Nature's Take: How will ChatGPT and generative AI transform research?
Nov 3, 2023
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The podcast discusses the impact of generative AIs on science and research, including the development of ChatGPT. They explore the perspective of a big publisher on using AI in scientific communication and discuss the challenges and collaboration required for regulating AI tools. The podcast also delves into the transformative power of AI in research and the potential for synthesizing information, while cautioning about the risks of fake papers.
Generative AI tools like ChatGPT and Bard have the potential to streamline scientific publishing by summarizing dense literature and assisting in coding, although risks of false information and biases must be addressed.
Generative AI tools can accelerate scientific discovery by imagining new proteins and drugs, but caution is required to avoid errors and biases in the generated content, emphasizing transparency and responsible use.
Deep dives
Generative AI: An Introduction to AI that Generates Text, Images, and More
Generative AI, such as Chat GPT, Bard, and Dal E, has gained popularity for its ability to generate text, images, and other content that appears almost human-like. These AI models have the potential to transform various fields, including science. They can be used to summarize dense literature, assist in coding, and help non-native English speakers improve their scientific writing. However, while generative AI tools have proven useful in many cases, they also pose risks. The text generated by these models can be misleading, leading to false information and misinformation. Additionally, there are concerns about biases in the training data and the impact on the data ecosystem. Despite the risks, generative AI tools are seen as valuable additions to scientific research and have the potential to revolutionize the way scientists ask questions, process data, and conduct experiments.
Scientists' Encouraging Response to Generative AI Tools
Scientists have embraced generative AI tools for various purposes. Many researchers use them to assist in writing code, as these tools provide cliched yet effective solutions. They are also helpful for non-native English speakers, who use them to improve the fluency and naturalness of their scientific papers. However, the use of generative AI tools in writing full papers is still seen as more of a gimmick than a practical approach. While some researchers have experimented with using generative AI for drug design and chemical processes, careful validation and discernment are crucial to avoid errors. Overall, generative AI tools have found a place in the scientific community, but their application is dependent on the researchers' ability to assess and verify the generated content.
The Implications and Risks of Generative AI in Science
Generative AI tools have the potential to accelerate scientific discovery by transforming various stages of the research process. For example, they can be employed to imagine new proteins and drugs, resulting in faster and more efficient drug research. However, using generative AI tools comes with risks. The false sense of security arising from their plausible text generation can lead to a lack of critical evaluation, especially when using generated code or text without proper verification. There are concerns about the impact on the data ecosystem, as these models heavily rely on existing imperfect datasets, potentially amplifying biases. Recognizing these risks, transparency and responsible use of generative AI tools are emphasized, with the need for clear methods section descriptions and cautious interpretation of their outputs.
Regulation and Collaboration in the Age of Generative AI
Given the potential risks and benefits of generative AI, efforts are being made to regulate its use in various contexts. The EU's AI Act requires producers of generative AI tools to disclose training data and be transparent about their usage. However, the challenges of enforcing transparency and preventing circumvention remain. Collaboration among governments, private sector researchers, and academics is essential for developing effective and appropriate regulations that cater to the global nature of scientific research. Scientific journals, like Nature, encourage the responsible use of generative AI tools, suggesting that their role should be acknowledged but not considered on par with human authorship. The focus should be on transparency and validation while considering the pitfalls of bias and the risk of counterfeit data or publications.
In the past year, generative AIs have been taking the world by storm. ChatGPT, Bard, DALL-E and more, are changing the nature of how content is produced. In science, they could help transform and streamline publishing. However, they also come with plenty of risks.
In this episode of Nature's Take we discuss how these AIs are impacting science and what the future might hold.