Whether we like it or not, robots are coming for our jobs. Self-driving cars will be the start, but rest assured, if it can be automated, it will be. Robots are even starting to edit movie trailers. As you plan out your career, it would be wise keep an eye on automation both for the ways it can speed up your own workflow and also for the ways it might make your job obsolete. While we are nowhere near losing many jobs to automation in film yet, the new EPICOLOR plugin from Lemke Software for FCPX and Resolve gives us a hint of what is coming with its automated grading tool that can be useful for small projects with tight turnarounds where a professional colorist might not be an option.

SAN FRANCISCO -- Google will invest $1 billion over the next five years in nonprofit organizations helping people adjust to the changing nature of work, the largest philanthropic pledge to date from the Internet giant. The announcement of the national digital skills initiative, made by Google CEO Sundar Pichai in Pittsburgh, Pa. Thursday, is a tacit acknowledgment from one of the world's most valuable companies that it bears some responsibility for rapid advances in technology that are radically reshaping industries and eliminating jobs in the U.S. and around the world. Over the next three years Goodwill, a major player in workforce development, aims to provide 1 million people with access to digital skills and career opportunities. CEO Sundar Pichai says Google has made $1 billion philanthropic pledge to help workers develop the skills they need for jobs in the new economy.

Imagine if you could anticipate equipment service needs in advance, accurately, and spend time only servicing the equipment that needs it when it needs it. Imagine, too, if you could make smarter production design decisions that optimize the overall manufacturing process. You can, with data you already have, by leveraging it using artificial intelligence (AI). You may have heard the terms analytics, advanced analytics, machine learning and AI. If you're in manufacturing, here's how to make sense of the terms analytics, advanced analytics, machine learning and AI.

A push to have robots mark English tasks in NAPLAN testing has come under attack, with a prominent US academic calling for a halt to the plans, claiming there are major flaws. From next year, NAPLAN persuasive writing tasks will be marked by an automated essay scoring system. They will be double-marked by a teacher. It is part of a plan to introduce fully automated marking and testing by 2020. The proposal has outraged teachers' unions, who argue it is impossible for a robot to score the subjective aspects of writing.

Akron Public Schools officials have issued warnings to parents about a suspicious drone flying near township schools and playgrounds that is attempting to lure kids away. "Witnesses have claimed that the voice in the drone has attempted to lure children off school grounds," she wrote in the letter obtained by the Beacon-Journal. Akron Public Schools spokesman Mark Williamson reiterated that the drone was seen or reported in evenings and over the weekend, but has not been present during school hours. Akron Police spokesman Rick Edwards said local law enforcement has not received complaints about the suspicious drone speaking to children near school grounds.

There are six snippets of code that made deep learning what it is today. This article covers the inventors and the background to their breakthroughs. Each story includes simple code samples on FloydHub and GitHub to play around with. To run the code examples on FloydHub, make sure you have installed the floyd command line tool and cloned the code examples I've provided to your local machine. If you are new to FloydHub, you might want to first read the getting started with FloydHub section in my earlier post.

Njideka Akunyili Crosby, a 2017 MacArthur Fellow, photographed in her studio in Los Angeles, CA on Wednesday September 13th, 2017.Photo courtesy of the MacArthur Foundation. The MacArthur Foundation announced today it has selected 24 individuals -- from photographers and historians, to computer scientists and psychologists -- for its annual "genius grant," given to those who have "extraordinary originality and dedication to their creative pursuits." How does someone become a so-called "genius"? We reached out to a few of them to ask about their "secret sauce." What are the quirks and habits that fuel their creativity and enhance their work?

Two years ago when I was living in New York City, my friend Sam came through town and was looking for a place to crash. We met at my apartment, took in the night skyline, and toasted to the opportunity to catch up. I had just spent the past few days deep in spreadsheets modeling the intricacies of my company's finances, and he was in the midst of modeling the impact of whether he should take a new job in a new city -- with all the different fixed costs, variable costs, cost of living, and other options. We ended up having an impassioned conversation deep into the night about the shortfalls of the financial services and tools available to us. We both had steady jobs, and might actually be making progress towards paying off our debt.

We consider multi-agent stochastic optimization problems over reproducing kernel Hilbert spaces (RKHS). In this setting, a network of interconnected agents aims to learn decision functions, i.e., nonlinear statistical models, that are optimal in terms of a global convex functional that aggregates data across the network, with only access to locally and sequentially observed samples. We propose solving this problem by allowing each agent to learn a local regression function while enforcing consensus constraints. We use a penalized variant of functional stochastic gradient descent operating simultaneously with low-dimensional subspace projections. These subspaces are constructed greedily by applying orthogonal matching pursuit to the sequence of kernel dictionaries and weights. By tuning the projection-induced bias, we propose an algorithm that allows for each individual agent to learn, based upon its locally observed data stream and message passing with its neighbors only, a regression function that is close to the globally optimal regression function. That is, we establish that with constant step-size selections agents' functions converge to a neighborhood of the globally optimal one while satisfying the consensus constraints as the penalty parameter is increased. Moreover, the complexity of the learned regression functions is guaranteed to remain finite. On both multi-class kernel logistic regression and multi-class kernel support vector classification with data generated from class-dependent Gaussian mixture models, we observe stable function estimation and state of the art performance for distributed online multi-class classification. Experiments on the Brodatz textures further substantiate the empirical validity of this approach.

If you are out to describe the truth, leave elegance to the tailor. Machine learning is everywhere now, from self-driving cars to Siri and Google Translate, to news recommendation systems and, of course, trading. In the investing world, machine learning is at an inflection point. What was bleeding edge is rapidly going mainstream. It's being incorporated into mainstream tools, news recommendation engines, sentiment analysis, stock screeners. And the software frameworks are increasingly commoditized, so you don't need to be a machine learning specialist to make your own models and predictions. If you're an old-school quant investor, you may have been trained in traditional statistics paradigms and want to see if machine learning can improve your models and predictions. If so, then this primer is for you! Even if you're not planning to build your own models, AI tools are proliferating, and investors who use them will want to know the concepts behind them. And machine learning is transforming society with huge investing implications, so investors should know basically how it works. In school, when we studied modeling and forecasting, we were probably studying statistical methods. Those methods were created by geniuses like Pascal, Gauss, and Bernoulli.