Unlike mammals who can hibernate in lean times, he added, birds need food on a nearly hour-to-hour basis, which means they’re powerful barometers of ecosystem health. Changes in their behavior give an early signal about other shifts down the food chain.
“Birds are affected by the same environmental processes, such as rainfall, that drive the timing of flowering and fruiting, of crops and insects,” [Alex Jahn] said.
Some of the work connecting these dots occurs in collaboration with other colleagues who track bird and animal species through the Movement Ecology Group of the Environmental Resilience Institute at IU. For example, the birds’ blood samples will inform the institute’s tick monitoring project, which seeks to understand how bug-borne diseases are spreading in the state.
Communications of the ACM, Daniel Weld and Gagan Bansal
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Despite intelligibility’s apparent value, it remains remarkably difficult to specify what makes a system “intelligible.” (We discuss desiderata for intelligible behavior later in this article.) In brief, we seek AI systems where it is clear what factors caused the system’s action,24 allowing the users to predict how changes to the situation would have led to alternative behaviors, and permits effective control of the AI by enabling interaction. As we will illustrate, there is a central tension between a concise explanation and an accurate one.
High school AI programs are still rare, and the development of an AI curriculum is in its early stages. To remedy this, David Touretzky, a computer science professor at Carnegie Mellon and founder of the AI for K-12 Initiative, has gathered educators, researchers and industry professionals to develop national guidelines for primary and secondary AI education.
The guidelines are based on a handful of big ideas, including teaching computers to learn from data, the challenges involved in making AI agents interact naturally with humans, and the positive and negative effects of AI on society.
“We want people to understand what’s going on so they can participate in the evolution of the technology as the technology is deployed,” Touretzky says.
The Brookings Institution; Nicol Turner Lee, Paul Resnick, and Genie Barton
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With algorithms appearing in a variety of applications, we argue that operators and other concerned stakeholders must be diligent in proactively addressing factors which contribute to bias. Surfacing and responding to algorithmic bias upfront can potentially avert harmful impacts to users and heavy liabilities against the operators and creators of algorithms, including computer programmers, government, and industry leaders. These actors comprise the audience for the series of mitigation proposals to be presented in this paper because they either build, license, distribute, or are tasked with regulating or legislating algorithmic decision-making to reduce discriminatory intent or effects.
Our research presents a framework for algorithmic hygiene, which identifies some specific causes of biases and employs best practices to identify and mitigate them. We also present a set of public policy recommendations, which promote the fair and ethical deployment of AI and machine learning technologies.
Deep in the heart of Germany, a team of scientists is trying to crack one of the fundamental problems of artificial intelligence: teaching a machine to understand cause and effect.
Their work could revolutionize how computers “learn,” making them more independent from human oversight and strengthening the hand of any country or company in possession of such next-generation AI tech.
But the scientists, many of whom were trained in European universities, are not working for any European firm or research body. They are employees of Amazon, the Seattle-based e-commerce behemoth that is locked in a global race for AI dominance against other U.S. tech giants, as well as new rivals from China, including Jack Ma’s Alibaba e-retail site.
Karl Gourgue is a 23-year-old graduate of New York University’s business school with concentrations in computing and data science. Gourgue was a consultant for a year at Capgemini after graduation, and was recruited by Google in October 2018. He hails from New Jersey and now lives in Ann Arbor, Michigan for his job as an associate account strategist at Google.
Novel pursuits in data science will get a boost at the University of Oregon thanks to a new seed funding program that will support new faculty partnerships and the development of innovative research and educational programs.
A joint initiative of the Office of the Vice President of Research and Innovation and the Presidential Initiative in Data Science, the Data Science Initiative Seed Funding Program launched officially May 21.
“The program will bring together existing faculty members and newly recruited scholars to use data science in innovative ways to make groundbreaking discoveries,” said Bill Cresko, founding director of the UO’s data science initiative.
David Jaffray will join the University of Texas MD Anderson Cancer Center this summer as its first-ever chief technology and digital officer, the provider said on Tuesday. … One of his core responsibilities will involve working with MD Anderson’s strategy and business development team to support data governance and integration across the organization’s campuses.
By combining 1.6B food item purchases with 1.1B medical prescriptions for the entire city of London for one year, researchers discovered that, to predict health outcomes, socio-economic conditions matter less than what previous research has shown: despite being of lower-income, certain areas are healthy, and that is because of what their residents eat.
For all of the hype about artificial intelligence (AI), most software is still geared toward engineers. To demystify AI and unlock its benefits, the MIT Quest for Intelligence created the Quest Bridge to bring new intelligence tools and ideas into classrooms, labs, and homes. This spring, more than a dozen Undergraduate Research Opportunities Program (UROP) students joined the project in its mission to make AI accessible to all. Undergraduates worked on applications designed to teach kids about AI, improve access to AI programs and infrastructure, and harness AI to improve literacy and mental health. Six projects are highlighted here.
Project Athena for cloud computing
Training an AI model often requires remote servers to handle the heavy number-crunching, but getting projects to the cloud and back is no trivial matter. To simplify the process, an undergraduate club called the MIT Machine Intelligence Community (MIC) is building an interface modeled after MIT’s Project Athena, which brought desktop computing to campus in the 1980s.
As the U.S. and China appear headed for a digital cold war, competing policy approaches to the same technologies are emerging. Artificial intelligence is a prime example: Policy makers in democratic societies should, in theory, be making sure it isn’t used to promote intellectual conformity or to persecute minorities and dissidents.
The idea that AI should be ethical and benefit society has led to the emergence of multiple versions of basic principles, drafted by governments, academics and industry groups. Last year, Chinese researchers Yi Zeng, Enmeng Lu and Cunqing Huangfu identified 27 such codes and made a website on which they can be compared. It makes a somewhat eerie impression, as if the various codes form a data set on which an AI algorithm could be trained to spew forth ethical principles for its peers.
Joe Plecker, Top 100 Teacher and Chief Swing Officer of the Swing Index app, which is part-owned by out parent company, ran the reigning PGA Championship winner Brooks Koepka’s golf swing through our Swing AI machine. With numbers like these, expect more majors in the future.
Two new research reports on wellbeing in universities, from the charity Education Support Partnership, suggest Harris’s emotional struggle is commonplace.
What is the point of higher education if it doesn’t make people happy?
One qualitative study found that academics are often isolated and anxious, in a system they feel is driven by financial targets and what one called a “treadmill of justification”.
A second survey, by the polling company YouGov for the charity, found that 55% of higher education professionals describe themselves as stressed, and nearly four in 10 had considered leaving the sector in the past two years as a result of health pressures.
One academic said: “I remember a time of camaraderie and collegiality. Now, the external pressures isolate and spotlight individuals.”
Data analytics and advanced technology are driving the financial industry. Creighton University doesn’t want its students to be left behind and created a FinTech degree to ensure that.
Among the first of its kind in the country, Creighton University Heider College of Business students will be able to earn a degree in Finance and Technology, beginning in the fall. Close to half of the curriculum will be comprised of newly developed classes focused on Python programming, machine learning, blockchain and the foundations of FinTech. The degree is aimed at arming students with the skills necessary in today’s financial markets and to give them a deep understanding of how tech is disrupting the financial sector.
On May 23, the Georgia Institute of Technology marked the grand opening of Coda, the flagship building for the Institute’s Technology Square — an area that Georgia Tech President G.P. “Bud” Peterson has called “the Southeast’s premier innovation neighborhood.”
Coda, developed by Portman Holdings and Databank, is special in many ways. At 755,000 square feet, Coda is believed to be the largest structure of its kind: a facility built to actively encourage the collaborations between university researchers —including students — and industry that can lead to new technologies.
Harvard University, Center for Geographic Analysis Center for Geographic Analysis
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Cambridge, MA June 17, starting at 8:30 a.m. Organizer: Harvard’s Center for Geographic Analysis Center for Geographic Analysis. “This symposium is focused on strategies for driving innovation and creating lasting global urban projects that emphasize data generation and mapping.” [free, rsvp requested]
New York, NY June 18, starting at 8:45 a.m., Dolby 88 Screening Room (1350 Avenue of the Americas). “David McCandless, founder of Information is Beautiful, shares his concept-driven process and method for creating successful visualizations in this rare workshop.” [$$$]
“This survey gives us [Data Visualization Society] an opportunity to understand the differences in practice, tools and audiences of anyone making data visualization. Whether you are focused on data visualization in your work or simply using it as a skill to accomplish your main goal, we want to hear from you.”
“Forbes, in partnership with Meritech Capital, will evaluate hundreds of companies based on metrics like revenue, growth, and valuation, with a panel of experts weighing in on how innovative and mission-critical each company’s use of AI is (versus buzzwords thrown onto a slide-deck).” Deadline for nominations is June 28.
Santa Barbara, CA August 5-9 at National Center for Ecological Analysis and Synthesis. “This five-day workshop is designed to help researchers stay abreast of current best practices and initiatives and get started on acquiring good data science skills to maximize their productivity, share their data with the scientific community effectively and efficiently, and benefit from the re-use of their data by others.” Deadline to apply is July 8.
Boston, MA November 4-5. “The 2019 PQG conference will focus on the computational algorithms, quantitative models, as well as data integration techniques that are under active development to answer these important questions.” Deadline to submit an abstract is September 30.
With this release, a new and reworked Helvetica for the digital age has arrived. I’d argue its arrival is 10 years or so overdue, but nonetheless, it is an exemplary update of the most popular font ever. Helvetica Now is full of features and goodies, as we’ll discover below.
arXiv, Computer Science > Computer Vision and Pattern Recognition; Tae-Hyun Oh, Tali Dekel, Changil Kim, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Wojciech Matusik
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How much can we infer about a person’s looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of that person speaking. We design and train a deep neural network to perform this task using millions of natural Internet/YouTube videos of people speaking. During training, our model learns voice-face correlations that allow it to produce images that capture various physical attributes of the speakers such as age, gender and ethnicity. This is done in a self-supervised manner, by utilizing the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly. We evaluate and numerically quantify how–and in what manner–our Speech2Face reconstructions, obtained directly from audio, resemble the true face images of the speakers.
“My twin brother Afshine and I created this set of illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class, which I am currently TA-ing at Stanford.”
“I have aimed to build a robust phishing detection mechanism using machine learning tools and techniques. The reported SOTA accuracy score on the dataset I used for the purpose is 97% (according to this publication Swarm Intelligence Approaches for Parameter Setting of Deep Learning Neural Network: Case Study on Phishing Websites Classification). I was able to achieve that score using a different approach that too in a very nominal amount of computation time. The machine learning models shown in the project can be easily served as REST API endpoints which can further be used in conjunction with add-ons to detect phishing websites in real-time.”