I believe that 2018 is going to be a fundamental moment in the history of technology, where we are able to unleash the power of IT to increase productivity and mercifully release bottlenecks that liberate people to focus on what they do best. In Part 2 of my technology predictions for 2018 (read Part 1 here), I explore the opportunities that lie in Augmented Reality (AR) and how our reliance on machines will increase. "Emerging technologies over the next decade have the potential to solve some of the intractable problems that humanity has faced for so long." It won't be long until the lines between'real' reality and augmented reality blur entirely. In fact, we are already starting to witness disruptions in some industries; DAQRI's state-of-the-art devices can bring AR to medical training facilities by helping plan surgeries before they happen through detailed 3D visualizations, and they are also revolutionizing the manufacturing industry through their ability to display control room information in real-time and visualize thermal data to pinpoint congestion issues deep within a pipe.
Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automation, humans now also interact with non-human agents of varying complexity as part of their day-to-day activities; substantial work is being done to integrate increasingly intelligent machine agents into human work and play. With increases in the cognitive, sensory, and motor capacity of these agents, intelligent machinery for human assistance can now reasonably be considered to engage in joint action with humans---i.e., two or more agents adapting their behaviour and their understanding of each other so as to progress in shared objectives or goals. The mechanisms, conditions, and opportunities for skillful joint action in human-machine partnerships is of great interest to multiple communities. Despite this, human-machine joint action is as yet under-explored, especially in cases where a human and an intelligent machine interact in a persistent way during the course of real-time, daily-life experience. In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task. In a case study with a single participant, we provide an example of human-agent coordination and decision-making involving prediction learning on the part of the human and the machine agent, and control learning on the part of the machine agent wherein audio communication signals are used to cue its human partner in service of acquiring shared reward. These comparisons suggest the utility of studying human-machine coordination in a virtual reality environment, and identify further research that will expand our understanding of persistent human-machine joint action.
The reported clash between the two technology titans is proof that not everyone sees the benefits and dangers of artificial intelligence in the same light. Yet from Facebook's algorithms to Tesla's self-driving cars, it's clear that AI isn't science fiction any longer--and that we're already at the cusp of a new era, with AI poised to deliver transformational change in business and society. As we explain in our book Human Machine: Reimagining Work in the Age of AI, which is based on research with 1,500 organizations, the fundamental rules by which organizations run are being rewritten daily. As businesses deploy AI systems--from machine learning to computer vision to deep learning--some will see modest short-term productivity gains. But others--by understanding and taking advantage of the true nature of AI's impact--will attain breakthrough improvements in performance, often by developing game-changing innovations.
Multiagent learning is an important tool for long-lasting human-machine systems (HMS). Most multiagent learning algorithms to date have focused on learning a best response to the strategies of other agents in the system. While such an approach is acceptable in some domains, it is not successful in others, such as when humans and machines interact in social dilemma-like situations, such as those arising when human attention is a scarce resource shared by multiple agents. In this paper, we discuss and show (through a user study) how multiagent learning algorithms must be aware of reputational equilibrium in order to establish neglect tolerant interactions.