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Janos Haits

Best AI Writer, Content Generator & Copywriting Assistant | Easy-Peasy.AI - 0 views

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    "Why choose one AI tool when you can have them all? MARKY: ChatGPT like AI chat with real-time data, vision, and PDF AI Chat Build no-code AI Bots by training on your own data. Embed on any website or share via URL"
Janos Haits

Free AI Tools: The Full List of over 5,000 AI sites | 2024 | Aixploria - 0 views

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    "All Free AI Tools The full list of all free AI sites"
Janos Haits

Notion AI - 0 views

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    "Introducing Notion AI Leverage the limitless power of AI in any Notion page. Write faster, think bigger, and augment your creativity. Like magic! Join the waitlist. Watch 1 minute demo"
Janos Haits

Illuminate - 0 views

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    "Illuminate is an experimental technology that uses AI to adapt content to your learning preferences. Discussions are generated with AI voices and are grounded in published academic papers. Generated content is provided for informational purposes only and may sometimes be offensive or inaccurate, so you should confirm any facts independently in the original content. AI voices are experimental and may sometimes make mistakes. Your feedback is helpful in improving the technology for everyone."
Janos Haits

YC AI - 0 views

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    "Our long-term goal is to democratize AI. We want to level the playing field for startups to ensure that innovation doesn't get locked up in large companies like Google or Facebook. If you're starting an AI company, we want to help you succeed. Apply here and mention this post in your application."
Janos Haits

The #1 Directory for AI Detector Tools - DetectorTools.ai - 0 views

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    "The #1 Directory for AI Detector Tools"
Janos Haits

Home - POWER AI - 0 views

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    "DISCOVER POWERFUL AI TOOLS : World's Largest AI Tool Directory, Updated Daily."
Janos Haits

Dust - Cracking team productivity with AI - 0 views

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    "Cracking team productivity with AI The way we work is changing. Break down knowledge silos and amplify team performance with data-augmented, customizable and secure AI assistants."
Janos Haits

Best AI Tools And Resources | Powerusers AI - 0 views

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    "Hand Picked AI Tools to boost your business productivity"
Janos Haits

Codestral: Hello, World! | Mistral AI | Frontier AI in your hands - 0 views

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    "Codestral: Hello, World! Empowering developers and democratising coding with Mistral AI."
Janos Haits

MaxAI.me: 1-Click AI Everywhere (GPT-4, Claude 3, Gemini 1.5) - 0 views

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    "1-click AI anywhere, Less time, Better results. Use personalized 1-click AI anywhere to save hours every day. Powered by ChatGPT, Claude 3, Gemini 1.5, GPT-4, and more."
Janos Haits

Coze: Next-Gen AI Chatbot Developing Platform - 0 views

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    "Coze your way to AI bot creation Next-generation AI chatbot building platform. Quickly create bots without coding and publish them on various platforms"
Janos Haits

Illuminate - 0 views

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    "Turn academic papers into AI-generated audio discussions About this experiment Illuminate is an experimental technology that uses AI to adapt content to your learning preferences."
Janos Haits

AI Excel Bot | Use AI To Generate Excel Formulas In Seconds - 0 views

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    "Write Excel and Google Sheets Formulas 10x Faster With AI"
Abdelrahman Ogail

Simulated annealing - Wikipedia, the free encyclopedia - 1 views

  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
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  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
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    Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration - provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima-which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
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    A natural AI approach
Abdelrahman Ogail

Production system - Wikipedia, the free encyclopedia - 0 views

  • A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.
  • A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.
Janos Haits

Artificial Intelligence (AI) Machine Learning Advanced Technology Platform - 0 views

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    "Our 2021.AI platform offers everything your team needs in one open platform, allowing your organization to manage team collaboration across heterogeneous infrastructure efficiently and deploy models effectively. Should you decide that you do not have the appetite to build such capacity and capabilities in-house, we will offer you data sciences as a service, ensuring your participation in harvesting and maximizing business benefits with a minimal organizational imprint."
Janos Haits

Futurepedia - The Largest AI Tools Directory | Home - 0 views

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    "THE LARGEST AI TOOLS DIRECTORY, UPDATED DAILY."
Janos Haits

wizdom.ai - intelligence for everyone - 0 views

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    "wizdom.ai is a result of extensive R&D by our team of data scientists, programmers, analysts, designers, quality engineers, product managers & process managers. The startup from the University of Oxford was founded by Tahir, Sadia, Rifaqat, David, Atikah and Asif."
Janos Haits

Meet Khanmigo, Khan Academy's AI-powered teaching assistant & tutor - 0 views

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    "Meet Khanmigo, your go-to AI tool for learning and teaching. Now just $4/month.*"
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