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UW-Green Bay’s Rising Phoenix Program Expands with Computer Science Pathway for Students Across the Region – Inside UW-Green Bay News – UWGB

Program addresses the computer science workforce shortage in Northeast Wisconsin

Green Bay, Wis.The Rising Phoenix Early College High School Program is expanding through a new collaboration between UW-Green Bay and the Computer Science Talent Ecosystem Youth (CSTEY). The Rising Phoenix Computer Science Pathway program is modeled after the inaugural Rising Phoenix cohort with the Manitowoc Public School District that launched in Fall 2020 by expanding the program across the CESA 7 region.

According to CSTEY, computer science is one of the fastest growing careers in the region with at least 2,000 new jobs created by 2022. To keep up with new job creation, the region requires a higher output of computer scientists with bachelors degrees, who are ready to meet computing needs of employers. The Rising Phoenix Computer Science Pathway fast-forwards bachelor degree attainment by providing the opportunity for students to get a two-year head start by completing an Associate of Arts and Sciences (AAS) degree while in high school.

We are proud to expand the Rising Phoenix program with a specialized pathway in computer science in order to provide skilled computer scientists to employers in the region, said UW-Green Bay Chancellor Michael Alexander. Rising Phoenix strengthens the bridge between school districts, UW-Green Bay and area employers in order to fill critical workforce shortages in the region. We are proud to partner with CSTEY and their partners to provide innovative workforce solutions.

Current sophomores from participating high schools in CESA 7 will have the opportunity to apply for the Rising Phoenix Program and enroll during their junior and senior years of high school. When admitted to the program, students will be concurrently enrolled at their high school and UW-Green Bay. Rising Phoenix courses are selected to meet Wisconsin high school graduation requirements and the UW-Green Bay Associate of Arts and Sciences (AAS) degree. Students are offered a flexible combination of courses that include existing dual credit options offered in each students high school, UW-Green Bay courses offered at campuses in Green Bay, Marinette, Manitowoc and Sheboygan and online courses. In this way, students can take courses and participate in extra-curricular activities at their high school. With Rising Phoenix, students get to experience college coursework in the familiar environment of their high school with dedicated support and gain confidence by learning alongside other college students at a college campus.

Students enrolling in the program as high school juniors will have the opportunity to earn an AAS degree by the time they graduate high school. Students who complete an AAS degree will have the first two years of a bachelors degree completed as well as 12 credits in computer science. Students will earn a digital badge in Computer Science Principles to validate and provide a shareable record of their accomplishment. The AAS will apply whether students decide to continue at UW-Green Bay, transfer to a different college or university or enter the workforce upon graduation with an earned college credential. Students tuition and textbook costs will be covered by their school district through the Early College Credit Program and UW-Green Bay Dual Enrollment Access Academy.

We are excited with the opportunities this partnership will present to students in our services area, said CESA 7 Agency Administrator Jeff Dickert. We know jobs in the computer science field are in high demand in Wisconsin and this creates another way for our schools to prepare our kids to be college and career ready.

Committed student support and coaching are the main advantages of the program. Every student enrolled will work consistently with UW-Green Bays Rising Phoenix student success coach, who will:

To learn more about the program and express interest, school districts in CESA 7 should visit https://www.uwgb.edu/computer-science-districts/ for more information and to express interest in providing this option for students.

About the University of Wisconsin-Green BayEstablished in 1965, UW-Green Bay is a public institution serving 9,276 undergraduate, graduate and doctoral students and nearly 80,000 continuing education enrollees each year across all campus locations. We educate students from pre-college through retirement and offer 200+ degrees, programs and certificates. UW-Green Bay graduates are resilient, inclusive, sustaining and engaged members of their communities, ready to rise to fearlessly face challenges, solve problems and embrace diverse ideas and people. With four campus locations, the University welcomes students from every corner of the world. In 2020, UW-Green Bay was the fastest growing UW school in Wisconsin. For more information, visit http://www.uwgb.edu.

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Machines that see the world more like humans do – MIT News

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects.

The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene. Probabilistic inference allows the system to infer whether mismatches are likely due to noise or to errors in the scene interpretation that need to be corrected by further processing.

This common-sense safeguard allows the system to detect and correct many errors that plague the deep-learning approaches that have also been used for computer vision. Probabilistic programming also makes it possible to infer probable contact relationships between objects in the scene, and use common-sense reasoning about these contacts to infer more accurate positions for objects.

If you dont know about the contact relationships, then you could say that an object is floating above the table that would be a valid explanation. As humans, it is obvious to us that this is physically unrealistic and the object resting on top of the table is a more likely pose of the object. Because our reasoning system is aware of this sort of knowledge, it can infer more accurate poses. That is a key insight of this work, says lead author Nishad Gothoskar, an electrical engineering and computer science (EECS) PhD student with the Probabilistic Computing Project.

In addition to improving the safety of self-driving cars, this work could enhance the performance of computer perception systems that must interpret complicated arrangements of objects, like a robot tasked with cleaning a cluttered kitchen.

Gothoskars co-authors include recent EECS PhD graduate Marco Cusumano-Towner; research engineer Ben Zinberg; visiting student Matin Ghavamizadeh; Falk Pollok, a software engineer in the MIT-IBM Watson AI Lab; recent EECS masters graduate Austin Garrett; Dan Gutfreund, a principal investigator in the MIT-IBM Watson AI Lab; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences (BCS) and a member of the Computer Science and Artificial Intelligence Laboratory; and senior author Vikash K. Mansinghka, principal research scientist and leader of the Probabilistic Computing Project in BCS. The research is being presented at the Conference on Neural Information Processing Systems in December.

A blast from the past

To develop the system, called 3D Scene Perception via Probabilistic Programming (3DP3), the researchers drew on a concept from the early days of AI research, which is that computer vision can be thought of as the "inverse" of computer graphics.

Computer graphics focuses on generating images based on the representation of a scene; computer vision can be seen as the inverse of this process. Gothoskar and his collaborators made this technique more learnable and scalable by incorporating it into a framework built using probabilistic programming.

Probabilistic programming allows us to write down our knowledge about some aspects of the world in a way a computer can interpret, but at the same time, it allows us to express what we dont know, the uncertainty. So, the system is able to automatically learn from data and also automatically detect when the rules dont hold, Cusumano-Towner explains.

In this case, the model is encoded with prior knowledge about 3D scenes. For instance, 3DP3 knows that scenes are composed of different objects, and that these objects often lay flat on top of each other but they may not always be in such simple relationships. This enables the model to reason about a scene with more common sense.

Learning shapes and scenes

To analyze an image of a scene, 3DP3 first learns about the objects in that scene. After being shown only five images of an object, each taken from a different angle, 3DP3 learns the objects shape and estimates the volume it would occupy in space.

If I show you an object from five different perspectives, you can build a pretty good representation of that object. Youd understand its color, its shape, and youd be able to recognize that object in many different scenes, Gothoskar says.

Mansinghka adds, "This is way less data than deep-learning approaches. For example, the Dense Fusion neural object detection system requires thousands of training examples for each object type. In contrast, 3DP3 only requires a few images per object, and reports uncertainty about the parts of each objects' shape that it doesn't know."

The 3DP3 system generates a graph to represent the scene, where each object is a node and the lines that connect the nodes indicate which objects are in contact with one another. This enables 3DP3 to produce a more accurate estimation of how the objects are arranged. (Deep-learning approaches rely on depth images to estimate object poses, but these methods dont produce a graph structure of contact relationships, so their estimations are less accurate.)

Outperforming baseline models

The researchers compared 3DP3 with several deep-learning systems, all tasked with estimating the poses of 3D objects in a scene.

In nearly all instances, 3DP3 generated more accurate poses than other models and performed far better when some objects were partially obstructing others. And 3DP3 only needed to see five images of each object, while each of the baseline models it outperformed needed thousands of images for training.

When used in conjunction with another model, 3DP3 was able to improve its accuracy. For instance, a deep-learning model might predict that a bowl is floating slightly above a table, but because 3DP3 has knowledge of the contact relationships and can see that this is an unlikely configuration, it is able to make a correction by aligning the bowl with the table.

I found it surprising to see how large the errors from deep learning could sometimes be producing scene representations where objects really didnt match with what people would perceive. I also found it surprising that only a little bit of model-based inference in our causal probabilistic program was enough to detect and fix these errors. Of course, there is still a long way to go to make it fast and robust enough for challenging real-time vision systems but for the first time, we're seeing probabilistic programming and structured causal models improving robustness over deep learning on hard 3D vision benchmarks, Mansinghka says.

In the future, the researchers would like to push the system further so it can learn about an object from a single image, or a single frame in a movie, and then be able to detect that object robustly in different scenes. They would also like to explore the use of 3DP3 to gather training data for a neural network. It is often difficult for humans to manually label images with 3D geometry, so 3DP3 could be used to generate more complex image labels.

The 3DP3 system combines low-fidelity graphics modeling with common-sense reasoning to correct large scene interpretation errors made by deep learning neural nets. This type of approach could have broad applicability as it addresses important failure modes of deep learning. The MIT researchers accomplishment also shows how probabilistic programming technology previously developed under DARPAs Probabilistic Programming for Advancing Machine Learning (PPAML) program can be applied to solve central problems of common-sense AI under DARPAs current Machine Common Sense (MCS) program, says Matt Turek, DARPA Program Manager for the Machine Common Sense Program, who was not involved in this research, though the program partially funded the study.

Additional funders include the Singapore Defense Science and Technology Agency collaboration with the MIT Schwarzman College of Computing, Intels Probabilistic Computing Center, the MIT-IBM Watson AI Lab, the Aphorism Foundation, and the Siegel Family Foundation.

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Apple, Boys & Girls Clubs team up to offer coding opportunities to kids, teens – Apple Newsroom

December 6, 2021

UPDATE

Apple teams up with Boys&GirlsClubs of America to bring new coding opportunities to young learners across the country

In celebration of Computer Science Education Week, Apple and Boys & Girls Clubs of America today launched a new program that will bring coding to Boys & Girls Clubs in more than a dozen US cities. This new collaboration will bring coding with Swift to tens of thousands of students across the country, building on Apples existing partnership with Boys & Girls Clubs of America through the companys Community Education Initiative in support of its Racial Equity and Justice Initiative.

Using iPad and Apples free Everyone Can Code curriculum and with ongoing professional support from Apple educators kids and teens at local Boys & Girls Clubs will integrate coding into their programming, giving students the opportunity to create and collaborate on the basics of app design and development, with an emphasis on critical thinking and creative problem-solving.

At Apple, we believe education is a force for equity, and that all learners should have the opportunity to explore and develop coding skills for their future, said Lisa Jackson, Apples vice president of Environment, Policy, and Social Initiatives. Together with the Boys & Girls Clubs of America, weve already introduced thousands of students to innovative technology experiences, and we are thrilled to expand our partnership to bring coding with Swift to even more communities across the country.

Boys & Girls Clubs of America is committed to helping youth reach their full potential, which includes equipping young people with critical thinking and problem-solving skills that will serve them for years to come, said Jim Clark, Boys & Girls Clubs of Americas president and CEO. We are thrilled to partner with Apple to enhance Club programming with innovative and educational coding activities that will build kids and teens engagement and opportunity in technology.

The program will initially launch in 10 new regions, including Atlanta; Austin, Texas; metro D.C.; Miami-Dade County, Florida; Wake County, North Carolina; and Silicon Valley, with the goal of expanding coding opportunities to clubs nationwide. Programming has already launched in Atlantic City, New Jersey; Chicago; Detroit; Nashville, Tennessee; and Newark, New Jersey, where engagement will continue to expand.

In New Jersey, Boys & Girls Club of Atlantic City opened a Design Lab and a STEAM Lab last year to support creativity, coding, and career development programming and the Club is opening a second STEAM Lab in January to create additional opportunities for young learners. The labs are equipped with iPad and Mac computers, and curricula incorporate Everyone Can Code, Everyone Can Create, and Develop in Swift. To prepare its students for future academic and professional pursuits, the Club is also launching a new STEAM preapprenticeship program that will teach students the foundations of working on iPad and Mac, eventually giving them the tools to seek a formal App Development with Swift certification.

Working with Apple this past year has been transformative for our students, who have had the opportunity to explore entirely new ways of thinking, creating, and pursuing their passions, said Stephanie Koch, Boys & Girls Club of Atlantic Citys CEO. The young people we work with are the future of Atlantic City, and were proud to partner with Apple to help them gain new skills to grow as learners and prepare for jobs in the 21st-century economy.

In Detroit, Apple helped support Boys & Girls Clubs of Southeastern Michigans summer Code to Career coding course and app challenge. The program brought together young adults ages 18 to 24 to learn the foundations of human interface design and the Swift coding language, using Apples Everyone Can Code curriculum. Students worked in small groups to create app prototypes designed to solve a challenge within the community including fashion sustainability, using hip-hop to build a sense of community, and improving city mobility. The club is now expanding this work further, bringing new devices and coding programming to its 11 locations across Greater Detroit.

Todays announcement builds on a 2020 initiative through which Apple donated 2,500 devices to Boys & Girls Clubs of America locations in Alabama; Arizona; California; Connecticut; Georgia; Idaho; Illinois; Louisiana; Massachusetts, Michigan; Minnesota; New Jersey; New York; Ohio; Oregon; Pennsylvania; Tennessee; Texas; Washington, D.C.; and Wisconsin.

Press Contacts

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UCF Professor Greg Welch Named to the National Academy of Inventors – UCF

UCF Professor Greg Welch is one of 164 national faculty members who have been named to the National Academy of Inventors and one of only 10 from Florida this year. Welch is the AdventHealth Endowed Chair in Healthcare Simulationin UCFs College of Nursing. The computer scientist and engineer is also the co-director of theUCF Synthetic Reality Laboratory.

The NAI Fellows Program recognizes academic inventors who have demonstrated a spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on the quality of life, economic development, and the welfare of society. Election to NAI Fellow is the highest professional distinction accorded solely to academic inventors, according to the NAI. The years honorees will be formally appointed at the Fellows Induction Ceremony during the 11th Annual Meeting of the National Academy of Inventors in June 2022 in Phoenix, Arizona.

The Chicago native who also holds additional faculty appointments in UCFs College of Engineering and Computer Scienceand in theUCF Institute for Simulation and Training within the School of Modeling, Simulation and Training is the universitys 17th NAI fellow. His work has resulted in 18 ideas or products that have eventually been granted patents including 10 at UCF, which the Technology Transfer Office is working to license to companies. The key to innovation is collaboration, time to think and finding solutions to real problems, he recently said.

Welch has a vast range of experience from working at NASAs Jet Propulsion Lab on the Voyager project to serving as a research professor at University of North Carolina Chapel Hill for many years before joining UCF in 2011.

The past two years have been exceptionally accomplished ones for Welch. In 2020, he was named a Pegasus Professor the highest honor bestowed to faculty at the university. Last month he also earned recognition at the TechConnect World Conference.

To date, NAI Fellows hold more than 48,000 issued U.S. patents, which have generated over 13,000 licensed technologies and companies, and created more than one million jobs. In addition, over $3 trillion in revenue has been generated based on NAI Fellow discoveries.

The 2021 Fellow class hails from 116 research universities and governmental and non-profit research institutes worldwide. They collectively hold over 4,800 issued U.S. patents.Among the new class of fellowsare 33 members of the National Academiesof Sciences, Engineering, and Medicine, and three Nobel Laureates, as well as other honors and distinctions.Their collective body of research and entrepreneurship covers a broad range of scientific disciplines involved with technology transfer of their inventions for the benefit of society.

Two faculty members, Pegasus Professor of Optics and Photonics Martin Richardson and NanoScience Technology Center Professor James Hickman, were among the 2020 cohort of fellows. Earlier this year, UCF was ranked No. 25 in the nation among public universities for producing patents.

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Physicists have coaxed ultracold atoms into an elusive form of quantum matter – Science News Magazine

An elusive form of matter called a quantum spin liquid isnt a liquid, and it doesnt spin but it sure is quantum.

Predicted nearly 50 years ago, quantum spin liquids have long evaded definitive detection in the laboratory. But now, a lattice of ultracold atoms held in place with lasers has shown hallmarks of the long-sought form of matter, researchers report in the Dec. 3 Science.

Quantum entanglement goes into overdrive in the newly fashioned material. Even atoms on opposite sides of the lattice share entanglement, or quantum links, meaning that the properties of distant atoms are correlated with one another. Its very, very entangled, says physicist Giulia Semeghini of Harvard University, a coauthor of the new study. If you pick any two points of your system, they are connected to each other through this huge entanglement. This strong, long-range entanglement could prove useful for building quantum computers, the researchers say.

The new material matches predictions for a quantum spin liquid, although its makeup strays a bit from conventional expectations. While the traditional idea of a quantum spin liquid relies on the quantum property of spin, which gives atoms magnetic fields, the new material is based on different atomic quirks.

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A standard quantum spin liquid should arise among atoms whose spins are in conflict. Spin causes atoms to act as tiny magnets. Normally, at low temperatures, those atoms would align their magnetic poles in a regular pattern. For example, if one atom points up, its neighbors point down. But if atoms are arranged in a triangle, for example, each atom has two neighbors that themselves point in opposite directions. That arrangement leaves the third one with nowhere to turn it cant oppose both of its neighbors at once.

So atoms in quantum spin liquids refuse to choose (SN: 9/21/21). Instead, the atoms wind up in a superposition, a quantum combination of spin up and down, and each atoms state is linked with those of its compatriots. The atoms are constantly fluctuating and never settle down into an orderly arrangement of spins, similarly to how atoms in a normal liquid are scattered about rather than arranged in a regularly repeating pattern, hence the name.

Conclusive evidence of quantum spin liquids has been hard to come by in solid materials. In the new study, the researchers took a different tack: They created an artificial material composed of 219 trapped rubidium atoms cooled to a temperature of around 10 microkelvins (about 273.15 Celsius). The array of atoms, known as a programmable quantum simulator, allows scientists to fine-tune how atoms interact to investigate exotic forms of quantum matter.

In the new experiment, rather than the atoms spins being in opposition, a different property created disagreement. The researchers used lasers to put the atoms into Rydberg states, meaning one of an atoms electrons is bumped to a very high energy level (SN: 8/29/16). If one atom is in a Rydberg state, its neighbors prefer not to be. That setup begets a Rydberg-or-not discord, analogous to the spin-up and -down battle in a traditional quantum spin liquid.

The scientists confirmed the quantum spin liquid effect by studying the properties of atoms that fell along loops traced through the material. According to quantum math, those atoms should have exhibited certain properties unique to quantum spin liquids. The results matched expectations for a quantum spin liquid and revealed that long-range entanglement was present.

Notably, the materials entanglement is topological. That means it is described by a branch of mathematics called topology, in which an object is defined by certain geometrical properties, for example, its number of holes (SN: 10/4/16). Topology can protect information from being destroyed: A bagel that falls off the counter will still have exactly one hole, for example. This information-preserving feature could be a boon to quantum computers, which must grapple with fragile, easily destroyed quantum information that makes calculations subject to mistakes (SN: 6/22/20).

Whether the material truly qualifies as a quantum spin liquid, despite not being based on spin, depends on your choice of language, says theoretical physicist Christopher Laumann of Boston University, who was not involved with the study. Some physicists use the term spin to describe other systems with two possible options, because it has the same mathematics as atomic spins that can point either up or down. Words have meaning, until they dont, he quips. It all depends how you spin them.

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What if Einstein never existed? – Big Think

If you ask the average person to name one scientist from any time or place in history, one of the most common names youre likely to hear is Albert Einstein. The iconic physicist was responsible for a remarkable number of scientific advances during the 20th century, and perhaps single-handedly overthrew the Newtonian physics that had dominated scientific thought for more than 200 years. His most famous equation, E = mc, is so prolific that even people who dont know what it means can recite it. He won the Nobel Prize for advances in quantum physics. And his most successful idea the general theory of relativity, our theory of gravity remains undefeated in all tests more than 100 years after Einstein first proposed it.

But what if Einstein had never existed? Would others have come along and made precisely the same advances? Would those advances have come quickly, or would they have taken such a long time that some of them might not yet have occurred? Would it have taken a genius of equal magnitude to bring his great achievements to fruition? Or do we severely overestimate just how rare and unique Einstein was, elevating him to an undeserved position in our minds based on the fact that he was simply in the right place at the right time with the right set of skills? Its a fascinating question to explore. Lets dive in.

Einstein had whats known as his miracle year in 1905, when he published a series of papers that would go on to revolutionize a number of areas in physics. But just prior to that, a great number of advances had recently occurred that threw many long-held assumptions about the Universe into great doubt. For over 200 years, Isaac Newton had stood unchallenged in the realm of mechanics: both in the terrestrial and celestial realms. His law of universal gravitation applied just as well to objects in the Solar System as it did to balls rolling down a hill, or cannonballs fired from a cannon.

In the eyes of a Newtonian physicist, the Universe was deterministic. If you could write down the positions, momenta, and masses of every object in the Universe, you could calculate how each of them would evolve to arbitrary precisions at any moment in time. Additionally, space and time were absolute entities, and the gravitational force traveled at infinite speeds, with instantaneous effects. Throughout the 1800s, the science of electromagnetism was developed as well, uncovering intricate relationships between electric charges, currents, electric and magnetic fields, and even light itself. In many ways, it seemed that physics was almost solved, given the successes of Newton, Maxwell, and others.

Until, that is, it wasnt. There were puzzles that seemed to hint at something new in many different directions. The first discoveries of radioactivity had already taken place, and it was realized that mass was actually lost when certain atoms decayed. The momenta of the decaying particles didnt appear to match the momenta of the parent particles, indicating that either something wasnt conserved or that something unseen was present. Atoms were determined not to be fundamental, but made of positively charged atomic nuclei and discrete, negatively charged electrons.

But there were two challenges to Newton that seemed, somehow, more important than all of the others.

The first confusing observation was the orbit of Mercury. Whereas all of the other planets obeyed Newtons laws to the limits of our precision in measuring them, Mercury did not. Despite accounting for the precession of the equinoxes and the effects of the other planets, Mercurys orbits failed to match predictions by a minuscule but significant amount. The extra 43 arc-seconds-per-century of precession led many to hypothesize the existence of Vulcan, a planet inner to Mercury, but none was there to be discovered.

The second was perhaps even more puzzling: When objects moved close to the speed of light, they no longer obeyed Newtons equations of motion. If you were on a train at 100 miles per hour and threw a baseball at 100 miles per hour in the forward direction, the ball would move at 200 miles per hour. Intuitively, this is what youd expect to occur, and also what does occur when you perform the experiment for yourself.

But if youre on a moving train and you shine a beam of light forward, backward, or any other direction, it always moves at the speed of light, regardless of how the train is moving. In fact, its also true regardless of how quickly the observer watching the light is moving.

Moreover, if youre on a moving train and you throw a ball, but the train and ball are both traveling close to the speed of light, addition doesnt work the way were used to. If the train moves at 60% the speed of light and you throw the ball forward at 60% the speed of light, it doesnt move at 120% the speed of light, but only at ~88% the speed of light. Although we were able to describe whats happening, we couldnt explain it. And thats where Einstein came onto the scene.

Although its difficult to condense the entirety of his achievements into even a single article, perhaps his most momentous discoveries and advances are as follows.

The equation E = mc: When atoms decay, they lose mass. Where does that mass go if its not conserved? Einstein had the answer: It gets converted into energy. Moreover, Einstein had the correct answer: It gets converted, specifically, into the amount of energy described by his famous equation, E = mc. It works the other way as well; weve since created masses in the form of matter-antimatter pairs from pure energy based on this equation. In every circumstance its ever been tested under, E = mc is a success.

Special Relativity: When objects move close to the speed of light, how do they behave? They move in a variety of counterintuitive ways, but all are described by the theory of special relativity. There is a speed limit to the Universe: the speed of light in a vacuum, at which all massless entities in a vacuum move precisely. If you have mass, you can never reach, but only approach that speed. The laws of special relativity dictate how objects moving near the speed of light accelerate, add or subtract in velocity, and how time dilates and lengths contract for them.

The photoelectric effect: When you shine direct sunlight on a piece of conducting metal, it can kick the most loosely held electrons off of it. If you increase the lights intensity, more electrons get kicked off, while if you decrease the lights intensity, fewer electrons get kicked off. But heres where it gets weird: Einstein discovered that it wasnt based on the lights total intensity, but on the intensity of light above a certain energy threshold. Ultraviolet light only would cause the ionization, not visible or infrared, regardless of the intensity. Einstein showed that lights energy was quantized into individual photons, and that the number of ionizing photons determined how many electrons got kicked off; nothing else would do it.

General relativity: This was the biggest, most hard-fought revolution of all: a new theory of gravity governing the Universe. Space and time were not absolute, but made a fabric through which all objects, including all forms of matter and energy, traveled. Spacetime would curve and evolve owing to the presence and distribution of matter and energy, and that curved spacetime told matter and energy how to move. When put to the test, Einsteins relativity succeeded where Newton failed, explaining Mercurys orbit and predicting how starlight would deflect during a solar eclipse. Since it was first proposed, General Relativity has never been experimentally or observationally contradicted.

In addition to this, there were many other advances that Einstein himself played a major role in initiating. He discovered Brownian motion; he co-discovered the statistical rules under which boson particles operated; he contributed substantially to the foundations of quantum mechanics through the Einstein-Podolsky-Rosen paradox; and he arguably invented the idea of wormholes through the Einstein-Rosen bridge. His scientific career of contributions was truly legendary.

And yet, there are many reasons to believe that despite the unparalleled career that Einstein had, the full suite of advances that were made by Einstein would have been made by others in very short order without him. Its impossible to know for certain, but for all that we laud the genius of Einstein and hold him up as a singular example of how one incredible mind can change our conception of the Universe as he, in fact, actually did pretty much everything that occurred on account of Einstein would have occurred without him.

Prior to Einstein, back in the 1880s, physicist J.J. Thomson, discoverer of the electron, began thinking that the electric and magnetic fields of a moving, charged particle must carry energy with them. He attempted to quantify the amount of that energy. It was complicated, but a simplified set of assumptions allowed Oliver Heaviside to make a calculation: He determined the amount of effective mass that a charged particle carried was proportional to the electric field energy (E) divided by the speed of light (c) squared. Heaviside had a proportionality constant in there of 4/3 that was different from the true value of 1 in his 1889 calculation, as would Fritz Hasenhrl in 1904 and 1905. Henri Poincar independently derived E = mc in 1900, but didnt understand the implications of his derivations.

Without Einstein, we were already perilously close to his most famous equation; it seems unrealistic to expect we wouldnt have gotten the rest of the way there in short order had he not come along.

Similarly, we were already extremely close to special relativity. The Michelson-Morley experiment had demonstrated that light always moved at a constant speed, and it had disproven the most popular aether models. Hendrik Lorentz had already uncovered the transformation equations that determined how velocities added and how time dilated, and independently along with George FitzGerald, determined how lengths contracted in the direction of motion. In many ways, these were the building blocks that led Einstein to develop the theory of special relativity. However, it was Einstein who put it together. Again, its difficult to imagine that Lorentz, Poincar, and others working at the interface of electromagnetism and the speed of light wouldnt have taken similar leaps to arrive at this profound conclusion. Even without Einstein, we were already so close.

Max Plancks work with light set the stage for the discovery of the photoelectric effect; it surely would have occurred with or without Einstein.

Fermi and Dirac worked out the statistics for fermions (the other type of particle, besides bosons), while it was Satyendra Bose who worked them out for the particles that bear his name; Einstein was merely the recipient of Boses correspondence.

Quantum mechanics, arguably, would have developed just as well in the absence of Einstein.

But general relativity is the big one. With special relativity already under his belt, Einstein set about to fold in gravity. While Einsteins equivalence principle the realization that gravitation caused an acceleration, and that all accelerations were indistinguishable to the observer is what led him there, with Einstein himself calling it his happiest thought that left him unable to sleep for three days, others were thinking along the same lines.

Of all the advances that Einstein made, this was the one that his peers were farthest behind when he put it forth. Still, while it might have taken many years or even decades, the fact that others were already so close to thinking precisely along the same lines as Einstein leads us to believe that even if Einstein had never existed, general relativity would eventually have fallen into the realm of human knowledge.

We typically have a narrative in how science advances: that one individual, through a sheer stroke of genius, spots the key advance or way of thinking that everyone else had missed. Without that one individual, humanity would never have gained that remarkable knowledge that was stored away.

But when we examine the situation in greater detail, we find that many individuals were often nipping at the heels of that discovery just before it was made. In fact, when we look back through history, we find that many people had similar realizations to one another at about the same time. Alexei Starobinskii put many of the pieces of inflation together before Alan Guth did; Georges Lematre and Howard Robertson put together the expanding Universe before Hubble did; and Sin-Itiro Tomonaga worked out the calculations of quantum electrodynamics before Julian Schwinger and Richard Feynman did.

Einstein was the first to cross the finish line on a number of independent and remarkable scientific fronts. But had he never come along, many others were close behind him. Although he may have possessed every bit of dazzling genius that we often attribute to him, one thing is almost certain: Genius is not as unique and rare as we often assume it to be. With a lot of hard work and a little luck, almost any properly trained scientist can make a revolutionary breakthrough simply by stumbling upon the right realization at the right time.

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What if Einstein never existed? - Big Think

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Air Liquide Enters a Long-term Partnership to Secure Its Supply of Helium-3 – Business Wire

PARIS--(BUSINESS WIRE)--Regulatory News:

Air Liquide (Paris:AI) has entered into a long-term agreement with Laurentis Energy Partners, a leader in the clean-energy industry, to produce and distribute helium-3 (3He). This molecule is a rare isotope of helium used in quantum computing, quantum science, astrophysics, neutron detection, medical imaging and, in the future, fusion. Thanks to this new partnership, Air Liquide will be able to deliver large quantities of helium-3 to its customers around the world.

Laurentis Energy Partners will extract helium-3 as a by-product of the energy produced by the Darlington power generating station in Canada. As an expert in gas management and extreme cryogenics, Air Liquide will purify the molecule, then package and distribute it to its customers globally. The production will start by the end of this year.

Helium-3 is a very rare and stable isotope of helium. Compared to the most common isotope (helium-4), helium-3 has unique physical properties, such as a lower liquefaction temperature. Thanks to this, helium-3 has a wide range of applications, from neutron detection for security gates to deep science where helium-3 allows to reach temperatures close to absolute zero. In particular, helium-3 is necessary to produce the ultra cold required by quantum computers that harness quantum physics to process exponentially more data compared to classical computers. The promise of quantum computing is accelerated development in many fields including the search for new drugs, the discovery of new materials, and even cyber defense. As a medical isotope, He-3 can also be used to produce highly detailed Magnetic Resonance Imaging (MRI) of airways in the lung.

Emilie Mouren-Renouard, Member of the Air Liquide Executive Committee, in charge of Innovation, Digital and IT, Intellectual Property and the Global Markets & Technologies World Business Unit, said: One year after the acquisition of CryoConcept, specialized in technologies allowing to reach very low temperatures (close to absolute zero), this major agreement reinforces our core competencies when it comes to extreme cryogenics and deep tech, and illustrates our ambition to push back the frontiers of science. In particular, it enables us to offer our customers an ever more comprehensive range of products and services including helium-3 rare gas. It also confirms the willingness of Air Liquide to take part in the growing market driven by the quantum revolution and serve customers leveraging its expertise in the field of ultra low temperatures."

Global Markets & TechnologiesThe GM&T World Business Unit delivers technological solutions - molecules, equipment and services - to support the markets of energy transition and deep tech, in order to drive Air Liquide sustainable growth. GM&T employs 2,200 people worldwide, and generated a 2020 revenue of 579 million euros.

A world leader in gases, technologies and services for Industry and Health, Air Liquide is present in 78 countries with approximately 64,500 employees and serves more than 3.8 million customers and patients. Oxygen, nitrogen and hydrogen are essential small molecules for life, matter and energy. They embody Air Liquides scientific territory and have been at the core of the companys activities since its creation in 1902.

Air Liquides ambition is to be a leader in its industry, deliver long term performance and contribute to sustainability - with a strong commitment to climate change and energy transition at the heart of its strategy. The companys customer-centric transformation strategy aims at profitable, regular and responsible growth over the long term. It relies on operational excellence, selective investments, open innovation and a network organization implemented by the Group worldwide. Through the commitment and inventiveness of its people, Air Liquide leverages energy and environment transition, changes in healthcare and digitization, and delivers greater value to all its stakeholders.

Air Liquides revenue amounted to more than 20 billion euros in 2020. Air Liquide is listed on the Euronext Paris stock exchange (compartment A) and belongs to the CAC 40, EURO STOXX 50 and FTSE4Good indexes.

http://www.airliquide.com Follow us on Twitter @airliquidegroup

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Fujitsu SD WAN and ISS are first users of quantum seciurity – Capacity Media

20h | Alan Burkitt-Gray

Fujitsu and a company working with the International Space Station have been named as among the first users of Quantum Origin, whats claimed to be the worlds first commercial product built using quantum computers.

Cambridge Quantum, which is now part of the US-UK group Quantinuum, says it can fit quantum-level security to existing networks, including software-defined wide area networks (SD-WANs) from Fujitsu, which has incorporated the technology into its products.

Duncan Jones, head of cyber security at Cambridge Quantum, said last night: We are kick-starting the quantum cyber security industry. He said the company will start to distribute [quantum] keys into cloud platforms.

Houtan Houshmand, principal architect at Fujitsu, said his company was planning to incorporate the technology into its SD-WAN products.

David Zuniga, business development manager at Axiom Space, said the technology has been tested on the International Space Station (ISS) and would lead to space tourism with researchers and scientists [who] could do their work in space with total security.

Cambridge Quantum founder and Quantinuum CEO Ilyas Khan said: This product could be used by anyone.

He said it should be used by organisations worrying about the threat from people sequestering data storing encrypted information for the time when quantum computers will also be available to decrypt it.

You cannot afford to be asleep at the wheel, said Khan. When should we be worried? Of course, now. He said existing classical systems could be protected by a quantum computer.

Jones said that the Quantum Origin typical end point might be a hardware security module that could be added to existing infrastructure. For large enterprises to add this might be a year or two, he said. Smaller businesses were slightly further out.

On prices, he said that a typical key using existing technology costs about US$1 a month. He implied that a Quantum Origin key would be cheaper but did not go into details.

Fujitsus Houshmand was also asked about pricing. I cant provide a cost, he said, saying that what Fujitsu has done so far is just a proof of concept.

Jones said that Quantinuum, which is a joint venture of Cambridge Quantum and Honeywell, is forming a number of partnerships, naming military supplier Thalys and public key infrastructure (PKI) specialist Keyfactor. This is how the technology will diffuse into the market.

He said: We want to make this product broadly available, but accepted that there were global security considerations. There are export control laws. We have to do a lot of due diligence.

Zuniga at Axiom Space, which is training its own crew for the ISS and is planning its own private space station, said that the US operating segment of the ISS, where Quantum Origin is to be used, has a firewall to keep our data secure from the Russian sector. If we cant secure our data, it hurts a really expensive asset thats floating in space.

Khan, asked about possible exports to China and Russia, said: We are answerable to the regulators. We are an American and a British company. Were not actually able to sell to adversaries.

Houshmand at Fujitsu agreed: We have to stay rigidly compliant.

Elaborating on the technology, Jones said: Quantum Origin is a cloud-based platform that uses a quantum computer from Quantinuum to product cryptographic keys.

He was asked whether companies had five years, as is often suggested, to install quantum-level protection for their data. Theyre wrong by about five years, he said.

Jones said Quantum Origin keys are the strongest that have ever been created or could ever be created, because they use quantum physics to produce truly random numbers.

Khan noted that the beta version of Quantum Origin has been tested on an IBM quantum network.

Quantinuum and Cambridge Quantum has a number of clients that have tested the technology, but they are operating under a non-disclosure agreement (NDA), said Khan.

We have been working for a number of years now on a method to efficiently and effectively use the unique features of quantum computers in order to provide our customers with a defence against adversaries and criminals now and in the future once quantum computers are prevalent, he said.

He added: Quantum Origin gives us the ability to be safe from the most sophisticated and powerful threats today as well threats from quantum computers in the future.

Jones said: When we talk about protecting systems using quantum-powered technologies, were not just talking about protecting them from future threats. From large-scale takedowns of organisations, to nation state hackers and the worrying potential of hack now, decrypt later attacks, the threats are very real today, and very much here to stay. Responsible enterprises need to deploy every defence possible to ensure maximum protection at the encryption level today and tomorrow.

A quantum of disruptiion: Capacity's feature about quantum technology, its threat to data security and what it is also doing to protect security, is here

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Fujitsu SD WAN and ISS are first users of quantum seciurity - Capacity Media

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Physicist: Science, by Nature, Can’t Have a Theory of Everything – Walter Bradley Center for Natural and Artificial Intelligence

With admirable clarity, astronomer and physicist Marcelo Gleiser explains what a Theory of Everything is and is not: Its not about every detail of life that happens to us.

Its the search for a single, underlying force that unites the four fundamental forces of nature gravity, electromagnetism, the strong nuclear force, and the weak nuclear force into one single underlying force. Why havent we found it? Well, first, he says, We do not see this unity because it is only manifest at extremely high energies, well beyond what we can perceive even with our most powerful machines.

But second and more significantly there is a real question, Gleiser contends, whether science is by nature suited to finding such a force:

As the physicist Werner Heisenberg, of Uncertainty Principle fame, once wrote, What we observe is not Nature itself but Nature exposed to our methods of questioning. What we can say about Nature depends on how we measure it, with the precision and reach of our instruments dictating how far we can see. Therefore, no theory that attempts to unify current knowledge can seriously be considered a final theory or a TOE, given that we cannot ever be sure that we arent missing a huge piece of evidence.

That makes a lot of sense if we think about it. A Final Theory developed by science-minded people in ancient civilizations would not have included what we can learn from the microscope, the telescope, magnetic resonance imaging We are all at the mercy of what we cant know. As Gleiser puts it,

How are we to know that there isnt a fifth or sixth force lurking out there in the depths? We cannot know, and quite often, hints of a new force are announced in the media. To put it differently, our perennially myopic view of nature precludes any theory from being complete. Nature doesnt care how compelling we think our ideas are.

Generally speaking, the more we know, the more we find out we dont know. We fill in blanks and then more blanks appear beside them. One of the blanks, instead of just being filled in, may lead to a whole new discovery.

As Gleiser puts it, The very process of discovery leads to more unknowns. And they may be smaller or larger.

For example, in 1977, Carl Woese (19282012) almost accidentally discovered a huge and significant Third Kingdom of life, the Archaea which are neither bacteria nor more complex life forms (eukaryotes).

The fifth and sixth forces may be out there too.

Science is not, at any time in the foreseeable future, going to be all tied down and delivered in a box.

You may also wish to read: Can quantum physics, neuroscience merge as quantum consciousness? Physicist Marcelo Gleiser looks at the pros and cons of current theories. The problem is, if we assume that the mind is nothing more than the brain, there may be nothing we can discover about how it works.

and

Does science disprove free will? A physicist says no. Michael Egnor: Marcelo Gleiser notes that the mind is not a solar system with strict deterministic laws. Apart from simple laws governing neurons, we have no clue what laws the mind follows, though it does show complex nonlinear dynamics.

Also: Astronomer: We cant just assume countless Earths out there. He points out that the Principle of Mediocrity is based on faulty logical reasoning. Marcelo Gleiser notes that the starting point of the Mediocrity Principle assumes countless Earths. Thats not a conclusion from evidence. Its bad logic.

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Physicist: Science, by Nature, Can't Have a Theory of Everything - Walter Bradley Center for Natural and Artificial Intelligence

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UNSW researcher honoured for outreach in the physics community – UNSW Newsroom

UNSW Engineering, Scientia Professor Andrea Morello has been recognised for his outstanding outreach work in the physics field by the Australian Institute of Physics New South Wales (AIP NSW).

Prof. Morello is a renowned international leader in the field of quantum computing and has led the development and launch of the worlds first bachelors degree in Quantum Engineering at UNSW Sydney.

In its eighth year, the AIP NSW Community Outreach to Physics Awardis presented to individuals that seek to achieve activities that engage and contribute to public participation within physics communities.

Prof. Morellos outreach achievements include a popular YouTube channel, contribution to science initiatives for students, and artistic collaborations.

His YouTube video series on explaining quantum computing, building quantum computers and quantum phenomena in everyday life has attracted over 10 million views.

Prof. Morello has contributed to several popular science initiatives to engage students and younger audiences, including the National Youth Science Forum and World Science Festival, as well as being featured in the Australian Broadcasting Corporation Science elevator pitch series.

I am truly honoured by this award. As much as I love basic research, pushing the boundaries of human knowledge isn't worth much if I don't share it with the public, Prof. Morello said.

I have been fortunate to have, over the years, the opportunity to interact with many outstanding science communicators, who have involved me in their activities, and inspired me to work on outreach myself.

In a ceremony on Friday, Prof. Morello was presented the AIP NSW Community Outreach to Physics Award. Photo: Supplied.

Collaborations with visual and literary artists have also seen him engage with wider audiences.

Visual art created by UNSW Art & Designs Professor Paul Thomas, inspired by Prof. Morellos quantum bits and quantum chaos research, has been exhibited internationally.

And together with award-winning writer Bernard Cohen, Prof. Morello has initiated a project to work with NSW schools to develop experiential learning activities that bring together science and creative writing.

I thank my creative arts collaborators, Professor Paul Thomas and Bernard Cohen, who helped me see things from a very different perspective and find new angles to convey the fascination for science through different channels.

UNSW Dean of Engineering Professor Stephen Foster congratulated Prof. Morello on his outreach achievements.

Congratulations to Prof. Morello on receiving this prestigious award acknowledging his relentless advocacy work in fostering closeness between science and the community, reflecting UNSWs Values in Action.

UNSW Deputy Vice-Chancellor Academic, Professor Merlin Crossley also applauded Prof. Morellos engagement initiatives.

Through his depth of knowledge Prof. Morello has helped inform the public, here in Australia and across the world, about the opportunities and prospects for quantum computing that are now appearing on the horizon, said Prof. Crossley.

The Australian Institute of Physics is an organisation dedicated to promoting the role of physics in research, education, industry and the community.

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