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The HR Technology Market: Trends and Disruptions for 2018

#artificialintelligence

Digital disruption A new industrial revolution, but people matter HR Tech 2017 Keynote 3 Copyright 2017 Deloitte Development LLC. Robots Can cost as low as $25,000* 250,000 purchased globally in 2016** *Source: Robots: The new low-cost worker, Dhara Ranasinghe, CNBC, April 10, 2015. The future of work Robotics, AI, sensors are here now HR Tech 2017 Keynote 4 Copyright 2017 Deloitte Development LLC. Quantified self: arriving now $1.8 billion in venture invested in wearables since 2016 Source: CB Insights HR Tech 2017 Keynote 5 Copyright 2017 Deloitte Development LLC. The "average" US worker now spends 25% of their day reading or answering emails Fewer than 16% of companies have a program to "simplify work" or help employees deal with stress. The average mobile phone user checks their device 150 times a day. The "average" US worker works 47 hours and 49% work 50 hours or more per week, with 20% at 60 hours per week 40% of the US population believes it is impossible to succeed at work and have a balanced family life.



Online Learning of Power Transmission Dynamics

arXiv.org Machine Learning

We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.


Fast and Strong Convergence of Online Learning Algorithms

arXiv.org Machine Learning

In this paper, we study the online learning algorithm without explicit regularization terms. This algorithm is essentially a stochastic gradient descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially decaying step size in each iteration can play a role of regularization to ensure the generalization ability of online learning algorithm. We develop a novel capacity dependent analysis on the performance of the last iterate of online learning algorithm. The contribution of this paper is two-fold. First, our nice analysis can lead to the convergence rate in the standard mean square distance which is the best so far. Second, we establish, for the first time, the strong convergence of the last iterate with polynomially decaying step sizes in the RKHS norm. We demonstrate that the theoretical analysis established in this paper fully exploits the fine structure of the underlying RKHS, and thus can lead to sharp error estimates of online learning algorithm.


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.


Google X's online course teaches you to build flying cars

Daily Mail - Science & tech

You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Online education provider Udacity, which is owned by Google X and Kitty Hawk founder Sebastian Thrun, has announced two new'nanodegrees'. One course will teach users the basics of driverless car engineering, while another will show students how to make systems for autonomous flying vehicles. You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Education provider Udacity has announced two new'nanodegrees' teaching users to make driverless or flying vehicles, such as the AeroMobil car pictured here Students will learn the basics of autonomous flight, including vehicle state planning and estimation, as well as motion planning.


The Whys and Hows of Becoming a Robotics Engineer

#artificialintelligence

In 2015, a poll of 200 senior corporate executives conducted by the National Robotics Education Foundation identified robotics as a major source of jobs for the United States. Indeed, some 81% of respondents agreed that robotics was the top area of job growth for the nation. Not that this should come as a surprise: as the demand for smart factories and automation increases, so does the need for robots. According to Nearshore Americas, smart factories are expected to add $500 billion to the global economy in 2017. In a survey conducted by technology consulting firm Capgemini, more than half of the respondents claimed to have invested $100 million or more into smart factory initiatives over the last five years.


3 Industries You Probably Didn't Know Were Using Machine Learning Udacity

#artificialintelligence

Say Machine Learning to someone, and if they recognize the term, they'll probably think, "tech company." But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Think about it like this: If you were a farmer, and someone came to you and said, there's a technology out there that can accurately predict your crop yields, would you be interested? Well, this is exactly what Descartes Labs does.


From Elon Musk to Bill Gates: Tech's Most Dubious Promises

WIRED

Last week, Elon Musk dashed off 125 characters announcing a remarkably ambitious plan to send Amtrak to an early grave. "Just received verbal govt approval for The Boring Company to build an underground NY-Phil-Balt-DC Hyperloop. NY-DC in 29 mins," he proclaimed in a tweet. Sign up to get Backchannel's weekly newsletter. Yet something about this particular moonshot seemed off.


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.