Rida Qadri ( ridaqadri@google.com) is a senior research scientist at Google Research, Mountain View, CA, USA. Michael Madaio ( madaiom@google.com) is a senior research scientist at Google Research, New York, NY, USA. Mary L. Gray ( mlg@microsoft.com) is a senior principal researcher at Microsoft Research, Cambridge, MA, USA.

To a ddress these gaps, prior work has sought to evaluate the cultural representations within AI generated output, b ut with few exceptions [30, 67], mostly through quantified, metricized approaches to representation such as statistical similarities and benchmark-style scoring [49, 84]. However, the use of these methods presumes that representation is an o bjective construct with an empirical, definitive ground truth that outputs can be compared against [e.g., 42, 84] [fo r a critique of ground truth, see 59]. Given limitations of these computational methods, evaluation of representation is reduced to basic recognition or factual generation of artifacts. Even when human feedback on representation is sought, it is solicited through narrow, constrained, quantitative scales from anonymized crowdworkers who often do not have th e lived experiences to evaluate nuances of cultural representation of other cultures. However, this approach to measuring representation is in contravention to decades of scholarship in the social sciences that emphasizes the subjective nature of representation, where judgments about representation in visual media are constructed in conversation with the viewer's lived experiences and the broader context within which an image is Permission to make digital or hard copies of all or part of thi s work for personal or classroom use is granted without fee pr ovided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

In this paper, we sketch a programme for AI driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our model for how AI based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI based models can help synthesize knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticization, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge, (2) transferability, i.e., the ability to transfer what has been learned in one context to another, and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

For most people living through the latest pandemic, the urgent questions are often "when questions." When indoor establishments should lift capacity limits. When mask requirements should be dropped. When family, friends, and strangers should reconnect across household lines. For millions of other people, the question is more like whether. Whether there ever will be an opening. Whether they will be welcome participants. Whether the reengineered social relationships for post-pandemic life will include them. Because when the physical world is utterly open, the social world can be closed to these people.

Three friends were having morning tea on a farm in the Northern Rivers region in New South Wales (NSW), Australia, when they noticed a drilling rig setting up in a neighbor's property on the opposite side of the valley. They had never heard of the coal seam gas (CSG) industry, nor had they previously considered activism. That drilling rig, however, was enough to push them into action. The group soon became instrumental in establishing the anti-CSG movement, a movement whose activism resulted in the NSW government suspending gas exploration licenses in the area in 2014.2 By 2015, the government had bought back a petroleum exploration license covering 500,000 hectares across the region.3 Mining companies, like companies in many industries, have been struggling with the difference between having a legal license to operate and a moral4 one. The colloquial version of this is the distinction between what one could do and what one should do--just because something is technically possible and economically feasible doesn't mean that the people it affects will find it morally acceptable. Without the acceptance of the community, firms find themselves dealing with "never-ending demands" from "local troublemakers" hearing that "the company has done nothing for us"--all resulting in costs, financial and nonfinancial,5 that weigh projects down. A company can have the best intentions, investing in (what it thought were) all the right things, and still experience opposition from within the community. It may work to understand local mores and invest in the community's social infrastructure--improving access to health care and education, upgrading roads and electricity services, and fostering economic activity in the region resulting in bustling local businesses and a healthy employment market--to no avail. Without the community's acceptance, without a moral license, the mining companies in NSW found themselves struggling. This moral license is commonly called a social license, a phrase coined in the '90s, and represents the ongoing acceptance and approval of a mining development by a local community. Since then, it has become increasingly recognized within the mining industry that firms must work with local communities to obtain, and then maintain, a social license to operate (SLO).6 The concept of a social license to operate has developed over time and been adopted by a range of industries that affect the physical environment they operate in, such as logging or pulp and paper mills. What has any of this to do with artificial intelligence (AI)?

The IEEE International Solid-State Circuits Conference (ISSCC) is the premier global forum for solid-state circuit research in both academia and industry. This year's conference in San Francisco was the 65th annual, and drew over 3,000 attendees. Among the show's highlights were new circuit design techniques and system-on-chip innovations that stand to accelerate on-chip machine learning. And machine learning did indeed take center stage. In his opening keynote, David Patterson from Google and UC Berkeley explained that although we have reached the end of Dennard scaling and Moore's Law, we can continue to improve computational and energy efficiency by adopting domain-specific architectures.

"I returned, and saw under the sun, that the race is not to the swift, nor the battle to the strong, neither yet bread to the wise, nor yet riches to men of understanding, nor yet favour to men of skill; but time and chance happeneth to them all." Chance appears to name a single, unitary thing. But its genealogy, its family history, turns out to be a tangled one. One way to understand its branching origins is to turn to literature: We may look, in turn, to two very different novels. Anton Chigurh, the antagonist of Cormac McCarthy's novel No Country for Old Men, forces his victims to guess the outcome of a coin toss, taking their life if they guess in error. That chance is entirely contained, not in Chigurh, but in the toss--in nature itself. This is one source of uncertainty.

To cite this article: Gina Neff. The biggest challenge for the use of "big data" in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. "I FOUND THE BUZZ AS FEVERISHLY LOUD AROUND HEALTH INFORMATION INNOVATION AS IT WAS DURING MY RESEARCH ON THE FIRST DOT-COM BOOM." To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom. One of our findings from this research seems at first blush so obvious that it is hard to believe it has been overlooked in the design and implementation of health-care innovation technologies.

In this position paper, the author proposes the use of social networks of characters as an AI narrative generation mechanism. The first part of the paper offers examples of recent research by literary critics on the relationship between character networks and narrative structure. The second part of the paper offers a simple example of story generation based on a structural balance network model.

There is a growing trend towards the convergence of cyber-physical systems (CPS) and social computing, which will lead to the emergence of smart communities composed of various objects (including both human individuals and physical things) that interact and cooperate with each other. These smart communities promise to enable a number of innovative applications and services that will improve the quality of life. This position paper addresses some opportunities and challenges of building smart communities characterized by cyber-physical and social intelligence.