DARPA: Superior AI

Defence Advanced Research Projects Agency
Superior Artificial Intelligence (AI)
Phase 1, Budget Period: 09/12/2016-09/11/2017
Phase 2, Budget Period: 09/12/2017-09/11/2018

DARPA Site Visit March 20, 2018

The goal of the project is to substantially advance the state of the art in artificial intelligence and, as a major necessity of achieving this goal, increase our understanding of the mechanisms that underlie biological intelligence. We will avoid methods and systems that may appear intelligent, but are, in fact, hard coded and not intelligent. Many current systems give the appearance of intelligence, however, these systems cannot adapt to changing circumstances or truly interpret and make decisions based on input. Systems that can do these things represent the future of computer technology.

This work builds energy aware neurocomputers to solve problems that cannot be addressed by today's AI technology. Successful completion of this work allows going beyond the state-of-the-art AI, which is represented by Deep Learning (DL). In the overall problem setting of DL, resource constraints are often ignored, or have just a secondary role. DL typically requires huge amount of data/ time/ parameters/ energy/ computational power, which are not readily available in various scenarios. Target applications include rapid response to emergency situations based on incomplete and disparate information, supporting graceful degradation in the case of physical damage or resource constraints, and real time speech recognition in noisy and cluttered background.

The project provides an approach to machine learning using brain-inspired spiking neural networks (SNNs). In Phase 2 we develop a unified software platform BindsNET, which is capable of pattern recognition on images and videos using unsupervised learning techniques such as spike-time dependent plasticity (STDP) and self-organizing maps (SOMs).

The project consists of four primary tasks with the underlying goal to build superior AI:

  1. Energy aware computing for improved energy utilization.
  2. Biologically inspired efficient learning algorithms.
  3. Architectures supporting energy aware computing and powerful memories.
  4. Hardware implementation of energy aware computing.