In 2011, IBM's Watson competed on the game show Jeopardy! winning against the two best players of all time, Brad Rutter and Ken Jennings (Ferrucci et al. 2010). Since this demonstration, IBM has expanded its research program in artificial intelligence (AI), including the areas of natural language processing and machine learning (Kelly and Hamm 2013). Ultimately, IBM sees the opportunity to develop cognitive computing -a unified and universal platform for computational intelligence (Modha et al. 2011). But how might cognitive computing work in real environmentsand in concert with people?In 2013, our group within IBM Research started to explore how to embed cognitive computing in physical environments. We built a Cognitive Environments Laboratory (CEL) (see figure 1) as a living lab to explore how people and cognitive computing come together.Our effort focuses not only on the physical and computational substrate, but also on the users' experience. We envision a fluid and natural interaction that extends through time across multiple environments (office, meeting room, living room, car, mobile). In this view, cognitive computing systems are always on and available to engage with people in the environment. The system appears to follow individual users, or groups of users, as they change environments, seamlessly connecting the users to available input and output devices and extending their reach beyond their own cognitive and sensory abilities.We call this symbiotic cognitive computing: computation that takes place when people and intelligent agents come together in a physical space to interact with one another. The intelligent agents use a computational substrate of "cogs" for visual object recognition, natural language parsing, probabilistic decision support, and other functions. the book The Society of Mind where Marvin Minsky likened agents to "cogs of great machines" (Minsky 1988). These cogs are available to intelligent agents through programmatic interfaces and to human participants through user interfaces.Our long-term goal is to produce a physical and computational environment that measurably improves the performance of groups on key tasks requiring large amounts of data and significant mental effort, such as information discovery, situational assessment, product design, and strategic decision making. To date, we have focused specifically on building a cognitive environment, a physical space embedded with cognitive computing systems, to support business meetings for strategic decision making. Other applications include corporate meetings exploring potential mergers and acquisitions, executive meetings on whether to purchase oil fields, and utility company meetings to address electrical grid outages. These meetings often bring together a group of participants with varied roles, skills, expertise, and points of view. They involve making decisions with a large number of high-impact choices that need to be evaluated on multiple dimensions taking into account large amounts of structured and unstructured data.While...

The difficulty of developing and deploying commercial web applications increases as the number of technologies they use increases and as the interactions between these technologies become more complex. This paper describes a way to avoid this increasing complexity by re-examining the basic requirements of web applications. Our approach is to first separate client concerns from server concerns, and then to reduce the interaction between client and server to its most elemental: parameter passing. We define a simplified programming model for form-based web applications and we use XForms and a subset of J2EE as enabling technologies. We describe our implementation of an MVC-based application builder for this model, which automatically generates the code needed to marshal input and output data between clients and servers. This marshalling uses type checking and other forms of validation on both clients and servers. We also show how our programming model and application builder support the customization of web applications for different execution targets, including, for example, different client devices.

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Under what conditions can a simple polygon be tiled by parallelograms? In this paper we give matching necessary and sufficient conditions on the polygon to be tilable and characterize the set of possible tilings. We also provide an etficient algorithm for constructing a tiling.

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