Introduction:
Physics is involved in every day’s life and almost have an impact on everything of human life. From describing motion, forces, energy as an ordinary experience of everyday life to the usage of modern technology things like driving cars, using Mobil phones, everything involves physics. An easy example to see in the daily life is a simple lever that converts the small energy into a prominent force. The transportation industry is another example which is describing Newton’s motion laws and has made our life more comfortable than ever. As time changed and technology has become an undeniable part of human life, the role of Physics has also become more critical.
Physics and AI:
The modern fast speed processor, data, and the neural network have emerged new fields of study artificial intelligence, which is not new. Still, due to the above component, it has now made accessible to be used, and the researcher is converting from traditional methods to modern AI-based methods. Learn about these machine learning models, algorithms, and AI-based methods from Intellipaat’s Machine learning certification course.
From some past years, Governments of developed countries like the USA and European countries have started to invest in AI research. It will have a significant impact in a short span of time, and the trillions dollar market is emerging.
But if you consider physics and usage of artificial intelligence, the scientist will say they have started using AI from the 1980s and call it machine learning. This machine learning has changed the game entirely, and from astrophysics to online calculators, AI has proven its need in every field of technology.
Role of AI in Physics:
Artificial intelligence is making a significant impact on all the fields of science, including physics. The artificial intelligence has enabled humans to solve in seconds or in less time the problems which were very hard and even impossible to be resolved by a human. One of the most significant discoveries of physics is the Higgs boson particle. The researcher took about half of a century to discover these particles but was unable to find. It was AI, which has enabled the researcher by using the processor of the quantum computer and neural network to detect these particles. The quantum processor, along with neural network-enabled scientist to identify the pattern of particles collision. It was utterly impossible without the help of AI.
Usually, where mechanics are involved, you may apply both physics and artificial intelligence. Such as automated industries where many types of machinery and the significant process is engaged, physics and AI help people to automate the process and make life easy. There is a big roll of AI in the defense system. Automated defense systems like a missile rocket are using AI and have a revolutionary impact on defense systems of countries. Robotic is another hot topic of AI and physics together. Robotic technology has involved AI to identify intelligent tasks along with physics enabling it to do them. There are a lot of other examples where AI and physics are working together.
Examples of AI contributions to real-life physics:
Data Analysis
The application of artificial intelligence, a significant amount of data gathered from experiments, environments, and other worlds by using powerful scientific tools, has given a lot of discoveries. Powerful machine learning techniques have given you a chance to extract scientific knowledge from experiments and make theories, conclusion, etc. Now the experimental facility is available on a massive scale. The neutron and synchrotron are now an essential part of scientific research, which has allowed the researcher to work on different structures and properties of the material. A large amount of data is being produced, and it is making it very difficult for the researcher to build any conclusion and theory. It now Application of AI, which will step into and make life easy and give us meaning full result and solve complexity, embeds. From the last ten years, the involvement of AI in science, including real physics, has increased. The grouping of neutron and working with intelligent software have enabled humans to give a result within no time.
Data modeling
In current days a large amount of data is available for the process, which is resulted in a delay in making any specific conclusion. One option to reach on the meaningful result is modeling the data by using artificial intelligence. This technique may be difficult for the researcher. Still, the person who has a watchful eye on the latest industry trends may value it much. The pharmaceutical industries have a large amount of data, and it is tough for them to utilize it without the involvement of the AI model. To make this data valuable and get full benefit need the involvement of artificial intelligence embedded model.
Statistical model and physics
Statistics is tremendously helping subject and almost all branches of science using statically model to reach out to some theories. Artificial intelligence has a remarkable impact on statistics, and nearly all statically models are now available in artificial intelligence. Artificial intelligence has made statistics a straightforward subject to help the science to conclude. The statistical tools like R, STATA, and SPSS are dealing with all branches of science. Mathematical calculation like mean, median, regression, sampling, probability, and many more are helping scientists to conclude any theory, and AI has made it very easy to solve all problems.
Astrophysics and Machine learning
A rapid growth in the field of astronomy increases a large amount of data and complexity. To deal with these changes, an astronomer has developed the tools with the help of artificial intelligence to mine the data and extract useful information. Machine learning is become very popular among astronomers and is very useful for a variety of tasks. These tasks are impossible to be solved without the help of artificial intelligence. Keeping in mind the changes and challenges associated with them, astronomers are focusing on techniques of machine learning and deep learning. Supervised and unsupervised machine learning techniques are presented for the application of such technologies and performance on the data set. The main focus on unsupervised learning algorithms, shallow artificial neural network, dimension reduction, and visualization specifically is essential for the scientists to use in their research to get new knowledge from existing data, and discoveries can be facilitated.