This talk discusses how AI is changing science by allowing huge amounts of data to be analyzed to discover patterns, increasing reproducibility of research, and supporting qualitative research. However, challenges also exist around developing best practices for AI, preventing misinformation, ensuring data remains in the public domain, and designing effective human-AI interaction. The talk calls for addressing these challenges to maximize AI's benefits for science.

1. This talk was not
generated with ChatGPT:
how AI is changing science
Elena Simperl
King’s College London & Open Data Institute
@esimperl

2. 80 years of
AI
2

3. Open and
closed source
large
language
models
3
[Source: Zhao et al. 2023]

4. Doing science
Analysing huge amounts of heterogeneous scientific data to discover
patterns and trends
4

5. Doing science better
Increasing reproducibility, analysing research literature, co-generating
code, supporting qualitative research, finding scientific data
5

6. Using scientific data responsibly
Generating synthetic data, privacy-aware AI, AI data governance
6

7. Making science more accessible and inclusive
Allowing more people to engage with scientific evidence, facilitating
citizen science
7

8. Challenge: code of practice
8

9. Challenge: misinformation
9
[Artwork by Gaëtan Robillard]

10. Challenge: digital
commons
9/3/20XX Presentation Title 10

11. Challenge: data
11
[Source: Zhao et al. 2023]
[Source: AI Index Report 2023]

12. Challenge: human-AI interaction
12
[Source: Microsoft]

13. Thank you
13
Elena Simperl
@esimperl