Cooney goes on, "With Fun LoL DARPA is looking for information about mathematical frameworks, architectures, and methods that would help answer questions such as: What are the number of examples necessary for training to achieve a given accuracy performance? What are important trade-offs and their implications? How close is the expected achievable performance of a learning algorithm compared to what can be achieved at the limit? What are the effects of noise and error in the training data? What are the potential gains possible due to the statistical structure of the model generating the data?"

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? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined.

Bookmark this page by right-clicking here and choosing "Add to Favorites" The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is requesting information on research related to the investigation and characterization of fundamental limits of machine learning with supportive theoretical foundations. Although the main focus is on machine learning, extensions and implications for human-machine systems are also of interest. The notion of fundamental limits here means that the conclusion about achievable performance limits should hold independent of specific learning methods or algorithms.