IBM's cognitive computing chip functions like a human brain, heralds our demise
engadget.com
“IBM has now taken another step toward human-like artificial intelligence, with an experimental chip designed to function like a real brain. Developed as part of a DARPA project called SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics), IBM’s so-called “neurosynaptic computing chip” features a silicon core capable of digitally replicating the brain’s neurons, synapses and axons.” Read more…
(source: IBM Research, via Engadget)
Partnerships Between Humans and Machines Will Define the New Era of Computing « A Smarter Planet Blog
asmarterplanet.com
Dr. James Hendler, Professor and Computer Science Department Head, Rensselaer Polytechnic Institute
By Dr. James Hendler
Every single student in the Department of Computer Science here at Rensselaer Polytechnic Institute has the potential to revolutionize computing. But with the arrival of Watson at Rensselaer, they’re even better positioned to do so.
Watson has caused the researchers in my field of artificial intelligence (AI) to rethink some of our basic assumptions. Watson’s cognitive computing is a breakthrough technology, and it’s really amazing to be here at Rensselaer, where we will be the first university to get our hands on this amazing system.
With 90 percent of the world’s data generated in the past two years, the ability for people and even traditional computing systems to make sense of this data has grown complex. The addition of Watson to our campus is very timely considering the growth of what some have termed “Big Data.”
In 1976, Joseph Weizenbaum, a leading computer scientist, wrote a book called Computer Power and Human Reason: From Judgement to Calculation, in which he criticized the field of AI for trying to replace human creativity and thought with the power of computers. He suggested that humans and computers were inherently different, and that trying to get computers to think like humans was an insurmountable task, if it was possible at all.
IBM Unveils Chip Prototypes That Mimic Human Brain
soc.li IBM says the prototype “cognitive computing” chips are designed to act like a brain—to quickly collect and analyze information, make decisions based on the findings and learn from its mistakes.
“We now are entering the Cognitive Systems Era, in which a new generation of computing systems is emerging with embedded data analytics, automated management and data-centric architectures in which the storage, memory, switching and processing are moving ever closer to the data. In today's big data era, computers essentially process a series of "if then, what" equations, which enables cognitive systems to learn, adapt, and ultimately hypothesize and suggest answers. Delivering these capabilities will require a fundamental shift in the way computing progress has been achieved for decades.”
—Innovator Chat: How Watson Can Transform HealthcareIBM announces a milestone in pursuit of chips that behave like human brains
Associated Press, August 18, 2011
SAN FRANCISCO—Computers, like humans, can learn. But when Google tries to fill in your search box based only on a few keystrokes, or your iPhone predicts words as you type a text message, it’s only a narrow mimicry of what the human brain is capable.
The challenge in training a computer to behave like a human brain is technological and physiological, testing the limits of computer and brain science. But researchers from IBM Corp. say they’ve made a key step toward combining the two worlds.
The company announced Thursday that it has built two prototype chips that it says process data more like how humans digest information than the chips that now power PCs and supercomputers.
The chips represent a significant milestone in a six-year-long project that has involved 100 researchers and some $41 million in funding from the government’s Defense Advanced Research Projects Agency, or DARPA. IBM has also committed an undisclosed amount of money.
The prototypes offer further evidence of the growing importance of “parallel processing,” or computers doing multiple tasks simultaneously. That is important for rendering graphics and crunching large amounts of data.
The uses of the IBM chips so far are prosaic, such as steering a simulated car through a maze, or playing Pong. It may be a decade or longer before the chips make their way out of the lab and into actual products.
But what’s important is not what the chips are doing, but how they’re doing it, says Giulio Tononi, a professor of psychiatry at the University of Wisconsin at Madison who worked with IBM on the project.
The chips’ ability to adapt to types of information that it wasn’t specifically programmed to expect is a key feature.
Technologists have long imagined computers that learn like humans. Your iPhone or Google’s servers can be programmed to predict certain behavior based on past events. But the techniques being explored by IBM and other companies and university research labs around “cognitive computing” could lead to chips that are better able to adapt to unexpected information.
Dharmendra Modha, project leader for IBM Research, said the new chips have parts that behave like digital “neurons” and “synapses” that make them different than other chips. Each “core,” or processing engine, has computing, communication and memory functions.
“You have to throw out virtually everything we know about how these chips are designed,” he said. “The key, key, key difference really is the memory and the processor are very closely brought together. There’s a massive, massive amount of parallelism.”
The project is part of the same research that led to IBM’s announcement in 2009 that it had simulated a cat’s cerebral cortex, the thinking part of the brain, using a massive supercomputer. Using progressively bigger supercomputers, IBM had previously simulated 40 percent of a mouse’s brain in 2006, a rat’s full brain in 2007, and 1 percent of a human’s cerebral cortex in 2009.
A computer with the power of the human brain is not yet near. But Modha said the latest development is an important step.
IBM Unveils Neuromorphic Cognitive Computing Chip
kurzweilai.netSyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) is a multi-year IBM initiative whose bold long-term goal is to create a chip system with ten billion neurons and a hundred trillion synapses, while consuming 1 kilowatt of power and occupying less then 2 liters of volume. I like people who can set the bar high, but that’s simply a stratospheric goal. This may be achieved in 20-30 years. Either way this is really cool tech. A neuromorphic, or cognitive computing chip, goes beyond traditional von Neumann architecture in a way that is meant to replicate the neuron-axon-synapse interaction. Memory here is integrated with the processor in a way to “mimic the brain’s event driven, distributed and parallel processing.”
I.B.M. Chief on Watson, Cognitive Computing and Her Tenure - NYTimes.com
bits.blogs.nytimes.comVirginia M. Rometty, the chief executive and
Watson is the room-size computer that defeated its human rivals to become a “Jeopardy!” champion. The answer, Ms. Rometty said at Fortune’s Most Powerful Women summit, is that Watson is in medical school.
The computer is working with many health care organizations to learn medical data so it can diagnose cancer, and that is just the beginning. It has so far ingested 80 percent of the world’s medical data.
“Watch it work, and it’s almost as if he’s talking to a colleague,” Ms. Rometty said. “The beauty of it is it tells you what’s right or wrong,” and explains with confidence why it believes what it says.
Watson’s skills – from “Jeopardy!” to oncology to working with banks and call centers – are part of I.B.M.’s bigger aim. It is called cognitive computing, which she described as machines that can learn.
“In this world of huge and big data, you won’t be able to program machines for everything they should know,” said Ms. Rometty. “These machines will have to learn what is right, what is wrong, what is a pattern.”
It is the third wave of computing, she said. At first, computers could count. Today, they are programmed to follow “if this, then that.” Next they will need to discover and learn on their own, she said, not just as a search engine, but proactively. The next era, for all jobs, not just those in computing, will be to help businesses sift big data, she said.
“It’s going to be a whole era to help you get through this, and everyone’s job, from chief marketing officer to chief of police, their jobs are going to be redefined by that,” she said.
NEWS: Cognitive Computing - When Computers Become Brains
The gnomes at IBM’s research labs were not content to make merely a genius computer that could beat any human at the game of jeopardy. They had to go and create a new kind of machine intelligence that mimics the actual human brain.
Watson, the reigning jeopardy champ, is smart, but it’s still recognizably a computer. This new stuff is something completely different. IBM is setting out to build an electronic brain from the ground up.
Cognitive computing, as the new field is called, takes computing concepts to a whole new level. Earlier this week, Dharmendra Modha, who works at IBM’s Almaden Research Center, regaled a roomful of analysts with what cognitive computing can do and how IBM is going about making a machine that thinks the way we do. His own blog on the subject is here.
First Modha described the challenges, which involve aspects of neuroscience, supercomputing, and nanotechnology.
The human brain integrates memory and processing together, weighs less than 3 lbs, occupies about a two-liter volume, and uses less power than a light bulb. It operates as a massively parallel distributed processor. It is event driven, that is, it reacts to things in its environment, uses little power when active and even less while resting. It is a reconfigurable, fault-tolerant learning system. It is excellent at pattern recognition and teasing out relationships.
A computer, on the other hand, has separate memory and processing. It does its work sequentially for the most part and is run by a clock. The clock, like a drum majorette in a military band, drives every instruction and piece of data to its next location — musical chairs with enough chairs. As clock rates increase to drive data faster, power consumption goes up dramatically, and even at rest these machines need a lot of electricity. More importantly, computers have to be programmed. They are hard wired and fault prone. They are good at executing defined algorithms and performing analytics.
With $41 million in funding from the Defense Advanced Research Projects Agency (DARPA), the scientists at the Almaden lab set out to make a brain in a project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE).
The rough analogy between a brain and a computer posits roles for cell types — neurons, axons, and synapses — that correspond to machine elements — processors, communications links, and memory. The matches are not exact, as brain cells’ functions are less distinct from each other than the computer elements. But the key is that the brain elements all reside near each other, and activity in any given complex is stimulated by activity from adjacent complexes. That is, thoughts stimulate other thoughts.
Modha and his team set out to map and synthesize a wiring diagram for the brain, no trivial task, as the brain has 22 billion neurons and 220 trillion synapses. In May 2009, the team managed to simulate a system with 1 billion neurons, roughly the brain of a lower mammal. Except that it operates at one-thousandth of real time, not enough to perform what Modha called “the four Fs”: food, fight, flight, and mating.
But the structure of this machine is entirely different from today’s commercial computers. The memory and processing elements are built close together. It has no clock. Operations are asynchronous and event driven; that is, they have no predetermined order or schedule. And instead of being programmed, they learn. Just like us.
Part of getting the power down to brain-like levels is not storing temporary results (caching, in industry jargon). Sensing stimulates action, which is sensed and acted upon further. And so on.
The team recently built a smaller hardware version of the brain simulation, one with just 256 neurons, 262,000 programmable synapses, and 65,000 learning synapses. The good news is that this machine runs at within an order of magnitude of the power that a real brain consumes. With its primitive capabilities, this brainlette is capable of spatial navigation, machine vision, pattern recognition, and associative memory and can do evidence-based hypothesis generation. It has a “mind’s eye” that can see a pattern, for example, a badly written number, and generate a good guess as to what the actual number is. Already better than our Precambrian ancestors.
Modha pointed out that this type of reasoning is a lot like that of a typical right hemisphere in the brain: intuitive, parallel, synthetic. Not content with half a brain, Modha envisions adding a typical von Neumann-type computer, which acts more like a reasoning left hemisphere, to the mix, and having the two share information, just like a real brain.
When this brain is ready to go to market, I’m going to send my own on holiday and let Modha’s do my thinking for me.
Oh, and, by the way, in case you were wondering whether the SyNAPSE project has caused Watson to be put out to pasture, nothing could be further from the truth. Watson is alive and well and moving on to new, more practical applications.
For example, since jeopardy contestants can’t “call a friend,” Watson was constrained to the data that could be loaded directly into the machine (no Internet searches), but in the latest application of Watson technology — medical diagnoses — the Internet is easily added to the corpus within the machine, allowing Watson to search a much wider range of unstructured data before rendering an answer.
Watson had to hit the bell faster than the human contestants, but the doctors seeking advice on a strange set of symptoms can easily wait a half hour or longer. So, Watson can make more considered choices. Watson at work is a serious tool.
All this genius is causing my brain to explode.
Disclosure: Endpoint has a consulting relationship with IBM.
© 2011 Endpoint Technologies Associates, Inc. All rights reserved.
Twitter: RogerKay
Source: Forbes - Cognitive Computing
Roger Kay, Contributor
I cover endpoints and their interrelation with the cloud.
“Researchers at the Cognitive Computing Research Group at the University of Memphis claim that cognitive computing, like the Roman God Janus, has two faces. In the case of cognitive computing, they claim there is a science face and an engineering face. "The science face fleshes out the global workspace theory of consciousness into a full cognitive model of how minds work. The engineering face of cognitive computing explores architectural designs for software information agents and cognitive robots that promise more flexible, more human-like intelligence within their domains." ["Cognitive Computing Research Group," University of Memphis] Frankly, the business world is more interested in the engineering face of cognitive computing (i.e., how artificial intelligence can help companies better understand the world in which they operate); however, you really can't have one face without the other. That's why commercial firms as well as academic institutions are pursuing cognitive computing.”
—Enterra Insights: Cognitive ComputingNEWS: Cognitive Computing - When Computers Become Brains
The gnomes at IBM’s research labs were not content to make merely a genius computer that could beat any human at the game of jeopardy. They had to go and create a new kind of machine intelligence that mimics the actual human brain.
Watson, the reigning jeopardy champ, is smart, but it’s still recognizably a computer. This new stuff is something completely different. IBM is setting out to build an electronic brain from the ground up.
Cognitive computing, as the new field is called, takes computing concepts to a whole new level. Earlier this week, Dharmendra Modha, who works at IBM’s Almaden Research Center, regaled a roomful of analysts with what cognitive computing can do and how IBM is going about making a machine that thinks the way we do. His own blog on the subject is here.
First Modha described the challenges, which involve aspects of neuroscience, supercomputing, and nanotechnology.
The human brain integrates memory and processing together, weighs less than 3 lbs, occupies about a two-liter volume, and uses less power than a light bulb. It operates as a massively parallel distributed processor. It is event driven, that is, it reacts to things in its environment, uses little power when active and even less while resting. It is a reconfigurable, fault-tolerant learning system. It is excellent at pattern recognition and teasing out relationships.
A computer, on the other hand, has separate memory and processing. It does its work sequentially for the most part and is run by a clock. The clock, like a drum majorette in a military band, drives every instruction and piece of data to its next location — musical chairs with enough chairs. As clock rates increase to drive data faster, power consumption goes up dramatically, and even at rest these machines need a lot of electricity. More importantly, computers have to be programmed. They are hard wired and fault prone. They are good at executing defined algorithms and performing analytics.
With $41 million in funding from the Defense Advanced Research Projects Agency (DARPA), the scientists at the Almaden lab set out to make a brain in a project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE).
The rough analogy between a brain and a computer posits roles for cell types — neurons, axons, and synapses — that correspond to machine elements — processors, communications links, and memory. The matches are not exact, as brain cells’ functions are less distinct from each other than the computer elements. But the key is that the brain elements all reside near each other, and activity in any given complex is stimulated by activity from adjacent complexes. That is, thoughts stimulate other thoughts.
Modha and his team set out to map and synthesize a wiring diagram for the brain, no trivial task, as the brain has 22 billion neurons and 220 trillion synapses. In May 2009, the team managed to simulate a system with 1 billion neurons, roughly the brain of a lower mammal. Except that it operates at one-thousandth of real time, not enough to perform what Modha called “the four Fs”: food, fight, flight, and mating.
But the structure of this machine is entirely different from today’s commercial computers. The memory and processing elements are built close together. It has no clock. Operations are asynchronous and event driven; that is, they have no predetermined order or schedule. And instead of being programmed, they learn. Just like us.
Part of getting the power down to brain-like levels is not storing temporary results (caching, in industry jargon). Sensing stimulates action, which is sensed and acted upon further. And so on.
The team recently built a smaller hardware version of the brain simulation, one with just 256 neurons, 262,000 programmable synapses, and 65,000 learning synapses. The good news is that this machine runs at within an order of magnitude of the power that a real brain consumes. With its primitive capabilities, this brainlette is capable of spatial navigation, machine vision, pattern recognition, and associative memory and can do evidence-based hypothesis generation. It has a “mind’s eye” that can see a pattern, for example, a badly written number, and generate a good guess as to what the actual number is. Already better than our Precambrian ancestors.
Modha pointed out that this type of reasoning is a lot like that of a typical right hemisphere in the brain: intuitive, parallel, synthetic. Not content with half a brain, Modha envisions adding a typical von Neumann-type computer, which acts more like a reasoning left hemisphere, to the mix, and having the two share information, just like a real brain.
When this brain is ready to go to market, I’m going to send my own on holiday and let Modha’s do my thinking for me.
Oh, and, by the way, in case you were wondering whether the SyNAPSE project has caused Watson to be put out to pasture, nothing could be further from the truth. Watson is alive and well and moving on to new, more practical applications.
For example, since jeopardy contestants can’t “call a friend,” Watson was constrained to the data that could be loaded directly into the machine (no Internet searches), but in the latest application of Watson technology — medical diagnoses — the Internet is easily added to the corpus within the machine, allowing Watson to search a much wider range of unstructured data before rendering an answer.
Watson had to hit the bell faster than the human contestants, but the doctors seeking advice on a strange set of symptoms can easily wait a half hour or longer. So, Watson can make more considered choices. Watson at work is a serious tool.
All this genius is causing my brain to explode.
Disclosure: Endpoint has a consulting relationship with IBM.
© 2011 Endpoint Technologies Associates, Inc. All rights reserved.
Twitter: RogerKay
Source: Forbes - Cognitive Computing
Roger Kay, Contributor
I cover endpoints and their interrelation with the cloud.
“A cognitive system like Watson accesses structured and unstructured information within an associated knowledge base to return responses that are not simply data but contextualized information that can inform users' actions and guide their decisions. This is a gigantic leap beyond human decision-making using experience based on random sources from the industry and internal sets of reports and data. This innovative new approach to computing is designed to aid humans by working with natural language – English in the case of today's Watson." Smith goes on to provide a brief primer about cognitive computing. He writes: "For those of you who are not used to the word cognitive, the foundation of cognition is the sum of all the thinking processes that contribute to gaining knowledge for problem-solving. In computational systems these processes are modeled using hardware and software; machine-based cognition thus is a step toward imbuing an artificial system with attributes we typically consider human: the abilities to think and learn. Watson builds on a foundation of evidence from preexisting decisions and knowledge sources that it can load for reference in future inquiries. The evidence-based reasoning that Watson employs to answer question is part of the big deal in its approach.”
—Enterra Insights: Cognitive Computing
Adaptive, brain-like systems give robots complex behaviors
ine-news.orgA detailed look at HP’s efforts in applying memristor technology to DARPA’s cognitive computing initiatives via Neuromorphic Engineer. (How’s that for a publication title!) Be warned — the link is to a PDF. [The article was found via a neat website that’s also worth exploring in greater depth, Neurdron.)
Classical implementations of large-scale neural systems in computers use resources such as central processing unit (CPU) and graphics processing unit (GPU) cores, mass memory storage, and parallelization algorithms. Designs for such systems must cope with power dissipation from data transmission between processing and memory units. By some estimates, this loss is millions of times the power required to actually compute, in the sense of creating meaningful new register contents. Such a high transmission loss is unavoidable as long as memory and computation are physically distant. The creation of an electronic brain stuffed into the volume of a mammalian brain is thus impossible via conventional technology.
The Defense Advanced Research Projects Agency (DARPA)-sponsored Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project is looking for hardware solutions that reduce power consumption by electronic synapses to achieve memory density of 1015 bits per square centimeter. One approach is based on memristive devices. The memristor, initially theorized by University of California, Berkeley Professor Leon Chua and later discovered by HP Labs, has the unique property of remembering its stimulation history in its resistive state. It does not require power to maintain its memory, making it ideal for implementing dense, low-power synapses supporting large-scale neural models. The challenge is to build a software platform able to exploit the memristor’s capacities.
This platform, named Cog ex Machina3 (Cog), is being developed at Hewlett-Packard by Greg Snider. Cog abstracts away the underlying hardware and allocates processing resources by computational algorithms based on CPU/GPU availability. Cog exposes a programming interface that enforces synchronous parallel processing of neural data encoded as multidimensional arrays (tensors).
Our Modular Neural Exploring Traveling Agent (MoNETA) project,4 supported by DARPA/SyNAPSE via a subcontract with HP, uses Cog to progressively implement complex, wholebrain systems able to leverage the power of memristive hardware that is yet to be designed. MoNETA is the brain of an animat, a neuromorphic agent autonomously learning to perform complex behaviors in a virtual environment. It combines visual scene analysis, spatial navigation, and plasticity. The system is intended to replicate a rodent’s learning to swim to a submerged platform in the Morris water maze task, a behavior that involves cooperation among several brain areas. The MoNETA brain will eventually implement many cortical and subcortical areas that will allow an animat or robot to engage with a virtual or real environment.
IBM: Computers Will See, Hear, Taste, Smell and Touch in 5 Years
mashable.comThe five senses are really all part of one grand concept: cognitive computing, which involves machines experiencing the world more like a human would. For example, a cognizant computer wouldn’t see a painting as merely a set of data points describing color, pigment and brush stroke; rather, it would truly see the object holistically as a painting, and be able to know what that means.
