Artificial intelligence. We've been reading and watching science fiction with walking, talking robots for nearly a century. Researchers have been tinkering with it for decades. Have we come any closer to android production factories? Not quite. But the CALO project, under the direction of SRI International, is looking at making headway in basic intelligence for widely used computer software.

CALO, or Cognitive Assistant that Learns and Organizes, is a very ambitious collaboration between more than twenty different organizations. "The goal of the project is to create cognitive software systems, that is, systems that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise," states SRI's CALO information page.

CALO brings together many experts from different fields of artificial intelligence, like machine learning, natural language processing, and Semantic Web technologies. Groups work on a different piece of CALO, which will be part of the whole functionality.

The project is being funded by the Defense Advanced Research Projects Agency (DARPA) under its Perceptive Assistant that Learns (PAL) program. The PAL program is expected to spawn innovative ideas that bring new science, fundamental approaches to current problems, and algorithms and tools and yield technology of significant value to the military. SRI was awarded the first two phases of a five-year contract to develop a personalized cognitive assistant.

While it’s not the artificial intelligence made popular by science-fiction writers like Dick and Asimov, CALO looks to be genuinely helpful to its targeted end-users, government agencies and possibly business. The PAL project is aimed at military use, but future packages or derivatives of CALO could be very helpful to business professionals that are constantly on the move by helping them schedule meetings and prioritize information.

The package can assist users in this way by analyzing patterns in information such as e-mail correspondence. Information importance can be learned by CALO so that the data is pushed to the top of the list judged by which projects and people it comes from.

The system's speech recognition abilities can also put data prioritization to use in meetings. The software can prioritize the data it gathers in regards to the user's projects and create lists and make appointments with involved parties.

One of the strengths of the system is that it can learn the needs of individuals through their habits and interactions, much like a personal assistant of the human kind. Rather than offering canned advice and only acting on human intervention, the software can make assumptions about a user's needs and plan accordingly. It will even be able to reschedule meetings if participants become unable to attend. Whether or not it would inform other attendees was not specified, but imagine if the system was interconnected to other learning systems on a network, that it could very well inform those assistants, who could in-turn inform their users of the change.

In an intranet situation, such as for a large business or the given military agency application, the system would be incredibly beneficial in that rather than depending on humans, who sometimes aren't at their desks or transpose numbers in a date or time, the software agents could work together seamlessly and accurately. Since the system is a learning system, mistakes are not probably out of the question, but replacing human error and time delay may outweigh the occasional mis-prioritized e-mail -- which the system could learn was mis-prioritized, reducing the likelihood of a similar mistake in the future.

One of the most challenging endeavors for the project is creating a consistent data system that CALO can use for decision making. Gathered data will likely be very disjointed and uncertain. To use this data, various members of the project are working on a probability consistency engine. This engine brings together two of the traditional approaches to artificial intelligence: logic and probability. Probability will be good for finding related data in the chaotic data the software gathers, while logic will better handle the meaning of the data.

Adam Cheyer, program director of the artificial-intelligence center at SRI says of the project, "What’s different and has never been done before in this way is the truly integrated approach of bringing all of these technologies and all of these capabilities into a single system. It takes a system of this size to give you something that can understand and organize so much information."

While the CALO project will probably not be able to respond to a user's mood, play them in a game of poker, or drive a car, the ambitious undertaking promises evolution in the artificial intelligence field by combining so many different types of systems, methods and applications. Some of the key features for a true AI are in its ability to learn from many different sources of data, adapt in adverse situations and interact with humans on a level that we are comfortable with. While not housed in an attractive mechanical body, CALO could show us the first steps in unified systems capable of such performance.