IBM Debuts Brainlike Cognitive Computer | WPN World People News

Today IBM debuts the world's first cognitive computer chips, which I called cognizers in my book "Cognizers--Neural Networks and Machines that Think" (John Wiley & Sons, October 1988). By replicating the functions of neurons and synapses in the human brain, IBM has crafted the world's first chips aimed at taming the overwhelming wealth of information in multiple sensor data-streams by learning to adapt like human brains. The chips have already beat humans at the game "Pong" and promise to impart humanlike abilities of all sorts of future cognitive computers.

Principal investigator Dharmendra Modha in front of the brain-wall at IBM Research, where the operation of the neurons and synapses in IBM's cognitive computers are visualized.

The traditional computers that we all use today are actually based on an antiquated design first proposed by John von Neumann in 1945. The so-called von Neumann architecture artificially separates programming from memory--putting the processor on one core and its memory on others. Unfortunately, this division of labor makes it incredibly difficult to combine the knowledge gleaned from multiple data streams--the No. 1 unsolved problem facing computer systems today. Cognitive computers, on the other hand, replicate the way the human brain distributes processing and memory among the same circuitry, which in the brain is composed of neurons and synapses.

"Our chip represents a sharp departure in architecture from the tradition von Neumann computers," said Dharmendra Modha, project leader for IBM Research. "All memory functions are integrated with program functions, creating a kind of social network of neurons with all their software stored in synapses."

Neurons are tiny cells that by their very nature integrate inputs from multiple sources, which in the brain are the other neurons, of which there are billions. The brain uses its neurons together to solve problems by integrating the pulses received over dendrites from other neurons until a threshold is exceeded, at which point it fires a pulse down its output axon, then resets and starts integrating anew. Firing rates are typically 10 Hz, with power only being consumed when a pulse is actually produced, thus enabling ultra-low-power operation for brainlike computers even though they using billions of neurons.

The other major component of brains are and trillions of synapses that add weight to the pulses emitted by firing neurons. Even though a neuron might be connected to thousands of nearby neighbors, each of these connections is enhanced or mitigated by a synapse, which holds the memories of the brain. Often used connections between two neurons will grow a synaptic connection that is large and fast, thereby enabling it to quickly contribute to pattern recognition tasks--such as recognizing your friends’ faces. Seldom used connections, on the other hand, are small and weak, thereby requiring extra time to recognize patterns under their control, such as the outline of the new 2012 Chrysler 200 that you have only seen a few times.

IBM claims this type of architecture has wide and deep applications that can easily make sense of today's increasingly common multiple simultaneous sensor data streams. For instance, a cognitive computer could easily monitor thousands of sensor inputs measuring the ocean's temperature, pressure, wave height, acoustics and tide, then issue super-accurate tsunami warnings. In grocery stores, a sensor-studded stocking glove could monitor the color, smell, texture and temperature of produce as it is being put on store shelves, and immediately flag any spoiled or contaminated items.

The new cognitive computer chips are being created under a DARPA (Defense Advanced Research Project Agency) program called SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics). IBM and its university research partners just received a $21 million infusion of cash to continue with the project. Already the research partners have crafted a simulation of a complete cat brain--called Blue Matter--and more recently have mapped the entire wiring diagram of a monkey brain. Using the latest neurological science to craft algorithms to accurately model these brain functions, then they used nanotechnology to implement the core architecture of its cognitive computer chips by implementing its supercomputer model in nanoscale semiconductors. The ultimate goal of the project is to build an artificial brain similar in size, capability and power consumption to a human brain.

Source : Smarter Technology