If neural networks learn, can they learn the skills to become an assistant to scientists to aid in the scientific discovery?

Written by Administrator on 23 February 2025. Posted in Wonders.

Yes, the sources suggest that neural networks can learn the skills to become assistants to scientists and aid in scientific discovery.

Here's how:

Pattern Recognition and Correlation: Neural networks excel at identifying patterns and correlations in large datasets, which can help scientists in various ways.
Hypothesis Generation: AI systems like the AI co-scientist are designed to generate novel research hypotheses and experimental protocols. This directly addresses Icke's concern about AI's inability to formulate hypotheses.
Prediction and Modeling: AI can build models that understand the physics and chemistry of natural phenomena. These models can then be used to efficiently search through combinatorial spaces to find optimal solutions. AlphaFold is an example of AI solving a complex problem and unlocking new avenues of discovery in disease understanding and drug design.
Interdisciplinary Applications: AI facilitates interdisciplinary work by bringing together experts from different fields. This collaboration can lead to significant advances in areas where multiple disciplines intersect.
Automation of tasks: AI can automate tasks such as literature review, summarization, and "deep research," allowing scientists to focus on higher-level reasoning and experimentation.
Accelerating Discovery: AI systems can accelerate scientific discovery by acting as collaborative tools for scientists. For example, Isomorphic aims to revolutionize drug discovery, potentially reducing the time to design a drug from years to months.
Self-Improvement: AI systems can use automated feedback to iteratively generate, evaluate, and refine hypotheses. The AI co-scientist, for example, leverages test-time compute scaling to improve its outputs.
Understanding Complex Systems: Demis Hassabis posits that classical computers, when used in the right way, can model complex systems, even quantum systems. He also advocates applying the scientific method, including neuroscience techniques, to better understand AI systems.

The development of AlphaFold showcases AI's ability to make significant contributions to solving complex scientific problems. Similarly, the AI co-scientist is designed to function as a virtual scientific collaborator, assisting scientists in generating novel hypotheses and research proposals.