Tag Archives: Yang Xu

Artificial intelligence and metaphors

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This is a different approach to artificial intelligence. From a June 27, 2017 news item on ScienceDaily,

Ask Siri to find a math tutor to help you “grasp” calculus and she’s likely to respond that your request is beyond her abilities. That’s because metaphors like “grasp” are difficult for Apple’s voice-controlled personal assistant to, well, grasp.

But new UC Berkeley research suggests that Siri and other digital helpers could someday learn the algorithms that humans have used for centuries to create and understand metaphorical language.

Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary.

The results, published in the journal Cognitive Psychology, demonstrate how throughout history humans have used language that originally described palpable experiences such as “grasping an object” to describe more intangible concepts such as “grasping an idea.”

Unfortunately, this image is not the best quality,

Scientists have created historical maps showing the evolution of metaphoric language. (Image courtesy of Mahesh Srinivasan)

A June 27, 2017 University of California at Berkeley (or UC Berkeley) news release by Yasmin Anwar, which originated the news item,

“The use of concrete language to talk about abstract ideas may unlock mysteries about how we are able to communicate and conceptualize things we can never see or touch,” said study senior author Mahesh Srinivasan, an assistant professor of psychology at UC Berkeley. “Our results may also pave the way for future advances in artificial intelligence.”

The findings provide the first large-scale evidence that the creation of new metaphorical word meanings is systematic, researchers said. They can also inform efforts to design natural language processing systems like Siri to help them understand creativity in human language.

“Although such systems are capable of understanding many words, they are often tripped up by creative uses of words that go beyond their existing, pre-programmed vocabularies,” said study lead author Yang Xu, a postdoctoral researcher in linguistics and cognitive science at UC Berkeley.

“This work brings opportunities toward modeling metaphorical words at a broad scale, ultimately allowing the construction of artificial intelligence systems that are capable of creating and comprehending metaphorical language,” he added.

Srinivasan and Xu conducted the study with Lehigh University psychology professor Barbara Malt.

Using the Metaphor Map of English database, researchers examined more than 5,000 examples from the past millennium in which word meanings from one semantic domain, such as “water,” were extended to another semantic domain, such as “mind.”

Researchers called the original semantic domain the “source domain” and the domain that the metaphorical meaning was extended to, the “target domain.”

More than 1,400 online participants were recruited to rate semantic domains such as “water” or “mind” according to the degree to which they were related to the external world (light, plants), animate things (humans, animals), or intense emotions (excitement, fear).

These ratings were fed into computational models that the researchers had developed to predict which semantic domains had been the sources or targets of metaphorical extension.

In comparing their computational predictions against the actual historical record provided by the Metaphor Map of English, researchers found that their models correctly forecast about 75 percent of recorded metaphorical language mappings over the past millennium.

Furthermore, they found that the degree to which a domain is tied to experience in the external world, such as “grasping a rope,” was the primary predictor of how a word would take on a new metaphorical meaning such as “grasping an idea.”

For example, time and again, researchers found that words associated with textiles, digestive organs, wetness, solidity and plants were more likely to provide sources for metaphorical extension, while mental and emotional states, such as excitement, pride and fear were more likely to be the targets of metaphorical extension.

Scientists have created historical maps showing the evolution of metaphoric language. (Image courtesy of Mahesh Srinivasan)

Here’s a link to and a citation for the paper,

Evolution of word meanings through metaphorical mapping: Systematicity over the past millennium by Yang Xu, Barbara C. Malt, Mahesh Srinivasan. Cognitive Psychology Volume 96, August 2017, Pages 41–53 DOI: https://doi.org/10.1016/j.cogpsych.2017.05.005

The early web version of this paper is behind a paywall.

For anyone interested in the ‘Metaphor Map of English’ database mentioned in the news release, you find it here on the University of Glasgow website. By the way, it also seems to be known as ‘Mapping Metaphor with the Historical Thesaurus‘.

3D printed biomimetic blood vessel networks

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An artificial blood vessel network that could lead the way to regenerating biologically-based blood vessel networks has been printed in 3D at the University of California at San Diego (UCSD) according to a March 2, 2017 news item on ScienceDaily,

Nanoengineers at the University of California San Diego have 3D printed a lifelike, functional blood vessel network that could pave the way toward artificial organs and regenerative therapies.

The new research, led by nanoengineering professor Shaochen Chen, addresses one of the biggest challenges in tissue engineering: creating lifelike tissues and organs with functioning vasculature — networks of blood vessels that can transport blood, nutrients, waste and other biological materials — and do so safely when implanted inside the body.

A March 2, 2017 UCSD news release (also on EurekAlert), which originated the news item, explains why this is an important development,

Researchers from other labs have used different 3D printing technologies to create artificial blood vessels. But existing technologies are slow, costly and mainly produce simple structures, such as a single blood vessel — a tube, basically. These blood vessels also are not capable of integrating with the body’s own vascular system.

“Almost all tissues and organs need blood vessels to survive and work properly. This is a big bottleneck in making organ transplants, which are in high demand but in short supply,” said Chen, who leads the Nanobiomaterials, Bioprinting, and Tissue Engineering Lab at UC San Diego. “3D bioprinting organs can help bridge this gap, and our lab has taken a big step toward that goal.”

Chen’s lab has 3D printed a vasculature network that can safely integrate with the body’s own network to circulate blood. These blood vessels branch out into many series of smaller vessels, similar to the blood vessel structures found in the body. The work was published in Biomaterials.

Chen’s team developed an innovative bioprinting technology, using their own homemade 3D printers, to rapidly produce intricate 3D microstructures that mimic the sophisticated designs and functions of biological tissues. Chen’s lab has used this technology in the past to create liver tissue and microscopic fish that can swim in the body to detect and remove toxins.

Researchers first create a 3D model of the biological structure on a computer. The computer then transfers 2D snapshots of the model to millions of microscopic-sized mirrors, which are each digitally controlled to project patterns of UV light in the form of these snapshots. The UV patterns are shined onto a solution containing live cells and light-sensitive polymers that solidify upon exposure to UV light. The structure is rapidly printed one layer at a time, in a continuous fashion, creating a 3D solid polymer scaffold encapsulating live cells that will grow and become biological tissue.

“We can directly print detailed microvasculature structures in extremely high resolution. Other 3D printing technologies produce the equivalent of ‘pixelated’ structures in comparison and usually require sacrificial materials and additional steps to create the vessels,” said Wei Zhu, a postdoctoral scholar in Chen’s lab and a lead researcher on the project.

And this entire process takes just a few seconds — a vast improvement over competing bioprinting methods, which normally take hours just to print simple structures. The process also uses materials that are inexpensive and biocompatible.

Chen’s team used medical imaging to create a digital pattern of a blood vessel network found in the body. Using their technology, they printed a structure containing endothelial cells, which are cells that form the inner lining of blood vessels.

The entire structure fits onto a small area measuring 4 millimeters × 5 millimeters, 600 micrometers thick (as thick as a stack containing 12 strands of human hair).

Researchers cultured several structures in vitro for one day, then grafted the resulting tissues into skin wounds of mice. After two weeks, the researchers examined the implants and found that they had successfully grown into and merged with the host blood vessel network, allowing blood to circulate normally.

Chen noted that the implanted blood vessels are not yet capable of other functions, such as transporting nutrients and waste. “We still have a lot of work to do to improve these materials. This is a promising step toward the future of tissue regeneration and repair,” he said.

Moving forward, Chen and his team are working on building patient-specific tissues using human induced pluripotent stem cells, which would prevent transplants from being attacked by a patient’s immune system. And since these cells are derived from a patient’s skin cells, researchers won’t need to extract any cells from inside the body to build new tissue. The team’s ultimate goal is to move their work to clinical trials. “It will take at least several years before we reach that goal,” Chen said.

Here’s a link to and a citation for the paper,

Direct 3D bioprinting of prevascularized tissue constructs with complex microarchitecture by Wei Zhu, Xin Qu, Jie Zhu, Xuanyi Ma, Sherrina Patel, Justin Liu, Pengrui Wang, Cheuk Sun Edwin Lai, Maling Gou, Yang Xu, Kang Zhang, Shaochen Chen. Biomaterials 124 (April 2017) 106-15 http://dx.doi.org/10.1016/j.biomaterials.2017.01.042

This paper is behind a paywall.

There is also an open access copy here on the university website but I cannot confirm that it is identical to the version in the journal.