When modelling data that have an inherent sequential structure to them, such as the sequence of words in language or the sequence of milliseconds in financial market data, your first choice from the universe of Deep Learning algorithms is typically going to be a Recurrent Neural Network. RNNs enable information from previous time steps to influence the present one. The problem with vanilla RNNs, however, is that the influence quickly drops off. While the preceding step (e.g., the previous word or market-feed update) can have a great impact on the current time step, the information from, say, ten steps (ten words or ten market-feed updates) earlier is limited to having minimal or negligible impact on the current time step. The prevailing solution to this vanishing gradient issue is to add gates to individual RNN units, enabling the important information (e.g., a verb or market-movement-predicting signal) from previous steps to be retained, while less consequential information (like a stop word or a boring market update) from previous steps is forgotten.

NEW YORK--Things are starting out miserably for Bixby. Samsung's upstart artificial intelligence digital assistant got an "incomplete" grade when it first turned up on the Galaxy S8 and S8 smartphones that launched in March. The reason is that the voice-based commands that promise to make Bixby behave more like Apple's Siri, Microsoft's Cortana, Amazon's Alexa, and Google's Assistant were delayed, at least in the U.S. (Bixby Voice is fully operational in South Korea, where Samsung is based.) Now the Korea Herald is reporting further delays to the English-speaking version of Bixby, apparently because Samsung can't amass the "big data" required for a good enough deep learning-based experience. Samsung's Bixby finally gets a voice -- sort of While Samsung recently granted early access to select S8 and S8 users who expressed interest in trying out Bixby Voice, me among them, I'm being kind to suggest that Bixby has a lot of catching up to do compared to its rivals.

Smartphones are getting increasingly feature-rich and with that, the need for high performance chipsets is also increasing. For processors to perform at par with evolving smartphone technology such as virtual reality and artificial intelligence, they will need to sort bandwidth issues between the processor and RAM. A research team from the Massachusetts Institute of Technology (MIT) and Stanford University might have a solution. It has developed a prototype processor made of Graphene carbon nanotubes, with a resistive RAM layer on top. The prototype effectively combines RAM and CPU, which will not only save space in a smartphone motherboard, it will also create better performance.

The Intel Software and Services Group (SSG) connects Intel to the worldwide software community. SSG strives to bring competitive advantage to Intel platforms by helping independent software vendors, operating system developers, OEMs, channel members and systems integrators deliver exceptional customer value and achieve differentiation on Intel processor technologies. SSG provides global leadership to the software community through its technical expertise, industry enabling activities, and developer products and programs.

Particle physicists began fiddling with artificial intelligence (AI) in the late 1980s, just as the term "neural network" captured the public's imagination. Their field lends itself to AI and machine-learning algorithms because nearly every experiment centers on finding subtle spatial patterns in the countless, similar readouts of complex particle detectors--just the sort of thing at which AI excels. "It took us several years to convince people that this is not just some magic, hocus-pocus, black box stuff," says Boaz Klima, of Fermi National Accelerator Laboratory (Fermilab) in Batavia, Illinois, one of the first physicists to embrace the techniques. Neural networks search for fingerprints of new particles in the debris of collisions at the LHC. Particle physicists strive to understand the inner workings of the universe by smashing subatomic particles together with enormous energies to blast out exotic new bits of matter.

DeepMind, Google's London-based artificial intelligence research branch, is launching a team at the University of Alberta in Canada. DeepMind is launching a team at the university partly for proximity to the broader AI research community in Canada. A number of leading AI researchers in Silicon Valley hail from Canada, where they plugged away at deep learning, a complex automated process of data analysis, during a period when that technology -- now popular at major tech companies -- was considered by the larger computer science community to be a dead end. Plus, almost a dozen DeepMind staff came from the university, according to a blog post by DeepMind co-founder and CEO Demis Hassabis announcing the new lab. An Alberta PhD and a former post doc from the school played key roles in one of DeepMind's hallmark accomplishments, getting its AlphaGo software to beat the human world champion at Chinese strategy game Go. "Our hope is that this collaboration will help turbocharge Edmonton's growth as a technology and research hub," wrote Hassabis, "attracting even more world-class AI researchers to the region and helping to keep them there too."

What if an algorithm could tell you whether you have cancer based on your CT scan or mammography exam? While I am certain that radiologists' creative work will be necessary in the future to solve complex issues and supervising diagnostic processes; AI will definitely become part of their daily routine in diagnosing simpler cases and taking over repetitive tasks. So rather than getting threatened by it, we should familiarize with how it could help change the course of radiology for the better. There is a lot of hype and plenty of fear around artificial intelligence and its impact on the future of healthcare. There are many signs pointing towards the fact that AI will completely move the world of medicine.

As malware developers use more advanced methods to create new malware, the gap between the detection rates of deep learning vs traditional machine learning will grow wider; and in coming years it will be critical to rely on deep learning in order to have a realistic chance of foiling the most sophisticated attacks.

I am one of the authors of ShuffleNet. We've designed a new convolutional neural network structure for mobile platforms which utilizes pointwise group convolution and channel shuffle. Under the budget of 40MFLOPS, we've achieved 6.7% absolute top-1 error reduction on ImageNet classification compared to MobileNets. Empirically, our network with approximately the same error runs 13x faster than AlexNet on an ARM platform.

It's rare for Canadians to come out and assert global leadership in anything (barring hockey and winter coats), but here we are, on the brink of adding artificial intelligence (AI) to the list. This is no small measure. It requires us to move away from the understated modesty that often defines our national character and demands that we take action to be able to declare our place on the world stage. Thankfully, we have the goods to declare. Seminal breakthroughs such as deep learning and reinforcement learning, which have resulted in unprecedented technological transformation and are currently fuelling the AI engine, were brought to life by Canadian universities.