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Processors That Work Like Brains Will Accelerate Artificial Intelligence | MIT Technology Review

Processors That Work Like Brains Will Accelerate Artificial Intelligence | MIT Technology Review | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
Microchips modeled on the brain may excel at tasks that baffle today’s computers.

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Mlik Sahib's curator insight, December 19, 2013 11:01 PM

"Neuroscientist Henry Markram, who discovered spike-timing-dependent plasticity, has attacked Modha’s work on networks of simulated neurons, saying their behavior is too simplistic. He believes that successfully emulating the brain’s faculties requires copying synapses down to the molecular scale; the behavior of neurons is influenced by the interactions of dozens of ion channels and thousands of proteins, he notes, and there are numerous types of synapses, all of which behave in nonlinear, or chaotic, ways. In Markram’s view, capturing the capabilities of a real brain would require scientists to incorporate all those features.

The DARPA teams counter that they don’t have to capture the full complexity of brains to get useful things done, and that successive generations of their chips can be expected to come closer to representing biology. HRL hopes to improve its chips by enabling the silicon neurons to regulate their own firing rate as those in brains do, and IBM is wiring the connections between cores on its latest neuromorphic chip in a new way, using insights from simulations of the connections between different regions of the cortex of a macaque."

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Rescooped by Chang Eop Kim from Global Brain
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Processors That Work Like Brains Will Accelerate Artificial Intelligence | MIT Technology Review

Processors That Work Like Brains Will Accelerate Artificial Intelligence | MIT Technology Review | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
Microchips modeled on the brain may excel at tasks that baffle today’s computers.

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Mlik Sahib's curator insight, December 19, 2013 11:01 PM

"Neuroscientist Henry Markram, who discovered spike-timing-dependent plasticity, has attacked Modha’s work on networks of simulated neurons, saying their behavior is too simplistic. He believes that successfully emulating the brain’s faculties requires copying synapses down to the molecular scale; the behavior of neurons is influenced by the interactions of dozens of ion channels and thousands of proteins, he notes, and there are numerous types of synapses, all of which behave in nonlinear, or chaotic, ways. In Markram’s view, capturing the capabilities of a real brain would require scientists to incorporate all those features.

The DARPA teams counter that they don’t have to capture the full complexity of brains to get useful things done, and that successive generations of their chips can be expected to come closer to representing biology. HRL hopes to improve its chips by enabling the silicon neurons to regulate their own firing rate as those in brains do, and IBM is wiring the connections between cores on its latest neuromorphic chip in a new way, using insights from simulations of the connections between different regions of the cortex of a macaque."

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tomorrows_world.jpg (JPEG Image, 976 × 2700 pixels)

tomorrows_world.jpg (JPEG Image, 976 × 2700 pixels) | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it

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Chang Eop Kim's insight:

재미로 볼만함 ㅎㅎ 

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Manuel Mühlbauer's curator insight, November 14, 2013 3:09 AM

what might happen during your lifetime ???

Javier Antonio Bellina's curator insight, August 5, 10:56 AM

Interesante y original línea de tiempo futurista, iniciada en 2012.

Joseph Kim's curator insight, August 18, 5:59 AM

what do we need to consider for clinical research?

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Qualcomm Reveals Neural Network Progress | EE Times

Qualcomm Reveals Neural Network Progress | EE Times | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
Qualcomm recently revealed details of its work to create neural network chips modeled on the human brain at the EmTech conference.

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Rescooped by Chang Eop Kim from Talks
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Rodney Brooks: Why we will rely on robots

Scaremongers play on the idea that robots will simply replace people on the job. In fact, they can become our essential collaborators, freeing us up to spend time on less mundane and mechanical challenges. Rodney Brooks points out how valuable this could be as the number of working-age adults drops and the number of retirees swells. He introduces us to Baxter, the robot with eyes that move and arms that react to touch, which could work alongside an aging population -- and learn to help them at home, too.

 

http://www.ted.com/talks/rodney_brooks_why_we_will_rely_on_robots.html


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Rescooped by Chang Eop Kim from Biobit: Computational Neuroscience & Biocomputation
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Interim report #BRAIN initiative (advisory committee to NIH director)


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Google scientist Jeff Dean on how neural networks are improving everything Google does - Puget Sound Business Journal

Google scientist Jeff Dean on how neural networks are improving everything Google does - Puget Sound Business Journal | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
If you've ever been mystified by how Google knows what you're looking for before...

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A new era of cognitive computing | ITWeb

A new era of cognitive computing | ITWeb | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
The next frontier for Watson, IBM’s supercomputer, is a biologically-inspired, truly cognitive underlying computer system.

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Spaceweaver's curator insight, February 19, 2013 3:13 PM

Highly recommended read - the future of learning machines

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Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data - Sebans Curve

Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data - Sebans Curve | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it

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Complejidady Economía's curator insight, September 28, 2013 4:44 PM

DT statistics,
DT time-frequency analysis, and 
DT low-dimensional reductions 
The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems.

 

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The Future Fabric of Data Analysis | Simons Foundation

The Future Fabric of Data Analysis | Simons Foundation | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
The nature of computing has changed dramatically over the last decade, and more innovation is needed to weather the gathering data storm.

Via Nima Dehghani
Chang Eop Kim's insight:

big data technology 관련하여 아주 뻔한 내용들 말고, 정말 공부가 될만한 내용,과 전망이 있는 글. 

parralell computing, quantum computing, SQL, No-SQL, 수많은 AI agent들이 distributed network에서 알아서 작업하며 data의 이동, 통합 등을 manage하는 미래상까지.

빠르게 변해가는 동향들을 지속적으로 모니터링하고 공부해나갈 필요가 있겠다. 
 

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John Searle: Our shared condition -- consciousness

Philosopher John Searle lays out the case for studying human consciousness -- and systematically shoots down some of the common objections to taking it seriously. As we learn more about the brain processes that cause awareness, accepting that consciousness is a biological phenomenon is an important first step. And no, he says, consciousness is not a massive computer simulation.


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Luciano Lampi's curator insight, July 29, 2013 7:57 AM

"Consciousness is not a massive computer simulatio." A consciência não é uma simulação com emprego massiço de meios computação. Concordam? Do you Agree?

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The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations

The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it

The assessment of scientific publications is an integral part of the scientific process. Here we investigate three methods of assessing the merit of a scientific paper: subjective post-publication peer review, the number of citations gained by a paper, and the impact factor of the journal in which the article was published. We investigate these methods using two datasets in which subjective post-publication assessments of scientific publications have been made by experts. We find that there are moderate, but statistically significant, correlations between assessor scores, when two assessors have rated the same paper, and between assessor score and the number of citations a paper accrues. However, we show that assessor score depends strongly on the journal in which the paper is published, and that assessors tend to over-rate papers published in journals with high impact factors. If we control for this bias, we find that the correlation between assessor scores and between assessor score and the number of citations is weak, suggesting that scientists have little ability to judge either the intrinsic merit of a paper or its likely impact. We also show that the number of citations a paper receives is an extremely error-prone measure of scientific merit. Finally, we argue that the impact factor is likely to be a poor measure of merit, since it depends on subjective assessment. We conclude that the three measures of scientific merit considered here are poor; in particular subjective assessments are an error-prone, biased, and expensive method by which to assess merit. We argue that the impact factor may be the most satisfactory of the methods we have considered, since it is a form of pre-publication review. However, we emphasise that it is likely to be a very error-prone measure of merit that is qualitative, not quantitative.

 

Eyre-Walker A, Stoletzki N (2013) The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations. PLoS Biol 11(10): e1001675. http://dx.doi.org/10.1371/journal.pbio.1001675


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Rescooped by Chang Eop Kim from Biobit: Computational Neuroscience & Biocomputation
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Brain teaser: Physical Principles for Scalable Neural Recording


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Nima Dehghani's curator insight, September 22, 2013 6:45 PM

Here are some excerpts text from Mark Buchanan's editorial in Nature Physics on the paper by bunch of friends.

(The origianl paper (arXiv) can be found here:

http://arxiv.org/abs/1306.5709)

 

".... Our ability to monitor and record these detailed dynamics remains limited. Although the technology for recording neural activity with wire electrodes has advanced consistently for decades — doubling in resolution every seven years since the 1950s — we can still only observe the dynamics of at most a few hundred neurons on timescales fast enough (milliseconds) to capture their core behaviour. Optical microscopy can get readings on something like 100,000 neurons, but only every second or so. Magnetic resonance imaging allows non-invasive whole-brain recordings on a one-second timescale, but doesn't come close to resolving the activity of single neurons....

.... physicist Adam Marblestone and colleagues have recently tried to look at how some of the current trends in technology might play out in the future (preprint at http://arxiv.org/abs/1306.5709; 2013). One conclusion: recording from every neuron isn't as out of reach as you might think.

Any suitable brain-imaging technology must avoid interference with normal function, both in terms of the power that can be dissipated within the brain, and the alteration of the physical tissue through direct influence. Both limits restrict the scope for traditional electrode technologies, even with the development of ever thinner wires and smaller electrode impedances. Hence, the biggest advances will probably come from radically different technologies....In their article, Marblestone and colleagues quote Freeman Dyson from his book Imagined Worlds, thinking about how a future technology might place enough tiny devices into the brain to record from each and every neuron. “There is”, he noted, “no law of physics that declares such an observational tool to be impossible.” This reality might be closer than we think...."

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Stanford’s Artificial Neural Network Is The Biggest Ever_

Stanford’s Artificial Neural Network Is The Biggest Ever_ | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
It's 6.5 times bigger than the network Google premiered last year, which has learned to recognize YouTube cats.

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Complex systems made simple

Complex systems made simple | complexity, informatics, network, quantum, artificial intelligence, machine learning, big data | Scoop.it
Network scientists at Northeastern have designed an algorithm capable of identifying the subset of components that reveal a complex system's overall nature.

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Eli Levine's curator insight, July 10, 11:02 AM

Way cool. And useful.