Software Social Organisms: Implications for Measuring AI Progress

solve planning and scheduling problems that are beyond what unaided people can accomplish, sift through mountains of data (both structured and unstructured) to help us find answers, and robustly translate speech and handwriting into text. But these systems are carefully crafted for specific purposes, created and maintained by highly trained personnel who are experts in artificial intelligence and machine learning. There has been much less progress on building general-purpose AI systems, which could be trained and tasked to handle multiple jobs. Indeed, in my experience, today’s general-purpose AI systems tend to skate a very narrow line between catatonia and attention deficit disorder. People and other mammals, by contrast, are not like that. Consider dogs. A dog can be taught to do tasks like shaking hands, herding sheep, guarding a perimeter, and helping a blind person maneuver through the world. Instructing dogs can be done by people who don’t have privileged access to the internals of their minds. Dogs don’t blue screen. What if AI systems were as robust, trainable, and taskable as dogs? That would be a revolution in artificial intelligence. In my group’s research on the companion cognitive architecture (Forbus et al. 2009), we are working toward such a revolution. Our approach is to try to build software social organisms. By that we mean four things: First, companions should be able to work with people using natural interaction modalities. Our focus so far has been on natural language (for example, learning by reading [Forbus et al. 2007; Barbella and Forbus 2011]) and sketch understanding (Forbus et al. 2011). Second, companions should be able to learn and adapt over extended periods of time. This includes formulating their own learning goals and pursuing them, in order to improve themselves. Third, companions should be able to maintain themselves. This does not mean a 24-hour, 7-day-a-week operation — even people need to sleep, to consolidate learning. But Articles