and environmental challenges in pursuit of a sustainable future, while also advancing computer science as a discipline.
For over a decade, we have been deeply engaged in computational research to address societal and environmental challenges, while nurturing the new field of Computational Sustainability. Computational sustainability aims to identify, formalize, and provide solutions to computational problems concerning the balancing of environmental, economic, and societal needs for a sustainable future.18 Sustainability problems offer challenges but also opportunities for the advancement of the state of the art of computing and information science. While in recent years increasingly more computer and information scientists have engaged in research efforts focused on social good and sustainability, such computational expertise is far from the critical mass required to address the formidable societal and sustainability challenges that we face today. We hope our work in computational sustainability will inspire more computational scientists to pursue initiatives of broad societal impact.
A team from the University of Maryland (UMD) has been awarded $2 million by the National Science Foundation (NSF) for a quantum idea incubator aimed at developing quantum-based machine learning. The $26 million grant is funded by the Quantum Leap Big Idea Program and the Division of Electrical, Communications, and Cyber Systems in the Directorate for Engineering.
The new fall semester is upon us, and at elite private colleges and universities, it’s hard to find a trendier major than Computer Science. It’s also becoming more common for such institutions to prioritize integrating ethics into their CS studies, so students don’t just learn about how to build software, but whether or not they should build it in the first place. Of course, this begs questions about how much the ethics lessons such prestigious schools are teaching are actually making a positive impression on students.
But at a time when demand for qualified computer scientists is skyrocketing around the world and far exceeds supply, another kind of question might be even more important: Can computer science be transformed from a field largely led by elites into a profession that empowers vastly more working people, and one that trains them in a way that promotes ethics and an awareness of their impact on the world around them?
The University of Wisconsin–Madison has established a School of Computer, Data & Information Sciences (CDIS) in the College of Letters & Science to strengthen research and education on campus, prepare tech-savvy graduates to fill new kinds of jobs, support a wave of Wisconsin entrepreneurs and partner with industry to provide them with the competitive advantage of the latest technology.
Powerhouse technology companies met with US officials at Facebook’s headquarters on Wednesday to work on security efforts leading up to the 2020 US presidential election. The companies attending included Facebook, Google, Microsoft and Twitter, according to Facebook.
“Participants discussed their respective work, explored potential threats and identified further steps to improve planning and coordination,” said Nathaniel Gleicher, Facebook’s head of cybersecurity policy, in s statement. “Specifically, attendees talked about how industry and government could improve how we share information and coordinate our response to better detect and deter threats.”
New York, NY September 15. “This workshop is cohosted by UN Environment and SDSN TReNDS, and will focus on efforts to build a digital ecosystem for the environment. It is part of Bloomberg’s Data for Good Exchange.” [registration required]
London, England September 19-20. “A two day conference bringing the creative community together to debate, share and explore the future of data-driven stories.” [$$$]
Denver, CO September 10-14. “CEDIA Expo is the event that’s making smart homes genius. More than 20,000 home tech pros and 500+ exhibitors convene for the leading event in connected technology.” [$$$]
Pittsburgh, PA September 13, starting at 8:30 a.m., Carnegie Mellon University Heinz College. “The INTERSECT@CMU 2019 Conference on Health Care Innovation will address the rapidly changing landscape of health care, featuring panels of thought-leaders discussing emerging technologies, market systems, policy, delivery methods, and visions of the future.” [sold out]
“This was the eighteenth year that NVIDIA has invited PhD students to submit their research projects for consideration. Recipients are selected based on their academic achievements, professor nomination, and area of research. We have found this program to be a great way to support academia in its pursuit of cutting edge innovation, as well as an ideal avenue to introduce NVIDIA to the future leaders of our industry.” Deadline to apply is September 13.
“FastMRI is a joint research project between Facebook AI and NYU School of Medicine, a department of NYU Langone Health, which was created to investigate the use of AI to make magnetic resonance imaging (MRI) scans up to 10 times faster. Last year, we released the largest publicly available data set of raw MRI measurements, as well as open-source tools and baseline results to empower the larger AI and medical imaging research communities to help tackle this problem. To advance the state of the art as quickly as possible, we are also announcing the first ever fastMRI image reconstruction challenge based on this research, and releasing the challenge data set on September 5, 2019.” Deadline to submit reconstructions is September 19.
The long-term vision is to build a joint European laboratory similar to the European Molecular Biology Lab (EMBL) as described in the original open letter (https://ellis.eu/letter).
ELLIS sites are a key element towards achieving this goal as they will implement as much of the ELLIS vision as possible. ELLIS sites can be “ELLIS units” or “ELLIS institutes”. Neither units nor institutes need to be built from scratch but can leverage existing structures and funding. The goal of this call for proposals is to determine the places of scientific excellence in Europe that are best poised to become part of the envisioned European laboratory for machine learning and intelligent systems. The resulting consortium of ELLIS sites will raise the funds and develop the common infrastructure to implement the full ELLIS vision.
There are three opportunities per year to submit a proposal for ELLIS units or institutes. The first call for proposals (deadline November 1st 2019) focuses on ELLIS units only.
“The goal of the challenge is to produce technology that everyone can use to better detect when AI has been used to alter a video in order to mislead the viewer. The Deepfake Detection Challenge will include a data set and leaderboard, as well as grants and awards, to spur the industry to create new ways of detecting and preventing media manipulated via AI from being used to mislead others. The governance of the challenge will be facilitated and overseen by the Partnership on AI’s new Steering Committee on AI and Media Integrity, which is made up of a broad cross-sector coalition of organizations including Facebook, WITNESS, Microsoft, and others in civil society and the technology, media, and academic communities.” The challenge will run through March 2020.
Differentially-private data analysis is a principled approach that enables organizations to learn from the majority of their data while simultaneously ensuring that those results do not allow any individual’s data to be distinguished or re-identified. This type of analysis can be implemented in a wide variety of ways and for many different purposes. For example, if you are a health researcher, you may want to compare the average amount of time patients remain admitted across various hospitals in order to determine if there are differences in care. Differential privacy is a high-assurance, analytic means of ensuring that use cases like this are addressed in a privacy-preserving manner.
Today, we’re rolling out the open-source version of the differential privacy library that helps power some of Google’s core products. To make the library easy for developers to use, we’re focusing on features that can be particularly difficult to execute from scratch, like automatically calculating bounds on user contributions. It is now freely available to any organization or developer that wants to use it.
I remember once speaking with a machine learning researcher who worked at a large company. He told me that a product team had approached him with a very exciting idea that had to do with text summarisation. He started looking into the problem and made some very significant contributions over the course of 12 months – going so far as publishing papers in top-tier conferences about the topic. I asked him if his ideas made it into the product in question. Unfortunately, the answer was no: by the time his research was completed, the product team had moved on from this problem, and weren’t interested in having the solution anymore.
I’ve heard many variants of this story: they all capture a misaligned pace of work between product and machine learning teams. Ultimately, this leads to machine learning research never making it out of the lab. And yet, the best measure of impact for machine learning, if you work in a non-research institution, is whether you can use it to help your customers – and that means getting it out of the door.
Instead of obeying the professor’s note-taking ban, I sat in the back of the classroom and took notes secretly, scribbling feverishly on a small notepad whenever the professor looked away—until I was eventually caught pen-handed and had to fib about writing a letter to a friend back home. This episode prompted me to study note taking—something I’ve done for the past four decades. My objective has been to determine the value of note taking and how to best take notes. Here are seven note-taking tips.