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The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


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.


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.


How Artificial Intelligence Can Change Higher Education

#artificialintelligence

On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.


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.


Coursera co-founder, Andrew Ng, sets out to raise $150M for AI Fund

#artificialintelligence

Andrew Ng, one of the founders of Coursera, has set out to raise a $150 million fund – dubbed AI Fund – in order to invest in artificial intelligence startups. The news comes just a few months after he announced his own startup, deeplearning.ai. The fund's existence was revealed because of a filing with the US Securities and Exchange Commission (SEC). The document filed with the SEC was filed under Andrew Ng's name on 14 August. At the end of June, we reported that Ng had left the Chinese company, Baidu, where he was in charge of the AI team to form his new startup, deeplearning.ai.


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.


The Future of Jobs and Jobs Training

#artificialintelligence

Machines are eating humans' jobs talents. And it's not just about jobs that are repetitive and low-skill. Automation, robotics, algorithms and artificial intelligence (AI) in recent times have shown they can do equal or sometimes even better work than humans who are dermatologists, insurance claims adjusters, lawyers, seismic testers in oil fields, sports journalists and financial reporters, crew members on guided-missile destroyers, hiring managers, psychological testers, retail salespeople, and border patrol agents. Moreover, there is growing anxiety that technology developments on the near horizon will crush the jobs of the millions who drive cars and trucks, analyze medical tests and data, perform middle management chores, dispense medicine, trade stocks and evaluate markets, fight on battlefields, perform government functions, and even replace those who program software – that is, the creators of algorithms. People will create the jobs of the future, not simply train for them, ...