National Science Foundation backs Rice-led effort to create science-aware artificial intelligence
Rice University
HOUSTON — (Sept. 18, 2019) — A Rice University scientist and his colleagues are booting their search for dark matter into a study they hope will enhance all of data science.
Rice astroparticle physicist Christopher Tunnell and his team have received a $1 million National Science Foundation (NSF) grant to reimagine data science techniques and help push data-intensive physical sciences past the tipping point to discovery.
Experiments in the physical sciences are starting to produce thousands of terabytes of data, Tunnell said. “These datasets are fundamentally different from large datasets of everyday photos, text or video,” he said. “Ours relate to experiences of the natural world that only highly specialized instruments and sensors can ‘see.'”
In tackling this class of problem, the two-year project aims to influence the way data scientists use machine and deep learning in bioinformatics, computational biology, materials science and environmental sciences. Tunnell said the goal is to support these physical science communities through a “domain-enhanced” data science institute.
“In large astroparticle data sets, we often look for the faintest signals that anyone has ever attempted to measure,” said Tunnell, an assistant professor of physics and astronomy and computer science and lead investigator on the project.
“Science is incremental,” he said, explaining the domain-enhanced approach. “We have spent decades building up mankind’s most precise physical theories, which provide the foundation for these measurements. When using machine learning in this realm, the machine has to learn through its own ‘Phys 101.’ But the great artificial intelligence advancements of the last decade have been mostly in computer vision and natural language processing with a muted impact in physical sciences.”
Tunnell’s co-investigators are Waheed Bajwa, an associate professor of electrical and computer engineering at Rutgers University, and Hagit Shatkay, a professor of computer and information sciences at the University of Delaware. The team formed at an Ideas Lab run by the NSF and Knowinnovation that brought together scientists and engineers to facilitate novel data science ideas that did not fit any disciplinary mold.
The researchers argue that particle physics can serve as a driver for technological advances that are later used by other sciences in the same way that data-handling needs at the European Organization for Nuclear Research (known as CERN) led to the development of the World Wide Web.
“Our proposal focuses on one scientific application — in this case astroparticle physics — to test out multiple novel methods,” Tunnell said. “We are searching for solutions to a real-world problem rather than problems that fit our solution. That, in my view, is what interdisciplinary science is about.”
For the dark matter search, they need data science and machine-learning algorithms that improve measurements of particle interactions in their detectors. “This will simultaneously increase the ability to measure faint dark-matter signals while improving the precision of energy measurements,” Tunnell said. “It will help the experiment be sensitive to neutrinoless double-beta decay, a process that sheds light on the nature of neutrino mass and, potentially, why our universe is made of matter.”
He said they will employ probabilistic graphical models that allow them to encode their knowledge of science, as well as inverse problem formulations that teach machine-learning routines enough that they can learn the rest on their own.
Tunnell has already gained a foothold in the search for dark matter, even if the matter itself is not at hand. Earlier this year, he and colleagues at the XENON1T experiment announced in Nature they had found the first physical evidence of the material with the longest half-life ever measured. The sophisticated detector under a mountain in Italy discovered that Xenon 124 has a half-life of 18 sextillion years, demonstrating that the experiment and subsequent data science can measure exotic physical signals.
He noted the grant incorporates funds for educational outreach and training of data scientists in the techniques under development.
Tunnell’s group was formed as part of Rice’s Data Science Initiative, with additional seed funding for research from two Rice Creative Venture grants. “This work has already led to one discovery: a strong friendly interdisciplinary team interested in trying something new,” he said.
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Read the abstract at https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940209
This news release can be found online at news.rice.edu
Follow Rice News and Media Relations via Twitter @RiceUNews
Related materials:
Elemental old-timer makes the universe look like a toddler: http://news.rice.edu/2019/04/24/elemental-old-timer-makes-the-universe-look-like-a-toddler-2/
Rice Astroparticle (Tunnell group): https://astroparticle.web.rice.edu
Inspire Lab (Bajwa group): http://www.inspirelab.us
Computational Biomedicine Lab (Shatkey group): https://www.eecis.udel.edu/~compbio/
Rice Computer Science: https://cs.rice.edu/
Rice Department of Physics and Astronomy: https://physics.rice.edu/
Image for download:
https://news-network.rice.edu/news/files/2019/09/0909_DATA-1-web-1.jpg?CAPTION: Christopher Tunnell. (Credit: Jeff Fitlow/Rice University)
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I’ve been wondering for a while just how programmers can include proper scientific skepticism into AI. If it assumes all the data it assimilates is accurate and reliable, it will be pretty useless. But what kind of algorithm would be able to detect incompetence, bias or fraud?
Here is my explanation of Dark Matter. Generated by musing in a comfortable armchair.
Two objects each travelling at near the speed of light converge. Then they converge at a speed greater than the speed of light.
If they were diverging then they would be diverging at greater than the speed of light and thus would be unknown to each other, or to put it otherewise. Would be Dark wrt each other.
The number of particles that are moving away from earth at greater than the speed of light must be very large indeed. Hence lots and lots of Dark Matter in the universe from the perspective of the earth. QED.
Cheers everyone.
Alasdair
Dark matter may be nonsense, but your thought experiment is in violation of special relativity. Your two objects do not “converge/diverge at a speed greater than the speed of light” in their own reference frames which see the events as sub-c.
Spare me. Some journalistic BS just leaps out of the screen at you and gives you a real good slapping.
Like with climate claptrap, I’ll believe in their unexplained but new-found computational powers, whatever they may be, when it produces real verifiable results. Not just because “this is great because a million dollar grant says so”.
Dark Matter, Dark Energy, Black Holes.
Do you see a theme here?
Thinks that can’t be seen, thinks that can’t be touched, things that go bump in the night.
And a million dollar government budget.
I say we are being scammed.
I say it would make a great title for the new Batman movie …
“He said they will employ probabilistic graphical models that allow them to encode their knowledge of science, as well as inverse problem formulations that teach machine-learning routines enough that they can learn the rest on their own.”
That just sounds like bafflegab. They’re using the million bucks research money for something else….
“This project will develop innovative domain-enhanced data science methods that will be based on probabilistic graphical models and graph-regularized inverse problems.”
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Sounds like a bargain at 1 million.
Just make the data open-source.
Dark Matter = Electron as a particle
Dark Energy = Electron as a wave
Dark matter/dark energy is nothing more than a fudge factor, applied individually to each galaxy just to “fit” the gravity effects detected and the space acceleration they think they detect. This is NOT science, it is just a kludge.
The whole dark matter/dark energy is nothing but unscientific fudge factors to prop up a theory that isn’t working quite right.
I prefer quantum inertia as a calculable theory that either matches the findings or doesn’t (so if it doesn’t work it can be discarded, as science is supposed to do) and is not created just to match what is detected with what is expected.
“Its residential college system builds close-knit communities and lifelong friendships” – in other words: networks.
Anyway: what’s the use of supercomputer searches when there ain’t the slightest idea of what to search for.