IBM gets $16 million to bolster its brain-on-a-chip technology

The DARPA SyNAPSE project seeks to build systems that rapidly understand tons of data

The quest to mimic the best parts of human brain function on a highly intelligent computer to decypher tons of data quickly is heating up. 

IBM this week got $16.1 million to kick up its part of a Defense Advanced Research Projects Agency research program aimed at rapidly and efficiently put brain-like senses into actual hardware and software so that computers can process and understand data more rapidly.

IBM has now gotten $21 million to work on the program known as Systems of neuromorphic adaptive plastic scalable electronics (SyNAPSE) which includes researchers from  HRL Laboratories, which got $16.2 million in Oct. 2008, and others such as HP.

According to DARPA, the SyNAPSE program will create useful, intelligent machines.  In DARPA language: the agency is looking to develop electronic neuromorphic machine technology that is scalable to biological levels.  The goal is to develop systems capable of analyzing vast amounts of data from many sources in the blink of an eye, letting the military or civilian businesses make rapid decisions in time to have a significant impact on a given problem or situation.

According to DARPA, programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment.  In contrast, biological neural systems such as human brains, autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations, DARPA stated. 

As compared to biological systems for example, today's programmable machines are less efficient by a factor of one million to one billion in complex, real-world environments.  The SyNAPSE program seeks to break the programmable machine archetype and define a new path forward, DARPA stated.

DARPA goes on to state that realizing this ambitious goal will require the collaboration of numerous technical disciplines such as computational neuroscience, artificial neural networks, large-scale computation, neuromorphic VLSI, information science, cognitive science, materials science, unconventional nanometer-scale electronics, and CMOS design and fabrication. 

The agency ultimate envisions work in four key areas:

-Hardware implementation will likely include CMOS devices, novel synaptic components, and combinations of hard-wired and programmable/virtual connectivity. These will support critical information processing techniques observed in biological systems, such as spike encoding and spike time dependent plasticity.

-Architectures will support critical structures and functions observed in biological systems such as connectivity, hierarchical organization, core component circuitry, competitive self-organization, and modulatory/reinforcement systems. As in biological systems, processing will necessarily be maximally distributed, nonlinear, and inherently noise- and defect-tolerant.

-Large scale digital simulations of circuits and systems will be used to prove component and whole system functionality and to inform overall system development in advance of neuromorphic hardware implementation.

-Environments will be evolving, virtual platforms for the training, evaluation and benchmarking of intelligent machines in various aspects of perception, cognition, and response.

While SyNAPSE basically seeks to replicate human brain function, DARPA has another project that seeks to develop an artificial intelligence system that can read, learn and develop knowledge about all manner of digital material in a quick, cost effective way.  BBN Technology recently got $29.7 million to develop a prototype machine reading system that transforms prose into knowledge that can be interpreted by an artificial intelligence application.

The prototype is part of DARPA's Machine Reading Program (MRP) that wants to develop systems that can capture knowledge from naturally occurring text and transform it into the formal representations used by AI reasoning systems.

The idea is that such an intelligent learning system could gather and analyze information from the Web such as international technological advances or plans and rhetoric of political organizations and unleash a wide variety of new military and civilian AI applications from intelligent bots to personal tutors according to DARPA.

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