Results


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.


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, ...



Forecasting The Future And Explaining Silicon Valley's New Religions

#artificialintelligence

Yuval Noah Harari might be Silicon Valley's favorite historian. His last book, Sapiens: A Brief History of Humankind, which detailed the entirety of human history and how Homo Sapiens came to dominate the Earth, was blurbed by President Barack Obama and Bill Gates, and Mark Zuckerberg recommended it for his book club. And more than 100,000 students have taken Harari's online course. In his new book, Homo Deus: A Brief History of Tomorrow, Harari looks forward and hazards a few guesses on what comes next for humanity. These next chapters in our history range from the utopian to the horrific, he says.


IZA World of Labor - Who owns the robots rules the world

#artificialintelligence

The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3].


MIT, White House co-sponsor workshop on big-data privacy

AITopics Original Links

On Monday, MIT hosted a daylong workshop on big data and privacy, co-sponsored by the White House as part of a 90-day review of data privacy policy that President Barack Obama announced in a Jan. 17 speech on U.S. intelligence gathering. White House Counselor John Podesta, grounded by snow in Washington, delivered his keynote address and took questions over the phone. But Secretary of Commerce Penny Pritzker was on hand, as were MIT President L. Rafael Reif and a host of computer scientists from MIT, Harvard University, and Microsoft Research, who spoke about the technical challenges of protecting privacy in big data sets. In his brief opening remarks, Reif mentioned the promise of big data and the difficulties that managing it responsibly poses, and he offered the example of MIT's online-learning initiative, MITx, to illustrate both. "We want to study the huge quantities of data about how MITx students interact with our digital courses," he said.


Mistake Bounds for Binary Matrix Completion

Neural Information Processing Systems

We study the problem of completing a binary matrix in an online learning setting. On each trial we predict a matrix entry and then receive the true entry. We propose a Matrix Exponentiated Gradient algorithm [1] to solve this problem. We provide a mistake bound for the algorithm, which scales with the margin complexity [2, 3] of the underlying matrix. The bound suggests an interpretation where each row of the matrix is a prediction task over a finite set of objects, the columns. Using this we show that the algorithm makes a number of mistakes which is comparable up to a logarithmic factor to the number of mistakes made by the Kernel Perceptron with an optimal kernel in hindsight. We discuss applications of the algorithm to predicting as well as the best biclustering and to the problem of predicting the labeling of a graph without knowing the graph in advance.


IZA World of Labor - Who owns the robots rules the world

#artificialintelligence

The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3].


Elon Pew Future of the Internet Survey Report: Impacts of AI, Robotics by 2025

#artificialintelligence

Internet experts and highly engaged netizens participated in answering an eight-question survey fielded by Elon University and the Pew Internet Project from late November 2013 through early January 2014. Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025? Describe your expectation about the degree to which robots, digital agents, and AI tools will have disrupted white collar and blue collar jobs by 2025 and the social consequences emerging from that. Among the key themes emerging from 1,896 respondents' answers were: - Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. This page holds the content of the survey report, which is an organized look at respondents elaborations derived from 250 single-spaced pages of responses from ...