Long-term memory is significant for agents, in which insights play a crucial role. However, the emergence of irrelevant insight and the lack of general insight can greatly undermine the effectiveness of insight. To solve this problem, in this paper, we introduce Multi-Scale Insight Agent (MSI-Agent), an embodied agent designed to improve LLMs' planning and decision-making ability by summarizing and utilizing insight effectively across different scales. MSI achieves this through the experience selector, insight generator, and insight selector. Leveraging a three-part pipeline, MSI can generate task-specific and high-level insight, store it in a database, and then use relevant insight from it to aid in decision-making. Our experiments show that MSI outperforms another insight strategy when planning by GPT3.5. Moreover, We delve into the strategies for selecting seed experience and insight, aiming to provide LLM with more useful and relevant insight for better decision-making. Our observations also indicate that MSI exhibits better robustness when facing domain-shifting scenarios.

The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, finetuning them for specific tasks is resource-intensive and may diminish the model's generalization capabilities. Moreover, state-of-the-art language models like GPT-4 and Claude are primarily accessible through API calls, with their parametric weights remaining proprietary and unavailable to the public. This scenario emphasizes the growing need for new methodologies that allow learning from agent experiences without requiring parametric updates. To address these problems, we introduce the Experiential Learning (ExpeL) agent. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions. Our empirical results highlight the robust learning efficacy of the ExpeL agent, indicating a consistent enhancement in its performance as it accumulates experiences. We further explore the emerging capabilities and transfer learning potential of the ExpeL agent through qualitative observations and additional experiments.

The expression "Artificial Intelligence" (AI) is stacked with a lot of suppositions taken from many years of sci-fi motion movies about robots taking control over the world. A long way from being a part of a whimsical anecdote about your microwave creating emotions however, AI is as of now a common truth of modern life. Despite the fact that fantasies of robots who can have our spot at work while we go through our days golfing and going for long snacks might be some distance later on, what computer scientists call "narrow" or "weak" AI is as of now integrated into our everyday lives. Progressively, the issues presented by counterfeiting are on the web. The size of the issue fluctuates by brand, nation and platform, however, plainly a few brands and products are being disproportionately affected by counterfeit online deals such that they wouldn't be by physical shops or merchants.

The expression "Artificial Intelligence" (AI) is stacked with a lot of suppositions taken from many years of sci-fi motion movies about robots taking control over the world. A long way from being a part of a whimsical anecdote about your microwave creating emotions however, AI is as of now a common truth of modern life. Despite the fact that fantasies of robots who can have our spot at work while we go through our days golfing and going for long snacks might be some distance later on, what computer scientists call "narrow" or "weak" AI is as of now integrated into our everyday lives. Progressively, the issues presented by counterfeiting are on the web. The size of the issue fluctuates by brand, nation and platform, however, plainly a few brands and products are being disproportionately affected by counterfeit online deals such that they wouldn't be by physical shops or merchants.

CAMBRIDGE, MASSACHUSETTS – Has your child swallowed a small battery? In the future, a tiny robot made from pig gut could capture and expel it. Researchers at the Massachusetts Institute of Technology are designing an ingestible robot that could be used to patch wounds, deliver medicine or dislodge a foreign object. They call their experiment an "origami robot" because the accordion-shaped gadget gets folded up and frozen into an ice capsule. "You swallow the robot, and when it gets to your stomach the ice melts and the robot unfolds," said Daniela Rus, a professor who directs MIT's Computer Science and Artificial Intelligence Laboratory.