Alice M. Mulvehill

Introduction: Why This Book?

It is often said that the expression “May you live in interesting times!” is an ancient Chinese curse. In reality this story is probably apocryphal, but the notion behind it is not: times of great change can produce great opportunities, but also significant personal stress or major societal upheaval. Many things can cause change, but technological innovation is often a facilitator. And one challenge for people during times of change is understanding the realities of these technologies. It is a tough challenge to separate out the hype generated by those who stand to gain financially and otherwise from the truth of what is actually being achieved. News media and social networking sites offer little help; the reporters are often no more versed in the technologies than the people for whom they are writing. The optimists among them see reasons for hope. The pessimists, reasons for fear. And the truth, when it is finally found, usually lies in a more nuanced space, somewhere between the two.

Social Machines: Embracing the Blur

Real life is and must be full of all kinds of social constraint—the very processes from which society arises.

The Limits of Humans

People are amazing, especially when it comes to creativity and adaptability. Many of the cognitive skills that humans regularly use to solve problems, imagine, play, and create still stump computer scientists to even define, let alone program. Humans are, by evolution, amazing pattern recognizers, including our ability to see patterns not yet formed. However, the cognitive machinery that gives us this breadth also limits our ability to concentrate on a single thought deeply through many, many alternatives. While some people (like skilled planners or chess players) are better at generating and keeping track of numerous alternatives, the number of alternatives that most people can efficiently handle is well below the thousands of alternatives that computers can routinely generate and manage. Additionally, cognitive skills vary across people, are dependent on our genetics and experiences, and tend to degrade as we age.

What Computers Can’t Do–Yet

It is also the case that it is very hard to set boundaries on many of the things that we might say a computer is bad at.

The Games We Play

In the medical scenario presented last chapter, you saw that there are differences between what humans can do well and what computers can do well. In this chapter, we will explore this in more detail.

Augmenting Human Capabilities with AI

In order for machines and humans to more closely interact, and develop a more productive, efficient, and trusting symbiotic relationship, the interfaces used to support interaction will need to change.

Social Challenges for the Social Machine

Given that cognitive computing technologies enable the computer to search for, pattern match, parse, synthesize, and interpret volumes of data at a very fast rate, the fear being described by some researchers is that these systems will go beyond helping humans and instead will become the next dominant entity on earth.

Conclusion: Social Machines and the New Future

As we’ve discussed, computers have been moving from passive information providers living on desktop machines to active participants in our social sphere. The infrastructure of the Internet and the World Wide Web allows the proliferation of new applications, and our social interactions with machines provide them with new information that let them learn more about us and our world.

Leveraging Historical Experience to Evaluate and Adapt Courses of Action

In many planning domains there may be multiple potential solutions to a given problem. Each solution may require different resources, involve more or less risk, and result in desirable or undesirable effects. Reuse of historical plans is a strategy that can be employed to solve planning problems. While the retrieval of similar historical plans can be facilitated with sophisticated annotation and search engines, evaluating the usefulness of historical plans tends to be subjective, is context sensitive, and difficult when no single historical plan can be used to develop a new plan. Course of action (COA) evaluation is a method that can be used to compare a set of alternative solutions. An agent-based tool called MICCA (Mixed-Initiative Course of Action Critic Advisors) can aid human operators or software agents in evaluating and adapting historical plans for use in achieving one or more objectives in some current or future hypothetical world state. In this paper we introduce MICCA and describe how case base reasoning (CBR) and generative planning techniques are utilized to support COA evaluation and adaptation.

ForMAT and Parka: A Technology Integration Experiment and Beyond

This report describes a Technology Integration Experiment (TIE) between the University of Maryland and The MITRE Corporation which was undertaken as part of the (D)Arpa/Rome Laboratory Planning Initiative (ARPI). This work led to an integration of the UM Parka-DB tool into the MITRE ForMAT transportation planning tool.