Science | 6 minutes to read
From the earliest days of scientific inquiry, humanity has sought to understand the universe through observation, experimentation, and reasoning. The scientific method, pioneered by thinkers like Ibn al-Haytham, Roger Bacon, and Francis Bacon, has long been our most reliable tool in out scientific jurney. Yet, these early architects of systematic science inquiry could hardly have imagined the vast scientific and technological advancements their method would enable. From the Industrial Revolution to the digital age—and now the rise of artificial intelligence—the principles they established have propelled humanity into an era of discovery far beyond their era's imaginations.
The Role of Mathematics in Science
The old debate about whether mathematics and its practice are considered a scientific method, a science in itself, or a philosophical language has not been settled. Is studying mathematics, predicting results, and drawing conclusions using it constitute part of the scientific method? There is still ambiguity surrounding this science and where it might lead us as humans. However, there is a near consensus that mathematics is a tool we use to interpret phenomena, test hypotheses, and reach places and results that are beyond human capability without mathematics.
Mathematics, as we mentioned, is used to reach conclusions, but it does not always follow the steps of the traditional scientific method, as it is an indispensable tool in explaining scientific phenomena and testing hypotheses through modeling and mathematical analysis. Scientists use mathematics to build models that express relationships between variables to explain phenomena in nature and laboratories. It is also used to formulate predictions to be tested experimentally. Mathematics, particularly statistics, is used to analyze data collected from experiments and determine probabilities.
In addition, mathematics has played a pivotal role in the evolution of information technology, as Boolean algebra laid the foundation for digital logic used in designing electronic circuits and computers. Mathematics has also contributed to the development of many aspects of computing, such as encryption and algorithms. It continues to play a fundamental role in algorithms to this day, as every process a computer performs is managed and instructed by algorithms. every algorithm has a mathematical foundation that defines its strength, efficiency, and utility.
Mathematics and the AI Revolution
Mathematics did not stop there; rather, it stood behind the artificial intelligence revolution through an intricate relationship between all its components. For example; AI applies linear algebra to represent images as matrices and performs operations like convolution and transformation. Neural networks use calculus-based gradient descent to iteratively adjust weights, optimizing learning from large datasets and probability theory for decision making.
Can AI Enhance the Scientific Method?
The scientific method is not perfect, but it is our best tool for discovery. One of the most promising aspects of artificial intelligence is its potential to accelerate scientific discoveries. It opens up broad horizons for new scientific discoveries and precise predictions. Artificial intelligence can enhance nearly every stage of the scientific method.
The scientific process begins with observation and data collection. In fields like astronomy, genomics, climate science, and social sciences, the sheer volume of data has become overwhelming. AI can then analyze massive datasets far beyond human capacity, identifying patterns and generating insights and hypotheses that would take years for humans to uncover, Google's AI co-scientist is a recent example of AI powered system that generates hypotheses [1] .
Scientists then prepare experiments to test the hypotheses formulated in the previous step. This step is often challenging due to limitations in material and human resource. AI-driven simulations can test thousands of variables in a fraction of the time required for traditional experiments, reducing costs and expediting discoveries.
Self-Driving Laboratories
One of the most prominent examples of artificial intelligence LAB simulations is Self-Driving Laboratories (SDLs)- AI-powered robotic systems that conduct experiments, collect data, extract results, and suggest subsequent experiments. There have been precedents in this field. Examples include:
- Granda et al.'s machine learning system that predicts the outcomes of chemical reactions.
- The Adam and Eve project, conducted in the early 21st century, achieved a high level of automation in laboratory experiments.
Self-driving laboratories can be classified into five levels:
(nature.com) Amanda/Milad
A. Piecewise, where human users fully separate the experiment and computational system.
B. Semi-closed-loop, where the algorithm and robotic components partially communicate.
C. Closed-loop, where the human user has no influence in the goal seeking loop.
D. self-motivated experimental systems, where the computational system dictates its own objectives.
These AI-driven laboratories are transforming research by taking on the most demanding and time-consuming tasks, operating at speeds far beyond human capability. Scientists increasingly rely on SDLs to accelerate experiments that would otherwise take decades or even centuries, particularly in fields like biological experiments [2] and other fields that produce big data like astronomy and particle physics.
In addition to SDLs, other AI tools have been developed to assist and accelerate scientific research and lab experimentation. For example, ColabFold is an open-source software that predicts protein 3D structures from their raw sequence alignments faster by 40 to 60 times, and Atomwise uses machine learning to automatically identify proteins in complex compounds.
Anandkumar's Neural Operators
AI researcher Anandkumar has developed neural operators, an AI frameworks that learn function spaces to simulate complex physical processes. These models have significantly expedited simulations in fluid dynamics and material science, performing calculations of magnitude faster than the traditional methods.
Anandkumar’s work lights a path toward a future where AI and science reinforce one another: where scientific knowledge is deeply integrated with an AI’s understanding of the physical world, enhancing its capabilities; and where AI systems can generate and test new ideas. “Many labs, including us, are building towards this, "she says. "There's so many discoveries that are happening as we speak"[3].
Conclusion
Developments such as Self-driving laboratories and Anandkumar's Neural Operators are still in their early stages, yet there are high hopes for them. In conclusion, the combination of artificial intelligence, mathematics, and the scientific method forms a revolutionary triad redefining the boundaries of human knowledge. Each component aids and complements the others, pushing the boundaries of scientific discovery.
As we continue our scientific journey toward the future, the most important question emerges: How far can this development go, and are we ready to handle its consequences?
Read More:
The Scientific Method:
https://www.sciencebuddies.org/science-fair-projects/science-fair/steps-of-the-scientific-method
The Crucial Role of Mathematics in AI Development
https://medium.com/@Noodle_LLC/the-crucial-role-of-mathematics-in-ai-development-dd31d4d6e55c
ColabFold
https://www.nature.com/articles/s41592-022-01488-1
Atomwise
https://www.nature.com/articles/s41598-024-54655-z
https://ai2050.schmidtsciences.org/fellow/anima-anandkumar/
Resources:
[1] https://blog.google/feed/google-research-ai-co-scientist/
[2] https://en.wikipedia.org/wiki/Long-term_experiment
[3] https://time.com/7212504/time100-impact-awards-anima-anandkumar/
[3] https://time.com/7212504/time100-impact-awards-anima-anandkumar/