Universities, Rich in Data, Struggle to Capture Its Value

Needs more automatic matching to actual uses, needs, outcomes. 

Universities, Rich in Data, Struggle to Capture Its Value     By UCLA

December 29, 2022

Merging together data from various sources is a major challenge, respondents said.

Universities are struggling to make sense of their vast administrative datasets, according to researchers Christine L. Borgman at UCLA and Amy Brand at MIT.

The two surveyed senior officials focused on university data governance and management to identify a shortage of coordination and expertise. Respondents cited issues about commercial control of internal systems, local capacity for data-informed planning, a lack of databases of record, and few coordinated information management strategies and administrators with data-science backgrounds.

"We unexpectedly found a pervasive void of infrastructure thinking and a relatively limited set of data-informed planning successes," said Brand.

The researchers's study, published in Science, suggests universities could expand infrastructure investments to enhance access, integration, and intelligence, as well as fortify their data management capacity.

From UCLA   View Full Article   

Can Computers Understand Complex Words, Concepts?

Provocative questions on understanding concepts.

Can Computers Understand Complex Words, Concepts?

UCLA Newsroom

Stuart Wolpert, June 16, 2022

Researchers at the University of California, Los Angeles (UCLA), the Massachusetts Institute of Technology, and the National Institutes of Health found that artificial intelligence (AI) can learn complex word meanings and understand them like humans do. The researchers developed the "semantic projection" technique to study 52 word groups to determine whether an AI system could learn to sort meanings. The results of their research showed their method was very similar to human intuition. across many objects and contexts. Said UCLA's Idan Blank, "This machine learning system is much smarter than we thought; it contains very complex forms of knowledge, and this knowledge is organized in a very intuitive structure."

More Seeing Around the Corner

More on the topic.  Mentioned a number of times here, a seemingly impossible idea. But very useful for driving applications to anticipate and avoid problems. Thought I have no application for this idea, the  metaphor of getting data from sensors that can see beyond normal barriers is interesting to consider.  Sensing.

Engineering Researchers Develop Camera System to See Around Corners
UCLA Samueli Newsroom
July 14, 2020

Researchers at the University of California, Los Angeles (UCLA) and the Nara Institute of Science and Technology in Japan have developed a camera that can see around corners using the same phenomenon one observes when looking through polarized sunglasses. The researchers set in front of the camera lens a polarizer that only allows certain oscillation states to reach the lens. They also developed a novel algorithm that rearranges the polarization of light off a wall to show objects hidden around a corner. Said UCLA's Achuta Kadambi, "If the technology can be successfully applied to cameras enabling them to see around corners, it could help autonomous cars avoid accidents at blind spots, or allow biomedical engineers to create endoscopes that can help doctors see around organs."  ... ' 

Future of Computer Tech?

Points again to the 'Brain as a computing model' conundrum.   We have lots of brains we can study, observe, dissect.   But we still don't know their operational specs.   Algorithms 'work', because their input and output are simple.   Brains are not.    But its good we can get closer to the biomimicry of the brain for any hope of replicating their approach.

Brain-Like Device May Forecast Future of Computer Technology
By The Daily Bruin,  January 14, 2020

Researchers at UCLA and Japan's National Institute for Materials Science developed a device with the ability to mimic certain characteristics of the brain. The work is described in "Emergent Dynamics of Neuromorphic Nanowire Networks," published in Scientific Reports.

James Gimzewski, a chemistry professor at UCLA, and Adam Stieg, associate director of the California NanoSystems Institute at UCLA, helped create the device, which spans 10 square millimeters.

The device's small size is possible because of the number of networks within the device, all of which are made from silver nanowires, which have an average diameter of 360 nanometers.

"Because [the networks] self-assemble on the nanoscale, we can achieve a very high density of synaptic-like connections that wouldn't be achievable using normal computer chip technology," Gimzewski says.

Kelsey Scharnhorst, who assisted in research for this project, says the growing popularity of machine learning has led to a scramble to develop an option that can produce computational outputs in a timely, more-energy-efficient fashion.

"Instead of doing everything with algorithms . . . [computations] can be sped up an insane amount [with machine learning], and that can make a huge difference for a ton of technology," Scharnhorst says.

From The Daily Bruin  Full article at the link.

AI Device Identifies Objects at Speed of Light

Quite an impressive statement.   And delivery method.  much more at the link.

UCLA-developed artificial intelligence device identifies objects at the speed of light  Public Release: 

The 3D-printed artificial neural network can be used in medicine, robotics and security
UCLA Samueli School of Engineering ... 

A team of UCLA electrical and computer engineers has created a physical artificial neural network -- a device modeled on how the human brain works -- that can analyze large volumes of data and identify objects at the actual speed of light. The device was created using a 3D printer at the UCLA Samueli School of Engineering.
Numerous devices in everyday life today use computerized cameras to identify objects -- think of automated teller machines that can "read" handwritten dollar amounts when you deposit a check, or internet search engines that can quickly match photos to other similar images in their databases. But those systems rely on a piece of equipment to image the object, first by "seeing" it with a camera or optical sensor, then processing what it sees into data, and finally using computing programs to figure out what it is.  ... "