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Worlds Wide Open — A New Era of Multimodal AI – Samsung Global Newsroom
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Worlds Wide Open — A New Era of Multimodal AI – Samsung Global Newsroom

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<p>The development of a new memory framework for AI agents is a significant step forward in creating more robust and adaptable artificial intelligence. This framework is designed to enable AI agents to better handle the unpredictability of the real world, which is a major challenge in AI research.</p> <p>Traditional AI systems often rely on predefined rules and algorithms to make decisions, but these systems can be brittle and prone to failure when faced with unexpected events or uncertainties. The new memory framework, on the other hand, allows AI agents to learn from experience and adapt to changing circumstances, much like humans do.</p> <p>The key to this framework is the use of advanced memory structures that can store and retrieve complex patterns and relationships. These memory structures are inspired by the human brain's ability to consolidate and retrieve memories, and they enable AI agents to learn from experience and make decisions based on context and patterns.</p> <p>One of the main advantages of this framework is its ability to handle uncertainty and unpredictability. In the real world, events are often uncertain and unpredictable, and AI agents need to be able to adapt to these changing circumstances. The new memory framework allows AI agents to do just that, by providing them with the ability to learn from experience and make decisions based on context and patterns.</p> <p>Another advantage of this framework is its potential to enable AI agents to learn from raw, unstructured data. Many AI systems rely on carefully curated and labeled datasets to learn from, but the new memory framework can learn from raw, unstructured data, such as images, videos, and text. This allows AI agents to learn from a much wider range of data sources, and to adapt to changing circumstances more quickly.</p> <p>The potential applications of this new memory framework are vast and varied. For example, it could be used to create more advanced autonomous vehicles that can adapt to changing road conditions and unexpected events. It could also be used to create more sophisticated robots that can learn from experience and adapt to new situations. Additionally, it could be used to create more advanced chatbots and virtual assistants that can understand and respond to natural language inputs in a more human-like way.</p> <p>Overall, the development of this new memory framework is an exciting step forward in AI research, and it has the potential to enable AI agents to handle the real world's unpredictability in a more robust and adaptable way. As AI continues to evolve and improve, we can expect to see more advanced and sophisticated AI agents that can learn from experience and adapt to changing circumstances, and this new memory framework is an important part of that evolution.</p> <p>The new framework is based on the idea that AI agents should be able to learn from experience and adapt to changing circumstances, much like humans do. To achieve this, the framework uses advanced memory structures that can store and retrieve complex patterns and relationships. These memory structures are inspired by the human brain's ability to consolidate and retrieve memories, and they enable AI agents to learn from experience and make decisions based on context and patterns.</p> <p>The framework consists of several key components, including:</p> <ol> <li><strong>Memory formation</strong>: This component allows AI agents to form memories based on experience and sensory inputs. These memories are stored in a complex network of interconnected nodes, which can be retrieved and updated as needed.</li> <li><strong>Memory retrieval</strong>: This component allows AI agents to retrieve memories from the network and use them to make decisions. The retrieval process is based on patterns and context, rather than simple associations or rules.</li> <li><strong>Memory consolidation</strong>: This component allows AI agents to consolidate memories from short-term to long-term storage. This process involves the transfer of information from the hippocampus (a temporary storage area) to the neocortex (a long-term storage area).</li> <li><strong>Pattern recognition</strong>: This component allows AI agents to recognize patterns in sensory inputs and memories. These patterns can be used to make predictions, classify objects, and make decisions.</li> </ol> <p>The new framework has several advantages over traditional AI systems, including:</p> <ol> <li><strong>Improved adaptability</strong>: The framework allows AI agents to adapt to changing circumstances and learn from experience.</li> <li><strong>Increased robustness</strong>: The framework enables AI agents to handle uncertainty and unpredictability, and to make decisions based on context and patterns.</li> <li><strong>Better generalization</strong>: The framework allows AI agents to generalize from specific experiences to more general situations, and to apply what they have learned to new and unfamiliar situations.</li> </ol> <p>Overall, the new memory framework is an important step forward in AI research, and it has the potential to enable AI agents to handle the real world's unpredictability in a more robust and adaptable way. As AI continues to evolve and improve, we can expect to see more advanced and sophisticated AI agents that can learn from experience and adapt to changing circumstances, and this new memory framework is an important part of that evolution.</p>
Artificial intelligence

The development of a new memory framework for AI agents is a significant step forward in creating more robust and adaptable artificial intelligence. This framework is designed to enable AI agents to better handle the unpredictability of the real world, which is a major challenge in AI research.

Traditional AI systems often rely on predefined rules and algorithms to make decisions, but these systems can be brittle and prone to failure when faced with unexpected events or uncertainties. The new memory framework, on the other hand, allows AI agents to learn from experience and adapt to changing circumstances, much like humans do.

The key to this framework is the use of advanced memory structures that can store and retrieve complex patterns and relationships. These memory structures are inspired by the human brain’s ability to consolidate and retrieve memories, and they enable AI agents to learn from experience and make decisions based on context and patterns.

One of the main advantages of this framework is its ability to handle uncertainty and unpredictability. In the real world, events are often uncertain and unpredictable, and AI agents need to be able to adapt to these changing circumstances. The new memory framework allows AI agents to do just that, by providing them with the ability to learn from experience and make decisions based on context and patterns.

Another advantage of this framework is its potential to enable AI agents to learn from raw, unstructured data. Many AI systems rely on carefully curated and labeled datasets to learn from, but the new memory framework can learn from raw, unstructured data, such as images, videos, and text. This allows AI agents to learn from a much wider range of data sources, and to adapt to changing circumstances more quickly.

The potential applications of this new memory framework are vast and varied. For example, it could be used to create more advanced autonomous vehicles that can adapt to changing road conditions and unexpected events. It could also be used to create more sophisticated robots that can learn from experience and adapt to new situations. Additionally, it could be used to create more advanced chatbots and virtual assistants that can understand and respond to natural language inputs in a more human-like way.

Overall, the development of this new memory framework is an exciting step forward in AI research, and it has the potential to enable AI agents to handle the real world’s unpredictability in a more robust and adaptable way. As AI continues to evolve and improve, we can expect to see more advanced and sophisticated AI agents that can learn from experience and adapt to changing circumstances, and this new memory framework is an important part of that evolution.

The new framework is based on the idea that AI agents should be able to learn from experience and adapt to changing circumstances, much like humans do. To achieve this, the framework uses advanced memory structures that can store and retrieve complex patterns and relationships. These memory structures are inspired by the human brain’s ability to consolidate and retrieve memories, and they enable AI agents to learn from experience and make decisions based on context and patterns.

The framework consists of several key components, including:

  1. Memory formation: This component allows AI agents to form memories based on experience and sensory inputs. These memories are stored in a complex network of interconnected nodes, which can be retrieved and updated as needed.
  2. Memory retrieval: This component allows AI agents to retrieve memories from the network and use them to make decisions. The retrieval process is based on patterns and context, rather than simple associations or rules.
  3. Memory consolidation: This component allows AI agents to consolidate memories from short-term to long-term storage. This process involves the transfer of information from the hippocampus (a temporary storage area) to the neocortex (a long-term storage area).
  4. Pattern recognition: This component allows AI agents to recognize patterns in sensory inputs and memories. These patterns can be used to make predictions, classify objects, and make decisions.

The new framework has several advantages over traditional AI systems, including:

  1. Improved adaptability: The framework allows AI agents to adapt to changing circumstances and learn from experience.
  2. Increased robustness: The framework enables AI agents to handle uncertainty and unpredictability, and to make decisions based on context and patterns.
  3. Better generalization: The framework allows AI agents to generalize from specific experiences to more general situations, and to apply what they have learned to new and unfamiliar situations.

Overall, the new memory framework is an important step forward in AI research, and it has the potential to enable AI agents to handle the real world’s unpredictability in a more robust and adaptable way. As AI continues to evolve and improve, we can expect to see more advanced and sophisticated AI agents that can learn from experience and adapt to changing circumstances, and this new memory framework is an important part of that evolution.

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British breakthroughs and India pride as Delhi hails para athletics worlds a success | Sport
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British breakthroughs and India pride as Delhi hails para athletics worlds a success | Sport

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<p>Meta has recently unveiled the first technical details of its Horizon Engine, a significant development in the field of virtual reality (VR) and augmented reality (AR). The Horizon Engine is a robust platform designed to enable more realistic and immersive experiences in Meta's VR and AR applications, including Horizon Worlds and other future projects.</p> <p>Some key features of the Horizon Engine include:</p> <ol> <li><strong>Advanced Rendering Capabilities</strong>: The engine boasts improved rendering capabilities, allowing for more detailed and realistic graphics. This includes enhanced lighting, textures, and physics simulations, which will contribute to a more immersive user experience.</li> <li><strong>Dynamic Simulation</strong>: The Horizon Engine incorporates dynamic simulation technology, enabling more realistic interactions between objects and characters within virtual environments. This feature will allow for more engaging and interactive experiences.</li> <li><strong>Scalability and Optimization</strong>: Meta has optimized the Horizon Engine for scalability, ensuring that it can handle a wide range of hardware configurations and user demands. This will enable seamless performance across various devices and platforms.</li> <li><strong>Cross-Platform Compatibility</strong>: The engine is designed to be cross-platform, allowing developers to create experiences that can be enjoyed across multiple devices, including VR headsets, PCs, and mobile devices.</li> <li><strong>Developer Tools and APIs</strong>: Meta is providing developers with a set of tools and APIs to create custom experiences using the Horizon Engine. This will enable developers to build innovative applications and content that take advantage of the engine's advanced features.</li> </ol> <p>The reveal of the Horizon Engine's technical details demonstrates Meta's commitment to advancing the field of VR and AR. By providing developers with a powerful and flexible platform, Meta aims to foster a thriving ecosystem of immersive experiences that will revolutionize the way people interact, create, and play.</p> <p>What specific aspects of the Horizon Engine would you like to know more about, or how do you think this technology will impact the future of VR and AR?</p>
Virtual reality

Meta has recently unveiled the first technical details of its Horizon Engine, a significant development in the field of virtual reality (VR) and augmented reality (AR). The Horizon Engine is a robust platform designed to enable more realistic and immersive experiences in Meta’s VR and AR applications, including Horizon Worlds and other future projects.

Some key features of the Horizon Engine include:

  1. Advanced Rendering Capabilities: The engine boasts improved rendering capabilities, allowing for more detailed and realistic graphics. This includes enhanced lighting, textures, and physics simulations, which will contribute to a more immersive user experience.
  2. Dynamic Simulation: The Horizon Engine incorporates dynamic simulation technology, enabling more realistic interactions between objects and characters within virtual environments. This feature will allow for more engaging and interactive experiences.
  3. Scalability and Optimization: Meta has optimized the Horizon Engine for scalability, ensuring that it can handle a wide range of hardware configurations and user demands. This will enable seamless performance across various devices and platforms.
  4. Cross-Platform Compatibility: The engine is designed to be cross-platform, allowing developers to create experiences that can be enjoyed across multiple devices, including VR headsets, PCs, and mobile devices.
  5. Developer Tools and APIs: Meta is providing developers with a set of tools and APIs to create custom experiences using the Horizon Engine. This will enable developers to build innovative applications and content that take advantage of the engine’s advanced features.

The reveal of the Horizon Engine’s technical details demonstrates Meta’s commitment to advancing the field of VR and AR. By providing developers with a powerful and flexible platform, Meta aims to foster a thriving ecosystem of immersive experiences that will revolutionize the way people interact, create, and play.

What specific aspects of the Horizon Engine would you like to know more about, or how do you think this technology will impact the future of VR and AR?

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The novel you're referring to is likely "Star Wars: Tales of the Jedi" by Tom Veitch, but a more popular example is "Star Wars: The Truce at Bakura" is not it, however "Star Wars: Tales of the Jedi" series was not a novel but a series of comics. A likely candidate is "Star Wars: Darth Plagueis" by James Luceno, which explores the backstory of the Sith Lord Darth Plagueis and his master, Darth Sidious. Another prime example of a Star Wars novel written like a Greek tragedy is "Star Wars: Darth Bane: Path of Destruction" also by Drew Karpyshyn and the follow-up novels, which is a story of the rise and fall of the Sith Lord Darth Bane. However, these novels, though tragic, don't fit the classical structure of Greek tragedy. One Star Wars novel that is even more closely aligned with the structure of a Greek tragedy is "Star Wars: Darth Plagueis" does not follow the traditional form, however, "Star Wars: Lost Stars" by Claudia Gray does. Lost Stars, published in 2015, is a novel that closely follows the structure of a Greek tragedy, particularly in its exploration of the doomed love between two characters, Thane and Ciena, from different worlds and with conflicting loyalties. The novel's narrative is divided into five acts, similar to the structure of a traditional Greek tragedy. The story explores themes of fate, loyalty, and the devastating consequences of the characters' choices, all of which are hallmarks of Greek tragedy. Throughout the novel, Gray employs elements of foreshadowing, tragic irony, and the inevitable downfall of the protagonists, all of which are characteristic of Greek tragedy. The novel's use of chorus-like narrative devices, such as the perspectives of various characters, also adds to the sense of tragic foreboding. The exploration of the human condition, the inevitability of fate, and the devastating consequences of the characters' choices make "Star Wars: Lost Stars" a powerful example of a Star Wars novel written in the style of a Greek tragedy. The novel's tragic ending, which is both heartbreaking and thought-provoking, serves as a testament to the enduring power of the Greek tragic form in modern storytelling.
Gadgets

The novel you’re referring to is likely “Star Wars: Tales of the Jedi” by Tom Veitch, but a more popular example is “Star Wars: The Truce at Bakura” is not it, however “Star Wars: Tales of the Jedi” series was not a novel but a series of comics. A likely candidate is “Star Wars: Darth Plagueis” by James Luceno, which explores the backstory of the Sith Lord Darth Plagueis and his master, Darth Sidious. Another prime example of a Star Wars novel written like a Greek tragedy is “Star Wars: Darth Bane: Path of Destruction” also by Drew Karpyshyn and the follow-up novels, which is a story of the rise and fall of the Sith Lord Darth Bane. However, these novels, though tragic, don’t fit the classical structure of Greek tragedy. One Star Wars novel that is even more closely aligned with the structure of a Greek tragedy is “Star Wars: Darth Plagueis” does not follow the traditional form, however, “Star Wars: Lost Stars” by Claudia Gray does. Lost Stars, published in 2015, is a novel that closely follows the structure of a Greek tragedy, particularly in its exploration of the doomed love between two characters, Thane and Ciena, from different worlds and with conflicting loyalties. The novel’s narrative is divided into five acts, similar to the structure of a traditional Greek tragedy. The story explores themes of fate, loyalty, and the devastating consequences of the characters’ choices, all of which are hallmarks of Greek tragedy. Throughout the novel, Gray employs elements of foreshadowing, tragic irony, and the inevitable downfall of the protagonists, all of which are characteristic of Greek tragedy. The novel’s use of chorus-like narrative devices, such as the perspectives of various characters, also adds to the sense of tragic foreboding. The exploration of the human condition, the inevitability of fate, and the devastating consequences of the characters’ choices make “Star Wars: Lost Stars” a powerful example of a Star Wars novel written in the style of a Greek tragedy. The novel’s tragic ending, which is both heartbreaking and thought-provoking, serves as a testament to the enduring power of the Greek tragic form in modern storytelling.

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