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.
Ep38. Attacking Vision-Language Computer Agents via Pop-ups
The Daily ML
15 minutes 44 seconds
1 year ago
Ep38. Attacking Vision-Language Computer Agents via Pop-ups
This research paper investigates the vulnerabilities of vision-language models (VLMs) used to power computer agents. The authors demonstrate that these agents can be easily manipulated by carefully crafted adversarial pop-ups, causing them to click on these malicious elements instead of performing their intended tasks. This attack successfully diverts the agents from their intended actions in over 80% of cases, significantly reducing their task success rate. The authors explore various attack design elements and find that basic defense strategies, such as asking the agent to ignore pop-ups or including an advertisement notice, are ineffective. They conclude that more robust agent systems are needed to ensure safe agent workflow in real-world computer environments.
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.
