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Matvey Ezhov

Mapping the brain - MIT news - 2 views

web.mit.edu/...brain-mapping.html

brain neuroscience connectome

shared by Matvey Ezhov on 20 Feb 10 - Cached
  • To find connectomes, researchers will need to employ vast computing power to process images of the brain. But first, they need to teach the computers what to look for.
  • to manually trace connections between neurons
  • want to speed up the process dramatically by enlisting the help of high-powered computers.
  • ...5 more annotations...
  • To do that, they are teaching the computers to analyze the brain slices, using a common computer science technique called automated machine learning, which allows computers to change their behavior in response to new data.
  • With machine learning, the researchers teach computers to learn by example. They feed their computer electron micrographs as well as human tracings of these images. The computer then searches for an algorithm that allows it to imitate human performance.
  • Their eventual goal is to use computers to process the bulk of the images needed to create connectomes, but they expect that humans will still need to proofread the computers’ work.
  • Last year, the National Institutes of Health announced a five-year, $30 million Human Connectome Project to develop new techniques to figure out the connectivity of the human brain. That project is focused mainly on higher level, region-to-region connections. Sporns says he believes that a good draft of higher-level connections could be achieved within the five-year timeline of the NIH project, and that significant progress will also be made toward a neuron-to-neuron map.
    • Matvey Ezhov
      Matvey Ezhov on 21 Feb 10
       
      draft of human connectome within five years
  • Though only a handful of labs around the world are working on the connectome right now, Jain and Turaga expect that to change as tools for diagramming the brain improve. “It’s a common pattern in neuroscience: A few people will come up with new technology and pioneer some applications, and then everybody else will start to adopt it,” says Jain.
thinkahol *

A: This Computer Could Defeat You at 'Jeopardy!' Q: What is Watson? - 0 views

www.youtube.com/PBSNewsHour

A: This Computer Could Defeat You at 'Jeopardy!' Q: What is Watson?

shared by thinkahol * on 15 Feb 11 - Cached
  • thinkahol * on 15 Feb 11
     
    From: PBSNewsHour | February 14, 2011  | 1,877 viewsRead the transcript: http://to.pbs.org/enCpW3NewsHour Science correspondent Miles O'Brien goes head-to-circuit board with IBM's computer Watson on the game show "Jeopardy!" to explore the limits of language and artificial intelligence for machines.
thinkahol *

Future Intelligence | Watch Free Documentary Online - 0 views

topdocumentaryfilms.com/future-intelligence

Future Intelligence | Watch Free Documentary Online

shared by thinkahol * on 16 Mar 11 - No Cached
  • thinkahol * on 16 Mar 11
     
    Catch a first-time glimpse at smart technology that will put android helpers in the home, network commuters and entire cities to the Web, and bring us entertainment systems that can virtually make dreams come true. Advances in artificial intelligence are creating machines with near human-like mental agility. Intelligence will be embedded everywhere - even in our clothing, thanks to smaller, more powerful computers. Soon, we will be able to build computers with artificial intelligence and processing power that rivals the human brain. Intelligence will be everywhere, in our clothing, our vehicles and homes. Intelligent robots will serve us - until they don't feel like doing so anymore. And what happens then…?
Matvey Ezhov

PLoS Biology: Towards a Mathematical Theory of Cortical Micro-circuits (about Hawkins' ... - 1 views

www.ploscompbiol.org/...10.1371%2Fjournal.pcbi.1000532

neuroinformatics neuroscience HTM cortex PLoS hawkins

shared by Matvey Ezhov on 16 Oct 09 - Cached
Matvey Ezhov liked it
  • The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation.
    • Matvey Ezhov
      Matvey Ezhov on 16 Oct 09
       
      Statement needs checking
  • Friston recently expanded on this to suggest an inversion method for hierarchical Bayesian dynamic models and to point out that the brain, in principle, has the infrastructure needed to invert hierarchical dynamic models [6].
  • In a recent review, Hegde and Felleman pointed out that the “Bayesian framework is not yet a neural model. [The Bayesian] framework currently helps explain the computations that underlie various brain functions, but not how the brain implements these computations” [2]. This paper is an attempt to fill this gap by deriving a computational model for cortical circuits based on the mathematics of Bayesian belief propagation in the context of a particular Bayesian framework called Hierarchical Temporal Memory (HTM).
  • ...3 more annotations...
  • This paper's other author, George, recognized that the Memory-Prediction framework could be formulated in Bayesian terms and given a proper mathematical foundation [8],[9].
  • Several researchers have proposed detailed models for cortical circuits [10]–[12].
  • Other researchers [4],[13] have proposed detailed mechanisms by which Bayesian belief propagation techniques can be implemented in neurons.
    • Matvey Ezhov
      Matvey Ezhov on 26 Oct 09
       
      Николаю Сибирцеву: ты искал именно это
thinkahol *

YouTube - Jeff Hawkins on Artificial Intelligence - Part 1/5 - 0 views

www.youtube.com/watch

intelligence math computer neuroscience philosophy bayesian

shared by thinkahol * on 04 Jun 10 - Cached
  • thinkahol * on 04 Jun 10
     
    June 23, 2008 - The founder of Palm, Jeff Hawkins, solves the mystery of Artificial Intelligence and presents his theory at the RSA Conference 2008. He gives a brief tutorial on the neocortex and then explains how the brain stores memory and then describes how to use that knowledge to create artificial intelligence. This lecture is insightful and his theory will revolutionize computer science.
Matvey Ezhov

On Biological and Digital Intelligence - 0 views

www.goertzel.org/...icalAndDigitalIntelligence.htm

shared by Matvey Ezhov on 09 Sep 09 - Cached
  • In essence, Hawkins argues that, to whatever extent the concept of “consciousness” can’t be boiled down to brain theory, it’s simply a bunch of hooey.
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      Not true!
  • in which conscious experience is more foundational than physical systems or linguistic communications
  • Conscious experiences are associated with patterns, and patterns are associated with physical systems, but none of these is fully reducible to the other. 
  • ...22 more annotations...
  • He makes the correct point that roughly-human-level AI’s will have dramatically different strengths and weaknesses from human being, due to different sensors and actuators and different physical infrastructures for their cognitive dynamics.  But he doesn’t even touch the notion of self-modifying AI – the concept that once an AI gets smart enough to modify its own code, it’s likely to get exponentially smarter and smarter until it’s left us humans in the dust.
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      Совершенно не имеет отношения к теме, подход Хокинса легко масштабируется до сверх- и сверх-сверх-сверхчеловеческого интеллекта.
  • therefore if AI closely enough emulates the human brain it won’t radically self-modify either
  • Rather, I think the problem is that the field of AI has come to focus on “narrow AI” – programs that solve particularly, narrowly-defined problems – rather than “artificial general intelligence” (AGI). 
  • cognitive science, artificial general intelligence, philosophy of mind and abstract mathematics
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      т.о. Гортзел признается, что вообще принимает и не считает нужным принимать нейронауку в расчет, т.е. опирается только на эмпирические представления о том, как работает сознание.
  • So what we’re doing is creating commercial narrow AI programs, using the software framework that we’re building out with our AGI design in mind.
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      и в этом его большое отличие от платформы Хокинса, которая имеет одинаковую структуру для всех ее применений
  • I tend to largely agree with his take on the brain
  • I think he oversimplifies some things fairly seriously – giving them very brief mention when they’re actually quite long and complicated stories.  And some of these omissions, in my view, are not mere “biological details” but are rather points of serious importance for his program of abstracting principles from brain science and then re-concretizing these principles in the context of digital software.
  • One point Hawkins doesn’t really cover is how a mind/brain chooses which predictions to make, from among the many possible predictions that exist.
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      тут он вроде бы прав...
  • Hawkins proposes that there are neurons or neuronal groups that represent patterns as “tokens,” and that these tokens are then incorporated along with other neurons or neuronal groups into larger groupings representing more abstract patterns.  This seems clearly to be correct, but he doesn’t give much information on how these tokens are supposed to be formed. 
  • So, what’s wrong with Hawkins’ picture of brain function?  Nothing’s exactly wrong with it, so far as I can tell.
  • But Edelman then takes the concept one step further and talks about “neural maps” – assemblies of neuronal groups that carry out particular perception, cognition or action functions.  Neural maps, in essence, are sets of neuronal groups that host attractors of neurodynamics.  And Edelman then observes, astutely, that the dynamics of the population of neuronal groups, over time, is likely to obey a form of evolution by natural selection.
  • How fascinating if the brain also operates in this way!
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      да нифига... слов нет
  • Hawkins argues that creativity is essentially just metaphorical thinking, generalization based on memory.  While this is true in a grand sense, it’s not a very penetrating statement.
  • Evolutionary learning is the most powerful general search mechanism known to computer science, and is also hypothesized by Edelman to underly neural intelligence.  This sort of idea, it seems to me, should be part of any synthetic approach to brain function.
  • Hawkins mentions the notion, and observes correctly that Hebbian learning in the brain is a lot subtler than the simple version that Donald Hebb laid out in the late 40’s.   But he largely portrays these variations as biological details, and then shifts focus to the hierarchical architecture of the cortex. 
  • Hawkins’ critique of AI, which in my view is overly harsh.  He dismisses work on formal logic based reasoning as irrelevant to “real intelligence.” 
  • So – to sum up – I think Hawkins’ statements about brain function are pretty much correct
  • What he omits are, for instance,   The way the brain displays evolutionary learning as a consequence of the dynamics of multiple attractors involving sets of neural clusters The way the brain may emergently give rise to probabilistic reasoning via the statistical coordination of Hebbian learning
  • Learning of predictive patterns requires an explicit or implicit search through a large space of predictive patterns; evolutionary learning provides one approach to this problem, with computer science foundations and plausible connections to brain function; again, Hawkins does not propose any concrete alternative.
  • crucial question of how far one has to abstract away from brain function, to get to something that can be re-specialized into efficient computer software.  My intuition is that this will require a higher level of abstraction than Hawkins seems to believe.  But I stress that this is a matter of intuitive judgment – neither of us really knows.
  • Of course, to interpret the Novamente design as an “abstraction from the brain” is to interpret this phrase in a fairly extreme sense – we’re abstracting general processes like probabilistic inference and evolutionary learning and general properties like hierarchical structure from the brain, rather than particular algorithms. 
    • Matvey Ezhov
      Matvey Ezhov on 09 Sep 09
       
      наконец-то он сказал это
  • Although I’m (unsurprisingly) most psyched about the Novamente approach, I think it’s also quite worthwhile to pursue AGI approaches that are closer to the brain level – there’s a large space between detailed brain simulation and Novamente, including neuron-level simulations, neural-cluster-level simulations, and so forth. 
Matvey Ezhov

DirectX compute - будущее вычислений на видеокартах - PCNEWS.RU - 5 views

www.pcnews.ru/...-microsoft-windows-266308.html

GPUPU видеокарты вычисления на видеокартах DirectX Compute

shared by Matvey Ezhov on 27 Oct 09 - Cached
  • Nikolay Sibirtsev on 28 Oct 09
     
    Ага, вот о чем я говорил... Надо будет MSDN на эту тему поглядеть.
  • Matvey Ezhov on 28 Oct 09
     
    Еще есть OpenCL от Apple. На хабре много инфы и обзоров по нему.
  • Nikolay Sibirtsev on 28 Oct 09
     
    Только технологии Microsoft поддерживаются всеми производителями железа для ПК, т.к. Microsoft занимает подавляющую часть рынка. Остальные можно даже не рассматривать, т.к. они будут вытеснены, когда Microsoft внедрит свое решение.
mikhail-miguel

WolframAlpha - Compute expert-level answers in Math, Science, Society, Culture & Everyd... - 0 views

www.wolframalpha.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Wolfram|Alpha brings expert-level knowledge and capabilities to the broadest possible range of people-spanning all professions and education levels. WolframAlpha: Compute expert-level answers in Math, Science, Society, Culture & Everyday Life (wolframalpha.com).
mikhail-miguel

WolframAlpha - Compute expert-level answers in Math, Science, Society, Culture & Everyd... - 0 views

wolframalpha.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    WolframAlpha: Compute expert-level answers in Math, Science, Society, Culture & Everyday Life (wolframalpha.com).
mikhail-miguel

DiffusionBee - Generate Artificial Intelligence art on your computer with Stable Diffus... - 0 views

diffusionbee.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    DiffusionBee: Generate Artificial Intelligence art on your computer with Stable Diffusion (diffusionbee.com).
Matvey Ezhov

New Light On Nature Of Broca's Area: Rare Procedure Documents How Human Brain Computes ... - 0 views

www.sciencedaily.com/...091015141500.htm

brain neuroscience news

shared by Matvey Ezhov on 16 Oct 09 - Cached
  • Our task involved both reading and speaking, and we found that aspects of word identity, grammar and pronunciation are all computed within Broca's area.
thinkahol *

Jeff Hawkins on Artificial Intelligence - Part 1/5 - YouTube - 0 views

www.youtube.com/watch

Jeff Hawkins on Artificial Intelligence - Part 1_5 YouTube

shared by thinkahol * on 30 Oct 11 - No Cached
  • thinkahol * on 30 Oct 11
     
    The founder of Palm, Jeff Hawkins, solves the mystery of Artificial Intelligence and presents his theory at the RSA Conference 2008. He gives a brief tutorial on the neocortex and then explains how the brain stores memory and then describes how to use that knowledge to create artificial intelligence. This lecture is insightful and his theory will revolutionize computer science.
davidjones29

FICC 2018 - Future of Information and Communication Conference (FICC) 2018 - 0 views

saiconference.com/ficc

ai brain news science intelligence artificial network computing data-science technology trends

shared by davidjones29 on 28 Jun 17 - No Cached
  • davidjones29 on 28 Jun 17
     
    FICC 2018 aims to provide a forum for researchers from both academia and industry to share their latest research contributions and exchange knowledge with the common goal of shaping the future of Information and Communication. Join us, April 5-6, to explore discovery, progress, and achievements related to Communication, Data Science, Computing and Internet of Things. ficc@saiconference.com saiconference.com/ficc https://groups.diigo.com/group/communication-conference https://youtu.be/7Qw-ovNd7A8
mikhail-miguel

IMAGEAmigo - 0 views

imageamigo.com/photodepth

'Photo' Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    Compute a depth map from a single photo without a 3D camera! Cut objects out of an image, adjust focus or measure relative distances!
mikhail-miguel

Artificial Intelligence: A Guide for Thinking Humans - 0 views

www.amazon.com/...ref=tmm_aud_swatch_0

'Recommended Books' Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    This program includes an introduction read by the author. No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals its turbulent history and the recent surge of apparent successes, grand hopes, and emerging fears that surround AI. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent - really - are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant methods of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought that led to recent achievements. She meets with fellow experts like Douglas Hofstadter, the cognitive scientist and Pulitzer Prize - winning author of the modern classic Gödel, Escher, Bach, who explains why he is "terrified" about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much farther it has to go. Interweaving stories about the science and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and approachable accounts of the most interesting and provocative modern work in AI, flavored with Mitchell's humor and personal observations. This frank, lively book will prove an indispensable guide to understanding today's AI, its quest for "human-level" intelligence, and its impacts on all of our futures. PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
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