Neural Information Processing Systems http://nips.cc/

There's no denying the growing skills gap that currently looms over our workforce. The good news is that awareness is increasing. Business leaders and institutions recognize the fundamental need to invest in skills training programs for their people to stay competitive in today's digital economy. Unfortunately, while the skills gap challenge is well established, few are taking action, and the solutions are not moving quickly enough. In 2020, we can expect 200,000 tech jobs to go unfilled in Canada, according ICTC.

There are at least two clear trends that show a demand-supply mismatch in tech jobs in cutting-edge IT fields such as Artificial Intelligence and Machine Learning. One is via industry predictions that estimate growth in the AI market from $21.46 Bn to $190.61 Year on year growth is projected to be an impressive 36.62% during the same period. The second trend is more subtle. Big Indian IT firms in the US are reportedly'hoarding' employees in these two fields as they foresee a shortage of skilled experts.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. He's the former Enron chief executive whose scheming helped bankrupt PG&E during the California energy crisis. On Friday, Skilling was released from federal custody after serving 12 years in prison for multiple counts of securities fraud, conspiracy and other crimes. Home prices: The latest Case-Shiller home price index comes out Tuesday. In November, seasonally adjusted home prices for the benchmark 20-city index were up 0.3% month over month.

The workforce is not just the backbone of a business but also of the economy of a city. It is said that if the workforce is highly skilled, it could significantly impact the growth of an economy. Undoubtedly, this factor is another opportunity for cities to transform into smart cities. This is the rising era of artificial intelligence. And according to reports, if businesses leverage the benefits of AI for a competitive edge, they could gain a unique position in the marketplace.

We describe a set of Gaussian Process based approaches that can be used to solve non-linear Ordinary Differential Equations. We suggest an explicit probabilistic solver and two implicit methods, one analogous to Picard iteration and the other to gradient matching. All methods have greater accuracy than previously suggested Gaussian Process approaches. We also suggest a general approach that can yield error estimates from any standard ODE solver.