Valérie Pisano sips a cappuccino in Montreal's Caffè Italia, an unassuming gem in the heart of the city's Little Italy, and a place that she has known since she was a child. "Coming here is all about history and stability," said the newly appointed president and chief executive of Mila (formerly the Montreal Institute for Learning Algorithms), the Quebec epicentre of Canada's artificial-intelligence revolution. With a soccer match on the TV screen above and the steamy blasts of an espresso machine punctuating her words, Ms. Pisano spoke about the need to stay connected to the past while leading an organization that is literally inventing the future. "What we're trying to create is completely new, completely emergent… And as society quickly pivots to this new era, we need to ask, how do we anchor ourselves in the roots of who we are and what we stand for as humanity?" The future Ms. Pisano sees emerging is just around the corner – not just figuratively, but literally.

Over the last fifty years, the world has seen an impressive growth in science - in terms of publications, number of highly-trained scientists and impact of results. This is not only good for science, but also for everyone who benefits from the outcomes and applications of scientific research. An area that has seen much growth lately is artificial intelligence (AI). A full four thousand scientific papers were submitted to the International Joint Conference on Artificial Intelligence (IJCAI), the top international conference in AI, which was held in Stockholm earlier this summer. While growth in science and technology has happened in most countries and regions in the world, recent developments in Asia are particularly impressive.

Hundreds of thousands of people have gathered in the German city of Cologne, for Europe's largest video game trade show. All of the latest games are on display, as are the newest devices to play them on.

Unlike the previous three industrial revolutions (18th, 19th, and 20th Centuries), the 4th will be more decentralized, automated, and controlled interdependently (Qin, Liu, & Grosvenor, 2016). In the first industrial revolution, the factory achieved production primarily through machines powered by water and steam and heavy manpower. In the second, operations became slightly more complexed through machines powered by electricity supported by mass production and division of labour. The third industrial revolution ushered in the use of electronics and information technology, adding more complexity to the production process in making it more automated (Brettel, Friederichsen, Keller, & Rosenberg, 2014; Wolfgang, 2016). Undoubtedly, these three industrial revolutions would have impacted their countries' economies.

One way to measure progress in artificial intelligence is to chart victories by algorithms over champions of increasingly challenging games--checkers, chess, and, in 2016, Go. On Wednesday, five bots sought to extend AI's mastery to e-sports, in the fantasy battle game Dota 2. They failed, as a team of pro gamers from Brazil called paiN defended humanity's honor--for now. A crowd of thousands in Vancouver's hockey arena watched the bots battle paiN over 52 tense minutes packed with spells and firebolts. The human-machine contest was a side event to The International, a Dota 2 tournament that boasts the biggest purse in e-sports, at $25 million. The five bots that lost Wednesday were created by OpenAI, a research institute cofounded by Tesla CEO Elon Musk to work towards human-level artificial intelligence, and make the technology safe.

If you think it's been a problem up to this point, the fight against fake news is about to get a whole lot harder. That is thanks to artificial intelligence technology which is making the creation of so-called "deep fake" videos more convincing at a frankly terrifying rate. The latest development comes from an international team of researchers, lead by Germany's Max Planck Institute for Informatics. They have created a deep-learning A.I. system which is able to edit the facial expression of actors to accurately match dubbed voices. In addition, it can tweak gaze and head poses in videos, and even animate a person's eyes and eyebrows to match up with their mouths -- representing a step forward from previous work in this area.

Our relatively recent and rapid transformation into a data-driven, data-devouring society comes as no big surprise to Jeffrey Ullman, a pioneering figure in the field of data science. "There's a sense in which all of this was inevitable from the time that Gordon Moore described Moore's Law," says Ullman, emeritus Stanford W Ascherman Professor of Computer Science at Stanford University in California, whose students have included Google founder Sergey Brin. "Throughout my career, key parameters have been doubling every two years, which led to unimaginably large capabilities. When I started out, my first computer had, I think, 8,000 bytes of memory. Today Facebook has close to a petabyte of main memory." He pauses, then concedes: "It's kind of unimaginable."

Researchers have developed an artificial intelligence system to detect lung cancer on scans that radiologists fail to detect. The AI method can notice specks of lung cancer with about 95 percent accuracy compared with 65 percent by radiologists, according to research conducted by the University of Central Florida's Computer Vision Research Center. The researchers published their findings in the Cornell University Library before the Medical Image Computing and Computer Assisted Intervention Society's conference next month in Granada, Spain. Computed tomography, or CT, scans use computer-processed combinations of many X-ray measurements taken from different angles to produce cross-sectional images of specific areas of a scanned area. "I believe this will have a very big impact," Ulas Bagci, an engineering assistant professor at UCF, said in a press release.

When researchers working on developing a machine learning-based tool for detecting fake news realized there wasn't enough data to train their algorithms, they did the only rational thing: They crowd-sourced hundreds of bullshit news articles and fed them to the machine. The algorithm, which was developed by researchers from the University of Michigan and the University of Amsterdam, uses natural language processing (NLP) to search for specific patterns or linguistic cues that indicate a particular article is fake news. This is different from a fact checking algorithm that cross-references an article with other pieces to see if it contains inconsistent information – this machine learning solution could automate the detection process entirely. No offense to the Michigan/Amsterdam team but building an NLP algorithm to parse sentence structure and hone in on keywords isn't exactly the bleeding edge artificial intelligence work that drops jaws. Getting it to detect fake news better than people, however, is.

We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the reward signal is the diversity of effects that each strategy produces. We test the method empirically on a simulated planar robotic arm, and establish that the method is both able discriminate between strategies of dissimilar quality, even when the differences are tenuous, and that the resulting performance is competitive with the best fixed mixture of strategies.