Full title: Voyager: An Open-Ended Embodied Agent with Large Language Models - "the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention"

Abstract:

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that 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, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.

links: https://voyager.minedojo.org/

https://arxiv.org/abs/2305.16291

Link to the tool: https://clipdrop.co/stable-diffusion-reimagine

Summary:

• An MIT study provides evidence that AI language models may be capable of learning meaning, rather than just being "stochastic parrots".
• The team trained a model using the Karel programming language and showed that it was capable of semantically representing the current and future states of a program.
• The results of the study challenge the widely held view that language models merely represent superficial statistical patterns and syntax.

Fascinating new paper from Jin and Rinard at MIT that shows models might develop semantic understanding despite being trained on text: "We present evidence that language models can learn meaning despite being trained only to perform next token prediction on text, specifically a corpus of programs."

Our vision at IBM is to scale quantum systems to a size where they’ll be capable of solving the world’s most challenging problems. To get there, we’ve set our sights on a key milestone: deploying a quantum-centric supercomputer powered by 100,000 qubits by 2033.

Read more: https://research.ibm.com/blog/100k-qubit-supercomputer

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