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.

Last school year, Ohio's cash-strapped education department paid Capital High $1.4 million in taxpayer dollars to teach students on the verge of dropping out. But on a Thursday in May, students' workstations in the storefront charter school run by for-profit EdisonLearning resembled place settings for a dinner party where most guests never arrived. In one room, empty chairs faced 25 blank computer monitors. Just three students sat in a science lab down the hall, and nine more in an unlit classroom, including one youth who sprawled out, head down, sleeping. Only three of the more than 170 students on Capital's rolls attended class the required five hours that day, records obtained by ProPublica show. Almost two-thirds of the school's students never showed up; others left early. Nearly a third of the roster failed to attend class all week. Some stay away even longer. ProPublica reviewed 38 days of Capital High's records from late March to late May and found six students skipped 22 or more days straight with no excused absences. Two were gone the entire 38-day period. Under state rules, Capital should have unenrolled them after 21 consecutive unexcused absences. Though the school is largely funded on a per-student basis, the no-shows didn't hurt the school's revenue stream.

Artificial Intelligence (AI) is here. It's not a future notion but is being incorporated into businesses every day. Because it has the capacity to greatly improve the efficiency of many, selected processes, thereby freeing up time for employees to use elsewhere and offering better ROI. Many people fear Artifical Intelligence as they think AI systems will overtake humanity, posing very real threats to people in addition to the common belief that they will replace employees as they invade the working environment. Over time, AI may reduce the need for certain job roles but, as the business landscape evolves, more jobs will be created.

Hesse suggests people research what skills will be in-demand in their field. Gates says workers with skills in science, engineering and economics will soon be the most sought after. Alibaba founder and e-commerce titan Jack Ma thinks more companies will be looking for people with expertise in data analysis and collection in the future. Eric Schmidt, executive chairman of Google's parent company Alphabet, also believes data skills will be in-demand moving forward. Of course, learning new skills doesn't necessarily mean going back to school for a degree.