MIT Sloan Management Review and BCG have assembled an international panel of AI experts that includes academics and practitioners to help us gain insights into how responsible artificial intelligence (RAI) is being implemented in organizations worldwide. This month, we asked our expert panelists for reactions to the following provocation: Executives usually think of RAI as a technology issue. The results were wide-ranging, with 40% (8 out of 20) of our panelists either agreeing or strongly agreeing with the statement; 15% (3 out of 20) disagreeing or strongly disagreeing with it; and 45% (9 out of 20) expressing ambivalence, neither agreeing nor disagreeing. While our panelists differ on whether this sentiment is widely held among executives, a sizable fraction argue that it depends on which executives you ask. Our experts also contend that views are changing, with some offering ideas on how to accelerate this change.

The emergence of leading-edge autonomous driving technologies and mobility-as-a-service (MaaS), along with a greater push for cleaner plug-in hybrid electric vehicles (PHEVs) and the present era of hyper data connectivity, are creating huge opportunities to fundamentally change the way people travel. Despite the majority of people agreeing that the autonomous vehicle (AV) is going to be the next big thing in the future and that it offers tremendous benefits, such as reduced mobility and infrastructure costs, increased safety, increased mobility, increased customer satisfaction, reduced crime, and disruption to other industries that are based on mobility, there are still several valid questions on the safety, liability, legal framework, loss of driving-related jobs, and ethical dilemmas that have no clear answers yet. The companies that are leading in AV technology, connectivity, and automobile manufacturing are pouring in a significant amount of capital to secure a strong position in the driverless future, and at the same time, all players ranging from small startup component vendors to municipalities are actively seeking their roles in the ecosystem. This Tractica report examines the market and technology issues surrounding consumer AVs and presents 6-year revenue forecasts for the industry. Tractica addresses the crucial market drivers and challenges, in addition to assessing the most important technology issues that will influence market development.

As robotics technologies have advanced significantly in the past few years, robots for enterprise markets are becoming more affordable, productive, and smarter than ever before. According to a new report from Tractica, this trend is resulting in a significant increase in the number of enterprises within the agriculture, construction, warehousing and logistics, telepresence, customer service, and other sectors willing to invest in robots to cut costs and increase profits, while trusting robots to solve business challenges and productivity gaps. Tractica forecasts that worldwide shipments of enterprise robots will grow from approximately 83,000 units in 2016 to 1.2 million units annually by 2022, increasing at a compound annual growth rate (CAGR) of 57% during that period. Global revenue for the enterprise robotics market will increase from $5.9 billion in 2016 to $67.9 billion in 2022. "Just as robotics has transformed manufacturing and heavy industry in recent years, robots are beginning to impact enterprise work processes, with innovative and effective solutions being introduced with increased frequency," says research analyst Manoj Sahi.

The race for making perfect hardware to accelerate artificial intelligence (AI) applications is heating up and many companies are jumping in with their products and solutions. Of the three key parts of hardware infrastructure compute, storage, and networking compute has made significant progress in the last couple of years. The other two areas, storage and networking, are lagging behind and have yet to see major innovations pertaining to AI applications. Currently, the industry is going with the fastest available option and promoting that as a solution for deep learning. Over time, more research and development (RandD) will go into these areas and new products will emerge that are designed specifically for AI.The distributed computing infrastructure that allows AI applications to run on multiple machines is in its infancy.

When the draft version of a federal encryption bill got leaked this month, the verdict in the tech community was unanimous. Critics called it ludicrous and technically illiterate--and these were the kinder assessments of the "Compliance with Court Orders Act of 2016," proposed legislation authored by the offices of Senators Diane Feinstein and Richard Burr. The encryption issue is complex and the stakes are high, as evidenced by the recent battle between Apple and the FBI. Many other technology issues that the country is grappling with these days are just as complex, controversial, and critical--witness the debates over law enforcement's use of stingrays to track mobile phones or the growing concerns around drones, self-driving cars, and 3-D printing. Yet decisions about these technical issues are being handled by luddite lawmakers who sometimes boast about not owning a cell phone or never having sent an email.