Even as Siri and Alexa slip quietly into the flow of our lives and we watch IBM's Watson play Jeopardy! Marketers play on this to draw attention, declaring in intentionally stark blog posts that "AI is better than humans" and it "really is going to take people's jobs." We can speculate, we can worry, we can ponder the defeat of humanity in the face of AI, but that focuses on provocative possibilities, not the reality. To understand the potential for machine learning, we must stop obsessing about the forest and sharpen our focus on the trees. On a macro level, there's no doubt that machine learning will reduce the need for manual labor across many industries and roles, including the maintenance aspects of IT.

Obviously, it's artificial intelligence, but it is an intelligence we can use to help us determine what's useful in all of the big data being collected through the Internet of Things (IoT). And we can teach it to tell us what's important to investigate there, too. AI won't replace humans, but it will give us a huge boost. Paul Muller, vice president of strategic marketing at Hewlett Packard Enterprise, has a conversation with technology analyst Theo Priestley about AI, including what it is, how to use it, fears about it, and how it can help us long term. They talk about the myths around AI--such as it's a human replacement vehicle rather than a way to augment our work, or it's just software rather than hardware and software--and how artificial intelligence doesn't mean it's not intelligent and doesn't practice self-preservation.

The reality will lie between these two extremes. Based on research gathered from surveying Asian business leaders and human resources and AI professionals, this report argues that AI's future will cleave much more closely to the positive outcome. Moreover, this future appears to be approaching quickly: advances in deep learning and the rapid expansion of process automation in such diverse sectors as manufacturing, transportation, and financial services mean that AI's impact is growing exponentially with each passing year. Decision makers in all organizations must now begin to understand how AI will alter their own operational processes and those of suppliers, partners, and customers. Asia's business landscape is poised not only to benefit greatly from AI's rise, but also to define it.

But how do you separate hype from reality? How can your company apply AI to solve real business problems in 2017? In September 2016, Luminary Labs convened 30 executives in healthcare, machine learning, and analytics for a grounded discussion on these questions with machine learning expert Hilary Mason, founder and CEO of Fast Forward Labs, and Sandy Allerheiligen, VP of data science and predictive and economic modeling at Merck. Here's a synopsis of what we discussed, and what AI learnings your business should keep in mind for 2017. AI and the Near Term 3 We've all seen the sensational headlines: The robots are coming, and they'll take our jobs! AI can do your job faster and more accurately than you can!

Machine learning, artificial intelligence and augmented reality have been bubbling away in research labs for decades, but in recent years they have burst into business consciousness. For marketers, the rise in big data, programmatic advertising and marketing clouds have all coincided with the commercial emergence of cognitive computing -- the umbrella label for technologies that ingest data and then learn as their knowledge base grows. According to industry analyst Gartner, cognitive computing is a "disruptive platform with a shift more impactful than many other technologies in the past 20 years". For an industry which seems to reinvent itself every three years, this is a suitably bold claim. And yet, clearly, cognitive computing is already having a serious impact across a range of industry sectors.

It has been more than half a year since Google Inc. first revealed a piano-playing artificial intelligence that can take a few notes and turn them into a song. Now the search giant has decided to share the experience with the world with its AI Duet web app. Yotam Mann is the musician and coder behind the new AI, which he created with help from Google's Magenta and Creative Labs teams. In a video, Mann explained the process behind the AI and how it manages to play along with new music on the fly. "Making music using code isn't a new thing at all, but machine learning gives us a different way to go about it," said Mann. "If I was trying to make AI Duet with more traditional programming, I'd have to write out lots of rules."

The future of federal IT belongs to CIOs who can build flexible, nimble organizations able to maximize the advantage of existing technologies like cloud services and automated machine intelligence while laying the groundwork for a range of emerging technologies on the horizon. That's according to a new report on government technology trends for 2017 published Wednesday by Deloitte. Researchers identified eight technologies they believe have an opportunity to disrupt and change the way the federal government leverages information, data and software over the next two years. Some are a continuation of existing trends that are already established, like IT consolidation and greater reliance on cloud-based software and services. Others, like artificial intelligence, mixed reality and nanotechnology veer more into the outer edges of what is currently possible today, but may have far more relevance a few years down the line.

The technology industry is facing up to the world-shaking ramifications of artificial intelligence. There is now a recognition that AI will disrupt how societies operate, from education and employment to how data will be collected about people. Machine learning, a form of advanced pattern recognition that enables machines to make judgments by analysing large volumes of data, could greatly supplement human thought. But such soaring capabilities have stirred almost Frankenstein-like fears about whether developers can control their creations. Failures of autonomous systems -- like the death last yearof a US motorist in a partially self-driving car from Tesla Motors -- have led to a focus on safety, says Stuart Russell, a professor of computer science and AI expert at the University of California, Berkeley.

The retail space is changing drastically through the evolution of technology, such as artificial intelligence (AI), which can offer retailers better control over stock flows and better customer service. This technology, and in particular machine learning, can step in to help retailers cater to the needs of their customers in smarter ways. Retail goods, even the most niche products, are becoming more and more commoditised and price parity means retailers have to look continuously for more innovative ways to keep customers loyal. A high-end jewellery retailer, a sporting goods store and a grocer all have very different target markets and their customer's behaviour patterns are very different, but the beauty of machine learning, an emerging technology, is that it quickly adapts to any type of environment. Machine learning is a form of AI that allows computers to learn without being programmed.

DeepMind is now capable of teaching itself based on information it already possesses. In a significant step forward for artificial intelligence, Alphabet's hybrid system -- called a Differential Neural Computer (DNC) -- uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it. "These models can learn from examples like neural networks, but they can also store complex data like computers," wrote DeepMind researchers Alexander Graves and Greg Wayne. Much like the brain, the neural network uses an interconnected series of nodes to stimulate specific centers needed to complete a task. In this case, the AI is optimizing the nodes to find the quickest solution to deliver the desired outcome.