Category Archives: Computer Science
Town hall on CHIPS and Science Act envisions key role for Penn State – Pennsylvania State University
UNIVERSITY PARK, Pa. Penn State held a Town Hall meeting recently to discuss internal strategies around semiconductor technologies and taking on a key role in partnering with other universities and industry centered on the U.S. governments CHIPS (Creating Helpful Incentives to Produce Semiconductors) and Science Act, which was signed into law on Aug. 9, 2022. The intent of this meeting was to share Penn States significant and spanning capabilities and expertise and facilitate internal alignment. Meetings have already taken place and will continue to occur with other university, industry and state partners.
The CHIPS and Science Act spawned multiple funding-driven opportunities to position the U.S. as a leader in these fields, with various major new and upcoming programs sponsored by the Department of Commerce, the Department of Defense, Department of Energy and the National Science Foundation.
To best address some of these opportunities, Penn State is creating the Mid-Atlantic Semiconductor Hub (MASH) with nine other academic partners, industry and state governments to lead and leverage the cumulative expertise in this area.
As another example of Penn States current involvement in semiconductor programs, the recently funded Center for Heterogeneous Integration of Micro Electronic Systems (CHIMES) was a major step in partnering to address global needs in the semiconductor space. CHIMES was announced by the Semiconductor Research Corporation (SRC)s Joint University Microelectronics Program 2.0 (JUMP 2.0), a consortium of industrial partners in cooperation with the Defense Advanced Research Projects Agency (DARPA). The SRCfunded $32.7 million to the Penn State-led Center.
Several years ago, recognizing the gaps in semiconductor technology research in the United States and the dependency on other countries, Penn States Materials Research Institute (MRI) and Department of Electrical Engineering began prioritizing semiconductor research to better meet the needs of our national demand. Several industrial partners have also been part of this journey. Penn State has a deep commitment to interdisciplinary research as an institutional strength, including fields related to the CHIPS and Science Act such as semiconductor materials, devices, packaging, optics, thermal management/efficiencies, computation and quantum devices. This includes a strong history in 2D materials research. All of these opportunities come with significant emphasis on workforce development, where Penn State has considerable strength to train the next generation of leaders, scientists, engineers and manufacturers across all the manufacturing domains inherent in CHIPS needs.
"Semiconductor and chip technology have been tremendous strengths for Penn State for several years, visible in our national rankings as No. 1 and No. 2 for materials science and materials engineering. Stated Lora Weiss, senior vice president for Research, This is why Penn State is an obvious choice to lead programs around the CHIPS and Science Act,and bring global recognition back home to the United States.
Developing a strategy within the upcoming programs under the CHIPs and Science Act is the next step in leveraging Penn States record as a leader in the semiconductor space. The Town Hall was held to provide an update on this strategy and to discuss areas where Penn State excels and can bring positive momentum to national goals for semiconductor technology, packaging, workforce development and education.
"By partnering with neighboring universities within the Mid-Atlantic region, we are creating a synergistic hub that combines expertise in semiconductor and microelectronics with unique resources, skills, and strengths in packaging, communications, electronic design and workforce development, said Daniel Lopez, director of the MRIs Nanofabrication Laboratory and Liang Professor of Electrical Engineering and Computer Science. Our goal in creating MASH is to generate a new paradigm for coordinated workforce development and collaborative research that will quickly transition technologies and restore the preeminence of the U.S. in microelectronics.
Speakers in the open forum Town Hall included Lora Weiss, senior vice president for research, Daniel Lopez, director of MRIs Nanofabrication Laboratory and Liang Professor of Electrical Engineering and Computer Science; Susan Trolier-McKinstry, Evan Pugh University Professor and Steward S. Flaschen Professor of Materials Science and Engineering and Electrical Engineering; and Madhavan Swaminathan, head of electrical engineeringand William E. Leonhard Endowed Chair in Penn State College of Engineerings School of Electrical Engineering and Computer Science. The slides from the meeting can be downloaded at this link.
The University has also created a form for any faculty or industry members who would like to be part of the possible Hub initiative; the form can be found here. Questions or comments can be sent to chipsact@psu.edu.
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Town hall on CHIPS and Science Act envisions key role for Penn State - Pennsylvania State University
Computer science meets cinema: University of Waterloo researchers … – CTV News Kitchener
Published March 16, 2023 5:56 p.m. ET
Updated March 16, 2023 7:20 p.m. ET
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It's not a pairing you'd expect computer science and cinema.
Researchers from the University of Waterloo and Carleton University are closely analyzing the colour used in films to learn more about them.
The team looked at more than 29,000 North American movie trailers from 1960 to 2019. Using a technique called k-means clustering, they extract the dominant colours from each trailer to create a colour palette. And it's those colour palettes that show there's more than meets the eye when it comes to colour in films.
"One interesting trend we observed is colour sort of leaking out of films over the years," said Andreea Pocol, a Ph.D. candidate in computer science at Waterloo.
"It's not to say individual films don't use it. But, on a whole, those vibrant greens and oranges have disappeared," says Lesley Istead, adjunct assistant professor of computer science at Waterloo and assistant professor at the Carleton School of Information Technology.
They found specific colours are favoured in certain genres too. Horror, action and adventure films often use darker, grittier tones.
"Things like O Brother, Where Art Thou? Wonderful movie, the colour in it is very muted and sepia-toned. You remember that," Istead said.
Researchers point to the idea that colour could eventually find its way into streaming service suggestions too. They say that's because colour says a lot about the type of movie someone wants to watch.
"What if colour could be part of the recommendation for you? We see you like horror movies, we're observing all the horror movies you watch that have these colours. Here are some others that are similar," says Istead.
There is a much-anticipated sequel to this study, which is determining whether a movie might be a blockbuster or a flop.
"And we think colour might play a role in this, and that's where this research is headed," Istead said.
Pocol pointed to some examples where colour caters to specific audiences.
"Kids prefer more colourful movies. If you want a profitable movie, add a lot of strong, bright, saturated colours. Maybe adults prefer the more muted colour palettes," Pocol says.
It's these findings that could change the film industry forever. For directors, film production companies and even the average viewer, it helps paint a clearer picture of a motion picture.
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Even before Micron breaks ground, CNY colleges offer classes to train chip fab workers – syracuse.com
Syracuse, N.Y. Micron Technologys production of computer memory chips in Clay wont start for another three years, but one Central New York college is already preparing to offer new classes this fall to help meet the companys labor needs.
Onondaga Community College will offer a new supply chain management degree, providing students with the skills needed to manage the flow of goods and services that enable companies to transform raw materials into final products.
Anastasia Urtz, OCC provost and senior vice president, said the new degree will be applicable to several industries but especially Micron.
Micron has shared that it anticipates with the chip fabs they will build that, over time, they will need 9,000 direct employees, but then there will be many additional jobs that are in the supply chain that will develop around them, she said.
The new degree is just one change that local colleges and an economic development organization are planning to help Micron meet its substantial workforce needs in Central New York.
Micron announced in October it has selected White Pine Commerce Park in Clay for a massive computer memory chip plant, the largest in the nation, that will cost up to $100 billion over 20 years and employ up to 9,000 people directly.
In addition to the people employed directly by Micron, the company has said another 41,000 workers will be employed at supply chain companies that will locate nearby.
Micron cited the abundance of colleges and universities in Central New York as one of the reasons it chose the Clay site. Being able to fill the thousands of jobs it creates at the chip fab is important to the developments success.
In addition to a supply chain management degree, OCC plans to offer a new degree in construction management this fall. Urtz said that degree will be directly applicable to a number of major construction projects in Central New York, including the Micron chip fab.
Most of the initial jobs created by Micron will be in construction, and those jobs are likely to be long term because the company plans to build the Clay plant in phases over 20 years.
And those arent the only curriculum changes the college is planning in response to Microns selection of Central New York for its giant chip plant.
OCC offers degrees in electrical technology and mechanical technology. Since Micron will need workers with training in both electrical and mechanical technologies, the college plans this fall to combine the two into a single two-year degree, as well as a one-year certificate program, Urtz said.
Were revising our existing programs to join these together because, essentially, the semiconductor space is one where individuals would do well to have both the electrical skills and the mechanical skills, she said.
When you think through how these operations work, theyre robotic technologies. These run 24 hours a day, so there are both machine operators and individual technicians who will be making all of that mechanical infrastructure stay working. Its all highly skilled fields.
And while many details are still to be worked out, Urtz said the college is also looking into establishing apprenticeship opportunities with Micron and other employers so students can earn while learning.
Were trying to offer the best possible opportunity to get as many people as we can access to this incredible moment for us in Central New York, she said. We want everybody to have an opportunity to participate.
To prepare for training students for jobs at Micron, Urtz and other OCC officials traveled in December to a Micron chip plant opened in 2002 in Manassas, Virginia, and also visited Northern Virginia Community College in Manassas and nearby Norfolk State University.
Hargsoon Yoon, director of the Micron-NSU Nanofabrication Cleanroom training facility at Norfolk State University, said the facility helps to train graduate and undergraduate students for jobs in the semiconductor industry.
OCC is making plans to build a similar facility to train students for jobs at Micron and other semiconductor makers.
Micron provided $300,000 toward the more than $10 million cost of building the facility in Norfolk, with much of the rest of the funding coming from the National Science Foundation, Yoon said.
The training provided at the facility is part of bachelor, masters and doctorate degrees the university offers in electrical engineering, material science, chemistry and other scientific fields, he said.
Though many of the students who are trained at the facility go on to work at Micron, the training provided there is not specific to Micron and often helps students obtain jobs at other semiconductor makers, including Intel, too, he said.
Micron maintains a close relationship with the facility. The company holds job fairs at the school, and Micron officials regularly come to the training center to familiarize students with the work the company does in Manassas, according to Yoon. Micron also provides NSU students with internships, he said.
We provide a lot of exposure to Micron, he said.
Central New York colleges are expected to incorporate Microns specific needs into their curriculums in the form of new courses, laboratory work and minors added to the backbone of traditional degrees. But college officials say they are offering programs right now that will put students in prime positions to land jobs at the company.
Michael Carpenter, interim dean of the College of Engineering at SUNY Polytechnic Institute in Utica, said electrical and mechanical engineering and technology degrees offered by the college are aligned with what Micron will be looking for when it begins hiring. So there is no need for students to wait for Micron-specific classes to be announced, he said.
There are going to be a lot of workers who need to be trained and we have that opportunity for students to come in and find their niche and then move to the one that they prefer and set themselves up really well for their career, said Carpenter.
Syracuse Universitys College of Engineering and Computer Science is planning to expand its student enrollment by 50% over the next three to five years to help Micron meet its need for engineering talent, said J. Cole Smith, the colleges dean. The college has 2,450 undergraduate and graduate students, so the expansion will mean an increase of up to 1,225 students.
When we talked with Micron about the importance of developing the human resources they would need, one of their big concerns was that semiconductor manufacturing is a skill that is not something that a lot of students picked up because of where manufacturing has gone in this country, Smith said.
The college has already begun hiring more faculty to prepare for the increase in students, he said.
The college does not plan to change its engineering curriculum, but Smith said its classes will likely include trips to Microns fabrication facility in Clay to give students a first-hand look at semiconductor manufacturing.
The great thing about having a partner like Micron nearby is that they are going to supply a lot of facilities at their site that we can leverage, he said.
CenterState CEO, a Syracuse-based economic development and business leadership organization that helped to recruit Micron to Central New York, is making plans to upscale its Pathways to Apprenticeship program to expand the opportunity for local residents, particularly the unemployed and underemployed, to land construction jobs at Micron.
The 11-week program, launched in 2021, prepares Syracuse residents for apprenticeships in a broad range of construction fields. The training includes instruction in basic construction skills and how to read blueprints. Since its launch, 65 people have gone through the program.
Aimee Durfee, CenterStates director of workforce innovation, said the organization will partner with building trades unions and Micron to scale up the program to help meet the companys needs.
In addition, CenterState officials say they are holding discussions with colleges and universities throughout the region on specific programs they can offer to prepare workers for jobs at Micron.
Its got to be all hands on deck, said Dominic Robinson, CenterStates senior vice president of inclusive growth.
Rick Moriarty covers business news and consumer issues. Got a tip, comment or story idea? Contact him anytime: Email | Twitter | Facebook | 315-470-3148
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GPT-4 is here: what scientists think – Nature.com
The GPT-4 artificial-intelligence model is not yet widely available.Credit: Jaap Arriens/NurPhoto via Getty Images
Artificial intelligence company OpenAI this week unveiled GPT-4, the latest incarnation of the large language model that powers its popular chat bot ChatGPT. The company says GPT-4 contains big improvements it has already stunned people with its ability to create human-like text and generate images and computer code from almost any a prompt. Researchers say these abilities have the potential to transform science but some are frustrated that they cannot yet access the technology, its underlying code or information on how it was trained. That raises concern about the technologys safety and makes it less useful for research, say scientists.
One upgrade to GPT-4, released on 14 March, is that it can now handle images as well as text. And as a demonstration of its language prowess, Open AI, which is based in San Francisco, California, says that it passed the US bar legal exam with results in the ninetieth centile, compared with the tenth centile for the previous version of ChatGPT. But the tech is not yet widely accessible only to paid subscribers to ChatGPT so far have access.
ChatGPT listed as author on research papers: many scientists disapprove
Theres a waiting list at the moment so you cannot use it right now, Says Evi-Anne van Dis, a psychologist at the University of Amsterdam. But she has seen demos of GPT-4. We watched some videos in which they demonstrated capacities and its mind blowing, she says. One instance, she recounts, was a hand-drawn doodle of a website, which GPT-4 used to produce the computer code needed to build that website, as a demonstration of the ability to handle images as inputs.
But there is frustration in the science community over OpenAIs secrecy around how and what data the model was trained, and how it actually works. All of these closed-source models, they are essentially dead-ends in science, says Sasha Luccioni, a research scientist specializing in climate at HuggingFace, an open-source-AI community. They [OpenAI] can keep building upon their research, but for the community at large, its a dead end.
Andrew White, a chemical engineer at University of Rochester, has had privileged access to GPT-4 as a red-teamer: a person paid by OpenAI to test the platform to try and make it do something bad. He has had access to GPT-4 for the past six months, he says. Early on in the process, it didnt seem that different, compared with previous iterations.
Abstracts written by ChatGPT fool scientists
He put to the bot queries about what chemical reactions steps were needed to make a compound, predict the reaction yield, and choose a catalyst. At first, I was actually not that impressed, White says. It was really surprising because it would look so realistic, but it would hallucinate an atom here. It would skip a step there, he adds. But when as part of his red-team work he gave GPT-4 access to scientific papers, things changed dramatically. It made us realize that these models maybe arent so great just alone. But when you start connecting them to the Internet to tools like a retrosynthesis planner, or a calculator, all of a sudden, new kinds of abilities emerge.
And with those abilities come concerns. For instance, could GPT-4 allow dangerous chemicals to be made? With input from people such as White, OpenAI engineers fed back into their model to discourage GPT-4 from creating dangerous, illegal or damaging content, White says.
Outputting false information is another problem. Luccioni says that models like GPT-4, which exist to predict the next word in a sentence, cant be cured of coming up with fake facts known as hallucinating. You cant rely on these kinds of models because theres so much hallucination, she says. And this remains a concern in the latest version, she says, although OpenAI says that it has improved safety in GPT-4.
Without access to the data used for training, OpenAIs assurances about safety fall short for Luccioni. You dont know what the data is. So you cant improve it. I mean, its just completely impossible to do science with a model like this, she says.
How Nature readers are using ChatGPT
The mystery about how GPT-4 was trained is also a concern for van Diss colleague at Amsterdam, psychologist Claudi Bockting. Its very hard as a human being to be accountable for something that you cannot oversee, she says. One of the concerns is they could be far more biased than for instance, the bias that human beings have by themselves. Without being able to access the code behind GPT-4 it is impossible to see where the bias might have originated, or to remedy it, Luccioni explains.
Bockting and van Dis are also concerned that increasingly these AI systems are owned by big tech companies. They want to make sure the technology is properly tested and verified by scientists. This is also an opportunity because collaboration with big tech can of course, speed up processes, she adds.
Van Dis, Bockting and colleagues argued earlier this year for an urgent need to develop a set of living guidelines to govern how AI and tools such as GPT-4 are used and developed. They are concerned that any legislation around AI technologies will struggle to keep up with the pace of development. Bockting and van Dis have convened an invitational summit at the University of Amsterdam on 11 April to discuss these concerns, with representatives from organizations including UNESCOs science-ethics committee, Organisation for Economic Co-operation and Development and the World Economic Forum.
Despite the concern, GPT-4 and its future iterations will shake up science, says White. I think it's actually going to be a huge infrastructure change in science, almost like the internet was a big change, he says. It wont replace scientists, he adds, but could help with some tasks. I think we're going to start realizing we can connect papers, data programmes, libraries that we use and computational work or even robotic experiments.
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Chief data and analytics officers must lead upskilling initiatives in … – SiliconANGLE News
As data scientist hiring continues to boom, many organizations report sustained difficulty finding, attracting and retaining data science talent. Even as initiatives to upskill quantitative professionals grow, machine learning literacy remains low in many organizations.
Chief data and analytics officers, or CDAOs, must build development paths that support budding citizen data scientists with the right tools, training and structure. Even organizations that build high volumes of complex and accurate models must diligently foster data literacy and proper adoption of solutions.
Concerted education and culture change are necessary but can be difficult to achieve because of entrenched ways of doing things and the high critical mass of technical expertise required for enterprise data science. Here are steps that CDAOs can take to develop in-house talent and improve data science and machine learning literacy:
CDAOs building development paths for data science experts can start by raising the level of discourse around data science and machine learning in the organization. Ensure that all line-of-business leaders and decision makers have a clear understanding of how data scientists create value.
CDAOs must help employees who express an interest or aptitude become familiar with the basics of several machine learning techniques, such as regression, clustering and classification. Data scientists can regularly hold open sessions to discuss projects or aspects of data science they are passionate about. Encourage upskilling individuals to attend regular training, engage in new subjects and enter into healthy competition with peers to maintain their enthusiasm.
Chief data and analytics officers also must raise overall data science and machine learning awareness, adoption and literacy by providing centralized education resources and showcasing existing use cases and success stories, both internal and external. By 2024, 75% of organizations will have established a centralized data and analyticscenter of excellence to support federated D&A initiatives and prevent enterprise failure.
Talent alignment, career development and talent retention are the primary leadership demands needed to sustain successful upskilling initiatives. Citizen data scientists play an important role for CDAOs when it comes to talent recognition and development. CDAOs must understand citizen data scientists persona and recognize the skills that make for good CDS candidates.
Gauge potential candidates interest in a data science career and have them complete self-evaluations of their backgrounds to gather an inventory of what skills can be established. Gathering this information will be vital to designing training programs and making technology investments. Typically, the most promising candidates for upskilling have both educational and professional backgrounds in physics, chemistry, biology, actuarial science, computer science, engineering, finance, economics and mathematics.
Foundational data science and machine learning upskilling initiatives must be conducted for CDS candidates. There are three stages to this roadmap:
There is an abundance of education opportunities and retention challenges that can motivate organizations to upskill their data professionals at all levels and grow their CDS population. It is necessary for CDAOs to build repeatable and sustainable education programs by designing upskilling roadmaps for CDS candidates and expert data scientists. With a large number of tools available to citizen data scientists, CDAOs must navigate this landscape to match diverse users to appropriate solutions and corresponding educational paths.
Peter Krensky is a director analyst on Gartners Business Analytics and Data Science team, where he covers data science, machine learning and data and analytics education. He wrote this article for SiliconANGLE. Gartner analysts are providing additional analysis on topics of interest to CDAOs at the Gartner Data & Analytics Summit, taking place March 20-22 in Orlando, Florida.
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Chief data and analytics officers must lead upskilling initiatives in ... - SiliconANGLE News
3D-printed revolving devices can sense how they are moving – Science Daily
Integrating sensors into rotational mechanisms could make it possible for engineers to build smart hinges that know when a door has been opened, or gears inside a motor that tell a mechanic how fast they are rotating. MIT engineers have now developed a way to easily integrate sensors into these types of mechanisms, with 3D printing.
Even though advances in 3D printing enable rapid fabrication of rotational mechanisms, integrating sensors into the designs is still notoriously difficult. Due to the complexity of the rotating parts, sensors are typically embedded manually, after the device has already been produced.
However, manually integrating sensors is no easy task. Embed them inside a device and wires might get tangled in the rotating parts or obstruct their rotations, but mounting external sensors would increase the size of a mechanism and potentially limit its motion.
Instead, the new system the MIT researchers developed enables a maker to 3D print sensors directly into a mechanism's moving parts using conductive 3D printing filament. This gives devices the ability to sense their angular position, rotation speed, and direction of rotation.
With their system, called MechSense, a maker can manufacture rotational mechanisms with integrated sensors in just one pass using a multi-material 3D printer. These types of printers utilize multiple materials at the same time to fabricate a device.
To streamline the fabrication process, the researchers built a plugin for the computer-aided design software SolidWorks that automatically integrates sensors into a model of the mechanism, which could then be sent directly to the 3D printer for fabrication.
MechSense could enable engineers to rapidly prototype devices with rotating parts, like turbines or motors, while incorporating sensing directly into the designs. It could be especially useful in creating tangible user interfaces for augmented reality environments, where sensing is critical for tracking a user's movements and interaction with objects.
"A lot of the research that we do in our lab involves taking fabrication methods that factories or specialized institutions create and then making then accessible for people. 3D printing is a tool that a lot of people can afford to have in their homes. So how can we provide the average maker with the tools necessary to develop these types of interactive mechanisms? At the end of the day, this research all revolves around that goal," says Marwa AlAlawi, a mechanical engineering graduate student and lead author of a paper on MechSense.
AlAlawi's co-authors include Michael Wessely, a former postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) who is now an assistant professor at Aarhus University; and senior author Stefanie Mueller, an associate professor in the MIT departments of Electrical Engineering and Computer Science and Mechanical Engineering, and a member CSAIL; as well as others at MIT and collaborators from Accenture Labs. The research will be presented at the ACM CHI Conference on Human Factors in Computing Systems.
Built-in sensing
To incorporate sensors into a rotational mechanism in a way that would not disrupt the device's movement, the researchers leveraged capacitive sensing.
A capacitor consists of two plates of conductive material that have an insulating material sandwiched between them. If the overlapping area or distance between the conductive plates is changed, perhaps by rotating the mechanism, a capacitive sensor can detect resulting changes in the electric field between the plates. That information could then be used to calculate speed, for instance.
"In capacitive sensing, you don't necessarily need to have contact between the two opposing conductive plates to monitor changes in that specific sensor. We took advantage of that for our sensor design," AlAlawi says.
Rotational mechanisms typically consist of a rotational element located above, below, or next to a stationary element, like a gear spinning on a static shaft above a flat surface. The spinning gear is the rotational element and the flat surface beneath it is the stationary element.
The MechSense sensor includes three patches made from conductive material that are printed into the stationary plate, with each patch separated from its neighbors by nonconductive material. A fourth patch of conductive material, which has the same area as the other three patches, is printed into the rotating plate.
As the device spins, the patch on the rotating plate, called a floating capacitor, overlaps each of the patches on the stationary plate in turn. As the overlap between the rotating patch and each stationary patch changes (from completely covered, to half covered, to not covered at all), each patch individually detects the resulting change in capacitance.
The floating capacitor is not connected to any circuitry, so wires won't get tangled with rotating components.
Rather, the stationary patches are wired to electronics that use software the researchers developed to convert raw sensor data into estimations of angular position, direction of rotation, and rotation speed.
Enabling rapid prototyping
To simplify the sensor integration process for a user, the researchers built a SolidWorks extension. A maker specifies the rotating and stationary parts of their mechanism, as well as the center of rotation, and then the system automatically adds sensor patches to the model.
"It doesn't change the design at all. It just replaces part of the device with a different material, in this case conductive material," AlAlawi says.
The researchers used their system to prototype several devices, including a smart desk lamp that changes the color and brightness of its light depending on how the user rotates the bottom or middle of the lamp. They also produced a planetary gearbox, like those that are used in robotic arms, and a wheel that measures distance as it rolls across a surface.
As they prototyped, the team also conducted technical experiments to fine-tune their sensor design. They found that, as they reduced the size of the patches, the amount of error in the sensor data increased.
"In an effort to generate electronic devices with very little e-waste, we want devices with smaller footprints that can still perform well. If we take our same approach and perhaps use a different material or manufacturing process, I think we can scale down while accumulating less error using the same geometry," she says.
In addition to testing different materials, AlAlawi and her collaborators plan to explore how they could increase the robustness of their sensor design to external noise, and also develop printable sensors for other types of moving mechanisms.
This research was funded, in part, by Accenture Labs.
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3D-printed revolving devices can sense how they are moving - Science Daily
Faculty share international experiences to inspire others | The … – The University Record
When nursing and midwifery graduate students travel to Uganda for clinical immersion experiences, they see first-hand the ways pregnancy and birth medical care in South Sudanese refugee settlements contribute to maternal mortality.
Working with midwives in a different cultural setting gives students the opportunity to reflect on maternal care across the globe, Ruth Zielinski, clinical professor of nursing, said at a March 17 panel discussion.
Its something we talk a lot about in class, but to actually be able to live it, to reflect on it we do a lot of debriefing I think is really, really important, Zielinski said.
With international engagement as one of President Santa J. Onos priorities, the panel hostedby U-Ms Council on Global Engagement examined how studying abroad and international fieldwork can further that mission, and how the university can provide tools to help facultydevelop international programs.
Michigan has this tremendous body of resources, so you dont have to figure this out on your own, said David Wallace, a clinical associate professor of information in the School of Information, said.
There are so many areas, so many departments and centers that are doing this kind of training and preparation and understanding and it makes a huge difference for us.
Judith Pennywell, director of the International Center and CGE chair, greeted the packed University Hall in the Alexander G. Ruthven Building, and introduced the event titled Faculty-led study abroad and international group projects: success stories and lessons learned.
The discussion aimed to highlight ways in which faculty develop courses, fieldwork and program ideas, articulate the critical value of international experiential programs, outline ways to prepare to take students abroad, and inspire other faculty to do the same.
Besides Zielinski and Wallace, the panel included other faculty members who have created and led successful education-abroad programs for undergraduate and graduate students. Other participants were:
Valeria Bertacco, vice provost for engaged learning, Arthur F. Thurnau Professor, and the Mary Lou Dorf Collegiate Professor of Computer Science and Engineering, moderated the panel.
She first asked panelists what they hoped students could achieve abroad that they would not be able to do on campus.
Calixto, who has led several groups of students in Peru, Costa Rica, and Argentina for intensive Spanish experiences, said she wants students to immerse themselves in a new environment and experience the full context in which the language takes place.
By engaging with the community and using creativity to solve cultural barriers, she said, students will grow and cultivate as global citizens.
I want my students to feel the culture and to develop their own ways of navigating the culture, Calixto said. I believe that every place has a music, and thats what I want my students to feel to feel the music of the place they are in.
Napieralski and Wallace both spoke to the ways studying abroad can affect students in the long term. They said learning to navigate a new environment, monitor safety, and engage with new cultures allows students to explore their independence and develop life skills.
Its quite enlightening when a student opens up and realizes that this experience is something that they didnt really anticipate would affect them personally; of course, academically, intellectually, sure, but these are personal skills. Its the social moments and the collaborative spirit that comes out of the course, Napieralski said.
After the panel discussion, those at the event participated in roundtables to discuss developing faculty-led programming and projects.
They were also asked to identify barriers and brainstorm ways that their college, unit or the university at large can support faculty in making their dream programming a reality.
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Faculty share international experiences to inspire others | The ... - The University Record
Free computer science schools to be set up to develop tech talents – The Star Online
KUALA LUMPUR: An innovative peer-to-peer computer science school called 42 Malaysia (42MY) will be established in a joint venture between Khazanah Nasional Berhad (Khazanah) and Sunway Education Group (SEG) to accelerate the development of 10,000 skilled tech talents within the next decade.
According to Khazanah in a statement yesterday, 42MY is part of a global movement with a network of 47 campuses across 26 countries which offers a unique education opportunity free of charge for individuals aged 18 years and above, regardless of education and socioeconomic background.
The programmes innovative and scalable education model focuses on peer-to-peer and practical project-based learning approaches that resemble a real-work environment, providing learners the opportunity to learn to code while working on real-life industry projects with 42MYs participating corporate partners, Bernama reported.
Khazanah Nasional managing director Datuk Amirul Feisal Wan Zahir said the investment in 42MY was in line with the commitment to delivering socioeconomic impact for Malaysians through the Dana Impak mandate, a key foundation of the Advancing Malaysia strategic imperative.
He said in line with Budget 2023, Khazanah was committed to playing an integral role, along with other government-linked companies (GLCs) in funding high-growth start-ups in Malaysia to spur the countrys innovation and economic growth.
The availability of talent is crucial to the continuous development of any industry. To this end, 42MY will further bolster the domestic start-up and tech ecosystem by nurturing and developing Malaysian talents, complementing the nations digital economy development agenda, and strengthening its IR (Industrial Revolution) 4.0 capability, he said.
The joint venture with SEG demonstrates what can be achieved through collaboration and partnership, Amirul Feisal said, adding that Khazanah welcomes the opportunity to work with partners that share a similar vision in developing the nations digital and skilled talent supply.
Meanwhile, Jeffrey Cheah Foundation founder and trustee Tan Sri Dr Jeffrey Cheah said SEG will expand 42MY through new campuses in Johor, Penang and Kuching, and potentially across Malaysia, in the coming years.
With this expansion, we can provide more students across the country with quality education, discovering and nurturing more tech talents to establish Malaysia as the leading digital economy in Asean.
This partnership also reaffirms our commitment to nation-building and the 17 United Nations Sustainable Development Goals, he said.
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Free computer science schools to be set up to develop tech talents - The Star Online
The Future of Data Science Lies in Automation | Transforming Data … – TDWI
The Future of Data Science Lies in Automation
Parts of data science can be automated today, and more may be automated soon.
Data science is a wide-ranging field that has been successfully applied in both scientific and business domains. Companies have been heavily investing in all things data in their quest to become data-driven.
With every business-minded investment comes the idea of optimization, and data science is no different in that regard. Although companies are pouring in money, they are also thinking of ways to make the most out of those resources. Automation is an inevitable part of optimization and often the first course of action.
Data science may seem like a field thats nearly impossible to automate due to its inherent complexity. There are so many steps, from data extraction to modeling, all of which seem to require human input. Weve thought that way, however, about many things and still found ways to automate processes.
Breaking Down the Parts of Data Science
Data science can be separated into several distinct parts, which together define the field. These are data exploration, data engineering, model building, and interpretation.
Data exploration largely revolves around discovering the needs, goals, and requirements of a particular task. For example, an e-commerce business might have a reason to need all pricing data for a specific category from a variety of regions. Each needed data set has to come from some source (or a multitude of them), however, its not always clear how to find the right data.
Additionally, exploration will often involve working with some data sets to discover goal-driven questions, the potential for visualization, etc. These aspects require quite extensive human judgment and are domain- and goal-specific. As a result, automation for data exploration is likely somewhat far away.
Data engineering -- which is the process of actually acquiring, labeling, wrangling, and transforming data -- is often the most time-consuming aspect. Unfortunately, we have had little success in automating these tasks. It is possible to do so, however, mostly when a functioning and accurate model already exists. Automating labeling on novel data sets, however, still remains challenging.
The other two parts, however, have much more potential. Data interpretation, to some surprise, has been shown to have the potential for automation. In 2014, a group of researchers created a natural language model that could interpret basic regression models (and even draft a full report with explanations) with an impressive degree of veracity.
Since then, various business implementations have aimed to do the same thing for more actionable, less academic insights. Numerous companies, such as PowerBI, have integrated automated insight generation, albeit at a somewhat limited capacity. Soon enough, I believe well get complete overviews from business intelligence systems.
Model building -- the practice of selecting algorithms, tuning parameters, evaluating performance, and creating machine learning models -- has already seen a decent degree of successful automation through AutoML.
The Role of AutoML
Much data science work is done through machine learning (ML). Proper employment of ML can ease the predictive work that is most often the end goal for data science projects, at least in the business world.
AutoML has been making the rounds as the next step in data science. Part of machine learning, outside of getting all the data ready for modeling, is picking the correct algorithm and fine-tuning (hyper)parameters.
After data accuracy and veracity, the algorithm and parameters have the highest influence on predictive power. Although in many cases there is no perfect solution, theres plenty of wiggle room for optimization. Additionally, theres always some theoretical near-optimal solution that can be arrived at mostly through calculation and decision making.
Yet, arriving at these theoretical optimizations is exceedingly difficult. In most cases, the decisions will be heuristic and any errors will be removed after experimentation. Even with extensive industry experience and professionalism, there is just too much room for error.
AutoML systems, such as Python libraries (e.g., Auto-sklearn), use advancements in mathematics and computer science to automatically select algorithms and fine-tune parameters. Research and experimentation have shown that various AutoML systems can often optimize pipelines and deliver accurate results at uncanny rates.
Although AutoML does not and will not completely automate data science, it has the potential to take a significant portion of manual work off the shoulders of humans. Its potential lies in simplifying a usually difficult part of machine learning.
Making Machine Learning Easier
Automation is not only about optimizing resource costs; it also removes the barrier to entry for some activities. Machine learning has two major hurdles to its accessibility.
Data acquisition and engineering is the first obstacle. However, data acquisition has been made easier through the emergence of web scraping, public data sets, and other phenomena. Labeling and wrangling still remain largely unchanged, but finding the necessary data has often been the primary challenge in data science.
AutoML, however, makes machine learning more accessible by reducing the requirements for creating an optimized model. Currently, the technology can still run into issues when high-quality data is not available, so its definitely not a cure-all, and general machine learning knowledge is required.
Within the near future, however, AutoML has the most potential to completely automate a part of data science and provide easier access to the field for less experienced practitioners. Additionally, large language models or natural language processing will aid data scientists in producing easy-to-read interpretations.
Finally, I expect that data engineering will be next in line for automation. Data integration, normalization, and extraction can already be automated, and all that is needed is to find solutions that can be scaled.
About the Author
Julius erniauskas is the CEO of Oxylabs -- the formerly small startup in the public data collection industry that now employs over 400 specialists. Since joining the company in 2015, erniauskas successfully transformed the basic business idea of Oxylabs by employing his knowledge of big data and information technology trends. He implemented a brand-new company structure that led to the development of a sophisticated public web data gathering service. Today, he leads Oxylabs as a global provider of premium proxies and data scraping solutions, helping companies and entrepreneurs to realize their full potential by harnessing the power of external data. You can reach him on LinkedIn.
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The Future of Data Science Lies in Automation | Transforming Data ... - TDWI