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JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models paper page: Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the... number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy.show more

AK

535,480 subscribers

141,481 次观看 • 3 年前 •via X (Twitter)

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10 条评论

Bodhisattwa Majumder 的头像

Bodhisattwa Majumder3 年前

Continuous exploration and updates seem to be the way to beat the limited context for agents. Imagine struggling to fit one trajectory of 16K tokens but being able to fit 1000s with the help of a smart, persistent memory! Our new work:

Amplify with AI 的头像

Amplify with AI3 年前

I would watch a live stream of AI playing Minecraft. Seeing it reason about tasks, especially you get higher up in the tech tree of Minecraft, would be interesting. I'm sure you’d learn a thing or two just watching it play haha

Anas Aldadi 的头像

Anas Aldadi3 年前

Mention the original tweet duh, he barely got any recognition for his hardwork

鬼城之鬼的头像

鬼城之鬼3 年前

Oh ffs, is this what we are?

Emi 的头像

Emi3 年前

@jodemi_

Ajeya 的头像

Ajeya3 年前

Amazing work! Keep pushing the boundaries of AI!

DongNan 的头像

DongNan3 年前

Jais. Ai

Tommy D. Rossi 的头像

Tommy D. Rossi3 年前

Is this real-time?

MrDee@SOG🫡 的头像

MrDee@SOG🫡3 年前

Bro they beat you to Jarvis @karpathy

Dwid Lee 的头像

Dwid Lee3 年前

@memdotai mem it

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Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:

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Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

AK

344,019 次观看 • 2 年前

Wait Xiaomi has also released the best 9B model in the world?! This model is based on Qwen-9B... And you can easily run it even on 8GB of memory! - MIT license - Multimodal (image + text) - Solid for coding/agentic tasks Perfect to use locally and offline... even on a laptop with unified memory!

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85,496 次观看 • 1 个月前