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As conflicts become more complex and uncertain in the 21st century, quick pivots to new technologies will become increasingly important. The starting point for this rapid fielding must begin with more frequent, and more relational, lower level warfighter-technologist interaction.

The current system does this nominally, but the relationships usually are far removed from the waterfront or the front lines where many user-generated solutions could be discovered. Science advisers, often from places such as the U.S. Naval Research Laboratory or the Navy’s Space and Naval Warfare Systems Command (SPAWAR), are stationed regularly at critical commands throughout the services. They provide a useful link between incredibly intelligent technologists and seasoned veterans.

Unfortunately, many of the interactions are only with senior officers and not the muddy boot or deckplate warriors who know best what is giving them day-to-day headaches. Additionally, many of the officers dedicated to acquisition programs are years removed from being actual operators. With the accelerating pace of technological change, even a year away from operational status can leave noticeable knowledge gaps.

Experience certainly is valuable, but after spending a long period of time in the same profession, a person’s creativity sometimes is lost. Even known innovators can get stuck in ruts when not exposed to different views of the world over sustained periods of time. Furthermore, as technology evolves, the quickest adopters usually are the younger generations. They often have better insights into how to integrate emerging, generationally ubiquitous trends to their professions than do their seniors who relied on legacy systems.

The Defense Advanced Research Projects Agency (DARPA) is searching for companies to participate in its recently launched Probabilistic Programming for Advanced Machine Learning (PPAML) program. Probabilistic programming is an innovative approach to manage the uncertain information that computers use to understand data, manage results and infer insights. The PPAML seeks to increase the number of people who can successfully build machine learning applications as well as boost the effectiveness of current machine learning experts. In addition, the project will focus on creating more economical, robust and powerful applications that require less data to produce more accurate results. “Our goal is that future machine learning projects won’t require people to know everything about both the domain of interest and machine learning to build useful machine learning applications,” Kathleen Fisher, DARPA program manager, says. The three-phase program is scheduled to run for 46 months beginning this year and continuing to 2017. The agency is hosting a Proposers’ Day at the Executive Conference Center, Arlington, Virginia, on April 10, 2013, to familiarize potential participants with the PPAML’s technical objectives. Interested organizations must register by 5 p.m. on April 5, 2013. A DARPA special notice document describing the specific capabilities the agency is interested in is available online.