Minecraft

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Minecraft is becoming an important testbed for new AI techniques. The open-endedness of Minecraft makes it a good environment for training AI. Researchers have built Minecraft bots that can explore and expand their capabilities in the game's open world. They use GPT-4 to solve problems inside the game and generate objectives that help the agent explore the game, and code that improves the bot’s skill at the game over time. The language model generates objectives that help the agent explore the game, and code that improves the bot’s skill at the game over time. Microsoft, which owns Minecraft, is also training AI programs to play it, and the company recently announced Windows 11 Copilot, an operating system feature that will use Machine Learning (ML) and APIs to automate certain tasks. Microsoft has also reportedly developed AI that plays Minecraft based on fan commands.

It's worth noting that AI in Minecraft is still an active area of research and development, and there are likely to be more advancements and applications in the future. AI is being used in various ways to improve gameplay in Minecraft. Here are some examples:

  • Reinforcement Learning (RL): Researchers have used reinforcement learning techniques to train AI agents in Minecraft. These agents learn to navigate and interact with the game environment by receiving rewards or punishments based on their actions. Through trial and error, the AI agents can improve their gameplay skills and achieve specific objectives.
  • Data-driven AI: AI algorithms can analyze large amounts of gameplay data to identify patterns and optimize gameplay. By studying player behavior and preferences, AI can generate personalized recommendations, suggest improvements, and create more engaging experiences for players.
  • AI Mods: Modders have developed AI modifications for Minecraft that enhance the behavior and performance of in-game entities. These mods can improve the intelligence of non-player characters (NPCs), making them more responsive and realistic in their actions.
  • AI-assisted Gameplay: 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.
  • AI-generated Content: 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.



Voyager

Voyager is an AI agent powered by a LLM that has been introduced to the world of Minecraft. It continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components:

  1. an automatic curriculum that maximizes exploration
  2. an ever-growing skill library of executable code for storing and retrieving complex behaviors
  3. a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement.

In the context of Minecraft; 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, 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 agent to learn from. The Nvidia Voyager model is an embodied lifelong learning 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 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 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 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 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 agent's actions.

The process of building your own Large Language Model (LLM) using LangChain involves creating an LLM which interacts with a black box 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 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 vector database with program descriptions and their respective code. Environmental feedback guides the 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 Auto-GPT were executed for comparison, with 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, 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.


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. - Global Illumination: The Hidden Hand Behind Instagram, Pixar and More | CMSwire

CHUNGUS

CHUNGUS stands for Computational Humongous Unconventional Number and Graphics Unit by Sammyuri. The CPU is also very large. CHUNGUS 2: Electric Boogaloo - A Minecraft CPU capable of running Tetris, snake, connect 4, graph rendering

In order to achieve a 10 tick clock speed despite its enormous size, the CPU makes use of techniques such as an instruction pipeline, automatic data cache and simple branch prediction.

CPU specs - 8 bit data, 16 bit fixed size instruction length - 1Hz clock speed, 4 stage instruction pipeline (fetch - decode - execute - writeback) - 64 byte automatic 8-way associative data cache and 256 bytes RAM - Up to 256 addressable I/O ports - 7 general purpose registers - Over 40 ALU functions, including a hardware barrel shifter, multiplier, divider and square rooter - 32x128 byte program pages for a total of 4KiB program storage

Other hardware used in video - 32x32 buffered pixel screen, draw and erase pixels, rectangles, up to 8 4x4 sprites - 12x2 character ASCII text display - 2x 8-bit integer display (signed or unsigned) - 8-input NES-style controller - 3 bit pseudo-RNG

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