Artificial intelligence (AI) promises to revolutionise our lives, drive our cars, diagnose our health problems, and lead us into a new future where thinking machines do things that we're yet to imagine. Even billionaire entrepreneur Elon Musk, who admits he has access to some of the most cutting-edge AI, said recently that without some regulation "AI is a fundamental risk to the existence of human civilization". So what is the future of AI? Michael Milford and Peter Stratton are both heavily involved in AI research and they have different views on how it will impact on our lives in the future. How widespread is artificial intelligence today? Answering this question depends on what you consider to be "artificial intelligence".

Chatbots are "computer programs which conduct conversation through auditory or textual methods". Apple's Siri, Microsoft's Cortana, Google Assistant, and Amazon's Alexa are four of the most popular conversational agents today. They can help you get directions, check the scores of sports games, call people in your address book, and can accidently make you order a $170 dollhouse. These products all have auditory interfaces where the agent converses with you through audio messages. In this post, we'll be looking more at chatbots that operate solely on the textual front.

While the deep-learning technology behind many of today's AI advances seems new to most, it has been around for decades--simply needing the data and power available today to fuel it. Artificial-intelligence technology has begun to hit its stride, springing from research labs into real business and consumer applications. Earlier this year at the AI Frontiers conference in Santa Clara, California, we sat down with AI experts from some of the world's leading technology-first organizations to find out. An edited version of the experts' remarks follows the video. Li Deng, chief AI officer, Citadel: There are a few factors that really propelled AI to this current state--what many people call "the third wave." The first wave died because people were probably too naive.

There's a lot of fluff surrounding chatbots, so I wrote this post to lay out the basics. I first review the theory of conversation to give us a sense of what we are aiming for. I then discuss three classes of chatbots. The simplest class is purposeless mimicry agents, which only provide the illusion of conversation. Members of this class include ELIZA and chatbots based on deep learning sequence-to-sequence models. The second and next most sophisticated class comprises intention-based agents such as Amazon's Alexa and Apple's Siri. These agents have a simple understanding and can do real stuff, but they generally can't have multi-turn conversations. The third and most sophisticated class is conversational agents that can keep track of what has been said in the conversation and can switch topics when the human user desires. Conversation begins with shared reference.

To gather insights on the state of artificial intelligence (AI) and all its variants -- machine learning (ML), deep learning (DL), natural language processing (NLP), predictive analytics, and multiple neural networks -- we spoke with 22 executives who are familiar with AI. We asked them, "How can companies benefit from AI?" Here's what they told us: How else do you see companies benefitting from AI? Here's who we talked to:

DeepMind's AlphaGo beat Lee Sedol, the best human Go player in the world. It was a defining technological moment not unlike IBM's Deep Blue beating… Read More

When it comes to artificial intelligence, Mark Zuckerberg is more of a glass-half-full guy whereas Elon Musk sees the glass as half empty. Zuckerberg, Facebook's CEO, wrote a post Tuesday evening in which he shared his optimism over the rise of AI technologies like deep learning and how they could lead to breakthroughs in areas like healthcare and self-driving cars.

Being able to reason through potential future events is something humans are pretty good at doing, but that kind of ability is a real challenge when it comes to training AI. Taking those reasoning skills and using them to create a plan is even more difficult, but the Google DeepMind team has begun to tackle this problem. In a recent blog post, researchers describe new approaches they've developed for introducing "imagination-based planning" to AI. Other programs have been able to work in planning abilities, but only within limited environments. AlphaGo, for example, can do this well, as the researchers note in the blog post, however, they add that "environments like Go are'perfect' - they have clearly defined rules which allow outcomes to be predicted very accurately in almost every circumstance."

To showcase the latest in artificial intelligence, Microsoft recently hosted an "underground" tour, two days' worth of virtual reality demos, product prototypes, programming and platform innovation, research news and philosophical musings on the future of AI from technological, social and business perspectives. AI progress can be attributed to a number of factors, including advancements in processing power, powerful new algorithms, data availability, cloud computing, and machine and deep learning capabilities. One of the more compelling milestones that furthered the cause for many applications was Microsoft's achievement late last year of error rates that are on par with, if not better than, human benchmarks – under 5.9 percent for speech recognition and 3.5 percent for image recognition. Autonomous cars, smart homes, automated assistants, translation apps, virtual and augmented reality were all represented over the course of the event as part of the AI spectrum. But the most compelling discussions were those that went beyond technical wizardry (which was impressive in itself) to explore the social and cultural impacts of AI.

Disney Research used deep learning methods to develop a new means of assessing complex audience reactions to movies via facial expressions and demonstrated that the new technique outperformed conventional methods. The new method, called factorized variational autoencoders or FVAEs demonstrated a surprising ability to reliably predict a viewer's facial expressions for the remainder of the movie after observing an audience member for only a few minutes. While the experimental results are still preliminary, this approach demonstrates tremendous promise to more accurately model group facial expressions in a wide range of applications. "The FVAEs were able to learn concepts such as smiling and laughing on their own," said Zhiwei Deng, a Ph.D. student at Simon Fraser University who served as a lab associate at Disney Research. "What's more, they were able to show how these facial expressions correlated with humorous scenes." The researchers will present their findings at the IEEE Conference on Computer Vision and Pattern Recognition on July 22 in Honolulu.