Five years from now, company leaders will be looking back and wishing they developed an artificial intelligence strategy sooner, according to one of the veterans of the field. Andrew Ng, the cofounder of Coursera and the former machine learning chief at Chinese tech powerhouse Baidu, said that he thinks Fortune 500 businesses will find the rise of AI similar to the rise of the internet. Some top CEOs bemoan how their businesses were late to the party when it came to competing on the internet, and Ng said that the same thing will be true when it comes to AI. In his view, businesses are best off hiring a leader with deep knowledge of the field who can help build up an organization's knowledge and capabilities in a centralized way. That chief AI officer, as he described it, would be charged with helping to bring expertise in the field to the rest of the a company.

Google quietly released an academic paper that could provide a blueprint for the future of machine learning. Called "One Model to Learn Them All," it lays out a template for how to create a single machine learning model that can address multiple tasks well. The MultiModel, as the Google researchers call it, was trained on a variety of tasks, including translation, language parsing, speech recognition, image recognition, and object detection. While its results don't show radical improvements over existing approaches, they illustrate that training a machine learning system on a variety of tasks could help boost its overall performance. For example, the MultiModel improved its accuracy on machine translation, speech, and parsing tasks when trained on all of the operations it was capable of, compared to when the model was just trained on one operation.

First Amazon announced plans to fully automate its new brick and mortar store with robots. Then we learned that Foxconn plans to automate 30 percent of its factory workforce by 2018. And recently, Wendy's announced plans to add automated kiosks at more than 1,000 stores. One thing is clear -- robots are changing the way we live and work. And this is just the beginning.

One of the biggest limiting factors of artificial intelligence (AI) systems is that they can't think or conceptualize the world the way humans can. Rather than intuitively discerning patterns in chaos, like how you can identify a cat in a photograph instantly, traditional AI models require in-depth descriptions of what constitutes a "cat" object and how to identify one by evaluating individual groups of pixels within the image. Deep learning systems are starting to bypass the necessity for brute force computations, as evidenced by the landmark victory of AI program AlphaGo against an international champion of Go, a game once thought to be too intuitive and conceptual for AI to master. But a new, yet intuitively simple, leap forward in AI learning may be able to accelerate the pace of AI development even further. Google researcher and AI expert Ian Goodfellow is working on AI that belongs to a group of "generative models," which are designed to create images and sounds comparable to those you'd find in the real world. This is a deceptively difficult task, as AI programs must first conceptually understand what it is they're trying to replicate, a leap forward in intuitive thinking that has historically been reserved for human beings.

Nvidia wants its graphics processing units (GPUs) to handle the artificial intelligence computing and other heavy-duty parallel processing tasks in giant data centers. To that end, the company is launching a program that makes it easy for its big manufacturing partners to package its GPUs in AI supercomputers. Nvidia HGX is a kind of starter recipe for original design manufacturers (ODMs) -- Foxconn, Inventec, Quanta, and Wistron -- to package GPUs in data center computers, said Ian Buck, general manager of accelerated computing at Nvidia, in an interview with VentureBeat. Nvidia CEO Jen-Hsun Huang is announcing HGX at the Computex tech trade show in Taiwan today. HGX has already been used as the basis for the Microsoft Project Olympus initiative, Facebook's Big Basin systems, and Nvidia DGX-1TM AI supercomputers.

Steph Hay, head of conversation design at Capital One, knows that her job -- creating conversational interfaces for customers to access account information and complete financial tasks -- is counterintuitive. Most people don't like talking about money with other humans, let alone with a voice-enabled, anthropomorphized computer. Last year, however, Capital One launched an Alexa skill designed to allow customers to do just that: talk to Amazon's Alexa in order to track spending, check account balances, and pay bills. I sat down with Hay to discuss the design process behind the Alexa skill and how her team thinks about how people think about money. More so than most apps, conversational financial products -- banking products that users interact with through text or voice -- must fight to earn customer trust.