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.
All content for The Daily ML is the property of The Daily ML and is served directly from their servers
with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
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.
Ep30. Can Knowledge Editing Really Correct Hallucinations?
The Daily ML
17 minutes 5 seconds
1 year ago
Ep30. Can Knowledge Editing Really Correct Hallucinations?
This research investigates the effectiveness of knowledge editing techniques in correcting hallucinations in large language models (LLMs). The authors present HalluEditBench, a comprehensive benchmark that evaluates the performance of different knowledge editing methods across five dimensions: efficacy, generalization, portability, locality, and robustness. They discovered that while some knowledge editing methods show promising results on existing benchmarks, their effectiveness in correcting real-world hallucinations may be significantly lower, highlighting the need for more robust evaluation methods. Additionally, the study provides insights into the limitations of different editing methods, suggesting that no single method excels across all five dimensions. The authors conclude by emphasizing the importance of understanding the potential and limitations of knowledge editing techniques for achieving more accurate and reliable LLMs.
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.
