In this article I argue that achieving human-level AI is equivalent to learning how to create sufficiently smart software social organisms. This implies that no single test will be sufficient to measure progress. Instead, evaluations should be organized around showing increasing abilities to participate in our culture, as apprentices. This provides multiple dimensions within which progress can be measured, including how well different interaction modalities can be used, what range of domains can be tackled, what human-normed levels of knowledge they are able to acquire, as well as others. I begin by motivating the idea of software social organisms, drawing on ideas from other areas of cognitive science, and provide an analysis of the substrate capabilities that are needed in social organisms in terms closer to what is needed for computational modeling. Finally, the implications for evaluation are discussed.

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Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we propose the outlines of an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which both the example of action and its justification influence the moral intuitions of others. The signals an agent provides to others, and the signals received from others, help each agent determine which others are suitable cooperative partners, and which are likely to defect. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

After close to twenty years at IBM, where he began as an IBM Fellow and Chief Architect for the SOA Foundation, Rob High has developed a number of core technologies that back Big Blue's enterprise systems, including the suite of tools behind IBM WebSphere, and more recently, those that support the wide-ranging ambitions of the Watson cognitive computing platform. Although High gave the second day keynote this afternoon at the GPU Technology Conference, there was no mention of accelerated computing. Interestingly, while the talk was about software, specifically the machine learning behind Watson, there was also very little about the software underpinnings. Disappointing as this might have been for the hardware-oriented folks in the crowd hoping to understand how OpenPower Foundation-spurred efforts using GPU-backed, Power-based systems make Watson's gears turn (we can fairly assume that is the case), High did provide a summary of Watson's evolution since 2011 as well as a look ahead at what the Watson research teams are looking to next. High says he is frequently asked what about the differences between AI and cognitive computing, noting that while they aren't much different conceptually, the goal of the Watson team is far more about making humans better at what they do than recreating the human brain in machine form.

She continues, "He noted that the biggest benefit will be on the energy-efficiency side. This is a key aspect to making cognitive systems of the future a reality because all of the extremely sophisticated processing requires energy. Because many of these systems may be untethered, that energy will have to be carefully meted out. In addition to processing efficiency, the software must be efficient. 'There are lots of things we can think of that would change the way software gets constructed and where the time and energy is spent in your average computer system,' Rowen said. 'Just think about what we have done with the last 50 years of computing. It's not as if we take the old applications and run them eight orders of magnitude faster. What we do is come up with new kinds of applications, which are really new levels of abstraction.'"

All of these are concepts aim to make human types of problems computable, whether it be a self-driving car, a health care-providing robot, or a walking and talking assistant robot for the home or office. R&D teams around the world are working to create a whole new world of machines more intelligent than humans. Designing systems as complex as the human brain -- which is still largely a mystery -- is no small task. For example, tomorrow's bleeding edge cars will be the ultimate in efficient system-level design sophistication given the complexity, integration, interdependencies, safety, convenience and comfort required on so many levels. "It fundamentally changes the paradigm and even what we expect of processors to be doing," said Chris Rowen, a Cadence fellow and CTO of the IP Group.

Solutions architects are the experts on our team at understanding and implementing Watson technology. They have developed this expertise by providing technical support to our partners through multiple mediums. Through their work, they have a deep understanding and point of view about the Watson APIs, but also the cognitive landscape at large. I interviewed solutions architect, Chris Ackerson on his thoughts on Watson and cognitive computing, as well as his specific tips and resources. Where do you see the Watson APIs growing in 2016 and beyond?

Machine learning just can't be left out of major tech keynotes and conferences these days. And while Tay continues to be a topic of funny discussions rather than the serious ones it was intended to be, Microsoft is announcing a few updates to its suite of Machine learning tools. First up, is the rebranding. If you are a developer, and a hard-core one at that, you'd remember Microsoft's machine tools being tabbed under something called'Project Oxford'. However, it isn't just the re-christening which is happening here.

Many people and companies seem to think of "cognitive computing" as an area separate from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, "We have no desire to win Jeopardy," an allusion, of course, to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it's not an exotic science project. In fact, I'd argue that cognitive computing is a logical extension of analytics work.

Developing AI programs to master board games drove progress in the field -- including advances in techniques for search algorithms and evaluation functions. However, research in such "clean" game domains did not really address most real-life tasks that have a "messy" nature. Real-world tasks typically pose additional challenges, such as ambiguous, hidden or missing data, and "non-stationarity," meaning that the task can change unexpectedly over time. IBM is now turning our attention to solving these real-world challenges. IBM Research scientists Murray Campbell, Gerry Tesauro, and Eric Brown discuss "Extending Game-Based AI Research into the Wild."