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mikhail-miguel

LinkedIn Post Generator - Tool to improve LinkedIn post performance by analyzing the Li... - 0 views

postgenerator.app

Artificial-Intelligence AI

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  • mikhail-miguel on 23 Jul 23
     
    LinkedIn Post Generator: Improve LinkedIn post performance by analyzing the LinkedIn algorithm and providing Artificial Intelligence recommendations (postgenerator.app). LinkedIn Post Generator: Tool to improve LinkedIn post performance by analyzing the LinkedIn algorithm and providing Artificial Intelligence recommendations (postgenerator.app).
mikhail-miguel

LinkedIn Post Generator - Tool to improve LinkedIn post performance by analyzing the Li... - 0 views

www.postgenerator.app

Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    LinkedIn Post Generator: Improve LinkedIn post performance by analyzing the LinkedIn algorithm and providing Artificial Intelligence recommendations (postgenerator.app). LinkedIn Post Generator: Tool to improve LinkedIn post performance by analyzing the LinkedIn algorithm and providing Artificial Intelligence recommendations (postgenerator.app).
mikhail-miguel

AlphaDev - AlphaDev discovers faster sorting algorithms (deepmind.com). - 0 views

www.deepmind.com/...vers-faster-sorting-algorithms

Artificial-Intelligence AI

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  • mikhail-miguel on 23 Jul 23
     
    AlphaDev: AlphaDev discovers faster sorting algorithms (deepmind.com).
thinkahol *

I, algorithm: A new dawn for artificial intelligence - tech - 31 January 2011 - New Sci... - 0 views

www.newscientist.com/...r-artificial-intelligence.html

I algorithm: A dawn for artificial intelligence - tech 31 January 2011 New Scientist

shared by thinkahol * on 01 Feb 11 - Cached
  • thinkahol * on 01 Feb 11
     
    Artificial intelligence has finally become trustworthy enough to watch over everything from nuclear bombs to premature babies
mikhail-miguel

Composer - Build trading algorithms with AI, backtest them, then execute-all in one pla... - 0 views

www.composer.trade

Artificial-Intelligence AI

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  • mikhail-miguel on 09 Jul 23
     
    Composer: Build trading algorithms with AI, backtest them, then execute-all in one platform (composer.trade).
mikhail-miguel

SwagAI - SwagAI helps you find creative company swag with Artificial Intelligence algor... - 0 views

www.useslingshot.com/swagai

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    SwagAI: SwagAI helps you find creative company swag with Artificial Intelligence algorithms (useslingshot.com).
thinkahol *

Researcher builds machines that daydream - Oddware - Technology - News - iTnews.com.au - 0 views

www.itnews.com.au/...ds-machines-that-daydream.aspx

Researcher builds machines that daydream - Oddware Technology News iTnews.com.au

shared by thinkahol * on 03 Oct 10 - No Cached
  • thinkahol * on 03 Oct 10
     
    Algorithms let robots reflect and meditate.
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.
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

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.
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.
Matvey Ezhov

The AI Revolution Is On | Magazine - 11 views

www.wired.com/...ff_ai_essay_airevolution

ai wired scipop

shared by Matvey Ezhov on 07 Feb 11 - No Cached
  • But they represent a new forefront in the field of artificial intelligence. Today’s AI doesn’t try to re-create the brain. Instead, it uses machine learning, massive data sets, sophisticated sensors, and clever algorithms to master discrete tasks.
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