It's about to be in even more places as the industry in charge of developing it pushes for dominance on their own platforms and standards. While for them it means new technologies -- and more importantly products -- that can be packaged up and sold, for us it means consistency and assuredness in a technology still somewhat shrouded in mystery and fragmented across platforms as companies compete for supremacy. In an attempt to bring some clarity into the smart home, key industry leaders like Apple, Amazon and Google (who make the Siri, Alexa, and Assistant digital voice AI platforms that power many smart homes) joined forces through the Project Connected Home Over IP protocol. The idea is to ensure that all your smart home devices work with one another. The three big tech companies mainly utilise their AI technology through smart speakers, and other companies across appliances, automotive and consumer electronics make their products compatible.

"I'm using artificial intelligence techniques to predict the size of hailstorms," explained Gagne. Working with computer-simulated storms, he created software that is trained to determine which storms produce hail and then to recognize patterns associated with the storms behind the largest hailstones. "The shape of storms is really important." His latest work is published in Monthly Weather Review. Gagne's novel approach started with his PhD dissertation between 2014 and 2015.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale SA working, and protect trees for pulp and paper producer Suzano SA. In the future, it's a system that may be put to work in California, where deadly wildfires abound. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances.

The shape of a severe storm, such as this one, is an important factor in whether the storm produces hail and how large the hailstones are, but current hail-prediction techniques are typically not able to take the storm's entire structure into account. NCAR scientists are experimenting with a new machine-learning technique that can process images to weigh the impact of storm shape and potentially improve hail forecasts. This image is freely available for media and nonprofit use.) The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study from the National Center for Atmospheric Research (NCAR). Instead of zeroing in on the features of an individual face, scientists trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict.

Technology similar to what Facebook uses for recommending what friends you should "tag" may soon be coming to hailstorms. David Gagne, a machine learning scientist at the National Center for Atmospheric Research, is using facial recognition technology to unlock the secrets behind big hail. "I'm using artificial intelligence techniques to predict the size of hailstorms," explained Gagne. Working with computer-simulated storms, he created software that is trained to determine which storms produce hail and then to recognize patterns associated with the storms behind the largest hailstones. "The shape of storms is really important."

Facial recognition software could be used to detect hail storms - and their severity. That's according to scientists at the US National Center for Atmospheric Research, who've tested the software's effectiveness on meteorological data. Specifically, they found that a deep learning model called a convolutional neural network can spot the early signs as they happen - better than current methods. The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warnings. AI: The promising results, published in the American Meteorological Society's Monthly Weather Review, could be a game-changer for providing accurate weather warning Whether or not a storm produces hail hinges on myriad meteorological factors.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale working, and protect trees for pulp and paper producer Suzano. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances. By linking them to artificial intelligence, a small Portugal-based company working with IBM Corp. believes it can help tame the often unpredictable affects of climate change.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale SA working, and protect trees for pulp and paper producer Suzano SA. In the future, it's a system that may be put to work in California, where deadly wildfires abound. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances.

Meteorologists are starting to experiment with deep learning tech to predict severe weather patterns. David Gagne, a postdoctoral researcher at the US National Center for Atmospheric Research (NCAR), developed a simple convolutional neural network model to forecast the chances of hailstorms. In the last decade, severe storms caused about $14bn worth of damage and killed 94 people per year, Gagne said during a presentation at the GPU Technology Conference in San Jose, California. Meteorologists begin warning people of severe weather conditions the day before a hail event, but it's difficult to be precise. So Gagne wanted to see if deep learning could accurately identify the weather patterns leading up to hailstorms and reduce false alarms.

Everyone agrees that the competition to hire people who know how to build artificial intelligence systems is intense. It's turned once-staid academic conferences into frenzied meet markets for corporate recruiters and driven the salaries of the top researchers to seven figures. But how scarce AI talent really is has been something of an industry mystery. Last year Element AI Inc., a Montreal-based startup, estimated that there were fewer than 10,000 people in the world with the expertise needed to create machine learning systems. The figure was widely cited in media stories and among recruiting firms, although it wasn't clear how Element AI arrived at it.