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Georgia Tech Will Help Bring Critical Advancements to Online Learning as Part of Multimillion Dollar NSF Grant

#artificialintelligence

Georgia Tech is a major partner in a new National Science Foundation (NSF) Artificial Intelligence Research Institute focused on adult learning in online education, it was announced today. Led by the Georgia Research Alliance, the National AI Institute for Adult Learning in Online Education (ALOE) is one of 11 new NSF institutes created as part of an investment totaling $220 million. The ALOE Institute will develop new AI theories and techniques for enhancing the quality of online education for lifelong learning and workforce development. According to some projections, about 100 million American workers will need to be reskilled or upskilled over the next decade. With the increase of AI and automation, said Co-Principal Investigator and Georgia Tech lead Professor Ashok Goel, many jobs will be redefined. "There will be some loss of jobs, but mostly we will see individuals needing to learn a new skill to get a new job or to advance their career," said Goel, a professor of computer science and human-centered computing in Georgia Tech's School of Interactive Computing (IC) and the chief scientist with the Center for 21st Century Universities (C21U).


Confronting Structural Inequities in AI for Education

arXiv.org Artificial Intelligence

Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artificial intelligence technologies -- in education and beyond -- have led to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems' disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems and demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models' performance. We close with alternative visions for a more equitable future for educational AI research.


10 free online writing courses for getting real good at words

Mashable

Writing is a much-prized skill and a difficult one to master and, while some are naturally gifted in stringing sentences together, we all need to take the time to learn the craft. Whether you want to write your first novel, pen a poignant poem, pull together a screenplay, or create better business content, there is a free, online course out there to help. We've rounded up a list of free, online writing courses so you can find the perfect program of study to help you write gooderer. This eight-week online writing course is an introduction to the theory and practice of rhetoric, the art of persuasive writing and speech. Using selected speeches from prominent 20th-century Americans -- including Martin Luther King Jr., John F. Kennedy, Margaret Chase Smith, and Ronald Reagan -- to explore and analyze rhetorical structure and style, this course will teach you when and how to employ a variety of rhetorical devices in writing and speaking.


Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

arXiv.org Artificial Intelligence

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.


Millions of Americans Have Lost Jobs in the Pandemic -- And Robots and AI Are Replacing Them Faster Than Ever

#artificialintelligence

For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. "Sometimes, you're the first person that people see in the morning," says Collins, "and that human interaction can spark a lot of conversation." But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins' supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins' job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.


How 'Learning Engineering' Hopes to Speed Up Education - EdSurge News

CMU School of Computer Science

This story was published in partnership with The Moonshot Catalog. In the late 1960s, Nobel Prize-winning economist Herbert Simon posed the following thought exercise: Imagine you are an alien from Mars visiting a college on Earth, and you spend a day observing how professors teach their students. Simon argued that you would describe the process as "outrageous." "If we visited an organization responsible for designing, building and maintaining large bridges, we would expect to find employed there a number of trained and experienced professional engineers, thoroughly educated in mechanics and the other laws of nature that determine whether a bridge will stand or fall," he wrote in a 1967 issue of Education Record. "We find no one with a professional knowledge in the laws of learning, or the techniques for applying them," he wrote. Teaching at colleges is often done without any formal training. Mimicry of others who are equally untrained, instinct, and what feels right tend to provide the guidance. Reading back over a textbook or taking lecture notes with a highlighter at the ready is often done by students, for instance, but these practices have proven of limited merit, and in some cases even counterproductive in aiding recall. And while many educators believe that word problems in math class are tougher for students to grasp than ones with mathematical notation, research shows that the opposite is true.


Here Are the Top Data Science Influencers in 2019

#artificialintelligence

The term "influencer marketing" may call to mind jet-setting travel vloggers on YouTube or cool gamer kids streaming on Twitch. However, there are also many influencers working in the business and marketing realm. Data science influencers educate and inform on the subject of the scientific approach to extracting insights from data for real-world applications. Here are eight of the data science thought leaders topping influencer discovery searches. Andrew Ng's credentials speak for themselves.


10 Workplace Trends You'll See In 2018

#artificialintelligence

Every year I give my forecast for the top 10 workplace trends for the upcoming year. The purpose is to help prepare organizations for the future by collecting, assessing and reporting the trends that will most impact them. You can read my predictions from 2013, 2014, 2015, 2016 and 2017. These trends are based on hundreds of conversations with executives and workers, a series of national and global online surveys and secondary research from more than 450 different research sources, including colleges, consulting firms, non-profits, the government and trade associations. All economic indicators show a positive view of the U.S. economy in 2018.


Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment

arXiv.org Artificial Intelligence

Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.


jupyter/jupyter

@machinelearnbot

Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.