Machine learning is a rising star in the compute constellation, and for good reason. It has the ability to not only make life more convenient – think email spam filtering, shopping recommendations, and the like – but also to save lives by powering the intelligence behind autonomous vehicles, heart attack prediction, etc. While the applications of machine learning are bounded only by imagination, the execution of those applications is bounded by the available compute resources. Machine learning is compute-intensive and it turns out that traditional compute hardware is not well-suited for the task. Many machine learning shops have approached the problem with graphics processing units (GPUs), application-specific integrated circuits (ASICs) – for example, Google TensorFlow – or field-programmable gate arrays (FPGAs) – for example, Microsoft's investment in FPGAs for Azure and Amazon's announcement of FPGA instances.

Artificial intelligence is one of the hottest technology trends for 2017. And perhaps no company in the AI sector is hotter than NVIDIA, which has pushed from the desktop into the data center, evolving into a major player in high performance computing. NVIDIA's graphics processing (GPU) technology has been one of the biggest beneficiaries of the rise of specialized computing, gaining traction with workloads in supercomputing, artificial intelligence (AI) and connected cars. This trend is expected to accelerate in 2017, with more custom chips being introduced to target these workloads. After building a major beachhead in hyperscale data centers, NVIDIA's ambitions now extend to the enterprise data center. The company's new DGX-1 Deep Learning System is a "supercomputer in a box" – a hardware appliance designed to make AI data crunching more accessible.

Many technologies follow a'hockey stick' curve. A great number of them seem to be around for years, they are talked about within the IT industry, white papers are written about them, but it takes a long time for them to actually reach the threshold of public consciousness. This is certainly true for neural networks. This computational approach, loosely modeled on the way a biological brain solves problems, has at least in theory been discussed since as early as the 1940s. It is only recently, however, that neural networks are not only becoming a reality, but also finding real, value adding use cases.

In yet another episode of man versus machine, an artificial intelligence developed by Carnegie Mellon University has been absolutely dismantling a team of professional poker players, accumulating a staggering lead of almost $800,000. The showdown takes place as part of the "Brains vs. Artificial Intelligence" competition which pits a group of four poker pros against the crafty supercomputer Libratus in a heads-up game of No-Limit Texas Hold'em slated to continue for 120,000 hands. Last year, Facebook's VP of Design thought the TNW Conference main stage was the best she'd ever been on. Since January 11 when the contest initially kicked off, the players have now passed the midway point of the the race, having completed almost 65,000 hands in total. What is more intriguing is that so far Libratus has managed to keep an impressive lead over its human opponents, stacking up a profit of $794,392.

Last year I shared my collection of video resources for machine learning. It was unwieldy taking individual e-mail requests and updating a time-stamped blog post.

At CES 2017, plenty of car makers were showing off the latest tech. One of the most interesting developments is that popular digital assistants are making their way onto infotainment systems, with Nissan and BMW demonstrating Cortana. Cortana will be familiar to Windows users as it's built into Windows 10 as well as Windows phones. Although there's no firm date yet when you'll be able to buy a car with Cortana, both Nissan and BMW unveiled their plans at CES to bring the digital assistant to some models in their ranges. Nissan's demo showed the most advanced integration, with Cortana able to tailor preferences and settings based on who's driving, which suggests user profiles will be involved.

The coming year could see the rise of consumer-focused machine learning, and the beginning of the end for corporate omniscience about user data. That's one of the implications of the latest batch of influential predictions from financial firm Deloitte, which projects that more than 300 million machine learning-tailored smartphones will be sold this year. Right now, the most important machine learning algorithms -- the ones that let us control our phones with a voice command and get predictive directions to our next destination -- use mostly out-board computing power to do their jobs. They run primarily on remote data servers operated by companies like Google, and while these companies incur huge costs for running all that computation, they make it up through the increased insight into users' lives. Sure, they have to pay for the power to parse a command to set a calendar event for 9 p.m. Eastern on Friday.

A survey of business leaders has found Australian companies are the worst prepared for the arrival of artificial intelligence (AI) technologies among selected major economies, despite spending the second-largest amount of money on automation. Independent research agency Vanson Bourne was commissioned by IT company Infosys (which as a seller of an AI platform has a vested interest in promoting such technology) to poll 1,600 business leaders of companies with more than 1,000 staff and at least US$500m in annual revenue across Australia, China, the United States, Germany, France, India and the UK. According to the survey, released at the World Economic Forum last week, major Australian businesses invested an average of $7.9m last year in AI, behind only the US, but placed last in both the skills required for AI takeup and in plans to integrate AI. The Infosys Australia regional head, Andrew Groth, told the Guardian the survey demonstrates that Australia risks becoming uncompetitive. "The challenge is the skills situation," he said.

Last year, the first season of HBO's Westworld concluded as most stories about robots do: with the machine eliminating its maker. This shouldn't have been surprising. Across Hollywood, from Terminator to Ex Machina, the first thing artificially intelligent robots seem to do once they gain consciousness is go rogue. It makes for a cool ending, but if the writers of Westworld's season 2 want to aim for more science and less fiction, they might consider having their "hosts" not eliminate humans, but help them to prevent cyberattacks or improve cancer treatments. And what if the show's humans didn't live in fear of robots, but instead set some ground rules for how AI technology can be used meaningfully, for the benefit of all?