Skip to main content

Home/ Artificial Intelligence Research/ Group items tagged A:

Rss Feed Group items tagged

Filter: All | Bookmarks | Topics Simple Middle
Matvey Ezhov

PLoS Computational Biology: Qualia: The Geometry of Integrated Information - 1 views

www.ploscompbiol.org/...10.1371%2Fjournal.pcbi.1000462

qualia consciousness PLoS

shared by Matvey Ezhov on 23 Oct 09 - Cached
  • Matvey Ezhov on 23 Oct 09
     
    According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale-a shape that completely and univocally characterizes the quality of a conscious experience. Φ- the height of this shape-is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the "redness" of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating quali
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
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
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.
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.
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. 
mikhail-miguel

PromptVibes - A Massive Collection of Useful ChatGPT Prompts, that will turn you into a... - 0 views

www.promptvibes.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    A Massive Collection of useful ChatGPT Prompts, you can steal to make you a ChatGPT expert. PromptVibes: A Massive Collection of Useful ChatGPT Prompts, that will turn you into a ChatGPT Expert (promptvibes.com). PromptVibes: A Massive Collection of useful ChatGPT Prompts (promptvibes.com).
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
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.”
mikhail-miguel

The AI Product Manager's Handbook (+Free PDF Ed.) - 0 views

a.co/eCoUR9o

'Recommended Books' Artificial-Intelligence AI

shared by mikhail-miguel on 27 Jul 23 - No Cached
  • mikhail-miguel on 27 Jul 23
     
    Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Build products that leverage AI for the common good and commercial success Take macro data and use it to show your customers you're a source of truth Best practices and common pitfalls that impact companies while developing AI product Book Description Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed. The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products. You'll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You'll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you'll stay ahead of the curve in the rapidly evolving field of AI and ML. By the end of this book, you'll have understood how to navigate the world of AI from a product perspective. What you will learn Build AI products for the future using minimal resources Identify opportunities where AI can be leveraged to meet business needs Collaborate with cross-function
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.
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

Technology Review: Intelligence Explained (!) - 0 views

www.technologyreview.com/printer_friendly_article.aspx

intelligence ai neuroscience news brain neuroinformatics science

shared by Matvey Ezhov on 24 Oct 09 - Cached
  • "Scientists are now able to switch the focus from particular regions of the brain to the connections between those regions," says Sherif Karama, a psychiatrist and a neuroscientist at McGill University's Montreal Neurological Institute.
  • A quantifiable "general intelligence factor," known as g, can be statistically extracted from scores on a battery of intelligence tests.
  • In 2001, Thompson showed that it is correlated with volume in the frontal cortex, a result consistent with a number of studies that have linked intelligence to overall brain size.
  • ...3 more annotations...
  • In 2007, Jung and Richard Haier, now professor emeritus of psychology at the University of California, Irvine, developed the first comprehensive theory drawn from neuroimaging of how the brain gives rise to intelligence.
    • Matvey Ezhov
      Matvey Ezhov on 24 Oct 09
       
      Attention! To Research.
  • As we "evolved from worms to humans," says George Bartzokis, a professor of psychiatry at UCLA, the number of non-neural cells in the brain increased 50 times more than the number of neurons. He adds, "My hypothesis has always been that what gives us our cognitive capacity is not actually the number of neurons, which can vary tremendously between human individuals, but rather the quality of our connections."
  • The type of MRI typically used for medical scans does not show the finer details of the brain's white matter. But with a technique called diffusion tensor imaging (DTI), which uses the scanner's magnet to track the movement of water molecules in the brain, scientists have developed ways to map out neural wiring in detail. While water moves randomly within most brain tissue, it flows along the insulated neural fibers like current through a wire.
mikhail-miguel

AI is a Joke - Enter a topic and it will give you a joke along with an Artificial Intel... - 0 views

aiisajoke.com

Artificial-Intelligence AI

shared by mikhail-miguel on 23 Jul 23 - No Cached
  • mikhail-miguel on 23 Jul 23
     
    AI is a Joke: Enter a topic and it will give you a joke along with an Artificial Intelligence generated image (aiisajoke.com).
mikhail-miguel

AI is a Joke - Enter a topic and it will give you a joke along with an Artificial Intel... - 0 views

www.aiisajoke.com

Artificial-Intelligence AI

shared by mikhail-miguel on 23 Jul 23 - No Cached
  • mikhail-miguel on 23 Jul 23
     
    AI is a Joke: Enter a topic and it will give you a joke along with an Artificial Intelligence generated image (aiisajoke.com).
mikhail-miguel

Connected Papers - Getting a visual overview of a field of research (connectedpapers.com). - 0 views

www.connectedpapers.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Connected Papers: Explore connected papers in a visual graph (connectedpapers.com). Connected Papers: Getting a visual overview of a field of research (connectedpapers.com).
mikhail-miguel

Ai Sofiya - Artificial Intelligence Sofiya is a Super Artificial Intelligence Tool that... - 0 views

aisofiya.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Ai Sofiya: Artificial Intelligence Sofiya is a Super Artificial Intelligence Tool that can create Ad in a minute (aisofiya.com). Ai Sofiya: Super Artificial Intelligence Tool that can create Ad in a minute (aisofiya.com).
mikhail-miguel

Connected Papers - Getting a visual overview of a field of research (connectedpapers.com). - 0 views

connectedpapers.com

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    Connected Papers: Explore connected papers in a visual graph (connectedpapers.com). Connected Papers: Getting a visual overview of a field of research (connectedpapers.com).
mikhail-miguel

PromptVibes - A Massive Collection of Useful ChatGPT Prompts, that will turn you into a... - 0 views

promptvibes.com

Artificial-Intelligence AI

shared by mikhail-miguel on 23 Jul 23 - No Cached
  • mikhail-miguel on 23 Jul 23
     
    PromptVibes: A Massive Collection of Useful ChatGPT Prompts, that will turn you into a ChatGPT Expert (promptvibes.com). PromptVibes: A Massive Collection of useful ChatGPT Prompts (promptvibes.com).
mikhail-miguel

One Click Article Creator - A powerful tool designed to help content creators and marke... - 0 views

www.buildai.space/...meal-mate-guru-5e9ca

Artificial-Intelligence AI

shared by mikhail-miguel on 09 Jul 23 - No Cached
  • mikhail-miguel on 09 Jul 23
     
    One Click Article Creator: A powerful tool designed to help content creators and marketers generate high-quality articles with just a single click (buildai.space). One Click Article Creator: Designed to help content creators and marketers generate high-quality articles with just a single click (buildai.space).
1 - 20 of 299 Next › Last »
Showing 20▼ items per page