For more than five decades, DARPA has been a leader in generating groundbreaking research and development (R&D) that facilitated the advancement and application of rule-based and statistical-learning based AI technologies. Today, DARPA continues to lead innovation in AI research as it funds a broad portfolio of R&D programs, ranging from basic research to advanced technology development. DARPA believes this future, where systems are capable of acquiring new knowledge through generative contextual and explanatory models, will be realized upon the development and application of “Third Wave” AI technologies.
DARPA announced in September 2018 a multi-year investment of more than $2 billion in new and existing programs called the “AI Next” campaign. Key areas of the campaign include automating critical DoD business processes, such as security clearance vetting or accrediting software systems for operational deployment; improving the robustness and reliability of AI systems; enhancing the security and resiliency of machine learning and AI technologies; reducing power, data, and performance inefficiencies; and pioneering the next generation of AI algorithms and applications, such as “explainability” and common sense reasoning.
AI Next builds on DARPA‘s five decades of AI technology creation to define and to shape the future, always with the Department’s hardest problems in mind. Accordingly, DARPA will create powerful capabilities for the DoD by attending specifically to the following areas:
In addition to new and existing DARPA research, a key component of the campaign will be DARPA’s Artificial Intelligence Exploration (AIE) program, which was first announced in July 2018 and renewed in August 2019. AIE constitutes a series of high-risk, high payoff projects where researchers work to establish the feasibility of new AI concepts within 18 months of award. DARPA uses streamlined contracting procedures and funding mechanisms to move these efforts from proposal to project kick-off within three months of an opportunity announcement. Beginning in August 2019, forthcoming AIE Opportunities will be published on the FedBizOpps website under Program Announcement DARPA-PA-19-03; older AIE Opportunities were listed under DARPA-PA-18-02.
The advance of technology has evolved the roles of humans and machines in conflict from direct confrontations between humans to engagements mediated by machines. Originally, humans engaged in primitive forms of combat. With the advent of the industrial era, however, humans recognized that machines could greatly enhance their warfighting capabilities. Networks then enabled teleoperation, which eventually proved vulnerable to electronic attack and subject to constraint due to long signal propagation distances and times. The next stage in warfare will involve more capable autonomous systems, but before we can allow such machines to supplement human warfighters, they must achieve far greater levels of intelligence.
Traditionally, we have designed machines to handle well-defined, high-volume or high-speed tasks, freeing humans to focus on problems of ever-increasing complexity. In the 1950s and 1960s, early computers were automating tedious or laborious tasks. It was during this era that scientists realized it was possible to simulate human intelligence and the field of artificial intelligence (AI) was born. AI would be the means for enabling computers to solve problems and perform functions that would ordinarily require a human intellect.
Early work in AI emphasized handcrafted knowledge, and computer scientists constructed so-called expert systems that captured the specialized knowledge of experts in rules that the system could then apply to situations of interest. Such “first wave” AI technologies were quite successful – tax preparation software is a good example of an expert system – but the need to handcraft rules is costly and time-consuming and therefore limits the applicability of rules-based AI.
The past few years have seen an explosion of interest in a sub-field of AI dubbed machine learning that applies statistical and probabilistic methods to large data sets to create generalized representations that can be applied to future samples. Foremost among these approaches are deep learning (artificial) neural networks that can be trained to perform a variety of classification and prediction tasks when adequate historical data is available. Therein lies the rub, however, as the task of collecting, labelling, and vetting data on which to train such “second wave” AI techniques is prohibitively costly and time-consuming.
DARPA envisions a future in which machines are more than just tools that execute human-programmed rules or generalize from human-curated data sets. Rather, the machines DARPA envisions will function more as colleagues than as tools. Towards this end, DARPA research and development in human-machine symbiosis sets a goal to partner with machines. Enabling computing systems in this manner is of critical importance because sensor, information, and communication systems generate data at rates beyond which humans can assimilate, understand, and act. Incorporating these technologies in military systems that collaborate with warfighters will facilitate better decisions in complex, time-critical, battlefield environments; enable a shared understanding of massive, incomplete, and contradictory information; and empower unmanned systems to perform critical missions safely and with high degrees of autonomy. DARPA is focusing its investments on a third wave of AI that brings forth machines that understand and reason in context.
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