Artificial Intelligence To Reshape Deep Science Learning

Artificial Intelligence, beyond the hype and hysteria in headlines today, plays a growing role in daily life and business – with uses ranging from predictive text to Netflix recommendations to the detection of bank fraud. 

Much of that progress is thanks to researchers on the cutting edge of complex Scientific exploration. 

And there is more to come. 

Vagelis Papalexakis, top left, Barry C. Barish, Jonathan Richardson, Rutuja Gurav

At UC Riverside, a team of four scientists has laid out their vision for using Machine Learning to maintain, improve and design some of the most sophisticated scientific equipment on Earth.     "Using AI to tackle major scientific challenges not only has the potential to advance science, but also to trickle down to solving problems in everyday life," said Vagelis Papalexakis, associate professor of Computer Science and Engineering at UC Riverside. "GPS being a great example." 

A chapter on the UCR team's vision was published in April 2023 by World Scientific in the book "Artificial Intelligence for Science: A Deep Learning Revolution." 

Chapter 7, "Machine Learning for Complex Instrument Design and Optimization," explores how AI can refine, improve – even revolutionize – large-scale scientific experiments. The idea is to leverage machine learning to simulate, computationally, an immense range of possibilities for operations and design – not only saving time, money and resources through efficiencies and comprehensive improvements, but also exploring counterintuitive designs and ideas. 

"That sounds futuristic – and that is the hope," Papalexakis said. "We are asking, 'What is the promise of AI?'" 

His coauthors are Barry C. Barish, Nobel Laureate, California Institute of Technology professor of Physics emeritus, and UCR distinguished professor of Physics and Astronomy; Jonathan Richardson, UCR assistant professor of Physics and Astronomy; and Rutuja Gurav, a Ph.D. Candidate at UCR in computer science. 

Their approach could enhance the design and operation of elaborate engineering, including the Laser Interferometer Gravitational-wave Observatory. LIGO, managed by Caltech, comprises two sets of two 2.5-mile-long laser beams, in Washington state and Louisiana, that detect gravitational waves from cosmic phenomena such as pairs of black holes merging that emit no light and thus can't be observed visually. 

Gravitational waves help scientists understand the mysteries of space, the origins of the universe and fundamental laws of physics. LIGO itself has opened a new frontier in astronomy, with findings so groundbreaking that Barish, the former director of LIGO, shared the 2017 Nobel Prize in Physics. 

"Advances in experimental physics rely on our ability to develop highly complex state-of-the-art instruments," Barish said. "Machine learning is playing a larger and larger role in the conception, design and the implementation of such advanced experimental facilities. It is fair to say that AI is becoming a full partner in making new discoveries in physics." 

The new research envisioned would, for example, help scientists learn how to improve or even design end-to-end instruments in ways that improve their sensitivity and resilience to real-world sources of error, such as environmental noise. 

"Instead of doing this in a lab, AI would do the heavy lifting of testing potential designs and finding one that works best" for LIGO's massive infrastructure, Papalexakis said. "It's a computational way of simulating things that will aid significantly in the design of large-scale experiments." 

Such approaches would tap and adapt the technology that runs emerging public platforms such as ChatGPT and Bing AI, with large implications for scientific discovery and everyday innovation. 

The scientists noted that using AI to test, model and improve large scientific systems would not displace researchers or engineers. 

"Frontier experiments like LIGO are incredibly complex instruments, with dozens of interdependent control systems and thousands of data channels," Richardson said. "Our hope is that AI advances, such as those being pursued at UCR, will be able to recognize hidden associations in this sea of data that could diagnose operational problems. This, in turn, would inform new ways that we, as human physicists, can make physical changes that improve the performance of the detector." 

The research grew from a student's fascination and a fortuitous meeting. 

Gurav, a graduate student working in Papalexakis' computer science lab, brought a fascination with isolating gravitational waves from other noise. Then, a public lecture at UCR four years ago by gravitational-wave expert Barish led the group to meet, talk and collaborate on the project. 

Gurav praised her UCR mentors and said: "It is wonderful to see our work included in such an amazingly diverse collection of ideas on applied AI for natural sciences. It marks a special milestone in my rather unconventional Ph.D. Journey as an aspiring computer scientist who is deeply interested in exploring applications of machine learning to advance the frontiers of scientific discovery." 

Now that the chapter has been published, Papalexakis said, "I feel proud and a little terrified." Publicly laying out research directions for complex scientific study brings "a sense of responsibility that we don't take lightly. But I'm excited that people believe these things are worth investigating." 

Article by Gale Hammons  Cover image by Getty Images

Artificial Intelligence: The Latest Architecture And News

Courtesy of Geethanjali Raman and Mohik Acharya

This article was originally published on Common Edge.

It's here! The 21st-century digital renaissance has just churned out its latest debutante, and its swanky, sensational entrance has sent the world into an awed hysteria. Now sashaying effortlessly into the discipline of architecture, glittering with the promise of being immaculate, revolutionary, and invincible: ChatGPT. OpenAI's latest chatbot has been received with a frenzied reception that feels all too familiar, almost a déjà vu of sorts. The reason is this: Every time any technological innovation so much as peeks over the horizon of architecture, it is immediately shoved under a blinding spotlight and touted as the "next big thing." Even before it has been understood, absorbed, or ratified, the idea has already garnered a horde of those who vouch for it, and an even bigger horde of those who don't. Today, as everyone buckles up to be swept into the deluge of a new breakthrough, we turn an introspective gaze, unpacking where technology has led us, and what more lies in store.

https://www.Archdaily.Com/1001882/is-ai-really-the-next-big-thing-in-architectureGeethanjali Raman & Mohik Acharya

AI Expert: "Artificial Intelligence Does Not Justify Basic Income"

An Opposing View

Numerous researchers have predicted how a lot of jobs could be taken over by automation in the near future, potentially leading to massive unemployment. The numbers and timeline vary — some reports say it could be 7 percent of jobs in the next decade, others predict 850,000 jobs by 2030 — and indeed, one could argue that it is already happening.

The advent of automation has brought with it experts in various fields advocating for a universal basic income (UBI) to help people through their potential unemployment brought about by artificial intelligence (AI). Proponents argue that giving every citizen a lump sum of money could solve poverty while those on the other side of the argument insist that it could hurt the taxpayers more.

Now, one person particularly well-suited to weigh in on the discussion has said that current advancements in AI don't justify the implementation of a UBI.

Vincent Conitzer, a professor of computer science, economics, and philosophy at Duke University, says in an MIT article that while current advances in the field of AI have been impressive, the tech still couldn't replace a human being at most jobs. He argues that current AI systems have difficulty in understanding social norms and cannot pick up on subtle social cues. He points to an AI's difficulty in language as an example:

Current AI systems do not have a broad understanding of the world, including our social conventions, and they lack common sense. Language understanding is a good example of the problem; it is remarkably hard to get computers to answer many types of simple questions.

Conitzer also argues that current AI systems do not yet have the capability of true abstraction, saying that they cannot examine their own reasoning and generalize what's going on. It is because of this that he sees AI as not being creative enough. He uses Google DeepMind's AlphaGo and DeepDream to point out that the ability of these systems to think outside the box "is not the kind of creativity that truly gives one a new perspective on the situation at hand."

All in all, Conitzer says, AI systems have difficulty working in the real world. An AI worker may be able to do a specific, well-defined job sufficiently, but it still cannot replace a person who can do similar tasks in a messier real world. Therefore, he does not see the need for a basic income yet.

Credit: Michael Tyka Cautious Optimism

Conitzer does see a future in which AI could eliminate some jobs because parts of those jobs could be done by an automated system. However, he says most jobs, like therapists, counselors, and kindergarten teachers, are immune because they require a general understanding of the world and its people, which is a skill that current AI systems have difficulty replicating.

While Conitzer does admit that AI could progress much faster than he anticipates, he does not see the technology surpassing human capabilities in the short term. "The idea that recent progress in AI will prevent most people from meaningfully contributing to society is nonsense," he insists. However, he also advises that people still monitor and prepare for developments in AI:

We may have to make some changes in the way society works, including making it easier for displaced workers to retrain, and perhaps at times increasing public spending on (say) carefully selected infrastructure projects to counterbalance job losses in the private sector. We should also be mindful that advances in AI may come unexpectedly, and do our best to prepare and make society resilient to such shocks.

While preparing for the future of AI is good advice in theory, we won't truly know how the tech will change society in general (and the workforce in particular) until it happens. In the meantime, don't quit your day job.

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