We have been hearing a lot lately on the topic of American greatness, where it went, and how to reclaim it. But greatness is complicated, and our ideas of what is great and what is not have changed over time. In this column, I ask what the history of one mighty corporation--IBM--can tell us about the rise and fall of a particular kind of American greatness. Decade after decade, IBM has been one of the world's largest, most profitable, and most admired companies. Of all American businesses, only General Electric, Apple, Microsoft, and Exxon-Mobile have generated more wealth.a Despite recent troubles, it has been ranked in the 2010s as the number one company for leaders (Fortune), the greenest company (Newsweek), the second most valuable global brand (Interbrand), the second most respected company (Barron's) and the fifth most admired (Fortune). IBM technical contributions to computing are second to none. Its researchers won six Turing awards and, more startling, four Nobel prizes. Its engineers produced the first hard disk drive, the first floppy disk drive, the first architecture implemented over a range of diverse but compatible machines, the first widely used high-level programming language, the relational database, the first scientific supercomputer, the first RISC designs, and the first DRAM chip. As recently as 2014, IBM ranked ahead of its old adversary Microsoft on Fortune's lists of the largest U.S. companies (20th place) and of the firms most admired by managers (16th place). In ways good and bad, IBM has been at the forefront of changes in American business and in America's relationships with the world.

I think often of Ender's Game these days. In this award-winning 1985 science-fiction novel by Orson Scott Card (based on a 1977 short story with the same title), Ender is being trained at Battle School, an institution designed to make young children into military commanders against an unspecified enemy (http://bit.ly/2hYQMDF). Ender's team engages in a series of computer-simulated battles, eventually destroying the enemy's planet, only to learn then that the battles were very real and a real planet has been destroyed. I got involved in computing at age 16 because programming was fun. Later I discovered that developing algorithms was even more enjoyable.

Over 13 months in 2016–17 the U.S. Federal Communications Commission conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 bn, $10.05 bn of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 Ultra High Frequency (UHF) channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 bn (used to pay down the national debt) after covering costs (including retuning). A crucial element of the auction design was the construction of a solver, dubbed SAT-based Feasibility Checker (SATFC), that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote.

The rise of the machine economy risks social disruption by widening the gap between rich and poor in Britain, as automation threatens jobs generating £290bn in wages. Jobs accounting for a third of annual pay in the UK risk being automated, according to the study by the IPPR thinktank. Warning that low-paid roles are in the greatest danger, it urged ministers to head off the prospect of rising inequality by helping people retrain and share in the benefits from advances in technology. The study for the IPPR's commission on economic justice, which features senior business and public figures including the archbishop of Canterbury, called on the government to take a greater role in managing the adoption of robotics, artificial intelligence and other methods of job automation in the workforce. Mathew Lawrence, a senior research fellow at the IPPR, said: "Managed badly, the benefits of automation could be narrowly concentrated, benefiting those who own capital and highly skilled workers. The IPPR estimates that 44% of jobs in the UK economy could feasibly be automated, equating to more than 13.7 million people who together earn about £290bn. Although it doesn't give a forecast for how long this would take, it cited US research which estimates the changes could occur over the next 10 or 20 years. From the collective pay pool worth £290bn, middle-income jobs such as call-centre workers, secretaries and factory workers are likely to be hollowed out. Low-skilled workers could also lose their jobs or face fewer hours from greater levels of automation. At the same time the highest earners and workers able to retrain will gain higher pay thanks to rising productivity – which means more output being generated per hour worked. The research follows similar studies warning of the risks arising from the current rapid advances in technology, which have enabled machines to take on work that was once the preserve of humans. The Bank of England has said as many as 15m jobs in Britain are under threat. Measures called for in the IPPR report include a UK skills system to help retrain those affected by the introduction of machines into the workforce, as well as an ethics watchdog to oversee the use of automating technologies modelled on the Human Fertilisation and Embryology Authority, which regulates embryo research. Ministers are also being urged to consider new models of company ownership in the face of increasing returns to asset owners, because rising automation could result in higher profits for those who own companies - at the expense of workers' salaries. Carys Roberts, a research fellow at the IPPR, said: "Some people will get a pay rise while others are trapped in low-pay, low-productivity sectors.

This includes the physical world, where qualitative representations have a long track record of providing human-level reasoning and performance (Forbus 2014), but also in social reasoning (for example, degrees of blame [Tomai and Forbus 2007]). Qualitative representations carve up continuous phenomena into symbolic descriptions that serve as a bridge between perception and cognition, facilitate everyday reasoning and communication, and help ground expert reasoning. We close with some lessons (Forbus, Klenk, and Hinrichs 2009) is on higher-order learned and open problems. In Newell's (1990) timescale proposed that analogy involves the construction of decomposition of cognitive phenomena, conceptual mappings between two structured, relational representations. Thus to the other, based on the correspondences), and a we approximate subsystems whose operations occur score indicating the overall quality of the match. For which one is trying to reason about, and hence inferences example, in Companions constraint checking and are made from base to target by default.

If you AAAI-18 registration information is programs specifically for students, are representing a company, research now available at aaai.org/aaai18, and including the Doctoral Consortium, organization or university and would online registration can be completed at the Student Abstract Program, Lunch like to participate in the job fair, please regonline.com/aaai18. The deadline with a Fellow, and the Volunteer Program, send an email with your contact information for late registration rates is January 5, in addition to the following: to aaai18jobfair@aaai.org Nuance Communications, Inc. is sponsoring and make all the newcomers welcome! the Hilton New Orleans Riverside at The test will be organized, senior women in artificial intelligence. For complete informatio, includes Cynthia Dwork (Harvard / hours of informal mingling. The Job please see aaai.org/Conferences/

It is also important to remember that having a very sharp distinction of AI A rise in real-world applications of AI has stimulated for social good research is not always feasible, and significant interest from the public, media, and policy often unnecessary. While there has been significant makers. Along with this increasing attention has progress, there still exist many major challenges facing come a media-fueled concern about purported negative the design of effective AIbased approaches to deal consequences of AI, which often overlooks the with the difficulties in real-world domains. One of the societal benefits that AI is delivering and can deliver challenges is interpretability since most algorithms for in the near future. To address these concerns, the AI for social good problems need to be used by human symposium on Artificial Intelligence for the Social end users. Second, the lack of access to valuable data Good (AISOC-17) highlighted the benefits that AI can that could be crucial to the development of appropriate bring to society right now. It brought together AI algorithms is yet another challenge. Third, the researchers and researchers, practitioners, experts, data that we get from the real world is often noisy and and policy makers from a wide variety of domains.

For decades AI researchers have built agents that are capable of carrying out tasks that require human-level or human-like intelligence. During this time, questions of how these programs compared in kind to humans have surfaced and led to beneficial interdisciplinary discussions, but conceptual progress has been slower than technological progress. Within the past decade, the term agency has taken on new import as intelligent agents have become a noticeable part of our everyday lives. Research on autonomous vehicles and personal assistants has expanded into private industry with new and increasingly capable products surfacing as a matter of routine. This wider use of AI technologies has raised questions about legal and moral agency at the highest levels of government (National Science and Technology Council 2016) and drawn the interest of other academic disciplines and the general public. Within this context, the notion of an intelligent agent in AI is too coarse and in need of refinement. We suggest that the space of AI agents can be subdivided into classes, where each class is defined by an associated degree of control.

Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent’s understanding of its own plans and goals; its dynamic modeling of its interlocutor’s knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community. Instead, it requires a holistic approach to cognitive modeling of the type we are pursuing in a paradigm called OntoAgent.

The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on Integrated Cognition, but is extended here through a synthesis across three existing cognitive architectures: ACT-R, Sigma, and Soar. The resulting standard model spans key aspects of structure and processing, memory and content, learning, and perception and motor, and highlights loci of architectural agreement as well as disagreement with the consensus while identifying potential areas of remaining incompleteness. The hope is that this work will provide an important step toward engaging the broader community in further development of the standard model of the mind.