Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.

Google is building a new artificial intelligence lab in Montreal dedicated to deep learning, a technology that's rapidly reinventing not only Google but the rest of the internet's biggest players. Hugo Larochelle will run the new lab after joining Google from the Twitter, where he was part of the company's central AI team. It's a homecoming for Larochelle, who earned a PhD in machine learning from the University of Montreal and remains a professor at the Université de Sherbrooke. Yoshua Bengio, one of the founding fathers of the movement, calls him "one of the rising stars of deep learning." Intel Looks to a New Chip to Power the Coming Age of AI Giant Corporations Are Hoarding the World's AI Talent OpenAI Joins Microsoft on the Cloud's Next Big Front: Chips Giant Corporations Are Hoarding the World's AI Talent Giant Corporations Are Hoarding the World's AI Talent At the moment, Larochelle is the new lab's sole hire, but the idea is that he will build a sizable team inside Google's existing engineering office in Montreal.

With the increasingly rapid technological advancements natural language processing (NLP) and deep learning, development of virtual assistants and chatbots has exploded this year, and new applications are being explored everyday. One area of increasing interest is the improving of healthcare and medicine through the use of virtual assistants - we spoke to Cathy Pearl, Director of User Experience at Sense.ly, to learn more.

Fei-Fei Li is a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can "see." And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos--technology that has become commonplace on the world's biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world's biggest tech companies are remaking themselves around this breed of artificial intelligence. Intel Looks to a New Chip to Power the Coming Age of AI Giant Corporations Are Hoarding the World's AI Talent OpenAI Joins Microsoft on the Cloud's Next Big Front: Chips Facebook Manages to Squeeze an AI Into Its Mobile App Giant Corporations Are Hoarding the World's AI Talent Giant Corporations Are Hoarding the World's AI Talent Alongside a former Stanford researcher--Jia Li, who more recently ran research for the social networking service Snapchat--the China-born Fei-Fei will lead a team inside Google's cloud computing operation, building online services that any coder or company can use to build their own AI. This new Cloud Machine Learning Group is the latest example of AI not only re-shaping the technology that Google uses, but also changing how the company organizes and operates its business.

DeepMind, Google's artificial intelligence outfit, wants to streamline health care by using machine learning to provide medics with intelligent notifications. But not everyone is happy with the piles of data being shared with the company. The project will provide medics across a number of London hospitals with alerts about patients via an app called Streams. The app is meant to provide easy access to patient histories and test results for nurses and doctors. But its AI will also learn to track patterns in patients' blood test data and flag cases that show early signs of kidney injury to the appropriate doctors.

It's almost impossible to escape the impact frontier technologies are having on everyday life. At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning. These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution – more specifically, AI is the new industrial revolution. The most exciting and promising of these frontier technologies is the advancements happening in the deep learning space. While still nascent, it's deep learning percolating into your smartphone, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and helping us find solutions to climate change.

The venture capital community is seeing literally hundreds, if not thousands, of new companies focused on applying artificial intelligence to resolving business issues and improving consumer experience on and offline. This explosion of interest, on both sides of the Atlantic, is driven by the well-known trends of big data, faster processing speeds, more bandwidth and increasing broadband access. The market numbers are staggering: a financial services firm issued a 300-page report in 2015 explaining why the AI market is projected to grow to $153 billion by 2020: that's $83 billion for robotics and $70 billion for AI-based analytics. The joke in the VC industry is that any start-up that claims to use AI will expect at least a 20 percent valuation premium. There will indeed be great value created but also lots of wasted experiments.

At BlizzCon earlier this month in Anaheim, California, Blizzard announced an ambitious new project in collaboration with DeepMind, a leading artificial intelligence research company acquired by Google in 2014. After creating the AlphaGo AI that bested the world's top Go player earlier this year, DeepMind's next groundbreaking challenge will be StarCraft II. If DeepMind is able to build an AI that could learn how to beat top players such as Byun "ByuN" Hyun Woo in the complex real-time strategy, tactics and resource management of this game, it would be a giant step forward in AI research. And with DeepMind's interest in using its research to solve hard problems in areas such as healthcare and energy efficiency on a massive scale, this Starcraft II project could impact the whole world. Soon after AlphaGo's Go victory, there were signs that DeepMind would take on StarCraft next. This was not lost on legendary StarCraft player/commentator and former competitive chess player Dan "Artosis" Stemkosi, for whom StarCraft seemed like the logical next step for AI research after games like chess and Go.

There's no reason why machines cannot be curious and creative Subscribe to WIRED ADVERTISEMENT When I was a teenager in the 70s, my goal was to build a self-improving AI smarter than myself, then retire. So I studied maths and computer science. For the cover of my 1987 diploma thesis, I drew a robot that bootstraps itself in seemingly impossible fashion. The thesis was very ambitious and described the first concrete research on a self-rewriting "meta-program" which not only learns to improve its performance in some limited domain, but also learns to improve the learning algorithm itself, and the way it meta-learns the way it learns. Read more How to build an artificial brain This was the first in a decades-spanning series of papers on algorithms for recursive self-improvement, with the goal of building a super- intelligence.

Google DeepMind's artificial intelligence team, alongside researchers at the University of California, Berkeley, has trained AI machines to interact with objects in order to evaluate their properties without any prior awareness of physical laws. The research project drew inspiration from child development and sought to train AI to mirror human capacity to interact with physical objects and infer properties such as mass, friction, and malleability. The study, entitled Learning to perform physics experiments via deep reinforcement learning, explained that while recent advances in AI have achieved'superhuman performance' in complex control problems and other processing tasks, the machines still lack a common sense understanding of our physical world – 'it is not clear that these systems can rival the scientific intuition of even a young child.' Lead researcher Misha Denil and his team set about various trials in different virtual environments in which the AI was faced with a series of blocks and tasked with assessing their properties. In the first simulation, called Which is Heavier, the AI was given a set of four blocks which were the same size but varied in mass.