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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.
mikhail-miguel

AudioPen - Just hit record. Then start Speaking. AudioPen will transcribe when you're d... - 0 views

audiopen.ai

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    AudioPen: Just hit record. Then start Speaking. AudioPen will transcribe when you're done (audiopen.ai).
Philip Solars

The Must Have Solar Equipment - 0 views

Due to the increasing cost of electricity bills, I have finally decided to switch to solar energy. Aside from being free, it also helps save mother earth. I must admit that at first I was confused ...

started by Philip Solars on 28 Sep 12 no follow-up yet
mikhail-miguel

The Art of Prompt Engineering with ChatGPT: Accessible Edition (Learn AI Tools the Fun ... - 0 views

www.amazon.com/...B0BZ4T68Q4

'Certification RE Use of ChatGPT and MidJourney' Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    Accessible Edition To make 'The Art of Prompt Engineering with ChatGPT' as beautiful as possible we designed the layout and published it here as a pdf. However, this wasn't the best option for those who used a kindle to read, or for those who had accessibility needs. So this is the accessible edition, rebuilt using a reflow able format. If you bought the original and need to use the accessibility features, just email us at nathan@ChatGPTtrainings.com. Let's move beyond basic examples and 'test this prompt.' ChatGPT is an amazing AI tool that can change the way we work. Bill Gates recently said that ChatGPT is as important an invention as the internet, and it could change the world. To make the most of ChatGPT and go beyond simple uses, you need to master prompt engineering. Check out a sample chapter: www.ChatGPTtrainings.com March Update - 2 New Sections with 34 More Pages of Content For this monthly update, we added a new section on Advanced Prompt Engineering to help you take your ChatGPT skills to a higher level once you've learned the basics. This section covers the co-creation approach, where you take control, and [format] your output, where you'll learn my favorite way to get the exact results you want. We also included a new section on GPT-4, with information on how to start, debunking past hype, and looking at some new improvements. This book will keep evolving as ChatGPT grows, making sure that everything you read and learn stays up-to-date and relevant. All updates are free and automatic for Kindle copies, and if you bought a hardcopy, you can email me at Nathan@ChatGPTtrainings.com with your proof of purchase to get a PDF update. Why This Book? This book helps you learn the art of working with ChatGPT to get much better results. This skill, prompt engineering, is what sets good apart from great when using ChatGPT. Learn 4 key techniques and tools for writing better prompts Master 2 advanced prompt engineering tools to take your skills further F
mikhail-miguel

Building Consistent Characters with MidJourney and ChatGPT: Unlocking the Power of Visu... - 0 views

www.amazon.com/...ref=sr_1_2

'Certification RE Use of ChatGPT and MidJourney' Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    Tired of the same stock photos next to each news article, blog post, and presentation? Me too. So when I wrote "The Art of Prompt Engineering with ChatGPT," I decided to build custom illustrations using MidJourney. I used a great trick that allowed me to rebuild my characters in different contexts throughout the book. Due to popular demand, I'm launching this book to teach you how to build your own consistent characters. In this book, you will learn: How text-to-image AI tools work How to get set up with MidJourney How to write basic prompts in MidJourney How to develop a character in MidJourney How to contextualise your character How to build backgrounds and compositions Not only that, but I've also added an extra section on using ChatGPT within your character building process, which covers: How to get ChatGPT to develop your characters and build them into MidJourney prompts How to get ChatGPT to contextualise your characters based on any text and build them into MidJourney prompts How to get ChatGPT to build backgrounds for your contextualised characters and build them into MidJourney prompts Get Certified and Show off Your Knowledge! Early Adopters of AI Tools should be able to have their expertise visible to their professional network. Chapter 17 of this book sets you up with a project to implement everything you have learned by creating a visual children's story with ChatGPT and MidJourney. ChatGPT Trainings has put together a certification program that lets you submit your project as proof of your new abilities, and in return you will receive a recognised and verifiable certification that can be added to your LinkedIn Profile. Head over to www.ChatGPTtrainings.com/certifications for more information
mikhail-miguel

Opera ("Aria") browser - 0 views

www.opera.com/...browser-ai

Alternative-AI-Tools Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    Opera features an integrated AI called Aria that you can access from the sidebar. You can use a keyboard shortcut (CTRL or Command and /) to start using Aria as well. The AI is also available in Opera's Android browser. The AI stems from Opera's partnership with ChatGPT creator OpenAI. Aria connects to GPT to help answer users' queries. The AI incorporates live information from the web and it can generate text or code and answer support questions regarding Opera products. In addition, Opera One can generate contextual prompts for Aria when you right click or highlighting text in the browser. If you prefer to use ChatGPT or ChatSonic, you can access those from the Opera One sidebar too. Opera says users don't have to engage with the browser's AI features if they don't want to. For one thing, you'll need to be logged into an Opera account to use Aria.
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

Perceptual Learning Relies On Local Motion Signals To Learn Global Motion - 0 views

www.sciencedaily.com/...090921134836.htm

news brain neuroscience

shared by Matvey Ezhov on 09 Oct 09 - Cached
  • The brain first perceives changes in visual input (local motion) in the primary visual cortex. The local motion signals are then integrated in the later visual processing stages and interpreted as global motion in the higher-level processes. But when subjects in a recent experiment using moving dots were asked to detect global motion (the overall direction of the dots moving together), the results show that their learning relied on more local motion processes (the movement of dots in small areas) than global motion areas.
  • show that the improvement in detection of global motion is not due to learning of the global motion but to learning of local motion of the moving dots in the test.
Matvey Ezhov

Time-keeping Brain Neurons Discovered - 3 views

www.sciencedaily.com/...091019162921.htm

brain neuroscience ToRead news

shared by Matvey Ezhov on 23 Oct 09 - Cached
  • An MIT team led by Institute Professor Ann Graybiel has found groups of neurons in the primate brain that code time with extreme precision.
  • The neurons are located in the prefrontal cortex and the striatum, both of which play important roles in learning, movement and thought control.
  • The research team trained two macaque monkeys to perform a simple eye-movement task. After receiving the "go" signal, the monkeys were free to perform the task at their own speed. The researchers found neurons that consistently fired at specific times -- 100 milliseconds, 110 milliseconds, 150 milliseconds and so on -- after the "go" signal.
  • Volucer Volucer on 30 Oct 09
     
    Its would be difficult, if neurons of that kind have not be discovered. Obliviously, we have millions of it in our brains. For make time-keeping neurons we need (in simplest case) only 2 neurons with reciprocal connections. More units in circle - more time to delay - more time to "keep". Also, not single "time keeping neurons" but time keeping circles. Such clear understating of processes on neuronal level is completely impossible without Brainbug play experience. Think about it!
Matvey Ezhov

Do Bayesian statistics rule the brain? - 0 views

www.mindhacks.com/...do_bayesian_statisti.html

bayesian theory of the brain brain neuroscience ai science neuroinformatics

shared by Matvey Ezhov on 27 Oct 09 - Cached
  • 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.
Matvey Ezhov

Is this a unified theory of the brain? (Bayesian theory in New Scientist) - 1 views

reverendbayes.wordpress.com/...yesian-theory-in-new-scientist

bayesian theory of the brain brain neuroscience ai science neuroinformatics

shared by Matvey Ezhov on 27 Oct 09 - Cached
  • Neuroscientist Karl Friston and his colleagues have proposed a mathematical law that some are claiming is the nearest thing yet to a grand unified theory of the brain. From this single law, Friston’s group claims to be able to explain almost everything about our grey matter.
  • Friston’s ideas build on an existing theory known as the “Bayesian brain”, which conceptualises the brain as a probability machine that constantly makes predictions about the world and then updates them based on what it senses.
  • A crucial element of the approach is that the probabilities are based on experience, but they change when relevant new information, such as visual information about the object’s location, becomes available. “The brain is an inferential agent, optimising its models of what’s going on at this moment and in the future,” says Friston. In other words, the brain runs on Bayesian probability.
  • ...6 more annotations...
  • “In short, everything that can change in the brain will change to suppress prediction errors, from the firing of neurons to the wiring between them, and from the movements of our eyes to the choices we make in daily life,” he says.
  • Friston created a computer simulation of the cortex with layers of “neurons” passing signals back and forth. Signals going from higher to lower levels represent the brain’s internal predictions, while signals going the other way represent sensory input. As new information comes in, the higher neurons adjust their predictions according to Bayesian theory.
  • Volunteers watched two sets of moving dots, which sometimes moved in synchrony and at others more randomly, to change the predictability of the stimulus. The patterns of brain activity matched Friston’s model of the visual cortex reasonably well.
  • Friston’s results have earned praise for bringing together so many disparate strands of neuroscience. “It is quite certainly the most advanced conceptual framework regarding an application of these ideas to brain function in general,” says Wennekers. Marsel Mesulam, a cognitive neurologist from Northwestern University in Chicago, adds: “Friston’s work is pivotal. It resonates entirely with the sort of model that I would like to see emerge.”
  • “The final equation you write on a T-shirt will be quite simple,” Friston predicts.
  • There’s work still to be done, but for now Friston’s is the most promising approach we’ve got. “It will take time to spin off all of the consequences of the theory – but I take that property as a sure sign that this is a very important theory,” says Dehaene. “Most other models, including mine, are just models of one small aspect of the brain, very limited in their scope. This one falls much closer to a grand theory.”
Volucer Volucer

Bigger not necessarily better, when it comes to brains - 1 views

www.physorg.com/news177692594.html

AI ИИ brain neuroscience news наука

shared by Volucer Volucer on 22 Nov 09 - Cached
  • Volucer Volucer on 22 Nov 09
     
    "In fact, the models suggest that counting could be achieved with only a few hundred nerve cells and only a few thousand could be enough to generate consciousness. "
Matvey Ezhov

Accelerating Future » More Debate on Superintelligent AGI Goals - 6 views

www.acceleratingfuture.com/...more-debate-on-agi-goals

agi agi safety ai

shared by Matvey Ezhov on 08 Nov 10 - No Cached
  • Even if these simple AIs could modify their own utility functions, why would they? Nothing outside the utility function has the power to generate base “motivation”. I’m not sure why this is hard to understand. It’s not going to magically change when AIs become smarter.
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