In fall 2018, Tufts School of Engineering began to offer a bachelor’s program in data science. Additionally, the School of Engineering has recently added a 4+1 B.S./M.S. dual degree in data science to their programs and will begin accepting applications for a Master’s in Data Science starting in fall 2019. These programs were spearheaded by Associate Professors Shuchin Aeron of the Electrical and computer engineering department and Alva Couch of the computer science department. The new degrees reflect the university’s many efforts to meet the growing demand for skills in data analysis, as well as the enthusiasm of students and faculty to expand data science at Tufts.
The boundaries between art, design, science and technology are disappearing in a digital world. Today, artists use algorithms, scientists rely on visualization and designers are often focused on helping people navigate new technologies.
At the University of Chicago, the disciplines come together at the Media Arts, Data and Design Center, creating a new collaborative space for experimentation, discovery and impact. The MADD Center will support work by faculty, other academic appointees, students, staff and community partners through cutting-edge technologies. The 20,000-square-foot center in the John Crerar Library opened Feb. 25.
How did life begin on Earth? That is one of the oldest and most profound questions that humans have ever tried to answer. Over the past several hundred years, the scientific answers have come a long way. Scientists want to understand what processes create life – both here and, possibly, on other planets – but there are many unsolved puzzles. To help solve the enigma, NASA this month launched a new research consortium – uniting researchers across multiple scientific disciplines – called Prebiotic Chemistry and Early Earth Environments or PCE3.
Scientists at University of California, Riverside (UCR) and Rensselaer Polytechnic Institute (RPI) announced PCE3 on February 14, 2019.
Inside a small, rectangular room at the University of Washington is a series of shelves filled with more than 300 high-tech tools. There’s a collection of drones, cameras, and tablets, and even a mobile EEG kit, able to measure a brain’s electrical activity and detect stress levels in disaster victims. Each one has been meticulously organized, labeled, and packed away in a protective case, ready to be sent hundreds or even thousands of miles to the next natural disaster.
This is one of the three rooms that make up the RAPID Facility in Seattle, a first-of-its-kind center pushing the boundaries on natural disaster research, along with the world’s ability to mitigate the potentially catastrophic effects of these hazards.
One of the world’s foremost multidisciplinary research institutions, Weizmann is investing heavily in the burgeoning field of AI. Its expertise in computer vision, machine learning and robotics — as well as a culture that encourages collaborations across disciplines — makes AI a natural fit.
More than half a dozen scientists from across the institute do AI-related research at the new center, which was launched about a year ago. The center’s director is Shimon Ullman, a world leader in computer vision who was awarded the 2015 Israel Prize in mathematics and computer science research.
Accelerating trends in artificial intelligence (AI) point to significant geopolitical disruption in the years ahead. Much as the Industrial Revolution enabled the
rise of the United States and other advanced economies, so AI and machine learning are poised to reshape the global order.
Prospects for sustaining global competitiveness are now directly tied to the industrialization of AI. The industrial application of AI to a wide array of industries will mean a constant state of innovation. AI is predicted to reshape manufacturing, energy management, urban transportation,
agricultural production, labor markets, and financial management. Governments that can successfully cultivate a culture of disruptive innovation will be strategically positioned to lead in the twenty-first century. By contrast, governments that resist AI will find themselves facing a daunting future.
Over my eight-plus years at Google, leading a fairly large and successful team, I’ve developed a set of principles that are core to my own approach to management. Since I’m about to leave Google for a startup, I thought now would be a good time to capture and share these ideas more broadly. The following notes are a slightly modified version of a doc that I shared within Google a while back.
Google, DeepMind; Carl Elkin, Sims Witherspoon, Will Fadrhonc
from
Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.
Ask any smart virtual assistant to tell you a joke and the response is, well, lackluster. A flat, robotic voice drones out some simplistic humor. No laughter, no tone and no concern if you are even listening.
Today’s technology has reached a level where interaction between humans and computers is vital in everyday life. Unfortunately, natural communication between both sides has lagged far behind many other advances.
Julia Rayz is conducting artificial intelligence research involving humor, among other areas, to determine what is necessary through computational algorithms to create computer-human interaction that rivals a common conversation between people.
Startup Syntiant announced a pair of low-power neural-network accelerators for audio tasks. The NDP100 and NDP101 will detect sound patterns at power levels below 200 µW, enabling speech interfaces on a wide range of devices.
The new chips represent something of a surprise because they use digital techniques. The startup debuted last year, describing an approach for processing deep-learning jobs in the analog domain using an array of hundreds of thousands of multiply-accumulate units linked to NOR cells. Rival Mythic is taking a similar analog approach, initially attacking imaging and video apps, an area that Syntiant said it will target in next-generation chips in 2020.
Online life and social media have radically shifted the relative costs across rewards. It is now far easier to pursue immediate happiness, compared to the available options in, say, 1986. Emails, TikTok videos, the latest witty (or outraged) tweets, whatever your favorite online avocation may be — all are just a keystroke, click or swipe away. And as economists would predict, people are indeed seeking and finding far more momentary pleasures.
Consider the time I spend on Twitter. I can take a peek and have some fun pretty much anytime I want, and for free. Yet never do I think that I will someday look back and reminisce about all that time I spent scrolling through tweets.
The Stanford AI Lab Blog; Shushman Choudhury, Michelle Lee, and Andrey Kurenkov
from
Academic AI researchers are now routinely having an impact in industry, journalists are now increasingly turning to researchers for quotes, and people in general can agree that AI as a field has never had as much influence on society as it does today. At the very least, all AI researchers and engineers should be aware of the sorts of ethical hypotheticals and contingencies they may encounter in their work and how to respond to them.
In this piece, we intend to promote several best practices that we think AI researchers and engineers should be aware of. We focus primarily on what researchers can do to avoid unintended negative consequences of their work, and do not go into as much depth on the topics of what those negative consequences can be or how to deal with intentionally bad actors. The ideas we promote are not new (in fact, most of them are ideas suggested by prominent researchers who we shall credit), nor are they comprehensive, but they are practices we would like to highlight as a beginning to a larger discussion on this topic. Our hope is to promote awareness of these concepts and to inspire other researchers to join this discussion.
When MIT graduate student Matthew Claudel learned of the student computing challenges launched to accompany the three-day celebration of the MIT Stephen A. Schwarzman College of Computing that begins today, he eagerly signed on.
“We were excited about extending the definition of computation and exploring how it might relate to the arts,” says Claudel, who is studying in the Department of Urban Studies and Planning.
The Computing Connections Challenges involved monthlong challenges with themes such as connecting arts, community, and computing, or using machine learning to explore cross-disciplinary topics including health care, transportation, privacy, ethics, architecture, design, commerce, finance, poverty, neuroscience, linguistics, and more. Other challenges involved the application of computing to finance and to the world of sports.
Failed chemical reactions are often not reported, which means that vast amounts of potentially useful data are going to waste. Experiments show that machine learning can use such data to optimize the preparation of porous materials.
Alexandria, VA March 6-7. “We’re going to have 18 briefings in total. The goal of the colloquium is to go a bit deeper into the technology than we typically do. For many events, we try to appeal to a broad audience. For this one, it’s more of a technical audience.” [registration currently closed]
“We will be able to make up to four awards of up to £500 each available for the 2019-2020 academic year. This award will also be accompanied by a two year SSN membership.” Deadline for submissions is March 30.
Time series is a type of data that measures how things change over time. In time series, time isn’t just a metric, but a primary axis. This additional dimension represents both an opportunity and a constraint for time series data because it provides a source of additional information but makes time series problems more challenging, as specialized handling of the data is required. Moreover, this temporal structure can carry additional information, like trends and seasonality, that data scientists need to deal with in order to make their time series easier to model with any type of classical forecasting methods.
Neural networks can be useful for time series forecasting problems by eliminating the immediate need for massive feature engineering processes, data scaling procedures, and the need for making the data stationary by differencing.