The following blog is an abstract of an article I wrote for the Government of New South Wales, Australia, for use as part of a symposium on Education for a Changing World. To see the full article and a companion piece I wrote on the implications of these technologies for education, click here. I'd like to thank the New South Wales government for prompting me to return to an interest in artificial intelligence and its implications for education that first preoccupied me in the 1980s and for permission to reprint this abstract here. It is not a law of nature that new technologies will put a lot of people out of work in the short term, but then create just as many new jobs that are even better in the long term. What is distinctive about artificial intelligence technologies is that they embody the very thing that makes us so different from any other thing animate or inanimate on earth: high intelligence. It is now clear that intelligent agents already exceed human capacity in some domains of intelligent behavior.

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. The weight of each weak loss function is tuned within the training phase through the gradient propagation optimization method of the neural network. The approach is evaluated on several text classification datasets. We also evaluate its performance in various environments with several degrees of label noise. Experimental results indicate an improvement of the results and strong resilience against label noise in comparison with other methods.

The world's first floating nation is set to appear in the Pacific Ocean off the island of Tahiti in 2020. A handful of hotels, homes, offices, restaurants and more will be built in the next few years by the nonprofit Seasteading Institute, which hopes to'liberate humanity from politicians'. The radical plans, bankrolled by PayPal founder Peter Thiel, could see the creation of an independent nation that will float in international waters and operate within its own laws. In a new interview, Joe Quirk, president of the Seasteading Institute, said he wants to see'thousands' of rogue floating cities by 2050, each of them'offering different ways of governance'. The world's first floating nation is set to appear in the Pacific Ocean off the island of Tahiti in 2020 (artist's impression).

While you may believe you could easily figure out that the email from your grandma who is desperately asking you for money is not really an email from your grandma, not all phishing scams are that obvious and many people fall for them. In fact, a 2015 survey done by Intel Security covering 19,000 respondents from 144 countries, revealed that a staggering 80% misidentified at least one phishing email. Now, Netsafe, a non-profit organization in New Zealand with a focus on online safety, is fighting back. With their new initiative called Re:scam, Netsafe has deployed a well-educated, artificially intelligent chat-bot that can take on multiple personalities and engage in correspondence with scammers, wasting their time indefinitely or until the scammers themselves realize they are being scammed. The exchanges can be hilarious.

A bot aided by artificial intelligence today will start drawing up wills for clients in the Northern Territory.

We investigate the use of alternative divergences to Kullback-Leibler (KL) in variational inference(VI), based on the Variational Dropout \cite{kingma2015}. Stochastic gradient variational Bayes (SGVB) \cite{aevb} is a general framework for estimating the evidence lower bound (ELBO) in Variational Bayes. In this work, we extend the SGVB estimator with using Alpha-Divergences, which are alternative to divergences to VI' KL objective. The Gaussian dropout can be seen as a local reparametrization trick of the SGVB objective. We extend the Variational Dropout to use alpha divergences for variational inference. Our results compare $\alpha$-divergence variational dropout with standard variational dropout with correlated and uncorrelated weight noise. We show that the $\alpha$-divergence with $\alpha \rightarrow 1$ (or KL divergence) is still a good measure for use in variational inference, in spite of the efficient use of Alpha-divergences for Dropout VI \cite{Li17}. $\alpha \rightarrow 1$ can yield the lowest training error, and optimizes a good lower bound for the evidence lower bound (ELBO) among all values of the parameter $\alpha \in [0,\infty)$.

The trucks that roam the highways of the Australian outback are a lot bigger than the average 18-wheeler. Instead of towing one container, these road trains, as Australians refer to them, pull at least three self-tracking semitrailers behind them, which follow each other like train carriages. The trailers are packed with heavy goods--cattle, gas, coal, cars--and sent roaring through the continent's interior to deliver supplies to coastal cities. Fully loaded, road trains weigh up to 120 tons, and materialize on the shimmering horizon of outback roads as great mechanical beasts. As they pass at 70 miles per hour, you can feel the air velocity generated by the machine trying to suck you under the rig. Road trains are as much a part of the outback as red dirt or Akubra hats, signifiers of a rugged, Mad Max mythology that has come to define Australia's interior in the global imagination.

A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on a framework that is easy to extend with custom acquisition functions and models. The framework is thoroughly tested and well documented, and provides scalability. The current released version of GPflowOpt includes some standard single-objective acquisition functions, the state-of-the-art max-value entropy search, as well as a Bayesian multi-objective approach. Finally, it permits easy use of custom modeling strategies implemented in GPflow.

There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.

Space is a cold and barren place. Nothing can exist there, nothing!" Ludwig Von Drake, an obscure uncle of Donald Duck and a professor of astronomy, is sitting on a high stool in his observatory. When he sees that he is being filmed, he falls off and lands on the floor with a loud thump. "Now I can see stars I've never seen before!" he groans. He walks over to a table with a large pile of books on it. The thickest of them all is a guide to space travel that he wrote himself. In a 45 -minute- long monologue, he tells us in a thick German accent how mankind discovered the planets in our solar system and has fantasized about everything that might be crawling around on them. Every now and then, he picks up a book from the large pile and reads from it, and then throws it nonchalantly into a corner of the room. He tells us about Copernicus and Galileo, and about Kepler's dreams about Martians, Fontenelle's speculations about life on other planets, and even John Herschel's Great Moon Hoax. Science fiction comes to life in the colorful cartoon: Hairy space beings and flying saucers shoot across the screen. At the end, the professor has the last word. He finds all these fantasies poppycock; nothing can live in that empty, barren space! But, as he is speaking, Von Drake is kidnapped by a black Martian robot from one of his stories. The cartoon, Inside Outer Space, is part of Walt Disney's Wonderful World of Color, a television series from the 1960s. The absent minded duck professor hosts a number of episodes, each with their own topic: the history of flight, the color spectrum, space--all exciting stuff for American kids in the Space Age. Lou Allamandola spent his teenage years in the science- crazy 1960s. He grew up in a Catholic family in the state of New Jersey. His grandparents were immigrants from Italy, and he didn't learn to speak English until he went to school. He still clearly remembers the Disney cartoons with Ludwig Von Drake, which were broadcast on Saturday evenings. "Von Drake called the interstellar medium--the empty space between the stars and the planets--a barren place where nothing could exist," he tells me. "That was all we knew in the '60s.