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CCI’s Manuel Prez Quiones elected to board of directors of Computing Research Association – Inside UNC Charlotte

Manuel Prez Quiones, a professor in the Department of Software and Information Systems in the College of Computing and Informatics,was elected to the Board of Directors of the Computing Research Association, one of the nations most influential computing research and policy nonprofit organizations.

An active member of CRA, Prez Quiones has served on multiple high-level CRA search and steering committees and was formerly chair of a committee that together with CRA-W eventually became the Computing Research Associations Committee on Widening Participation in Computing Research. In his new role, he anticipates continuing collaborating with CRA to broaden industry participation across all underrepresented groups.

As a member of an underrepresented group in computing, I feel like Im entering a roomwhere few people like me have entered before, Prez Quiones said. Not in a million years would I have imagined this. It feels very special, because historically I have not seen people like me in those positions.

An advocate for broadening participation in computing, Prez Quiones is excited to share his unique perspective as a Latino and Puerto Rican leader in academia and his decades of experience in human-computer interaction and computer science education research toward the goal of creating a stronger, more equitable computing industry.

Based in Washington, D.C., CRA is composed of over 250 North American organizations active in computing research; academic departments of computing; laboratories and centers in industry, government and academia; and affiliated professional societies. The organization works closely with the National Science Foundation and supports several initiatives to expand computer science research funding as well as mentoring and outreach programs.

Along with his fellow newly elected and re-elected board members, Prez Quiones will serve a three-year term beginning July 1.

Prez Quiones, who joined UNC Charlotte in 2015, just completed a rotation as program officer at the National Science Foundation in the Education and Workforce Cluster part of the Computer and Information Sciences and Engineering Directorate.

He previously served on the faculties of Virginia Tech University and the University of Puerto Rico-Mayaquez and was a visiting professor at the U.S. Naval Academy and Northeastern University. He worked as a computer scientist at the federal Naval Research Lab in Washington D.C. while earning a D.Sc. in computer science from George Washington University, after completing bachelors and masters degrees at Ball State University.

We are all tremendously proud of our friend and colleague Dr. Prez Quiones for being elected to the board of directors of the leading national computing research organization, said Bojan Cukic, dean of the College of Computing and Informatics. Manuel represents the best of CCI and our University. His tireless efforts towards equitable participation of women and underrepresented minorities in computing and thoughtful mentorship have influenced many, not only at UNC Charlotte but across the world of computing.

Read the entire article on the College of Computing and Informatics website.

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Computer class instruction | | timesnews.net – Kingsport Times News

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Connecting the dots: Visiting scientists offer a glimpse into the future of data compression – The Killeen Daily Herald

With the proliferation of artificial intelligence shaping the modern marketplace, finding models and uses for the technology has become an increasingly important endeavor for computer scientists. Students, faculty and staff at Texas A&M UniversityCentral Texas were treated to a glimpse into research surrounding the field recently by a pair of visiting professors from Poland.

Dariusz Puchala, Ph.D., and Kamil Stokfiszewski, Ph.D., of Lodz University of Technology in Poland specialize in the field of data compression and are working with A&MCentral Texas Assistant Professor KhaldoonDhou, Ph.D., to develop advanced models of data compression and determine a means by which artificial intelligence platforms can be compressed for use in smaller device with less computational power.

Dhou, who teaches in the Subhani Department of Computer Information Systems, invited his colleagues to campus to continue the teams research and to introduce the Polish duo to his research methods at a United States university. Stokfiszewski said the research internship has been invaluable to their efforts.

Since our collaboration becomes intense, Dr. Dhou invited us to see how he works and to work on some things together, Stokfiszewski said.

As a team, the trio is working on developing compression models through which loss of data during compression goes unnoticed when the files are transferred and re-opened. For instance, a large photo taken in high resolution by a professional camera carries enough data that the file is not easily transferred. When compressed, some of the data is lost to make the file size smaller and more manageable. However, upon decompression, the idea is that the user will notice no difference in the file.

The trios work focuses on things much larger than a single photo, however. Stokfiszewski said the loss of data in compression is done in a smart way. In such a way that the human is quite satisfied.

Dhou said the model they developed has significantly out-performed international standards for data compression. Now, the group is continuing its work and focusing on artificial intelligence. Stokfiszewski said they are looking at the neural networks that drive artificial intelligence and trying to find a way to compress the AI models to work properly with far less computational demand.

Those neural network models are quite huge, he said. It takes quite an amount of RAM memory in our computers and mobile devices.

The group is focusing on compressing AI models so they can be loaded on mobile devices and still function properly. Puchala compared it to a self-contained black box a complicated device that produces useful information without revealing information about its internal workings. These devices can be used in various industries to transform specific data sets into useful outcomes.

It works more like the black box, Puchala said. We construct the black box. We have data and the black box learns something that allows it to generate results.

Dhou said not only is having Puchala and Stokfiszewski on campus beneficial to his research, but seeing his own research through their perspective is helping to shape his approach and how he teaches his students.

That is helping me to shape my lectures, Dhou said. When I teach students some concepts in programming and AI, I tell them about whats happening in the real world and that is giving them additional knowledge, not just what they take from the textbook.

Dhou said he tries to consistently add content to his lectures that students are unable to find with a simple online search.

My rule is to add something to my lectures and make them try to connect the dots and connect themselves to their world.

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Eight from MIT named 2024 Sloan Research Fellows – MIT News

Eight members of the MIT faculty are among 126 early-career researchers honored with 2024 Sloan Research Fellowships by the Alfred P. Sloan Foundation. Representing the departments of Chemistry, Electrical Engineering and Computer Science, and Physics, and the MIT Sloan School of Management, the awardees will receive a two-year, $75,000 fellowship to advance their research.

Sloan Research Fellowships are extraordinarily competitive awards involving the nominations of the most inventive and impactful early-career scientists across the U.S. and Canada, says Adam F. Falk, president of the Alfred P. Sloan Foundation. We look forward to seeing how fellows take leading roles shaping the research agenda within their respective fields.

Jacob Andreas is an associate professor in the Department of Electrical Engineering and Computer Science (EECS) as well as the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research aims to build intelligent systems that can communicate effectively using language and learn from human guidance. Jacob has been named a Kavli Fellow by the National Academy of Sciences, and has received the NSF CAREER award, MIT's Junior Bose and Kolokotrones teaching awards, and paper awards at ACL, ICML and NAACL.

Adam Belay, Jamieson Career Development Associate Professor of EECS in CSAIL, focuses on operating systems and networking, specifically developing practical and efficient methods for microsecond-scale distributed computing, which has many applications pertaining to resource management in data centers. His operating system, Caladan, reallocates server resources on a microsecond scale, resulting in high CPU utilization with low tail latency. Additionally, Belay has contributed to load balancing, and Application-Integrated Far Memory in OS designs.

Soonwon Choi, assistant professor of physics, is a researcher in the Center for Theoretical Physics, a division of the Laboratory for Nuclear Science. His research is focused on the intersection of quantum information and out-of-equilibrium dynamics of quantum many-body systems, specifically exploring the dynamical phenomena that occur in strongly interacting quantum many-body systems far from equilibrium and designing their novel applications for quantum information science. Recent contributions from Choi, recipient of the Inchon Award, include the development of simple methods to benchmark the quality of analog quantum simulators. His work allows for efficiently and easily characterizing quantum simulators, accelerating the goal of utilizing them in studying exotic phenomena in quantum materials that are difficult to synthesize in a laboratory.

Maryam Farboodi, the Jon D. Gruber Career Development Assistant Professor of Finance in the MIT Sloan School of Management, studies the economics of big data. She explores how big data technologies have changed trading strategies and financial outcomes, as well as the consequences of the emergence of big data for technological growth in the real economy. She also works on developing methodologies to estimate the value of data. Furthermore, Farboodi studies intermediation and network formation among financial institutions, and the spillovers to the real economy. She is also interested in how information frictions shape the local and global economic cycles.

Lina Necib PhD 17, an assistant professor of physics and a member of the MIT Kavli Institute for Astrophysics and Space Research, explores the origin of dark matter through a combination of simulations and observational data that correlate the dynamics of dark matter with that of the stars in the Milky Way. She has investigated the local dynamic structures in the solar neighborhood using the Gaia satellite, contributed to building a catalog of local accreted stars using machine learning techniques, and discovered a new stream called Nyx. Necib is interested in employing Gaia in conjunction with other spectroscopic surveys to understand the dark matter profile in the local solar neighborhood, the center of the galaxy, and in dwarf galaxies.

Arvind Satyanarayan in an assistant professor of computer science and leader of the CSAIL Visualization Group. Satyanarayan uses interactive data visualization as a petri dish to study intelligence augmentation, asking how computational representations and software systems help amplify our cognition and creativity while respecting our agency. His work has been recognized with an NSF CAREER award, best paper awards at academic venues such as ACM CHI and IEEE VIS, and honorable mentions among practitioners including Kantars Information is Beautiful Awards. Systems he helped develop are widely used in industry, on Wikipedia, and in the Jupyter/Python data science communities.

Assistant professor of physics and a member of the Kavli Institute Andrew Vanderburg explores the use of machine learning, especially deep neural networks, in the detection of exoplanets, or planets which orbit stars other than the sun. He is interested in developing cutting-edge techniques and methods to discover new planets outside of our solar system, and studying the planets we find to learn their detailed properties. Vanderburg conducts astronomical observations using facilities on Earth like the Magellan Telescopes in Chile as well as space-based observatories like the Transiting Exoplanet Survey Satellite and the James Webb Space Telescope. Once the data from these telescopes are in hand, they develop new analysis methods that help extract as much scientific value as possible.

Xiao Wang is a core institute member of the Broad Institute of MIT and Harvard, and the Thomas D. and Virginia Cabot Assistant Professor of Chemistry. She started her lab in 2019 to develop and apply new chemical, biophysical, and genomic tools to better probe and understand tissue function and dysfunction at the molecular level. Specifically, with in situ sequencing of nucleic acids as the core approach, Wang aims to develop high-resolution and highly-multiplexed molecular imaging methods across multiple scales toward understanding the physical and chemical basis of brain wiring and function. She is the recipient of a Packard Fellowship, NIH Directors New Innovator Award, and is a Searle Scholar.

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Programmer: How We Know Computers Won’t Surpass the Human Mind – Walter Bradley Center for Natural and Artificial Intelligence

Heres a brief excerpt from Chapter 22 of Minding the Brain (Discovery Institute Press, 2023), The Human Minds Sophisticated Algorithm and Its Implications, by programmer Winston Ewert. His discussion is based on the halting problem: No computer knows when another computer will halt, though humans do.

Is the human mind a computer? If not, what is it? Before we can answer, we must first clarify, what exactly is a computer? Historically, the term computer actually referred not to machines but to humans. Typically, these were teams of people working together to perform long and tedious calculations. They helped with such tasks as computing the positions of planets, producing mathematical tables, and simulating fluid dynamics. What made them computers was that they were following a procedure. They were not expected or allowed to engage in creative thinking or problem-solving; instead, every action they took was guided by the procedure given to them. All that our modern computers do is automate this procedure- following activity. Human computers and machine computers are similar in that they operate strictly by following a procedure.

What exactly constitutes a procedure? A procedure provides a step-by-step method for solving a particular class of problems. The procedure defines how to proceed at every step of the task, leaving no decision up to the judgment of the person or machine following the procedure. In the context of computers, these procedures are typically called algorithms.

However, not every task can be reduced to a procedure. Researchers working in theoretical computer science have proven that a number of tasks cannot be reduced to a procedure. There is no procedure that can be written that will reliably perform these tasks. For example, there is no procedure that determines whether a logical statement, in first-order or higher logic, follows from a given set of premises.

Is everything that the human mind can do reducible to a procedure or program, even if we are not consciously aware of the procedure? Could we, in principle, duplicate the abilities of the human using a computer program? Or are there at least some tasks that the human mind can accomplish which cannot be reduced to a procedure? Are there things that the human mind can do which could not be duplicated by any procedure or program?

We have sketched an argument that generating cognitive abilities requires greater cognitive ability. This has a number of interesting consequences: First, human cognitive ability will never be matched by artificial intelligence. We have argued that the only way to obtain an accurate partial halting detector is using a more powerful halting detector. When humans devise artificially intelligent systems, they use their internal powerful halting detection abilities to verify and/or construct the implicit halting detection present in the artificially intelligent system. However, they are only capable of devising a halting detector less powerful than the one they have. As such, we would expect that while humans will get better at building artificial intelligence systems, they will never be able to match themselves.

Second, the singularity will not happen. The idea of the singularity is that an artificially intelligent system will be able to build a slightly more intelligent artificial intelligence (AI) system. That system will, in turn, devise an even more intelligent system. This process, repeated over and over, will culminate in artificially intelligent systems which will leave humans far behind. However, the only way to obtain a partial halting detector is using a more powerful partial halting detector. An AI system cannot build a slightly more intelligent partial halting detector. Thus, the singularity will not occur.

Third, the human mind has a transcendent origin. Standard evolutionary theory claims that the human mind was produced by natural selection operating on random mutations. However, this would be a case of a very computationally simple process constructing an accurate, highly powerful halting detector. This cannot happen if the only way to obtain a partial halting detector is by using a more powerful halting detector. Instead, the human mind must have derived from something with more powerful halting detection abilities. Yet we cannot explain the human mind by an infinite regress of increasingly powerful partial halting detectors. Rather, the human mind must eventually be explained by a non-computational form of intelligence for whom the halting problem is no obstacle.

You may also wish to read: Programmers: Why materialism cant explain human creativity Eric Holloway and Robert Marks explain why its unlikely that the mind that enables human creativity is merely the product of animal evolution. The total space-time information capacity of the universe falls significantly short of the ability to generate meaningful text of only a few hundred letters.

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Generative AI: Unlocking the Power of Synthetic Data To Improve Software Testing – SciTechDaily

DataCebo, an MIT spinoff, leverages generative AI to produce synthetic data, aiding organizations in software testing, patient care improvement, and flight rerouting. Its Synthetic Data Vault, used by thousands, demonstrates the growing significance of synthetic data in ensuring privacy and enhancing data-driven decisions. Credit: SciTechDaily.com

MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.

Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms especially in scenarios where real-world data are limited or sensitive.

For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki 15, SM 16 believe the companys success is due to SDVs ability to revolutionize software testing.

DataCebo offers a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models. Credit: Courtesy of DataCebo. Edited by MIT News.

In 2016, Veeramachanenis group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.

Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.

Veeramachanenis group came across the problem because it was working with companies that wanted to share their data for research.

MIT helps you see all these different use cases, Patki explains. You work with finance companies and health care companies, and all those projects are useful to formulate solutions across industries.

In the next few years, synthetic data from generative models will transform all data work, Kalyan Veeramachaneni says. From left: Kalyan Veeramachaneni, Co-Founder; Andrew Montanez, Director of Engineering; and Neha Patki, Co-Founder, VP of Product. Credit: Courtesy of DataCebo

In 2020, the researchers founded DataCebo to build more SDV features for larger organizations. Since then, the use cases have been as impressive as theyve been varied.

With DataCebos new flight simulator, for instance, airlines can plan for rare weather events in a way that would be impossible using only historical data. In another application, SDV users synthesized medical records to predict health outcomes for patients with cystic fibrosis. A team from Norway recently used SDV to create synthetic student data to evaluate whether various admissions policies were meritocratic and free from bias.

In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data. Roughly 30,000 data scientists participated, building solutions and predicting outcomes based on the companys realistic data.

And as DataCebo has grown, its stayed true to its MIT roots: All of the companys current employees are MIT alumni.

Although their open-source tools are being used for a variety of use cases, the company is focused on growing its traction in software testing.

You need data to test these software applications, Veeramachaneni says. Traditionally, developers manually write scripts to create synthetic data. With generative models, created using SDV, you can learn from a sample of data collected and then sample a large volume of synthetic data (which has the same properties as real data), or create specific scenarios and edge cases, and use the data to test your application.

For example, if a bank wanted to test a program designed to reject transfers from accounts with no money in them, it would have to simulate many accounts simultaneously transacting. Doing that with data created manually would take a lot of time. With DataCebos generative models, customers can create any edge case they want to test.

Its common for industries to have data that is sensitive in some capacity, Patki says. Often when youre in a domain with sensitive data youre dealing with regulations, and even if there arent legal regulations, its in companies best interest to be diligent about who gets access to what at which time. So, synthetic data is always better from a privacy perspective.

Veeramachaneni believes DataCebo is advancing the field of what it calls synthetic enterprise data, or data generated from user behavior on large companies software applications.

Enterprise data of this kind is complex, and there is no universal availability of it, unlike language data, Veeramachaneni says. When folks use our publicly available software and report back if works on a certain pattern, we learn a lot of these unique patterns, and it allows us to improve our algorithms. From one perspective, we are building a corpus of these complex patterns, which for language and images is readily available.

DataCebo also recently released features to improve SDVs usefulness, including tools to assess the realism of the generated data, called the SDMetrics library as well as a way to compare models performances called SDGym.

Its about ensuring organizations trust this new data, Veeramachaneni says. [Our tools offer] programmable synthetic data, which means we allow enterprises to insert their specific insight and intuition to build more transparent models.

As companies in every industry rush to adopt AI and other data science tools, DataCebo is ultimately helping them do so in a way that is more transparent and responsible.

In the next few years, synthetic data from generative models will transform all data work, Veeramachaneni says. We believe 90 percent of enterprise operations can be done with synthetic data.

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Dealing with the limitations of our noisy world – MIT News

Tamara Broderick first set foot on MITs campus when she was a high school student, as a participant in the inaugural Womens Technology Program. The monthlong summer academic experience gives young women a hands-on introduction to engineering and computer science.

What is the probability that she would return to MIT years later, this time as a faculty member?

Thats a question Broderick could probably answer quantitatively using Bayesian inference, a statistical approach to probability that tries to quantify uncertainty by continuously updating ones assumptions as new data are obtained.

In her lab at MIT, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS) uses Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques.

Ive always been really interested in understanding not just What do we know from data analysis, but How well do we know it? says Broderick, who is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society. The reality is that we live in a noisy world, and we cant always get exactly the data that we want. How do we learn from data but at the same time recognize that there are limitations and deal appropriately with them?

Broadly, her focus is on helping people understand the confines of the statistical tools available to them and, sometimes, working with them to craft better tools for a particular situation.

For instance, her group recently collaborated with oceanographers to develop a machine-learning model that can make more accurate predictions about ocean currents. In another project, she and others worked with degenerative disease specialists on a tool that helps severely motor-impaired individuals utilize a computers graphical user interface by manipulating a single switch.

A common thread woven through her work is an emphasis on collaboration.

Working in data analysis, you get to hang out in everybodys backyard, so to speak. You really cant get bored because you can always be learning about some other field and thinking about how we can apply machine learning there, she says.

Hanging out in many academic backyards is especially appealing to Broderick, who struggled even from a young age to narrow down her interests.

A math mindset

Growing up in a suburb of Cleveland, Ohio, Broderick had an interest in math for as long as she can remember. She recalls being fascinated by the idea of what would happen if you kept adding a number to itself, starting with 1+1=2 and then 2+2=4.

I was maybe 5 years old, so I didnt know what powers of two were or anything like that. I was just really into math, she says.

Her father recognized her interest in the subject and enrolled her in a Johns Hopkins program called the Center for Talented Youth, which gave Broderick the opportunity to take three-week summer classes on a range of subjects, from astronomy to number theory to computer science.

Later, in high school, she conducted astrophysics research with a postdoc at Case Western University. In the summer of 2002, she spent four weeks at MIT as a member of the first class of the Womens Technology Program.

She especially enjoyed the freedom offered by the program, and its focus on using intuition and ingenuity to achieve high-level goals. For instance, the cohort was tasked with building a device with LEGOs that they could use to biopsy a grape suspended in Jell-O.

The program showed her how much creativity is involved in engineering and computer science, and piqued her interest in pursuing an academic career.

But when I got into college at Princeton, I could not decide math, physics, computer science they all seemed super-cool. I wanted to do all of it, she says.

She settled on pursuing an undergraduate math degree but took all the physics and computer science courses she could cram into her schedule.

Digging into data analysis

After receiving a Marshall Scholarship, Broderick spent two years at Cambridge University in the United Kingdom, earning a master of advanced study in mathematics and a master of philosophy in physics.

In the UK, she took a number of statistics and data analysis classes, including her first class on Bayesian data analysis in the field of machine learning.

It was a transformative experience, she recalls.

During my time in the U.K., I realized that I really like solving real-world problems that matter to people, and Bayesian inference was being used in some of the most important problems out there, she says.

Back in the U.S., Broderick headed to the University of California at Berkeley, where she joined the lab of Professor Michael I. Jordan as a grad student. She earned a PhD in statistics with a focus on Bayesian data analysis.

She decided to pursue a career in academia and was drawn to MIT by the collaborative nature of the EECS department and by how passionate and friendly her would-be colleagues were.

Her first impressions panned out, and Broderick says she has found a community at MIT that helps her be creative and explore hard, impactful problems with wide-ranging applications.

Ive been lucky to work with a really amazing set of students and postdocs in my lab brilliant and hard-working people whose hearts are in the right place, she says.

One of her teams recent projects involves a collaboration with an economist who studies the use of microcredit, or the lending of small amounts of money at very low interest rates, in impoverished areas.

The goal of microcredit programs is to raise people out of poverty. Economists run randomized control trials of villages in a region that receive or dont receive microcredit. They want to generalize the study results, predicting the expected outcome if one applies microcredit to other villages outside of their study.

But Broderick and her collaborators have found that results of some microcredit studies can be very brittle. Removing one or a few data points from the dataset can completely change the results. One issue is that researchers often use empirical averages, where a few very high or low data points can skew the results.

Using machine learning, she and her collaborators developed a method that can determine how many data points must be dropped to change the substantive conclusion of the study. With their tool, a scientist can see how brittle the results are.

Sometimes dropping a very small fraction of data can change the major results of a data analysis, and then we might worry how far those conclusions generalize to new scenarios. Are there ways we can flag that for people? That is what we are getting at with this work, she explains.

At the same time, she is continuing to collaborate with researchers in a range of fields, such as genetics, to understand the pros and cons of different machine-learning techniques and other data analysis tools.

Happy trails

Exploration is what drives Broderick as a researcher, and it also fuels one of her passions outside the lab. She and her husband enjoy collecting patches they earn by hiking all the trails in a park or trail system.

I think my hobby really combines my interests of being outdoors and spreadsheets, she says. With these hiking patches, you have to explore everything and then you see areas you wouldnt normally see. It is adventurous, in that way.

Theyve discovered some amazing hikes they would never have known about, but also embarked on more than a few total disaster hikes, she says. But each hike, whether a hidden gem or an overgrown mess, offers its own rewards.

And just like in her research, curiosity, open-mindedness, and a passion for problem-solving have never led her astray.

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Q&A: Is artificial intelligence defined the same way across disciplines? – Penn State University

UNIVERSITY PARK, Pa. Due to its rapid rise in everyday life, artificial intelligence (AI) technology has become increasingly relevant to social scientists. A team led by Penn State researchers reviewed a variety of social science literature and found that studies often defined AI differently. By drawing from some of these areas and computer science, the researchers created a single definition and framework that they said they hope will be compatible across disciplines.

Lead author HomeroGil de Ziga, Distinguished Professor in Media Effects and AI in the Donald P. Bellisario College of Communications at Penn State, said the definition is a starting point. It is purposefully broad so it can both adapt as AI evolves and boost interdisciplinary collaboration among researchers. The work, discussed by Gil de Ziga in the Q&A below, was published in the journal Political Communication with co-authors Timilehin Durotoye, a doctoral student in the Bellisario College, and Manuel Goyanes, assistant professor at the University Carlos III de Madrid.

Q: How did you identify the need for an artificial intelligence definition specifically for the social sciences?

Gil de Ziga: Obviously in society today, AI is picking up. Its not just scientific anymore. It has a human basis for all citizens. Regardless of the country that you're living in, AI is becoming more important. For computer scientists, its been around for decades. But for us who are thinking about how its going to be integrated in daily life, artificial intelligence is in its infancy. So, starting with computer science, we gathered different definitions from what had been written about AI. My co-authors and I found that there was not a large consensus about what AI is or what it might be. We realized that the definitions were not concrete and were often defined in a way so they fit a particular papers study.

Q: What is the definition that emerged from your study?

Gil de Ziga: Our definition says: AI is thetangible real-world capability of non-human machines or artificial entities to perform, task solve, communicate, interact and logically act akin to biological humans.

Q: How does your definition for AI differ from a discipline outside the social sciences?

Gil de Ziga: If someone is writing a study on Alexa, they might define artificial intelligence in a very particular way. For example, they may say AI is a machine that performs smart tasks. Or they may base it on the systems ability to interpret external data. When it comes to journalism and communication, the definitions might abandon the machine and instead define AI as a set of algorithms designed to generate and distribute media, text and images. So, thats why we wanted to combine all of these definitions and generate something that will work across disciplines.

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WilmU, Code Differently team up for 18 credit opportunity in tech – Milford LIVE

A new partnership between Wilmington University and a national coding group will provide Delaware residents the opportunity to earn a big chunk of college credits in concentrations like computer science, cybersecurity and data analysis.

The university has partnered with Code Differently, which provides hands-on training and education through coding classes that gives participants the technical and cognitive skills they need to succeed in technology-driven workplaces.

The partnership, announced Tuesday, provides Code Differently participants up to 18 college credits at WilmU.

Since its establishment in 2018, 800 First State adults have received software development training from Code Differently, with an 89% completion rate and an 85% work-placement rate.

According to the organization, the most recent group of participants included 15 students who started the 20-week coding course in February.

This collaboration with Code Differently speaks to our mission of providing opportunity and flexibility to students, and it also addresses our comprehensive focus on technology, said LaVerne Harmon, WilmU president.

Harmon said the school understands the high demand for skilled information technology professionals, and she suspects that need will continue to grow.

This partnership reflects an opportunity for innovation to meet accessibility in higher education, she said.

Stephanie Eldridge, CEO and co-founder of Code Differently, said the organization wants to eliminate barriers to learning and success, and is committed to the advancement of all of its participants.

Lindsay Rice, the WilmUs senior director of Academic Partnerships, said the partnership leverages what its participants have learned and provides an easy transfer to bachelors programs directly connecting to Code Differently programs.

As students embark on their educational journeys with WilmU, Rice said, they save time and money while earning a competitive degree.

Upon completion of our 20-week full stack coding program, this agreement allows all of our participants, past, present and future, to earn 18 credits that can be applied directly to in-demand undergraduate computer science degree programs at WilmU, Eldridge said. Our partnership with WilmU opens the door for all participants who realize that higher education is the other key to their success.

Raised in Doylestown, Pennsylvania, Jarek earned a B.A. in journalism and a B.A. in political science from Temple University in 2021. After running CNNs Michael Smerconishs YouTube channel, Jarek became a reporter for the Bucks County Herald before joining Delaware LIVE News.

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The best computer science universities in Latin America – AOL

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The coveted computer science degree has promised graduates six-figure salaries and exciting careers in high-tech spaces since the personal computer took off in the 1990s.

As an academic discipline, computer science dates back even further. In the 1960s, Purdue University became the first major institution to found a department dedicated to the practice, complete with history books written by faculty members because, at that point, none existed.

These days, the U.S., China, and Singapore are often associated with top-tier computer science degrees, but there are highly respected universities in nearly every corner of the world offering an education in the field. Revelo collected rankings from U.S. News and World Report to identify the top 10 universities for computer science in Latin America as part of a larger global analysis.

Slowly but steadily, Latin America has developed some of the world's most promising tech hubs. The region is outpacing others to become a top destination for developers, according to a 2023 report by HackerRank. Students considering studying computer science abroad can add universities in this large, diverse region to their list thanks to its commitment to internationalizing.

Web designers, software developers, computer network architects, research scientists, and systems administrators all leverage computer science knowledge as the basis of their job. And it's a job that analysts project to be in high demandeven as layoffs at major tech companies dominate headlines.

The typical U.S. worker in a computer science-based career today earns an income of $100,530, according to the Bureau of Labor Statistics. The bureau also estimates the industry will require 377,500 new workers each year for the next decade, as the industry is set to grow faster than the average growth rate of all other industries.

With the tech industry facing criticism for lacking diversity and products like artificial intelligence built on mostly white male perspectives, employers could benefit from looking beyond traditional institutions in recruiting efforts. From 2010-2020, the number of women graduating from U.S. schools with computer science degrees rose only 3%, and graduates from underrepresented racial groups remained flat at just over 20% of all graduates.

U.S. News and World Report ranked 778 universities globally with at least 250 academic research papers, calculating its subject scores on a 0-100 scale based on the number of publications and citations an institution received, its global and regional research reputation, and other factors.

- Location: Niteroi, Brazil - Computer science score: 18.0 out of 100 (#710 globally) - Overall score: 38.4 out of 100 (#1,017 globally) - Enrollment: 49,554

- Location: Fortaleza, Brazil - Computer science score: 20.0 out of 100 (#683 globally) - Overall score: 39.2 out of 100 (#977 globally) - Enrollment: Not available

- Location: Sao Carlos, Brazil - Computer science score: 22.4 out of 100 (#650 globally) - Overall score: 41.0 out of 100 (#896 globally) - Enrollment: Not available

- Location: Curitiba, Brazil - Computer science score: 23.5 out of 100 (#630 globally) - Overall score: 25.5 out of 100 (#1,603 globally) - Enrollment: Not available

- Location: Florianopolis, Brazil - Computer science score: 23.8 out of 100 (#626 globally) - Overall score: 47.9 out of 100 (#618 globally) - Enrollment: Not available

- Location: Brasilia, Brazil - Computer science score: 25.2 out of 100 (#605 globally) - Overall score: 45.4 out of 100 (#710 globally) - Enrollment: Not available

- Location: Buenos Aires, Argentina - Computer science score: 26.5 out of 100 (#588 globally) - Overall score: 53.8 out of 100 (#426 globally) - Enrollment: Not available

- Location: Mexico City, Mexico - Computer science score: 28.4 out of 100 (#556 globally) - Overall score: 54.3 out of 100 (#405 globally) - Enrollment: 172,729

- Location: Santiago, Chile - Computer science score: 32.4 out of 100 (#472 globally) - Overall score: 57.7 out of 100 (#314 globally) - Enrollment: 31,579

- Location: Curitiba, Brazil - Computer science score: 32.7 out of 100 (#470 globally) - Overall score: 42.6 out of 100 (#816 globally) - Enrollment: Not available

- Location: Sao Paulo, Brazil - Computer science score: 33.7 out of 100 (#449 globally) - Overall score: 51.4 out of 100 (#497 globally) - Enrollment: Not available

- Location: Mexico City, Mexico - Computer science score: 33.7 out of 100 (#449 globally) - Overall score: 36.7 out of 100 (#1,095 globally) - Enrollment: Not available

- Location: Monterrey, Mexico - Computer science score: 34.6 out of 100 (#438 globally) - Overall score: 44.2 out of 100 (#759 globally) - Enrollment: 49,696

- Location: Rio de Janeiro, Brazil - Computer science score: 35.9 out of 100 (#412 globally) - Overall score: 54.1 out of 100 (#413 globally) - Enrollment: 45,964

- Location: Recife, Brazil - Computer science score: 37.4 out of 100 (#396 globally) - Overall score: 40.9 out of 100 (#901 globally) - Enrollment: Not available

- Location: Porto Alegre, Brazil - Computer science score: 39.6 out of 100 (#364 globally) - Overall score: 53.6 out of 100 (#432 globally) - Enrollment: Not available

- Location: Belo Horizonte, Brazil - Computer science score: 44.3 out of 100 (#301 globally) - Overall score: 52.6 out of 100 (#468 globally) - Enrollment: Not available

- Location: Santiago, Chile - Computer science score: 45.0 out of 100 (#285 globally) - Overall score: 54.4 out of 100 (#400 globally) - Enrollment: 37,286

- Location: Campinas, Brazil - Computer science score: 48.2 out of 100 (#246 globally) - Overall score: 58.7 out of 100 (#294 globally) - Enrollment: 31,199

- Location: Sao Paulo, Brazil - Computer science score: 55.1 out of 100 (#154 globally) - Overall score: 68.5 out of 100 (#120 globally) - Enrollment: 82,010

This story features data reporting by Paxtyn Merten, writing by Dom DiFurio, and is part of a series utilizing data automation across 5 regions.

This story originally appeared on Revelo and was produced and distributed in partnership with Stacker Studio.

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