This research paper examines the impact of an artificial intelligence tool for materials discovery on the productivity and performance of scientists working in a large U.S. firm's R&D lab. The study exploits a randomized rollout of the AI tool across teams of scientists, allowing the researchers to draw causal inferences about the effects of the technology. The paper demonstrates that the AI tool significantly increases the rate of materials discovery, patent filings, and product innovation, but these benefits are unequally distributed among scientists. The researchers find that the AI tool is most beneficial to scientists with strong judgment skills, which involve the ability to evaluate and prioritize AI-generated candidate compounds. The study also reveals that the AI tool automates a significant portion of idea generation tasks, resulting in a reallocation of scientist labor towards judgment tasks. This reallocation, along with the increased demand for judgment skills, explains the heterogeneous impact of the AI tool on scientific performance.
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This research paper examines the impact of an artificial intelligence tool for materials discovery on the productivity and performance of scientists working in a large U.S. firm's R&D lab. The study exploits a randomized rollout of the AI tool across teams of scientists, allowing the researchers to draw causal inferences about the effects of the technology. The paper demonstrates that the AI tool significantly increases the rate of materials discovery, patent filings, and product innovation, but these benefits are unequally distributed among scientists. The researchers find that the AI tool is most beneficial to scientists with strong judgment skills, which involve the ability to evaluate and prioritize AI-generated candidate compounds. The study also reveals that the AI tool automates a significant portion of idea generation tasks, resulting in a reallocation of scientist labor towards judgment tasks. This reallocation, along with the increased demand for judgment skills, explains the heterogeneous impact of the AI tool on scientific performance.
Ep31. ROCKET-1: Master Open-World Interaction with Visual-Temporal Context Prompting
The Daily ML
16 minutes 12 seconds
1 year ago
Ep31. ROCKET-1: Master Open-World Interaction with Visual-Temporal Context Prompting
This paper describes the development of ROCKET-1, a novel hierarchical agent architecture that leverages visual-temporal context prompting to enable agents to master open-world interaction in Minecraft. This system utilizes a low-level policy (ROCKET-1) that predicts actions based on concatenated visual observations and segmentation masks, guided by a high-level reasoner. The key innovation lies in the visual-temporal context prompting protocol, which uses object segmentation from both past and present observations to effectively communicate spatial information. This approach allows for the successful completion of complex tasks, such as crafting and mining, in Minecraft, which were previously unattainable using traditional language-based prompting methods. The paper highlights the potential of visual-temporal context prompting to overcome the limitations of existing approaches and unlock the full potential of vision-language models for embodied decision-making.
The Daily ML
This research paper examines the impact of an artificial intelligence tool for materials discovery on the productivity and performance of scientists working in a large U.S. firm's R&D lab. The study exploits a randomized rollout of the AI tool across teams of scientists, allowing the researchers to draw causal inferences about the effects of the technology. The paper demonstrates that the AI tool significantly increases the rate of materials discovery, patent filings, and product innovation, but these benefits are unequally distributed among scientists. The researchers find that the AI tool is most beneficial to scientists with strong judgment skills, which involve the ability to evaluate and prioritize AI-generated candidate compounds. The study also reveals that the AI tool automates a significant portion of idea generation tasks, resulting in a reallocation of scientist labor towards judgment tasks. This reallocation, along with the increased demand for judgment skills, explains the heterogeneous impact of the AI tool on scientific performance.
