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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.
mikhail-miguel

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World - 0 views

www.amazon.com/...ref=mp_s_a_1_1

'Recommended Books' Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    One of The Sunday Times Business Books of the Year Artificial intelligence is smarter than humans. It can process information at lightning speed and remain focused on specific tasks without distraction. AI can see into the future, predicting outcomes and even use sensors to see around physical and virtual corners. So why does AI frequently get it so wrong? The answer is us. Humans design the algorithms that define the way that AI works, and the processed information reflects an imperfect world. Does that mean we are doomed? In Scary Smart, Mo Gawdat, the internationally best-selling author of Solve for Happy, draws on his considerable expertise to answer this question and to show what we can all do now to teach ourselves and our machines how to live better. With more than 30 years' experience working at the cutting-edge of technology and his former role as chief business officer of Google [X], no one is better placed than Mo Gawdat to explain how the Artificial Intelligence of the future works. By 2049, AI will be a billion times more intelligent than humans. Scary Smart explains how to fix the current trajectory now, to make sure that the AI of the future can preserve our species. This book offers a blueprint, pointing the way to what we can do to safeguard ourselves, those we love and the planet itself.
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.
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. 
thinkahol *

Building Gods | Watch Free Documentary Online - 0 views

topdocumentaryfilms.com/building-gods

Building Gods | Watch Free Documentary Online

shared by thinkahol * on 15 Dec 10 - No Cached
  • thinkahol * on 15 Dec 10
     
    This film by Ken Gumbs tackles the issue of pending greater-than-human artificial intelligence and the possible ramifications. Different individuals with different backgrounds are interviewed on the subject, including a theologian, a philosopher, a brain builder and a cyborg. A wide spectrum of topics are discussed, including trans-humanism, mind-machine mergers, uploading, and artificial super-intelligence.
Volucer Volucer

Equal numbers of neuronal and nonneuronal cells ma... [J Comp Neurol. 2009] - PubMed re... - 0 views

www.ncbi.nlm.nih.gov/...19226510

neuroscience brain science наука нейробиология ИИ

shared by Volucer Volucer on 19 Dec 09 - No Cached
  • Volucer Volucer on 19 Dec 09
     
    "We find that the adult male human brain contains on average 86.1 +/- 8.1 billion NeuN-positive cells ("neurons") and 84.6 +/- 9.8 billion NeuN-negative ("nonneuronal") cells. With only 19% of all neurons located in the cerebral cortex, greater cortical size (representing 82% of total brain mass) in humans compared with other primates does not reflect an increased relative number of cortical neurons. "
  • Volucer Volucer on 19 Dec 09
     
    New data about overall number of neurons in brain
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

Recursive Self-Improvement - The Transhumanist Wiki - 2 views

transhumanistwiki.com/...Recursive_Self-Improvement

ai classic AI

shared by Matvey Ezhov on 20 Oct 09 - Cached
  • True Artificial Intelligence would bypass problems of biological complexity and ethics, growing up on a substrate ideal for initiating Recursive Self-Improvement. (fully reprogrammable, ultrafast, the AI's "natural habitat".) This Artificial Intelligence would be based upon: 1) our current understanding of the central algorithms of intelligence, 2) our current knowledge of the brain, obtained through high-resolution fMRI and delicate Cognitive Science experiments, and 3) the kind of computing hardware available to AI designers.
  • Humans cannot conduct any of these enhancements to ourselves; the inherent structure of our biology and the limited level of our current technology makes this impossible.
  • Recursive Self-Improvement is the ability of a mind to genuinely improve its own intelligence. This might be accomplished through a variety of means; speeding up one's own hardware, redesigning one's own cognitive architecture for optimal intelligence, adding new components into one's own hardware, custom-designing specialized modules for recurrent tasks, and so on.
  • ...2 more annotations...
  • Unfortunately, the neurological structures corresponding to human intelligence are likely to be highly intricate, delicate, and biologically very complex (unnecessarily so; evolution exhibits no foresight, and most of the brain evolved in the absence of human General Intelligence).
  • 2) advances in Cognitive Science that indicate the complexity of certain brain areas is largely extraneous to intelligence,
    • Matvey Ezhov
      Matvey Ezhov on 20 Oct 09
       
      Очень серьезно допущение, которое может быть ошибочно. Нам известно, что все зоны кортекса участвуют в формировании модели мира индивида, а значит и сознания.
mikhail-miguel

AutoWrite App - Artificial Intelligence Writer with Human-like SEO (autowrite.app). - 0 views

autowrite.app

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    AutoWrite App: Artificial Intelligence Writer with Human-like SEO (autowrite.app).
mikhail-miguel

Human or Not - A social Turing Game (humanornot.ai). - 0 views

humanornot.ai

Artificial-Intelligence AI

shared by mikhail-miguel on 23 Jul 23 - No Cached
  • mikhail-miguel on 23 Jul 23
     
    Human or Not: A social Turing Game (humanornot.ai).
mikhail-miguel

ZeroGPT - Differentiates human vs. synthetic text (zerogpt.cc). - 0 views

zerogpt.cc

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    ZeroGPT: Differentiates human vs. synthetic text (zerogpt.cc).
mikhail-miguel

Stable Attribution - A tool which lets anyone find the human creators behind Artificial... - 0 views

www.stableattribution.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Stable Attribution: A tool which lets anyone find the human creators behind Artificial Intelligence generated images (stableattribution.com).
mikhail-miguel

Mighty Travels - Artificial Intelligence and human intelligence will save you up to 90%... - 0 views

mightytravels.com/...welcome.php

Artificial-Intelligence AI

shared by mikhail-miguel on 23 Jul 23 - No Cached
  • mikhail-miguel on 23 Jul 23
     
    Mighty Travels: Artificial Intelligence and human intelligence will save you up to 90% on airfare tickets and hotels (mightytravels.com).
mikhail-miguel

HumanFest - Chat Roulette-Style Experiment: Human or AI-Powered Bot? (humanfest.chat). - 0 views

humanfest.chat

Artificial-Intelligence

shared by mikhail-miguel on 23 Jul 23 - No Cached
  • mikhail-miguel on 23 Jul 23
     
    HumanFest: Chat Roulette-Style Experiment: Human or AI-Powered Bot? (humanfest.chat).
mikhail-miguel

ChatLit Codes - Generate human-like text with pre-trained models (chat.lit.codes). - 0 views

chat.lit.codes

Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    ChatLit Codes: Generate human-like text with pre-trained models (chat.lit.codes).
mikhail-miguel

Human or Not - A social Turing Game (humanornot.ai). - 0 views

www.humanornot.ai

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Human or Not: A social Turing Game (humanornot.ai).
mikhail-miguel

Robotalk Artificial Intelligence - Robotalk: The Smart Way to Transfer Knowledge from R... - 0 views

robotalk.ai

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Robotalk Artificial Intelligence: Robotalk: The Smart Way to Transfer Knowledge from Robots to Humans (robotalk.ai).
thinkahol *

Being No One - The MIT Press - 0 views

mitpress.mit.edu/...default.asp

Being No One - The MIT Press

shared by thinkahol * on 23 Feb 12 - Cached
  • thinkahol * on 23 Feb 12
     
    According to Thomas Metzinger, no such things as selves exist in the world: nobody ever had or was a self. All that exists are phenomenal selves, as they appear in conscious experience. The phenomenal self, however, is not a thing but an ongoing process; it is the content of a "transparent self-model." In Being No One, Metzinger, a German philosopher, draws strongly on neuroscientific research to present a representationalist and functional analysis of what a consciously experienced first-person perspective actually is. Building a bridge between the humanities and the empirical sciences of the mind, he develops new conceptual toolkits and metaphors; uses case studies of unusual states of mind such as agnosia, neglect, blindsight, and hallucinations; and offers new sets of multilevel constraints for the concept of consciousness. Metzinger's central question is: How exactly does strong, consciously experienced subjectivity emerge out of objective events in the natural world? His epistemic goal is to determine whether conscious experience, in particular the experience of being someone that results from the emergence of a phenomenal self, can be analyzed on subpersonal levels of description. He also asks if and how our Cartesian intuitions that subjective experiences as such can never be reductively explained are themselves ultimately rooted in the deeper representational structure of our conscious minds.
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