AI Has Exponentially Progressed Science and Other Areas of Study

An insight into challenges and successes with Artificial Intelligence and Science

https://tinyurl.com/msu4zun4

The amount of data generated by many of today's physics and astronomy studies is so tremendous that no human, or group of humans, could possibly compete.

Beginning in the 1950s, scientific AI has been attempting to understand human intelligence. Two decades later Data-centric AI was formulated to construct decision trees and later, neural networks. Artificial neural networks are computer-simulated networks of neurons that replicate the function of brains and can plow through mounds of data with little to no human input, emphasizing abnormalities and seeing patterns that people would never have noticed.

Scientists develop hypotheses that can then be tested with more observations using both simulation and observation. Both of these methods are different from generative modeling. Astrophysicist and one of generative modeling's most ardent supporters, Kevin Schawinski, says ​​through simulation, science has evolved. For some scientists, generative modeling and other cutting-edge methods are just more effective ways to do conventional research. However, most people concur that AI is having a significant influence and that it will continue to play an important role in research.

In 2016, Schawinski started using generative modeling. In essence, generative modeling asks how likely it is that you would see result Y given condition X. The strategy has shown to be quite effective and adaptable. Consider, for illustration, feeding a generative model a collection of pictures of people's faces, each with the age of the person indicated on it. The computer algorithm starts to make a relationship between older faces and a higher likelihood of wrinkles as it goes through these "training data."

Heidelberg Institute for Theoretical Studies physicist Kai Polsterer argues that as every undergraduate is instructed to do, it is essential that neural networks offer results as well as error bars. No one will take your scientific findings seriously if you make a measurement without disclosing an estimate of the associated error. On the other hand, human intuitions are frequently just as difficult to understand, according to Lenka Zdeborová, a researcher at CEA Saclay's Institute of Theoretical Physics in France.

There is one thing that AI cannot account for: creativity. Neural networks are designed to learn algorithms by taking in a massive amount of data and finding every pattern therein. When it comes to comparing one pattern to another and predicting when the pattern will change course, they fall short. The lack of social intelligence in neural nets is a significant contributing factor. Innovations are frequently intertwined with interpersonal ties. They are unable to look past the particular data set and its objective. Without the ability to conceptualize figuratively, creativity is almost impossible.

It’s difficult to determine if a machine that can learn physics or math that even the smartest humans alive cannot perform on their own will ever be built using only biological hardware. For now, great feats have been made in the world of AI and science alike.

---

Source Cites

Sq2. what are the most important advances in ai? One Hundred Year Study on Artificial Intelligence (AI100). (n.d.). Retrieved September 3, 2022, from https://ai100.stanford.edu/2021-report/standing-questions-and-responses/sq2-what-are-most-important-advances-ai

Brown, J. (2020, October 23). Growth in artificial intelligence is beyond exponential. Legacy Research Group. Retrieved September 3, 2022, from https://www.legacyresearch.com/the-daily-cut/growth-in-artificial-intelligence-is-beyond-exponential/