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Bill Gates famously said: The computer was born to solve problems that did not exist before. In a world where devices play an integral part in our daily lives, his opinion couldnt be more true. The proof is in the numbers.

The US Bureau of Labour Statistics projects that employment in computer and information technology occupations will grow 13% from 2020 to 2030, much faster than the average for all occupations. Its little wonder why. Computer science professionals are responsible for solving complex organisational problems with technical solutions. Hence, their ability to infiltrate any sector that comes to mind. For them, anything is possible with the right qualification.

Thankfully, most universities across the globe are ramping up their offerings to give their students a deeper understanding of emerging technologies. In fact, many are even developing programmes comprehensive enough to produce professionals capable of innovating these technologies instead. These dynamic institutions representing Hong Kong, Australia and New Zealand are proof:

Soon, all new undergraduates at PolyU will be required to take on foundational courses in emerging technologies and entrepreneurship. Source: The Hong Kong Polytechnic University

Few universities are as committed to producing successful students the way The Hong Kong Polytechnic University (PolyU) is. While rigorous, the experience is still holistic unsurprising from an institution that effortlessly balances 80 years of tradition with a progressive approach. At this top 100 university, undergraduate students arent expected to have it all figured out. Instead, they enjoy a year of exploration before deciding on a major a unique initiative designed to ensure aspirations are solidified before they are pursued in Year 2.

Its new departmental scheme-based admissions curriculum is set to integrate two 21st century elements: Artificial Intelligence and Data Analytics (AIDA) and Innovation and Entrepreneurship (IE). Both will be incorporated into the General University Requirement from the academic year 2022-23, ensuring that while students are gaining a solid academic foundation, they will be preparing for societys ever-changing manpower needs as well. They will be required to complete at least two credits of AIDA courses and at least one credit of IE courses.

Those sure of their decision to take the future-focused route will be able to enhance their newfound skills with one of the Faculty of Engineerings many undergraduate programmes like the BSc (Hons) in Computer Science, BSc (Hons) in Enterprise Information Systems or the BSc (Hons) in Financial Technology and Artificial Intelligence.

The BSc (Hons) Scheme in Computing and AI makes a perfect starting point for each. However, even if students choose to relish in another PolyU faculty, school or department; AIDA or IE courses will always be available as Secondary Majors. Click here to learn how else PolyU prepares students for the jobs of tomorrow.

This School of Computer Science is currently New Zealands largest, most diverse and accomplished division. Source: University of Auckland Facebook

There are many reasons why the University of Auckland ranked first in New Zealand for Computer Science and Information Technology, according to the QS World University Rankings by Subject 2019. It has also placed among the worlds top 100. Its School of Computer Science is behind the prestige.

Founded in 1981, it is currently New Zealands largest, most diverse and accomplished division. Students arrive from all over the world because they know their aspirations are welcomed. Here, a broad range of courses spanning most aspects of computer science are ready and waiting to teach the art of computing.

None of it would be possible without the experts behind the Bachelor of Science (BSc) majoring in Computer Science, Bachelor of Advanced Science (Honours) (BAdvSci(Hons)) specialising in Computer Science, and the Bachelor of Science (BSc) specialising in Data Science. While distinctive from one another, each programme was developed to fix the IT sectors ever-growing skills shortage. Thanks to their effectiveness, more graduates land employment every year with ease.

Our graduates are well prepared for the challenges of the dynamic and fast-growing software industry and are very well received by industry employers, confirms head of school and associate professor, Dr Giovanni Russello.

The School of Information Technology offers a wide array of programmes. Source: University of Queensland Facebook

Are you looking to pursue a bachelors degree in IT, or to hone your expertise in the field with a career-boosting masters qualification? You may want to consider doing so at the University of Queenslands School of Information Technology.

The school focuses on key areas such as: Cybersecurity and Software Engineering; Imaging, Sensing and Biomedical Engineering; Power, Energy, and Control Engineering; Human-Centred Computing; and Data Science. Here, research is facilitated at the Centre for Energy Data Innovation (CEDI) and ARC Training Centre for Information Resilience (CIRES).

The School of Information Technology offers a wide array of programmes. At the undergraduate and postgraduate levels, you can opt for a variety of programmes in Computer Science and Information Technology. These programmes are taught by award-winning teachers in a varied and extensive lineup of expertise. Students are provided with an in-depth understanding and set of skills in computational science, numerical simulation, applied mathematics, statistics, and data science.

*Some of the institutions featured in this article are commercial partners of Study International

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3 schools of computer science cracking the code to academic success - Study International News

Labeling data can be a chore. Its the main source of sustenance for computer-vision models; without it, theyd have a lot of difficulty identifying objects, people, and other important image characteristics. Yet producing just an hour of tagged and labeled data can take a whopping 800 hours of human time. Our high-fidelity understanding of the world develops as machines can better perceive and interact with our surroundings. But they need more help.

Scientists from MITs Computer Science and Artificial Intelligence Laboratory (CSAIL), Microsoft, and Cornell University have attempted to solve this problem plaguing vision models by creating STEGO, an algorithm that can jointly discover and segment objects without any human labels at all, down to the pixel.

STEGO learns something called semantic segmentation fancy speak for the process of assigning a label to every pixel in an image. Semantic segmentation is an important skill for todays computer-vision systems because images can be cluttered with objects. Even more challenging is that these objects don't always fit into literal boxes; algorithms tend to work better for discrete things like people and cars as opposed to stuff like vegetation, sky, and mashed potatoes. A previous system might simply perceive a nuanced scene of a dog playing in the park as just a dog, but by assigning every pixel of the image a label, STEGO can break the image into its main ingredients: a dog, sky, grass, and its owner.

Assigning every single pixel of the world a label is ambitious especially without any kind of feedback from humans. The majority of algorithms today get their knowledge from mounds of labeled data, which can take painstaking human-hours to source. Just imagine the excitement of labeling every pixel of 100,000 images! To discover these objects without a humans helpful guidance, STEGO looks for similar objects that appear throughout a dataset. It then associates these similar objects together to construct a consistent view of the world across all of the images it learns from.

Seeing the world

Machines that can see are crucial for a wide array of new and emerging technologies like self-driving cars and predictive modeling for medical diagnostics. Since STEGO can learn without labels, it can detect objects in many different domains, even those that humans dont yet understand fully.

If you're looking at oncological scans, the surface of planets, or high-resolution biological images, its hard to know what objects to look for without expert knowledge. In emerging domains, sometimes even human experts don't know what the right objects should be, says Mark Hamilton, a PhD student in electrical engineering and computer science at MIT, research affiliate of MIT CSAIL, software engineer at Microsoft, and lead author on a new paper about STEGO. In these types of situations where you want to design a method to operate at the boundaries of science, you can't rely on humans to figure it out before machines do.

STEGO was tested on a slew of visual domains spanning general images, driving images, and high-altitude aerial photographs. In each domain, STEGO was able to identify and segment relevant objects that were closely aligned with human judgments. STEGO's most diverse benchmark was the COCO-Stuff dataset, which is made up of diverse images from all over the world, from indoor scenes to people playing sports to trees and cows. In most cases, the previous state-of-the-art system could capture a low-resolution gist of a scene, but struggled on fine-grained details: A human was a blob, a motorcycle was captured as a person, and it couldnt recognize any geese. On the same scenes, STEGO doubled the performance of previous systems and discovered concepts like animals, buildings, people, furniture, and many others.

STEGO not only doubled the performance of prior systems on the COCO-Stuff benchmark, but made similar leaps forward in other visual domains. When applied to driverless car datasets, STEGO successfully segmented out roads, people, and street signs with much higher resolution and granularity than previous systems. On images from space, the system broke down every single square foot of the surface of the Earth into roads, vegetation, and buildings.

Connecting the pixels

STEGO which stands for Self-supervised Transformer with Energy-based Graph Optimization builds on top of the DINO algorithm, which learned about the world through 14 million images from the ImageNet database. STEGO refines the DINO backbone through a learning process that mimics our own way of stitching together pieces of the world to make meaning.

For example, you might consider two images of dogs walking in the park. Even though theyre different dogs, with different owners, in different parks, STEGO can tell (without humans) how each scenes objects relate to each other. The authors even probe STEGOs mind to see how each little, brown, furry thing in the images are similar, and likewise with other shared objects like grass and people. By connecting objects across images, STEGO builds a consistent view of the word.

The idea is that these types of algorithms can find consistent groupings in a largely automated fashion so we don't have to do that ourselves, says Hamilton. It might have taken years to understand complex visual datasets like biological imagery, but if we can avoid spending 1,000 hours combing through data and labeling it, we can find and discover new information that we might have missed. We hope this will help us understand the visual word in a more empirically grounded way.

Looking ahead

Despite its improvements, STEGO still faces certain challenges. One is that labels can be arbitrary. For example, the labels of the COCO-Stuff dataset distinguish between food-things like bananas and chicken wings, and food-stuff like grits and pasta. STEGO doesn't see much of a distinction there. In other cases, STEGO was confused by odd images like one of a banana sitting on a phone receiver where the receiver was labeled foodstuff, instead of raw material.

For upcoming work, theyre planning to explore giving STEGO a bit more flexibility than just labeling pixels into a fixed number of classes as things in the real world can sometimes be multiple things at the same time (like food, plant and fruit). The authors hope this will give the algorithm room for uncertainty, trade-offs, and more abstract thinking.

In making a general tool for understanding potentially complicated datasets, we hope that this type of an algorithm can automate the scientific process of object discovery from images. There's a lot of different domains where human labeling would be prohibitively expensive, or humans simply dont even know the specific structure, like in certain biological and astrophysical domains. We hope that future work enables application to a very broad scope of datasets. Since you don't need any human labels, we can now start to apply ML tools more broadly, says Hamilton.

STEGO is simple, elegant, and very effective. I consider unsupervised segmentation to be a benchmark for progress in image understanding, and a very difficult problem. The research community has made terrific progress in unsupervised image understanding with the adoption of transformer architectures, says Andrea Vedaldi, professor of computer vision and machine learning and a co-lead of the Visual Geometry Group at the engineering science department of the University of Oxford. This research provides perhaps the most direct and effective demonstration of this progress on unsupervised segmentation.

Hamilton wrote the paper alongside MIT CSAIL PhD student Zhoutong Zhang, Assistant Professor Bharath Hariharan of Cornell University, Associate Professor Noah Snavely of Cornell Tech, and MIT professor William T. Freeman. They will present the paper at the 2022 International Conference on Learning Representations (ICLR).

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A new state of the art for unsupervised computer vision - MIT News

The National Science Foundation has announced this year's winners of the prestigious Alan T. ... [+] Waterman Award, named after NSF's first director.

The U.S. National Science Foundation announced the winners of this year's Alan T. Waterman Award, which the agency describes as the nation's highest honor for early-career scientists and engineers.

The winners, revealed on Wednesday, April 20, were: Jessica E. Tierney, a geoscientist at the University of Arizona; Lara A. Thompson, a University of the District of Columbia biomedical engineer; and Daniel B. Larremore, a University of Colorado computer scientist.

This is the first year that three researchers have been selected to receive the award, which was established by Congress in 1975 and is named for Alan T. Waterman, NSFs first director.

As part of the award, each recipient will be given $1 million over five years to support research in their chosen field of science. The Waterman Award will be presented to all recipients at a ceremony during the National Science Board meeting, to be held on May 5 in Washington, D.C.

Jessica Tierney

Jessica Tierney earned a bachelor's, master's and a doctorate in geology from Brown University. Currently, she is an associate professor at the University of Arizona. She has been recognized as a Packard Foundation Fellow, an American Geophysical Union Fellow and is a lead author on the Intergovernmental Panel on Climate Change Sixth Assessment report.

Tierneys field of specialization is paleoclimatology, which involves the study of what Earth's past climate was like and why. Her research focuses on understanding past climate change in order to improve our understanding of future climate events. She specializes in developing organic geochemical records of paleoclimate, derived from biomarkers that are preserved in sediments and rocks.

According to the NSF release, Tierneys research has redefined the understanding of global temperature changes in the geologic past and developed a new quantitative understanding of temperature and climate sensitivity to past levels of carbon dioxide.

Lara Thompson

Lara Thompson earned a bachelor's in mechanical engineering from the University of Massachusetts Lowell, a master's in aeronautical and astronautical engineering from Stanford University, and a doctorate in biomedical engineering from the Harvard-MIT Division of Health Sciences and Technology program. She is currently an associate professor in the department of mechanical engineering at the University of the District of Columbia, where she also serves as the founding director of both the universitys Center for Biomechanical & Rehabilitation Engineering and its biomedical engineering program.

Thompson is recognized for her research in rehabilitation engineering, specifically for translating her findings on vestibular disorders in primates into interventions for individuals with balance, gait and postural impairments. She investigates various assistive technologies and robotics aimed towards improving balance in elderly individuals and survivors of stroke, as well as how such technologies can increase balance confidence and reduce the risk of falling for these groups of individuals.

Daniel Larremore

Daniel Larremore earned a bachelor's in chemical engineering from Washington University in St. Louis; a master's and doctorate in applied mathematics from the University of Colorado Boulder. Following his Ph.D work, he completed postdoctoral fellowships at the Harvard T.H. Chan School of Public Health and the Santa Fe Institute. Currently, he is an assistant professor at the University of Colorado Boulder in the department of computer science and the BioFrontiers Institute.

Larremore explores the ways that mathematical models can support scientific discovery and policy, specifically how math can be used to track and understand the spread of human diseases.

According to the NSF release, Larremore recently used computational epidemiology to answer urgent questions about the Covid-19 pandemic, such as: How should the first doses of a scarce vaccine be targeted to minimize deaths or infections? What role could widely available rapid testing play in mitigating viral transmission prior to the arrival of vaccines?

_________

"It is a great pleasure to honor these three outstanding scientists with the Waterman Award," said NSF Director Sethuraman Panchanathan in the agencys announcement. "They have clearly demonstrated a superb record of scientific achievements by using creative and innovative approaches that have further strengthened basic research in their respective fields. We are grateful to all of our honorees for the vital role they play in advancing the scientific enterprise. I am thrilled to congratulate each of them and look forward to their tremendous accomplishments in the future."

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NSF Recognizes Three University Scientists With The $1 Million Alan T. Waterman Award - Forbes

UC San Diego is one of the biggest research institutions in the world, known for its biology, engineering, and computer science programs. Students from all over come here to study different STEM-related fields, hoping that their hard work (and the prestige of the school) will help them get high-paying and fulfilling jobs.

Looking at it from a literature major perspective, it all seems a bit daunting.

Youve probably heard the same questions that I have. A literature major? At UCSD? Butwhy? People always question my dedication to the field, my interest in it, and even my future job prospects. I once overheard a girl ask her friend, who would ever be a lit major? She was not talking about me, but I still had to resist chiming in.

The funny thing is that being a literature major was never really my plan. I applied as one because I liked writing, but I had always assumed that my final degree would be in something else. When people asked me what I planned on studying, I would say Oh, Im a literature major but thats probably going to change. I had never felt a strong connection to the subject, seeing it as more of a placeholder before I discovered what I really wanted to do.

But over time, I began to realize that graduating with a degree in literature might actually be my final goal. I really like all of the classes that I have taken so far and Ive heard that they only get better from here. There are not any other majors that I would rather pursue. Unfortunately, the more set on this path I become, the more doubts appear.

In all honesty, I do not know what Im going to do with a literature degree. My parents always told me that with a degree in the humanities, you can do everything and you can do nothing, a sentiment that is not ideal for someone as indecisive as me. No matter how much I rationalize or look up jobs or make plans for grad school, there is always this voice telling me to choose something more practical with a clear career path. Sometimes those voices are in my head, sometimes they come from other people.

I dont mind when people make fun of my major. When it comes to friends I know it is not serious, and that I can always make fun of their majors in return. But every once in a while I get reminded that people with other majors are moving so much faster than I am. STEM majors know what jobs they want when they graduate. They know how they are going to pay their bills. They are cramming for finals week when I sit off to the side with my silly little book, making my silly little highlights. And while some of these may be exaggerations or flat out false, it is how I feel.

The work you do as a literature major, or even as a humanities major, is vastly different though not necessarily easier than what you study in hard science or math. And with a school that is mostly STEM majors, that difference can feel overwhelming. I cannot help but feel that, when everyone around me is talking about clicker questions or MATLAB, I am falling behind and making the wrong decision. That once we graduate, everyone else will know what they are doing while I am still figuring things out.

I guess what I am trying to say is that being a humanities major at a majority STEM school, at least in my experience, comes with a lot of doubt. It is hard and its scary to see everyone around you doing work that is so much more tangible than yours. It is difficult to have a major that does not apply to the job market well while everyone else around you does. This also applies to people who are not a part of the humanities division, but still have a lot of doubts about their future.

What helped me was realizing that college is not a competition. I am trying not to worry about what everyone else is doing, and what everyone else is studying because my path is going to be different anyway. All of our paths are going to be different. Nobody knows what they are doing every step of the way, even though from here it seems like they might. So relax, and take a deep breath. Everything is (probably) going to be fine.

Signing off,

Samantha

Photo by Annie Spratt from Unsplash

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Dear Humanities Majors, - The UCSD Guardian Online