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mikhail-miguel

What font is - What Font Is is a font finder tool that allows users to find any font fr... - 0 views

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    What font is: What Font Is is a font finder tool that allows users to find any font from any image (whatfontis.com).
mikhail-miguel

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

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    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

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    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.
mikhail-miguel

What-A-Prompt - Generates creative and optimized prompts for enhanced ChatGPT results (... - 0 views

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    What-A-Prompt: Generates creative and optimized prompts for enhanced ChatGPT results (freshly.ai).
Matvey Ezhov

Do Bayesian statistics rule the brain? - 0 views

  • Over the past decade, neuroscientists have found that real brains seem to work in this way. In perception and learning experiments, for example, people tend to make estimates - of the location or speed of a moving object, say - in a way that fits with Bayesian probability theory. There's also evidence that the brain makes internal predictions and updates them in a Bayesian manner. When you listen to someone talking, for example, your brain isn't simply receiving information, it also predicts what it expects to hear and constantly revises its predictions based on what information comes next. These predictions strongly influence what you actually hear, allowing you, for instance, to make sense of distorted or partially obscured speech.
  • In fact, making predictions and re-evaluating them seems to be a universal feature of the brain. At all times your brain is weighing its inputs and comparing them with internal predictions in order to make sense of the world.
mikhail-miguel

ResearchRabbit - ResearchRabbit learns what you love and improves its recommendations! ... - 0 views

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    ResearchRabbit: ResearchRabbit learns what you love and improves its recommendations! (researchrabbit.ai). ResearchRabbit: Supercharge your research workflows (researchrabbit.ai).
mikhail-miguel

What does this code do? - Rapidly grasp new code with a VS Code extension (whatdoesthis... - 0 views

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    What does this code do?: Rapidly grasp new code with a VS Code extension (whatdoesthiscodedo.com).
mikhail-miguel

What on earth? - Create stories with one-word prompts for fun learning (whatonearth.xyz). - 0 views

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    What on earth?: Create stories with one-word prompts for fun learning (whatonearth.xyz).
mikhail-miguel

ResearchRabbit - ResearchRabbit learns what you love and improves its recommendations! ... - 0 views

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    ResearchRabbit: ResearchRabbit learns what you love and improves its recommendations! (researchrabbit.ai). ResearchRabbit: Supercharge your research workflows (researchrabbit.ai).
mikhail-miguel

ProductBot - Decide what to buy on Amazon and get recommendations based on your prefere... - 0 views

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    ProductBot: Decide what to buy on Amazon and get recommendations based on your preferences (getproduct.help).
mikhail-miguel

ProductBot - Decide what to buy on Amazon and get recommendations based on your prefere... - 0 views

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    ProductBot: Decide what to buy on Amazon and get recommendations based on your preferences (getproduct.help).
mikhail-miguel

Have I Been Encoded - Keep track of what all the different AI's say about you! (haveibe... - 0 views

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    Have I Been Encoded: Keep track of what all the different AI's say about you! (haveibeenencoded.com).
mikhail-miguel

Buildt - AI-powered search to find & modify code by describing what it does (buildt.ai). - 0 views

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    Buildt: AI-powered search to find & modify code by describing what it does (buildt.ai).
thinkahol *

Being No One - The MIT Press - 0 views

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    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.
Matvey Ezhov

On Biological and Digital Intelligence - 0 views

  • 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
       
      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
       
      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
       
      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
       
      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
       
      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
       
      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
       
      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 *

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

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    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

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    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…?
thinkahol *

YouTube - Dr. Antonio Damasio on Self Comes to Mind - 0 views

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    "What Inspired You to Write Self Comes to Mind?"
aliamalhotra

Tech Savvy News: What Artificial intelligence do and how? - 1 views

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    The expense of Artificial Intelligence is getting less expensive regarding calculation control and as far as devices. Each new device/library is helping Artificial Intelligence Engineers to invest less energy in forecast issues.
aliamalhotra

What is an Engineer AI? - 1 views

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    Job of engineer AI and staffing it with individuals who can play out a half and half of information building, information science, and programming advancement undertakings.
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