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Who knows where the Dow DJIA, -0.11% and the S&P 500 SPX, 0.01% are headed? Maybe only the algo traders, who are mostly calling the shots nowadays. Which brings us to this call from Jefferies analysts looking to make money on the artificial intelligence trend. They suggest betting on hot chip-stock Nvidia NVDA, -0.08% and car-parts supplier Delphi Automotive DLPH, -0.47%, as they're likely to benefit from the greater usage of AI across industries. "Artificial intelligence and deep learning will be among the most important investment themes in the next 3-5 years," says Jefferies in a note, citing research from its analyst Mark Lipacis. Nvidia has emerged as the de facto standard in AI, providing the graphic chips used in Amazon AMZN, 1.40% and Google's GOOG, -0.04% GOOGL, -0.03% personal assistants, Baidu's BIDU, 0.78% speech recognition, Skype's translation tool and Netflix's NFLX, 0.02% recommendations, according to Lipacis.

In just the past two years, the industry has made great strides in artificial intelligence (AI) using artificial neural networks, better known as deep learning. With massive processing resources, massive amounts of data, and a software framework, a network of filters is created and optimized to perform select functions like image recognition. As the neural network learns, it develops models that can then be used by computing solutions with much less processing resources to perform the desired function on similar data. This is typically referred to as an inference engine or solution, which can be common processing elements like Central Processing Units (CPUs) and Graphics Processing Units (GPUs) or custom processing solutions. Most artificial intelligent applications, like Amazon Echo using Alexa, perform all or part of the processing in the cloud.

The race to become the hub of AI talent is on, and Canada has shown its hand. The Canadian government recently announced that it'll invest C$170m in The Vector Institute in Toronto, who's job will be to do research into the advancement of AI, and then help to implement their findings into AI start-ups and even big technology companies. Google, who invested $5m into the institute, has now announced that they'll also be setting up an AI lab in the Canadian city of Toronto. This AI lab will be headed up by Geoff Hinton, who was one of the behind the start of the deep learning movement and a professor at the University of Toronto. He's been working with Google since 2012, and his new position will enable him to continue his groundbreaking research into AI, as well as allowing him to move back to his home city.

Machine learning couldn't be hotter. A type of artificial intelligence that enables computers to learn to perform tasks and make predictions without explicit programming, machine learning has caught fire among the hip tech set, but remains a somewhat futuristic concept for most enterprises. But thanks to technological advances and emerging frameworks, machine learning may soon hit the mainstream. Consulting firm Deloitte expects to see a big increase in the use and adoption of machine learning in the coming year. This is in large part because the technology is becoming much more pervasive.

There is no doubt that artificial intelligence, machine learning, and neural networks have experienced huge strides in progress, but of their applications have been on things with "hard edges". Those include search results, translation, board games, etc. Recently, however, progress is also being made in areas of computer vision, imaging, and graphics, for applications that are usually considered more "subjective". Researchers from Adobe and Cornell University have developed a deep-learning neural network that does exactly that, and the results are very convincing indeed. "Style transfer" may not be a popular term, even for the tech savvy, but heavy users of social networking apps and services, like Facebook's Prisma, are already using it without even knowing it.

Artificial intelligence these days is sold as if it were a magic trick. Data is fed into a neural net – or black box – as a stream of jumbled numbers, and voilà! It comes out the other side completely transformed, like a rabbit pulled from a hat. That's possible in a lab, or even on a personal dev machine, with carefully cleaned and tuned data. However, it is takes a lot, an awful lot, of effort to scale machine-learning algorithms up to something resembling a multiuser service – something useful, in other words. Interest in AI is soaring.

As a wild stream after a wet season in African savanna diverges into many smaller streams forming lakes and puddles, so deep learning has diverged into a myriad of specialized architectures. Each architecture has a diagram. Here are some of them. Neural networks are conceptually simple, and that's their beauty. A bunch of homogenous, uniform units, arranged in layers, weighted connections between them, and that's all.

From a layman's perspective, I approached Nvidia's Deep Learning Institute event with a profound sense of unease. Would I be swept along in a sea of jargon and acronyms, furtively and futilely attempting to navigate the esoteric world of data science? In a Royal Institution lecture theatre populated by Microsoft delegates and Credit Suisse's IT team, would I feel totally and completely alienated by the formidable – not to mention inaccessible – world of deep learning? No, is the short answer to that. The world of deep learning is a complex one, I'll admit, but it's an electrifying one too: a phenomenon that wields innumerable possibilities for the streamlining and advancement of everyday life at home and in business.

From voice translation to self-driving automobile, AI's impact in everyday life will become more and more apparent this year. The AI and deep learning market will experience even more rapid technological advancement, very rapid growth and adoption, and increasing competition for both hardware and software platforms. While AI fears will remain, the public will become more cognisant and comfortable with social media AI applications. Deep learning training lends itself to what we call "High Density Processing". High density processing applies when algorithms are computationally intensive, having higher ratios of compute operations per byte of memory bandwidth.