SyNAPSE Project AI Neuromorphic Chip

IBM’s Aug 18th Press Release announced another significant milestone for the DARPA SyNAPSE project, the world’s best funded and arguably the “most likely to succeed” approach to creating a general artificial intelligence.

The release notes that the new chips represent a departure from traditional models of computing:

…. cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

To do this, IBM is combining principles from nanoscience, neuroscience and supercomputing as part of a multi-year cognitive computing initiative. The company and its university collaborators also announced they have been awarded approximately $21 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

As we’ve noted here many times, another remarkable project is the Blue Brain Project in Europe spearheaded by Dr. Henry Markram.     That team has joined with many others and is in the process of applying to the European Union for substantial funding – perhaps as much as 1.6 billion dollars.    Although Blue Brain tends to shy away from stating that their objective is a general artificial intelligence,  I would argue that they should have that goal and also that they are much more likely to be funded by stating that goal in no uncertain terms.

Unfortunately there remain many both in and outside of technology circles who believe the search for a general artificial intelligence is either dangerous or a waste of time and money.   Both these scenarios are possible but unlikely.   Sure, intelligence can be dangerous but given human history compared to technology history it seems odd to argue that we are more likely to create a Frankenstein than a helpful machine process.    Computers don’t kill people, people kill people.

In terms of a waste of time and money, clearly we humans have overrated our intelligence for some time – probably since the beginning of self-awareness.   There are few rational reasons to reject the idea that we cannot duplicate processes that are similar to our own thinking in a machine.   The advantages of machine based intelligence are likely to be  substantial – probably on the order of a new human age with vastly improved resource efficiency, poverty reduction, and more.  Thus the costs – currently measured in the low tens of millions – pale in comparison to almost all other government projects – many with massively dubious and negative ROIs.

http://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltext

One of the paper’s most notable items asserts that within a decade the project expects to have the computational scale needed for human level modelling, though it also notes that this is not the same as creating a model of the human brain – this may require computational structures yet to be invented.    However on balance it would seem the SyNAPSE project continues to build on their core assumptions, taking us ever closer to the holy grail of technology – a general artificial intelligence.

More at Dr. Modha’s blog , where we learn more about the new approaches the SyNAPSE team at IBM will take in an effort to achieve human quality cognition in a machine:

18 Aug 2011: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers.

In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing.

Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

How hard is it to build an intelligent machine? I don’t think it’s so hard ….   The basic idea I promote is that you mustn’t look for a magic bullet. You mustn’t look for one wonderful way to solve all problems. Instead you want to look for 20 or 30 ways to solve different kinds of problems. And to build some kind of higher administrative device that figures out what kind of problem you have and what method to use.

Now, if you take any particular researcher today, it’s very unlikely that that researcher is going to work on this architectural level of what the thinking machine should be like. Instead a typical researcher says, “I have a new way to use statistics to solve all problems.” Or: “I have a new way to make a system that imitates evolution. It does trials and finds the things that work and remembers the things that don’t and gets better that way.” And another one says, “It’s going to use formal logic and reasoning of a certain kind, and it will figure out everything.” So each researcher today is likely to have one particular idea, and that researcher is trying to show that he or she can make a machine that will solve all problems in that way.

I think this is a disease that has spread through my profession. Each practitioner thinks there’s one magic way to get a machine to be smart, and so they’re all wasting their time in a sense.

I was surprised to see his lack of optimism in the face of so much progress in areas I’d argue are very generalized indeed.     The DARPA  SyNAPSE project we’ve discussed several times here at Technology Report remains the best funded AI research to date, and lead researchers seem to feel optimistic that progress there could lead to a human scale general intelligence within several years rather than several decades that Minsky implies may be required given the new approaches.

Simply put, DARPA SyNAPSE  is creating a computing infrastructure to rival the human brain in terms of connectivity, and counting on the possibility that we are dealing mostly with *quantity of connections* rather than *quality of connections* when we talk about human level intelligence.

The other very promising project for generalized AI is somewhat at odds with the DARPA SyNAPSE view.    The Blue Brain project is also a promising development ground for general artificial intelligence, but the approach is very different as described by Dr. Henry Markram, the project manager at Blue Brain.      The Blue Brain team is focusing more on “reverse engineering” animal brains and eventually a human brain.

Given the new level of enthusiasm and funding from DARPA, it seems likely that progress will continue at a faster pace that at anytime in the past.

Ironically I think Minsky’s early optimism in the 1950s  was more justified than his current pessimism, though his observation that academics are working in too much isolation is certainly true.    I’m often surprised how many technologists don’t seem to understand many simple aspects of human biology and evolution and vica versa.     Human intelligence, though intriguing, continues to be overrated as an phenomenon of exceptional quality.    We’re a somewhat arrogant creature by evolutionary design, but that does not justify our self importance.    Machines surpass most of us in most compartmentalized aspects of intelligence and many aspects of creativity  (mathematics / translation and language / game playing / music / information retrival, etc, etc).    It seems reasonable that what we call “consciousness” may only require massive connectivity – perhaps something as simple as creating a fast, multitasked conversation between different parts of an artificial brain.

We’ve written a lot about DARPA SyNAPSE and the BLUE BRAIN projects, but a new exciting one in Europe (not sure about the name though) is called …

—————————–

Brain‐i‐Nets

Novel Brain‐Inspired Learning Paradigms for Large‐Scale
Neuronal Networks

• Our vision: To build a novel type of computing
machinery, exhibiting human‐like learning
capabilities.

• Our approach: To port leaning and adaptation
mechanisms in the brain to brain‐inspired
hardware.

• Our path: An interdisciplinary roadmap from
neuroscience to technology.
•Explore: Use cutting‐edge techniques to gain
insight in how the brain learns.

•Understand: Understand the results through
mathematical analysis and simulation.

• Implement: Study the implementation in novel
brain‐inspired hardware.

————————-

Read more at the University of Heidleberg’s Brain-i-Nets website

Remains, however, a great idea

Kudos to the folks at Quest Visual:  Questvisual.com.   Their free  iPhone application “Wordlens” allows real time translation by simply pointing your iPhone at a sign or other text.

As any traveler knows it’s very helpful to be able to interpret signs, menus, and  other text.   This is naturally very difficult in countries where you don’t speak the language.    Wordlens is only available in spanish now but other languages are on the way, and this is clearly a great step in the direction of our phones and handheld devices becoming “universal translators”

Via Singularity Hub

more at Quest Visual:  Questvisual.com

This is the article, about a new chip called a “Memristor”  that seemed to spawn a lot of discussion.   It’s a new approach under development as part of the DARPA SyNAPSE project.  [thanks to my good pal Roy K who always forwards me very interesting stuff!]

It’s encouraging to see the debate over computer consciousness take such a serious tone as this topic is arguably one of the most intriguing in all of human history.    Many experts believe we’ll see machines become self-aware within 10-20 years.    Given the massive computational superiority computers already enjoy over humans, one can make a strong case that a conscious computer – or more likely a computer + human brain hybrid – will  clone its intellect and improve that intellect over and over in a very short time, leading to levels of intelligence far beyond that of “normal” humans.

Also encouraging that pioneers in the field like the article’s authors:  Sean Lorenz, Heather Ames & Massimiliano Versace are willing to discuss this topic rather than shelve it as so many in computer research have done.    I think early inflated optimism about artificial intelligence led to so much disappointment in the computer community that the “old guard” programmers are being too stubborn now, especially in the face of very significant advances in the understanding of human neurobiology and in computational speeds and memory capacities.

From the post “ Moneta_And_The_C_Word”

Conclusion
Although Searle has argued for biological embodiment as a necessity for causation of consciousness, this paper puts forth the argument that biological embodiment is not the only embodiment that can produce consciousness. Instead, we argue that the brain is an optimal form of embodiment giving rise to consciousness because it can produce observable reports, oral reports, and observed and measured activity. The first two qualifications of consciousness can be replicated with computer simulations as discussed by the proponents of WBE. However, the third qualification requires a unique stipulation for embodiment that is able to self organize and generate unique global and local patterns of activities within its constituent elements. At this point in time, this is only achievable within brain tissue. However, with the advancement of neural chip development, we would argue that embodiment necessary for consciousness would be achievable in a new medium, the neuromorphic chip.

Watson’s brilliancy will be tested in February against Jeopardy’s best contestants:

http://www-03.ibm.com/innovation/us/watson/what-is-watson/the-next-grand-challenge.html

IBM had announced a Jeopardy test for Watson over a year ago and we are checking on why there has been such a long delay in the contest, now scheduled for three episodes in February 2011.

See Technology Report’s Jeopardy post from CES 2009

However the implications of this type of research go far beyond simple repair.   As science improves the current state of the art of brain implants (which now offer only rudimentary connections to actual brain functions), we are likely to see a spectacular increase in human intellectual capabilities.   Our current limitations to information processing include the very slow speeds with which we can interact with computers – usually via keyboards.   When implants will allow brains to *directly* interface with, for example, internet information, we are very likely to experience an explosion of human capabilities.