This is the first instalment of Generation AI, a regular column that will explore innovations being made in the vast area of artificial intelligence, in which Canada is a top global player. It was a big news item for the startup community when Microsoft announced last week the acquisition of Maluuba – a Montreal-based deep learning research lab for natural language understanding, and the doubling of its AI campus there. But the reality is, an acquisition by a major player is quickly becoming old news in the artificial intelligence (AI) world (a term that is also applied to the disciplines of deep learning and machine learning). Maluuba was founded in 2011 by Sam Pasupalak and Kaheer Suleman after they graduated from University of Waterloo. "We went through a number of accelerator programs including Velocity and Communitech when we started," Pasupalak says.

Every year, with the introduction of new social platforms and apps, brands face increased challenges of reaching their target customers. In addition to pivoting to keep up with changes to existing platforms, digital marketers have to create new strategies around emerging digital marketing trends. Luckily, along with new social media networks, technology advances enable the development of tools and software to improve digital marketing efforts. Industry leaders are seeing the opportunities and benefits of emerging trends for social media and content marketing. Here, we have identified what we think will be the 7 biggest trends to impact digital marketing for 2017.

If you're a Raspberry Pi developer that is at all interested in artificial intelligence (AI) and machine learning, we've got a treat in store for you. Google is looking to bring its AI and machine learning tools to the Raspberry Pi starting this year, but it wants your help and input to make it happen. Google has launched a survey that includes questions about how often developers spend working on software and hardware projects, and if they are interested in fields ranging from wearables to drones to IoT to robotics to 3D printing. It will use input gained from this survey to narrow its focus on the tools that are provided later this year. Thank you for taking the time to take our survey.

But first up are the things you may have missed, like a massive update for Google Voice, the name of the new Star Wars movie and why cassette sales are way, way up. Fitbit's recent acquisitions hint at a device we'd actually want to buy. It hasn't been a great year for wearables, with sluggish sales and underwhelming products dominating the space. Several smartwatches have disappeared over the last twelve months, and for Dan Cooper, devices from Apple, Samsung and Google that try to recreate the smartphone experience on the wrist just aren't compelling. However, those companies are increasingly the only games in town after the demise of low-power wearable companies like Pebble, Vector and Basis.

Steve Mirsky: Welcome to Scientific American's, Science Talk, posted on November 10, 2015. A short episode today for which I'll turn it over now to Scientific American's associate tech editor, Larry Greenemeier. Larry Greenemeier: Computers have always been good at doing things that are really complicated for us humans. On the other hand, computers have a really hard time recognizing a particular voice or face in a crowd; something most kids learn to do before they're even out of diapers. But things are changing fast. Over the next decade or so, machines will more easily mimic inherently human abilities.

"The basic premise is that human space exploration must be highly efficient, cost effective, and autonomous as placing humans beyond low Earth orbit is fraught with political economic, and technical difficulties," John D. Mathews, professor of electrical engineering, reported in the current issue of the Journal of the British Interplanetary Society. If aliens are out there, they have the same problems we do, they need to conserve resources, are limited by the laws of physics and they may not even be eager to meet us, according to Mathews. He suggests that "only by developing and deploying self-replicating robotic spacecraft -- and the incumbent communications systems -- can the human race efficiently explore even the asteroid belt, let alone the vast reaches of the Kuiper Belt, Oort Cloud, and beyond." Mathews assumes that any extraterrestrial would need to follow a similar path to the stars, sending robots rather than living beings, which would explain why SETI has not succeeded to date. "If they are like us, they too have a dysfunctional government and all the other problems plaguing us," said Mathews.

UC Berkeley researchers are tapping the cognitive smarts of babies, toddlers and preschoolers to program computers to think more like humans. "Children are the greatest learning machines in the universe. Imagine if computers could learn as much and as quickly as they do," said Alison Gopnik a developmental psychologist at UC Berkeley and author of "The Scientist in the Crib" and "The Philosophical Baby." In a wide range of experiments involving lollipops, flashing and spinning toys, and music makers, among other props, UC Berkeley researchers are finding that children -- at younger and younger ages -- are testing hypotheses, detecting statistical patterns and drawing conclusions while constantly adapting to changes. "Young children are capable of solving problems that still pose a challenge for computers, such as learning languages and figuring out causal relationships," said Tom Griffiths, director of UC Berkeley's Computational Cognitive Science Lab. "We are hoping to make computers smarter by making them a little more like children."

Some argue that robots do not have free will and therefore cannot be held morally accountable for their actions. But UW psychologists are finding that people don't have such a clear-cut view of humanoid robots. The researchers' latest results show that humans apply a moderate amount of morality and other human characteristics to robots that are equipped with social capabilities and are capable of harming humans. In this case, the harm was financial, not life-threatening. But it still demonstrated how humans react to robot errors.

Scientists are developing artificial intelligence solutions for image processing, which have applications in many areas including advertising, entertainment, education and healthcare. They have, for example, developed computer algorithms for facial age classification -- the automated assignment of individuals to predefined age groups based on their facial features as seen on video captures or still images. Improving the accuracy of facial age classification, however, is not easy. A person can teach a computer to make better guesses by running its algorithm through a large database of facial images of which the age is known using sets of labeled images, but acquiring such a database can be both time-consuming and expensive. The process might even breach privacy in certain countries.

Facial characteristics play a central role in our everyday assessments of other people. "The perception of dominance has been shown to be an important part of social roles at different stages of life, and to play a role in mate selection," said Mr. Rojas. If the information on which the evaluation of faces is based could be automatically learned, it could be modeled and used as a tool for designing better interactive systems. The team studied to what extent this information is learnable from the point of view of computer science. Specifically, the task was formulated with the intention of predicting 9 facial trait judgments (attractive, competent, trustworthy, dominant, mean, frightening, extroverted, threatening, and likable) using Machine learning techniques (a branch of artificial intelligence that uses examples to teach a program how to work).