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.
Ep34. What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
The Daily ML
10 minutes 20 seconds
1 year ago
Ep34. What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
This study investigates the gradient patterns of different layers in large language models (LLMs) during instruction tuning. The researchers compare the gradients of LLMs trained using "fast thinking" (without chain-of-thought reasoning) versus "slow thinking" (with detailed chain-of-thought reasoning). The study examines how these training methods affect gradient stability, response correctness, and the ability to distinguish between correct and irrelevant responses. They further analyze the impact of different initial models (pre-trained vs. instruction-tuned) on gradient behavior. The results show that "slow thinking" leads to more stable and efficient training, while "fast thinking" results in larger gradients and greater fluctuation across layers. The researchers also find that "slow thinking" helps distinguish correct responses from irrelevant responses, but this ability is not as pronounced in "fast thinking" training. Finally, the study explores the effects of response length and popularity on gradient patterns in knowledge-learning tasks, demonstrating that increasing response length alone does not necessarily mimic the effects of "slow thinking."
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.
