DARPA pushing a new field called the Machine Reading Program (MRP) that combines natural language processing and understanding, knowledge-based systems, knowledge acquisition, and machine learning. The goal is to create an automated “Reading System” that serves as a bridge
between knowledge contained in natural texts and the formal reasoning systems that need such knowledge. This is similar to the Cognitive Computing initiative at DARPA, which also has the goal to help humans with executive assistant style of capabilities.

So, any resulting technology breakthroughs in this field will have a direct impact on the business intelligence industry. Right now, business intelligence is focused on the structured data stored in (generally) relational databases. There are some forward thinking companies using natural language processing, but the field as a whole is still focused on pulling data from a database using SQL. The MRP will plug into that messy word of unstructured text – documents, emails, news feeds, blog posts, IM messages, and Twitter streams.

Business intelligence draws from other industries to provide the tools that can support a comprehensive and actionable view of the business landscape. Words like “Dashboard” and “Heads ups Display” did not originate in the business intelligence world, but they sure are a part of the vocabulary now. It is probably only a matter of time before the same is true for MRP.

Artificial intelligence (AI) systems continue to grow in use within the Department of
Defense as there is a consistent emphasis on using high technology as a strategic
advantage and reducing reliance on humans, both to save money and save lives.
Situation awareness, diagnostics, prognostics, planning, logistics – all are areas in
which AI systems are used or applicable. A great deal of the militarily relevant
knowledge that these systems need is presently expressed as natural-language text.
This knowledge may range from the local political and militant groups to infrastructure
and food supplies. The necessary information is available, but rarely in a form that can
be used by current AI systems.

For example, the U.S. military frequently faces impediments to 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,
conference agendas, etc. This information is often available on the World Wide 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.

Looking beyond intelligence applications, there is a wealth of information contained in
operations plans, charts from a commander’s regular update briefings, after-action
reports at all military echelons, lessons learned analyses, and similar textual documents
that are exploitable primarily by humans today. Diagnostic and trouble reports from
fielded equipment contain a wealth of information that could be used for failure analysis
and predictive maintenance, but only the simplest of analyses – often based solely on
document metadata or term extraction – are automated today.

DARPA’s envisioned Machine Reading Program (MRP) seeks to address the shortfalls
discussed above. Currently, nearly all successful AI systems succeed because they
possess sufficient consistent, relevant knowledge about a problem domain. However,
since large amounts of knowledge are almost always needed for this success, AI
systems require this knowledge to be expressed in a logical formalism of some type.
Manually encoding such knowledge can become prohibitively expensive. Since text is,
by far, 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.

The intent of the MRP described in this BAA is to enable just such a revolution. That is,
the goal of the MRP is to create an automated Reading System that serves as a bridge
between knowledge contained in natural texts and the formal reasoning systems that
need such knowledge. However, rather than choosing a particular mechanism for this
bridge, such as formalizing knowledge contained in natural text into a specific
representation, the MRP focuses instead on applying the knowledge productively. As a
consequence of this performance-based approach to the natural-language
understanding problem, the MRP will only assess reading capability by its impact on
some performance tasks. A relevant task could be a human performance task where a
task-specific interface is employed to provide the user access to automatically read text,
or it could be performance by a domain-specific AI system that requires information
inferred by the Reading System from source texts. In either case, the goal is not the
capture of (some or all of) the knowledge contained in a natural-language corpus in a
general-purpose logical format; rather, the goal is the successful execution of a
performance task in which knowledge contained in natural language is one essential
component.

There is a long history of work in natural language understanding, knowledge-based
systems, knowledge acquisition, and machine learning. All are areas that are
potentially relevant to the MRP.

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