The Borg lives: BBN gets $30 million for artificial intelligence wizard

The Borg Queen --

Developing a an artificial intelligence system that can read, learn and develop knowledge about all manner of digital material in a quick, cost effective way sounds like a bit of a pipe dream.  But those are some of the lofty items that are now on BBN Technology's plate as the firm this week got $29.7 million from the Air Force to develop a prototype machine reading system that transforms prose into knowledge that can be interpreted by an artificial intelligence application.

The prototype is part of the Defense Advanced Research Projects Agency's Machine Reading Program (MRP) that wants to develop systems that can capture knowledge from naturally occurring text and transform it into the formal representations used by AI reasoning systems.

The idea is that such an intelligent learning system could gather and analyze information from the Web such as international technological advances or plans and rhetoric of political organizations and unleash a wide variety of new military and civilian AI applications from intelligent bots to personal tutors according to DARPA.

As digitized text from library books world wide becomes available, new avenues of cultural awareness and historical research will be enabled. With techniques for effectively handling the incompatibilities between natural language and the language of formal inference, a system could, in principal, be constructed that maps between natural and formal languages in any subject domain, DARPA said.

"Strength is irrelevant, resistance is futile. We wish to improve ourselves. We will add your biological and technological distinctiveness to our own. Your culture will adapt to service ours." -The Borg

    DARPA said that nearly all successful AI systems today succeed because they possess sufficient consistent, relevant knowledge about a given problem. However, since large amounts of knowledge are almost always needed for this success, AI systems require this knowledge to be expressed in a logical formula of some type. Manually encoding such knowledge can become prohibitively expensive. Since text is the most flexible and ubiquitous medium used to capture knowledge about the diverse areas of human interest, it is natural to consider making it feasible for AI reasoning systems to employ this vast store of human knowledge. As AI systems currently cannot use such knowledge, it would be revolutionary if technology could be developed to bridge this gap, DARPA said.

   The main problem is reading and understanding. The necessary information is available, but rarely in a form that can be used by current AI systems, DARPA said.

   For example, the military frequently faces problems with stability and reconstruction operations in a new location due to the lack of understanding of the local situation. Similarly, strategic assessment of a foreign nation's science and technology base involves the continuous assessment of technical articles, bibliographies and conference agendas. This information is often available on the Web, and some tools to assist this analysis are available, but the process would be significantly enhanced by a system that could directly analyze the information found in these text sources.

   The same reasoning could be equally valuable if applied to other types of open-source intelligence analysis, including assessing military readiness and posturing; political speeches, actions, and more obscure messages; economic trends and sentiments; and propaganda from terrorist groups and even their hidden web-based communications.

DARPA lists some the technological goals of the new AI system as follows:

-Create a universal Reading System that can take any natural text and any reasoning context as input and can effectively apply the knowledge contained in that text in that reasoning context.

-Combine natural language and AI reasoning: Combine natural language and AI reasoning into new technology that provides the benefits of both.

-Develop a general-purpose text-reading reading system that can be used with any number of domain-specific reasoning systems.

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