"Physicists have developed a new method to detect extremely low-frequency gravitational waves, potentially illuminating the early universe and the nature of supermassive black holes. Credit: SciTechDaily.com" (ScitechDaily, Unlocking the Universe’s Deepest Rhythms With Gravitational Waves)
AI can probably make the next theories in physics. The reason for that is all empiric theories must be based on observations. Making theories and models is easy, but proving them is difficult. Observations determine if the theory is accepted or not. The problem with observations is that.
There is a limited number of large-size telescopes. And if researchers want to use things like gravitational waves to see things what happens inside a black hole's event horizon. They must collect information about gravity waves. And that happens by measuring gravity wave interaction with other wave movement types. That requires that the dataset can be collected from multiple wavelength observations.
The AI makes it possible to combine data from multiple sources. All computers calculate better than humans.
The AI can calculate precise points. When things like black holes or neutron stars collide. At that point the optical systems, radio telescopes, neutrino sensors, and gravity wave sensors to observe those events. That allows the system to create datasets from different systems. The dataset can work as a tool for making new models for particles' internal and external interactions and the shape of the material.
Theory and thinking are the same thing. When we create new theories, we create new thoughts. In that process, we must reshape the dataset that we have. In this case, the dataset is like a puzzle, and the researcher must collect pieces that they have to the entirety.
Things like the Theory of General relativity and quantum theories are the top in the series that began with Newton's gravity model. When new observation tools replace older ones. That thing means theories turn to models. Otherwise, theories can proven as wrong.
The problem with new theories and models is that some people think that some old models are dear to them. If some model has been a cornerstone of the science for over a hundred years it feels hard to deny that model. When researchers create theoretical models they must collect lots of information from books.
Or confirmed material that is collected about some topics. There are models. That we learn at elementary schools. The AI doesn't have an emotional attitude for that kind of thing. The AI isn't afraid to deny models if they are wrong. This makes the AI more effective thing than humans.
https://scitechdaily.com/ai-unlocks-the-secrets-of-dark-energy-in-groundbreaking-study/
https://scitechdaily.com/the-next-einstein-new-ai-can-develop-new-theories-of-physics/
https://scitechdaily.com/unlocking-the-universes-deepest-rhythms-with-gravitational-waves/
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