Defense Advanced Research Projects AgencyTagged Content List

Artificial Intelligence and Human-Computer Symbiosis Technologies

Technology to facilitate more intuitive interactions between humans and machines

Showing 9 results for AI + Math RSS
Advanced materials are increasingly embodying counterintuitive properties, such as extreme strength and super lightness, while additive manufacturing and other new technologies are vastly improving the ability to fashion these novel materials into shapes that would previously have been extremely costly or even impossible to create. Generating new designs that fully exploit these properties, however, has proven extremely challenging.
It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so?
Advances in artificial intelligence (AI) are making virtual and robotic assistants increasingly capable in performing complex tasks. For these “smart” machines to be considered safe and trustworthy collaborators with human partners, however, robots must be able to quickly assess a given situation and apply human social norms. Such norms are intuitively obvious to most people—for example, the result of growing up in a society where subtle or not-so-subtle cues are provided from childhood about how to appropriately behave in a group setting or respond to interpersonal situations. But teaching those rules to robots is a novel challenge.
June 14, 2016,
DARPA Conference Center
DARPA’s Information Innovation Office (I2O) is hosting a Proposers Day to provide information to potential proposers on the objectives of the upcoming Data-Driven Discovery of Models program. The program aims to develop semi-automated model discovery systems that enable non-expert users (i.e., users with subject matter expertise but no data science background) to create empirical models of real, complex processes. DARPA believes such a capability would increase the productivity of data scientists, and enable many more users to make predictions from data.
| AI | Complexity | Data | Math |
Machine learning has shown remarkable success across many application areas in recent years, leveraging advances in computing power and the availability of large sets of training data. It provides a tremendous opportunity to deploy data-driven systems in more complex and interactive tasks including personalized autonomy, agile robotics, self-driving vehicles, and smart cities. Despite dramatic progress, the machine learning community still lacks an understanding of the trade-offs and mathematical limitations of related technologies for a given domain, problem, or dataset.