Minecraft - Revision history

BPeat at 15:50, 26 November 2024

2024-11-26T15:50:42Z

BPeat at 16:39, 6 November 2024

2024-11-06T16:39:15Z

BPeat at 20:41, 16 June 2024

2024-06-16T20:41:53Z

BPeat at 12:56, 16 June 2024

2024-06-16T12:56:50Z

BPeat: /* CHUNGUS */

2024-06-13T11:06:19Z

‎CHUNGUS

BPeat at 10:51, 13 June 2024

2024-06-13T10:51:51Z

BPeat at 11:39, 11 May 2024

2024-05-11T11:39:43Z

BPeat at 02:18, 27 April 2024

2024-04-27T02:18:12Z

BPeat at 14:04, 23 March 2024

2024-03-23T14:04:31Z

BPeat at 01:44, 20 March 2024

2024-03-20T01:44:07Z

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 * [[Simulation]] ... [[Simulated Environment Learning]] ... [[World Models]] ... [[Minecraft]]: [[Minecraft#Voyager|Voyager]]
 * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
 * [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[Point Cloud]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]

@@ Line 20: / Line 20: @@
 * [[Simulation]] ... [[Simulated Environment Learning]] ... [[World Models]] ... [[Minecraft]]: [[Minecraft#Voyager|Voyager]]
-* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
+* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
 * [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[Point Cloud]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]

@@ Line 69: / Line 69: @@
 In the context of Minecraft; [https://jimfan.me/ Linxi “Jim” Fan], an AI researcher at the chipmaker [[NVIDIA]] asks, What if we sent [[GPT-4]] for free in Minecraft? I'm excited to announce Voyager. The first lifelong learning agent that plays Minecraft purely in context, Voyager continuously improves Itself by writing. The finding committing and retrieving code from a skill Library GPT for unlocks a new paradigm, training is code execution. Rather than [[Gradient Descent Optimization & Challenges|gradient descent]], trade model is a quote basic skills in Voyager iteratively composes rather than matrices of floats. We are pushing. No gradient architecture to its limit Voyager rapidly becomes a seasoned Explorer in Minecraft. It obtains 3.3 X more unique items travels to point 3 x longer distances and unlocks key Tech Tree Milestones up to Fifteen point three times faster than previous methods. Let generalist agents emerge in Minecraft and just to put a fine point on the excitement here, Jim continues. Generally capable of autonomous [[agents]] are the next Frontier of AI. It continuously explore plan to develop new skills and open-ended world's driven by survival and curiosity Minecraft is by far. Are the best test bed with endless possibilities for [[agents]]. Jim then goes on to explain how [[agents]] within the Voyager experiment actually write programs to achieve goals and then save successful programs in a database that they can pull back from in other words building a skill Library over time.
-Voyager interacts with [[GPT-4]] using blackbox queries, eliminating the need for [[fine-tuning]] model parameters. The [[agent]]'s skills are temporally extended, interpretable, and compositional, rapidly enhancing its capabilities and mitigating catastrophic forgetting. Minedojo provides a simulation suite with diverse open-ended and language-prompted tasks. In this 3D world, Voyager can explore different terrains, mine materials, craft tools, build structures, and make remarkable discoveries. Training data is sourced from YouTube videos, Wiki pages, and Reddit posts that revolve around Minecraft, offering a vast array of tutorials, information, and player discussions for the AI [[Agents|agent]] to learn from. The Nvidia Voyager model is an embodied lifelong learning [[Agents|agent]] that combines [[Reinforcement Learning (RL)]] and mutation learning to develop sophisticated strategies and skills. It comprises an automatic curriculum for exploration, a skill library to store complex behaviors, and a prompting mechanism to generate executable code for controlling the AI [[Agents|agent]]'s actions. [[GPT-4]] is utilized for prompting and in-context learning, enabling Voyager to interact with the Minecraft environment effectively. Other algorithms like react, reflection, and [[Agents#Auto-GPT|Auto-GPT]] are mentioned for comparison on the Mind Dojo platform. However, Voyager surpasses these baselines in terms of exploration, mastery of the game's tech tree, map traversal, generalization to unseen tasks, and overall performance.
+Voyager interacts with [[GPT-4]] using blackbox queries, eliminating the need for [[fine-tuning]] model parameters. The [[agent]]'s skills are temporally extended, interpretable, and compositional, rapidly enhancing its capabilities and mitigating catastrophic forgetting. Minedojo provides a [[simulation]] suite with diverse open-ended and language-prompted tasks. In this 3D world, Voyager can explore different terrains, mine materials, craft tools, build structures, and make remarkable discoveries. Training data is sourced from YouTube videos, Wiki pages, and Reddit posts that revolve around Minecraft, offering a vast array of tutorials, information, and player discussions for the AI [[Agents|agent]] to learn from. The Nvidia Voyager model is an embodied lifelong learning [[Agents|agent]] that combines [[Reinforcement Learning (RL)]] and mutation learning to develop sophisticated strategies and skills. It comprises an automatic curriculum for exploration, a skill library to store complex behaviors, and a prompting mechanism to generate executable code for controlling the AI [[Agents|agent]]'s actions. [[GPT-4]] is utilized for prompting and in-context learning, enabling Voyager to interact with the Minecraft environment effectively. Other algorithms like react, reflection, and [[Agents#Auto-GPT|Auto-GPT]] are mentioned for comparison on the Mind Dojo platform. However, Voyager surpasses these baselines in terms of exploration, mastery of the game's tech tree, map traversal, generalization to unseen tasks, and overall performance.
 The [[Agents|agent]] example involves the collection of 7,000 pages containing diverse content, such as text, images, tables, and diagrams. These pages are carefully structured to preserve layout information, including screenshots and bounding boxes of visual elements. In addition, a substantial amount of data from the r/Minecraft subreddit, including 340,000 posts and 6.6 million commands, was gathered. These posts cover a wide range of topics, including questions, showcases, tips, and discussions related to Minecraft. By [[fine-tuning]] large language models on this Reddit corpus, specific Minecraft concepts can be internalized, and advanced strategies can be developed. The traditional approach to training [[agents]] in Minecraft relies on reinforcement learning and mutation learning, focusing on primitive actions. However, these methods pose challenges in terms of systematic exploration, interpretability, and generalization. Introducing the Nvidia Voyager model, an embodied lifelong learning [[Agents|agent]], provides a solution. Voyager incorporates an automatic curriculum to maximize exploration, a skill library to store complex behaviors, and a prompting mechanism that generates executable code for controlling the [[Agents|agent]]'s actions.
-The process of building your own [[Large Language Model (LLM)]] using [[LangChain]] involves creating an [[Large Language Model (LLM)|LLM]] which interacts with a black box [[Large Language Model (LLM)|LLM]] ([[GPT-4]]). Voyager aims to solve progressively challenging tasks provided by an automatic curriculum generated by [[GPT-4]]. This approach focuses on discovering new and diverse items, resembling an in-context form of novelty search. The prompts given to [[GPT-4]] include directives for diverse behavior and constraints based on the [[Agents|agent]]'s current state, including inventory items like oak planks, sticks, and a crafting table. Additional context is incorporated using GPT-3.5 and the Wiki. To expand the skill library, GPT-3.5 and the MB key are used to introduce extra information. Each skill in the library is represented by executable code, such as an async function. [[GPT-4]] generates and verifies new skills, adding them to the skill library, which is stored as a [[Database|vector database]] with program descriptions and their respective code. Environmental feedback guides the [[Agents|agent]]'s actions, such as adjusting crafting choices based on available resources. The temperature in the experiment is generally set to zero, except for the automatic curriculum where it is increased to 0.1 for task diversity. The simulation environment utilizes Mind Dojo and the Mind Flavor JavaScript API for motor controls. Due to the higher cost of [[GPT-4]], it is primarily used for code generation, while GPT-3.5 handles quick question-answering and providing additional context. Control primitive APIs are implemented in the skill library, and Chain of Thought prompting is employed to guide [[GPT-4]] in explaining code failures, providing step-by-step plans, and generating new code. Voyager autonomously discovers new items and skills in Minecraft through exploration, outperforming baseline algorithms. React, Reflection, and [[Agents#Auto-GPT|Auto-GPT]] were executed for comparison, with [[Agents#Auto-GPT|Auto-GPT]] automating [[Natural Language Processing (NLP)]] tasks by decomposing goals into sub-goals and executing them in a loop. Voyager surpasses the baselines in various aspects, including longer traversals, exploration, Tech Tree mastery, map traversal, [[Few Shot Learning#Zero-Shot Learning|zero-shot generalization]], and overall performance. It represents the latest and most advanced model for replicating Minecraft's behavior and gameplay style. The video concludes by asking viewers for suggestions regarding other specific models they would like to learn about.
+The process of building your own [[Large Language Model (LLM)]] using [[LangChain]] involves creating an [[Large Language Model (LLM)|LLM]] which interacts with a black box [[Large Language Model (LLM)|LLM]] ([[GPT-4]]). Voyager aims to solve progressively challenging tasks provided by an automatic curriculum generated by [[GPT-4]]. This approach focuses on discovering new and diverse items, resembling an in-context form of novelty search. The prompts given to [[GPT-4]] include directives for diverse behavior and constraints based on the [[Agents|agent]]'s current state, including inventory items like oak planks, sticks, and a crafting table. Additional context is incorporated using GPT-3.5 and the Wiki. To expand the skill library, GPT-3.5 and the MB key are used to introduce extra information. Each skill in the library is represented by executable code, such as an async function. [[GPT-4]] generates and verifies new skills, adding them to the skill library, which is stored as a [[Database|vector database]] with program descriptions and their respective code. Environmental feedback guides the [[Agents|agent]]'s actions, such as adjusting crafting choices based on available resources. The temperature in the experiment is generally set to zero, except for the automatic curriculum where it is increased to 0.1 for task diversity. The [[simulation]] environment utilizes Mind Dojo and the Mind Flavor JavaScript API for motor controls. Due to the higher cost of [[GPT-4]], it is primarily used for code generation, while GPT-3.5 handles quick question-answering and providing additional context. Control primitive APIs are implemented in the skill library, and Chain of Thought prompting is employed to guide [[GPT-4]] in explaining code failures, providing step-by-step plans, and generating new code. Voyager autonomously discovers new items and skills in Minecraft through exploration, outperforming baseline algorithms. React, Reflection, and [[Agents#Auto-GPT|Auto-GPT]] were executed for comparison, with [[Agents#Auto-GPT|Auto-GPT]] automating [[Natural Language Processing (NLP)]] tasks by decomposing goals into sub-goals and executing them in a loop. Voyager surpasses the baselines in various aspects, including longer traversals, exploration, Tech Tree mastery, map traversal, [[Few Shot Learning#Zero-Shot Learning|zero-shot generalization]], and overall performance. It represents the latest and most advanced model for replicating Minecraft's behavior and gameplay style. The video concludes by asking viewers for suggestions regarding other specific models they would like to learn about.

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 [https://www.bing.com/news/search?q=Minecraft+Global+Illumination+OpenAI+Microsoft&qft=interval%3d%228%22 ...Bing News]
 * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
-* [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[3D Simulation Environments]] ... [[Simulated Environment Learning]] ... [[Point Cloud]]
+* [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[Point Cloud]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]

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 == CHUNGUS ==
-[[Emergence]]
+* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 CHUNGUS stands for Computational Humongous Unconventional Number and Graphics Unit by Sammyuri.

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 * <b>AI-assisted Gameplay</b>: Microsoft has reportedly developed AI that can play Minecraft based on fan commands. This AI allows players to interact with the game by entering commands and having the AI execute them in the game world. While this technology may not be available for public use yet, it showcases the potential for AI to enhance the gaming experience.
 * <b>AI-generated Content</b>: AI algorithms can generate new content for Minecraft, such as procedurally generated landscapes, structures, and quests. This can provide players with endless possibilities and keep the game fresh and exciting.
 <youtube>Xd5PLYl4Q5Q</youtube>
@@ Line 81: / Line 82: @@
 == Global Illumination ==
 Global Illumination isn't just any company in the vast sea of AI startups. It has carved a niche for themselves, adeptly leveraging artificial intelligence to craft innovative tools, state-of-the-art infrastructure and immersive digital experiences. Its expertise doesn't end here. Before this alliance, the team behind Global Illumination had its hands in designing and crafting early-stage products for social media giants like Instagram and Facebook. - [https://www.cmswire.com/digital-experience/chatgpt-creators-acquire-global-illumination/ Global Illumination: The Hidden Hand Behind Instagram, Pixar and More | CMSwire]

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 * <b>AI-generated Content</b>: AI algorithms can generate new content for Minecraft, such as procedurally generated landscapes, structures, and quests. This can provide players with endless possibilities and keep the game fresh and exciting.
 <youtube>70gs0E4yb8g</youtube>
 <youtube>I8P9mlnOJ4s</youtube>

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 * [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[3D Simulation Environments]] ... [[Simulated Environment Learning]] ... [[Point Cloud]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[Immersive Reality]] ... [[Metaverse]] ... [[Omniverse]] ... [[Transhumanism]] ... [[Religion]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]