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Tag: Arizona State University (ASU)

  • ASU ranks in top 10 for inventions, patents, licenses and startups among universities without medical schools

    ASU ranks in top 10 for inventions, patents, licenses and startups among universities without medical schools

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    BYLINE: Michelle Stermole, Senior Director, Public Relations and Strategic Communications, ASU Enterprise Partners

    Arizona State University ranks among the top 10 research institutions without a medical school for inventions disclosed, U.S. patents secured, license and option deals closed and startups launched, according to the Association of University Technology Managers‘ latest survey of 147 reporting institutions on licensing activities at U.S. and Canadian universities, hospitals and research institutions. 

    ASU, California Institute of Technology, Massachusetts Institute of Technology and North Carolina State University were the only universities that ranked in the top 10 across all four categories among institutions without medical schools. 

    In fiscal year 2021, ASU ranked higher than the previous fiscal year for three of the four key metrics among its peer group of institutions without medical schools, according to AUTM’s survey data. ASU rose one spot to third for U.S. patents issued, rose one spot to second for startups launched and rose two spots to seventh for license and option deals closed. It remained fifth for inventions disclosed, compared to fiscal year 2020.  

    Innovations from researchers in the Ira A. Fulton Schools of Engineering and the Biodesign Institute significantly boosted ASU’s results. 

    “ASU continues to excel in these performance indicators because its commitment to innovation and real-world impact is woven into the very fabric of its charter and is exemplified by its leaders,” said Kyle Siegal, senior vice president and chief patent counsel for Skysong Innovations. 

    Skysong Innovations, ASU’s exclusive technology transfer and intellectual property management organization, helps translate research into impact by protecting intellectual property developed in ASU labs and negotiating licensing deals with commercial partners who advance the technologies and develop solutions for society.

    The organization, fueled by ASU’s growing research enterprise, has cumulatively secured more than 1,400 U.S. patents for ASU and closed nearly 1,400 license or option deals with commercial partners during its years of service to ASU. Skysong Innovations has also facilitated more than 200 ASU startups that have collectively attracted more than $1.2 billion in external funding. 

    “One of our strengths as a university is reflected in ASU’s ability to facilitate research discoveries into startups — an area of growth that represents our commitment to the overall health of our local and national communities,” said Sally C. Morton, executive vice president of ASU’s Knowledge Enterprise. “Innovation at this speed and scale requires the ability to change the way we look at and solve problems; it takes commitment, teamwork, entrepreneurship, and community engagement, and ASU continues leading the way.”

    During fiscal year 2021, ASU had 301 invention disclosures, which is an innovation submitted by an ASU researcher for potential commercialization.

     

    ASU startups seek to solve global challenges

    FY21 saw the birth of new startups across a broad spectrum of sectors including biotech, solar energy and software.

    • GELF Energy was among the 21 ASU startups established in FY21. The company is developing microbial technologies that process food and sewage waste streams into transportable hydrogen gas for the maritime and transportation sectors. The technologies were invented by John Sabo, former ASU professor in the Global Institute of Sustainability and Innovation; Bruce Rittmann, a professor and director of the Biodesign Swette Center for Environmental Biotechnology; and Cesar Torres, a professor in the School for Engineering of Matter, Transport and Energy. 
    • Lifelab Studios is developing an online learning platform that connects learners in small communities to support each other as they use learning to achieve real-world goals. The platform was created by a team of researchers, programmers and artists, includingSasha Barab, a professor in theSchool for the Future of Innovation in Society and in the Mary Lou Fulton Teachers College and executive director of the Center for Games and Impact.
    • SunFlex Solar is developing high-efficiency interdigitated back-contact solar panels. The company was co-founded byKate Fisher, an assistant research technologist at theSchool of Electrical, Computer and Energy Engineering; Zachary Holman, an associate professor in the School of Electrical, Computer and Energy Engineering and director of faculty entrepreneurship in the Ira A. Fulton Schools of Engineering; Zhengshan “Jason” Yu, a research assistant professor, School of Electrical, Computer and Energy Engineering; and graduate research associate Barry Hartweg.
    • VaxSyna Inc. is another ASU startup established in FY21. The company is developing low-cost vaccines and therapeutics that can be easily modified to combat various pathogens associated with global health challenges. The technology was invented by Hugh Mason, an associate professor in the Biodesign Center for Immunotherapy, Vaccines and Virotherapy, and Mary Pardhe, an academic associate.

     

    Patented technologies advance early detection of neurological conditions

    There were 157 U.S. patents issued for ASU technologies during the period, and 81 licenses and option deals closed during that time.

    In October 2020, Skysong Innovations secured for ASU a patent exclusively licensed to Aural Analytics that covers the use of a patient’s speech to detect and track neurological conditions including Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease, multiple sclerosis, traumatic brain injury, stroke and other conditions where motor speech changes may occur.

    Aural Analytics, a venture-backed ASU spinout company, has built applications that use speech to detect subtle changes in brain health. The company was co-founded by Julie Liss, associate dean and professor in the College of Health Solutions, and Visar Berisha, an associate professor with a joint appointment in the College of Health Solutions and the School of Electrical, Computer and Energy Engineering.

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  • Do the math: ChatGPT sometimes can’t, expert says.

    Do the math: ChatGPT sometimes can’t, expert says.

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    BYLINE: Scott Bordow, Reporter, ASU News

    Newswise — Paulo Shakarian’s son wanted to have some fun with the natural-language processing tool ChatGPT recently, so he generated a fictitious movie script where Arnold Schwarzenegger fights Jean-Claude Van Damme.

    Welcome to the world of artificial intelligence.

    ChatGPT, which was designed by OpenAI, a small San Francisco company, is different from other large language models in that it allows the general public to experiment with it directly.

    Want to know what to do for your child’s birthday? Ask ChatGPT.

    Want poetry written in the style of William Shakespeare? ChatGPT will do that for you.

    RELATED: The pros and cons of ChatGPT

    But Shakarian, an associate professor at Arizona State University who runs Lab V-2 in the Ira A. Fulton Schools of Engineering — the lab examines challenges in the field of artificial intelligence — is not as sold on ChatGPT’s capability of higher-level reasoning. In a paper that was accepted to the Association for the Advancement of Artificial Intelligence for its spring symposium, Shakarian detailed results of a study in which he tested ChatGPT on 1,000 mathematical word problems.

    “Our initial tests on ChatGPT, done in early January, indicate that performance is significantly below the 60% accuracy for state-of-the-art algorithm for math word problem-solvers,” Shakarian said. “We are conducting a new experiment as OpenAI has stated that they have released a new version of ChatGPT with improvements in solving math problems.”

    ASU News talked to Shakarian about the paper and ChatGPT’s uses as a product.

    Editor’s note: The following interview has been edited for length and clarity.

    Question: For those not familiar with ChatGPT, what would you say it is and does?

    Answer: It’s designed around a concept called next word prediction, where for when you ask it something, it’s going to predict what the related words are based on a corpus (text and speech) data. It uses an underlying technology called the Transformer. This piece is critical because earlier technology … could only give good answers for, say, very short questions as opposed to something longer and more conversational.

    Q: So, what can someone do with ChatGPT?

    A: I think the practical applications in my view are probably going to be more in the creative and artistic space, as well as entertainment, where accuracy is not something that is going to be the most important thing. For things like a creative writing project, it could be really interesting. There was a recent story by a New York Times reporter that had a very long and strange conversation with the chat feature where the thing went a little bit off the rails. But on the flip side of that, having something that appears sentient … does give an impression there’s someone on the other end, and some people might find entertainment value in that. That said, there could be ethical implications with such uses as well, as these models can appear almost human and gain the trust of a user. However, designers have very little control over what they communicate to such a trusting and possibly vulnerable individual. These problems are related to ones of social engineering.

    Q: What are the limitations of ChatGPT?

    A: One really well-known limitation is the information in it only goes until the end of 2021. The reason for this is that ChatGPT uses what’s called a trained model, which means there’s a corpus of data used to train it. At some point that data has to stop, and it stopped at the end of 2021. So if you add new data, you usually have to start from scratch in this process. That’s significant because estimates for computational cost, just the cost of computers and electricity … is somewhere in the neighborhood of four to five million dollars. So to do that is very expensive, which is why the limitation of the data that’s used to go into it is significant.

    Q: So I couldn’t ask it about anything that happened in 2022, right?

    A: Right. Now, what has happened recently is Microsoft has announced using similar models created by OpenAI to power Bing (Microsoft’s web search engine). Instead of giving you a response, you type in your prompt and behind the scenes it’s generating search queries, and then taking those search results and putting them back into the language model and using that to give you an answer.

    Q: Sounds like Google.

    A: It is, except it’s using the language models as layers to communicate between the human and the search engine. Let’s say you have a query around buying a car, and you have specifications about the size of the vehicle because maybe you have a small garage or something. Where before you might have to do some research to kind of identify the sizes of various vehicles, and then you do another set of searches around identifying which ones meet the criteria, what happens with the new Bing is you just have one prompt that goes in and it’s using the language model to do a bunch of different searches all at once. Then it combines it together to give you an end result.

    Q: So it’s a quicker process, essentially.

    A: Yeah. From the search engine perspective, that’s where there could be some advantages. But there’s also some serious drawbacks because the language model, both in the creation of the queries and in compiling the results together, makes no difference between, say, adding in an extra sentence to kind of make something more readable versus adding in an extra sentence with some false information that just kind of sounds related to the topic. Because of that, people who have been experimenting on this have noticed that it has factual errors in the results; and by factual, I mean discrepancies between the final results and what the search engine actually found. So these are some of the problems that these companies will need to overcome.

    Q: What were you trying to find out with your paper and what did the results tell you?

    A: When ChatGPT first came out, there were all kinds of comments about how it was bad at math. There is a line of research in the field of natural language processing where people have studied how to create algorithms to solve mathematical word problems. Take a word problem that a junior high student would see that would maybe lead to a system of equations, nothing too bad, like two trains going at different speeds (to the same place). You can use algebra to solve those simultaneous questions. One key aspect about these math word problems is that they require multiple steps of inference. This simply means that once you take a look at the problem, there’s kind of a translation step, which is taking the words and turning it into the equations. These are all multiple steps we’ve done in high school, and we wanted to see if ChatGPT could correctly do these steps. What we can conclude is one of the limitations with ChatGPT is it’s just not capable of doing good multistep logical inference. And this makes sense because the underlying technology really wasn’t designed for that.

    Original Article in ASU News.

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  • Allen Coral Atlas at ASU launches improved tool to uncover reef threats and support conservation measures

    Allen Coral Atlas at ASU launches improved tool to uncover reef threats and support conservation measures

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    Newswise — The loss of coral reefs is a serious threat to the health of marine ecosystems around the world. 

    Rising ocean temperatures and coastal pollution are among many environmental stressors that contribute to the degradation of critical coral reef environments. Additional threats including deforestation, agricultural pollutants and land development, are damaging coastal marine zones at an alarming rate.

    Today, the Allen Coral Atlas at Arizona State University is launching a novel turbidity monitoring tool, which is part of a new toolkit called “Reef Threats.” The Reef Threats system provides global, real-time, integrated data on bleaching, ocean temperature and turbidity. Turbidity is the ‘muck’, mostly from neighboring land use, that can harm coastal coral habitats. 

    The expanded capability of the Atlas’s monitoring system will provide crucial information for conservation managers around the world tasked with deciding where and how to best protect, support and save coral reefs.

    “Each Allen Coral Atlas monitoring tool we create offers new insight into how conditions are changing on coral reefs,” says Greg Asner, director of ASU’s Center for Global Discovery and Conservation with the Julie Ann Wrigley Global Futures Laboratory.

    “The new Reef Threats toolkit will link changes in ocean temperature, turbidity and coral bleaching to coral loss and reef change over time. This is important because now we’ll see both the human drivers and the reef response with increasing breadth and detail. We’re hopeful that innovative mitigation measures will emerge for coral reefs worldwide,” says Asner.

    Brianna Bambic leads the Allen Coral Atlas engagement team by facilitating workshops and field opportunities to use data from the Atlas in real time. Working directly with researchers, students, governments, and coastal managers in reef communities around the world, Bambic says the new tool will make a global impact in reef management.

    “In a time of increasing human disturbance both on land and in our oceans, dynamic turbidity monitoring at this scale will drastically improve time and efficiency, as well as prioritize areas for conservation,” says Bambic, senior manager of global engagement with the ASU Center for Global Discovery and Conservation. “These new data can help local communities make more informed decisions about where to restore reefs and mangroves, and it will help identify sources of pollution caused by coastal land development and urban runoff.”

    Having a visual, real-time tool provides an immediate focus on conservation action, and can help reduce the time it takes to complete a report. For example, the Ministry of Environment of Sri Lanka is creating an Environmentally Sensitive Areas map of Sri Lanka. The Atlas data will dramatically cut down the time and resources it takes to compile these reports, thus more time can be used for mitigation and conservation action.

    Bambic says with real-time feedback to see where the coast is being disturbed, coastal communities can monitor if and when their restoration efforts are making a difference. 

    What is ocean turbidity?

    Turbid water is cloudy and heavy with sediment, contaminants and pollutants stemming from land damage and disturbances. Coastal ocean turbidity is an accepted index of water quality that has been widely applied in field-based water quality monitoring programs. For example, the United States Geological Survey and National Water Quality Program use this index.

    However, field-based point recordings have extremely limited spatial coverage. As a result, it is challenging to scale field data to large regions to capture the extent, temporal variation and sources of turbid waters. 

    Saving coral reefs requires the identification and reduction of local stressors and the cumulative impacts caused by human activities, particularly overfishing, coastal water pollution and land development.

    “The muck smothers corals that generate habitat for other marine species and for humans. The improved turbidity monitor uses satellite imagery taken on a regular basis worldwide,” says Asner. “The tool uses European Sentinel-2 data, and while it does come with some satellite-based artifacts, it’s important to push our monitoring boundaries to provide timely, detailed information about the health of coral reefs.”

    Mapping the health of coral reefs

    The Atlas uses satellite imagery, advanced analytics and global collaboration to create maps of and monitor threats to marine ecosystems’ benthic and geomorphic data in unprecedented detail. The Atlas is a collaborative project led by the ASU Center for Global Discovery and Conservation Science in partnership with Vulcan Inc., Planet Inc., the University of Queensland and the Coral Reef Alliance.

     

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