Beyond the hype and hysteria in today’s headlines, artificial intelligence is playing an ever-greater role in everyday life and business, with uses ranging from predictive text to Netflix recommendations to bank fraud detection.
Much of that progress is due to researchers at the forefront of complex scientific exploration.
More to come.
At the University of California, Riverside, a team of four scientists developed a vision of using machine learning to maintain, improve, and design the most sophisticated scientific instruments on the planet.
“Using AI to tackle major scientific problems has the potential to not only advance science, but also to solve problems in everyday life,” says Computer Science Engineering at the University of California, Riverside. Associate Professor Vagelis Paparexakis said: “GPS is a good example.”
A chapter on the UCR team’s vision was published in April 2023 in the World Scientific book Artificial Intelligence for Science: The Deep Learning Revolution.
Chapter 7, “Machine Learning for Complex Instrument Design and Optimization,” explores how AI can refine, improve, and even revolutionize large-scale scientific experiments. The idea is to leverage machine learning to computationally simulate a vast range of manipulation possibilities. and Design – Explore counterintuitive designs and ideas as well as save time, money and resources through efficiency and comprehensive improvement.
“It sounds futuristic, but that’s the hope,” Paparexakis said. “We are asking, ‘What is the future of AI?'”
His co-author is Barry C. Barrish, Nobel Laureate, Professor Emeritus of Physics at the California Institute of Technology, and Distinguished Professor of Physics and Astronomy at UCR. Jonathan Richardson, UCR Assistant Professor of Physics and Astronomy. and Dr. Rutuja Glav. Candidate for UCR in Computer Science.
Their approach could enhance the design and operation of sophisticated engineering, including laser interferometer gravitational-wave observatories. Managed by the California Institute of Technology, LIGO consists of two sets of 2.5-mile-long laser beams in Washington and Louisiana that emit gravitational forces from cosmic phenomena such as the unobservable merger of a pair of black holes that emit no light. Detect waves. visually.
Gravitational waves help scientists understand the mysteries of the universe, the origin of the universe, and the fundamental laws of physics. LIGO itself broke new ground in astronomy, and its discovery was so groundbreaking that Barrish, the former director of LIGO, shared the 2017 Nobel Prize in Physics.
“Advancements in experimental physics depend on our ability to develop highly complex, state-of-the-art instruments,” Barrish said. “Machine learning is playing an increasingly important role in the conception, design and implementation of these advanced laboratories. AI is becoming the perfect partner for new discoveries in physics. It’s no exaggeration.”
The envisioned new research will explore how scientists can improve, or even design, end-to-end instruments in ways that make them more sensitive and resilient to real-world sources of error, such as environmental noise. will help you learn.
Paparexakis said of LIGO’s massive infrastructure, “Instead of doing this in the lab, AI will do the heavy lifting of testing potential designs and finding the best one.” “It’s a way of simulating things with a computer, and it’s very useful for planning large-scale experiments.”
Such an approach will be adapted using the technologies that power emerging public platforms such as ChatGPT and Bing AI, which will have a profound impact on scientific discovery and everyday innovation.
Scientists have pointed out that using AI to test, model, and improve large-scale scientific systems will not replace researchers and engineers.
“A state-of-the-art experiment like LIGO is an incredibly complex instrument with dozens of interdependent control systems and thousands of data channels,” Richardson said. “Our hope is that advances in AI, such as those underway at UCR, will help us recognize hidden connections in a sea of data that could potentially diagnose operational problems. This, in turn, will tell us new ways that we, human physicists, can make physical changes to improve the performance of our detectors.”
This research began with a student’s curiosity and a chance encounter.
Grav, a graduate student working in Papa Rexakis’ computer science lab, became interested in separating gravitational waves from other noise. Then, in the wake of his public talk at UCR four years ago by gravitational wave expert Barish, the group got together, talked, and collaborated on the project.
Commending the UCR leadership, Mr Gulav said: “It’s great to see our work included in an amazingly diverse collection of ideas about AI applied to the natural sciences. I continue my journey as an aspiring computer scientist with a deep interest in exploring applications of machine learning to advance the frontiers of scientific discovery.”
Now that the chapter is published, Paparexakis said she was “proud and a little bit scared”. Publicly pointing out the research direction of complex scientific research “does bring a sense of responsibility that cannot be taken lightly. But I am thrilled that people believe these things are worth investigating.”
