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.
Ep40. A Comprehensive Survey of Small Language Models in the Era of Large Language Models
The Daily ML
27 minutes 17 seconds
12 months ago
Ep40. A Comprehensive Survey of Small Language Models in the Era of Large Language Models
This paper provides a comprehensive survey of small language models (SLMs) in the context of large language models (LLMs). The authors discuss the benefits of SLMs over LLMs, including their low inference latency, cost-effectiveness, and ease of customization. They also explore the various techniques used to develop and enhance SLMs, including architecture design, training methods, and model compression. The paper goes on to analyze the applications of SLMs in various NLP tasks, such as question answering, coding, and web search. Finally, the authors address the trustworthiness of SLMs and identify several promising future research directions.
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.
