Daily Archives: February 27, 2020

Here is our annual list of technological advances that we believe will make a real difference in solving important problems. How do we pick? We avoid the one-off tricks, the overhyped new gadgets. Instead we look for those breakthroughs that will truly change how we live and work.

This story is part of our March/April 2020 Issue

Were excited to announce that with this years list were also launching our very first editorial podcast, Deep Tech, which will explore the the people, places, and ideas featured in our most ambitious journalism. Have a listen here.

Later this year, Dutch researchers will complete a quantum internet between Delft and the Hague.

Yoshi Sodeoka

An internet based on quantum physics will soon enable inherently secure communication. A team led by Stephanie Wehner, at Delft University of Technology, is building a network connecting four cities in the Netherlands entirely by means of quantum technology. Messages sent over this network will be unhackable.

In the last few years, scientists have learned to transmit pairs of photons across fiber-optic cables in a way that absolutely protects the information encoded in them. A team in China used a form of the technology to construct a 2,000-kilometer network backbone between Beijing and Shanghaibut that project relies partly on classical components that periodically break the quantum link before establishing a new one, introducing the risk of hacking.

The Delft network, in contrast, will be the first to transmit information between cities using quantum techniques from end to end.

The technology relies on a quantum behavior of atomic particles called entanglement. Entangled photons cant be covertly read without disrupting their content.

But entangled particles are difficult to create, and harder still to transmit over long distances. Wehners team has demonstrated it can send them more than 1.5 kilometers (0.93 miles), and they are confident they can set up a quantum link between Delft and the Hague by around the end of this year. Ensuring an unbroken connection over greater distances will require quantum repeaters that extend the network.

Such repeaters are currently in design at Delft and elsewhere. The first should be completed in the next five to six years, says Wehner, with a global quantum network following by the end of the decade.

Russ Juskalian

Novel drugs are being designed to treatunique genetic mutations.

Julia Dufoss

Heres a definition of a hopeless case: a child with a fatal disease so exceedingly rare that not only is there no treatment, theres not even anyone in a lab coat studying it. Too rare to care, goes the saying.

Thats about to change, thanks to new classes of drugs that can be tailored to a persons genes. If an extremely rare disease is caused by a specific DNA mistakeas several thousand aretheres now at least a fighting chance for a genetic fix.

One such case is that of Mila Makovec, a little girl suffering from a devastating illness caused by a unique genetic mutation, who got a drug manufactured just for her. Her case made the New England Journal of Medicine in October, after doctors moved from a readout of her genetic error to a treatment in just a year. They called the drug milasen, after her.

The treatment hasnt cured Mila. But it seems to have stabilized her condition: it has reduced her seizures, and she has begun to stand and walk with assistance.

Milas treatment was possible because creating a gene medicine has never been faster or had a better chance of working. The new medicines might take the form of gene replacement, gene editing, or antisense (the type Mila received), a sort of molecular eraser, which erases or fixes erroneous genetic messages. What the treatments have in common is that they can be programmed, in digital fashion and with digital speed, to correct or compensate for inherited diseases, letter for DNA letter.

How many stories like Milas are there? So far, just a handful.

But more are on the way. Where researchers would have once seen obstacles and said Im sorry, they now see solutions in DNA and think maybe they can help.

The real challenge for n-of-1 treatments (a reference to the number of people who get the drug) is that they defy just about every accepted notion of how pharmaceuticals should be developed, tested, and sold. Who will pay for these drugs when they help one person, but still take large teams to design and manufacture?

Antonio Regalado

The rise of digitalcurrency has massive ramifications forfinancial privacy.

Last June Facebook unveiled a global digital currency called Libra. The idea triggered a backlash and Libra may never launch, at least not in the way it was originally envisioned. But its still made a difference: just days after Facebooks announcement, an official from the Peoples Bank of China implied that it would speed the development of its own digital currency in response. Now China is poised to become the first major economy to issue a digital version of its money, which it intends as a replacement for physical cash.

Chinas leaders apparently see Libra, meant to be backed by a reserve that will be mostly US dollars, as a threat: it could reinforce Americas disproportionate power over the global financial system, which stems from the dollars role as the worlds de facto reserve currency. Some suspect China intends to promote its digital renminbi internationally.

Now Facebooks Libra pitch has become geopolitical. In October, CEO Mark Zuckerberg promised Congress that Libra will extend Americas financial leadership as well as our democratic values and oversight around the world. The digital money wars have begun.

Mike Orcutt

Drugs that try to treat ailments bytargeting a natural aging process in the body have shown promise.

Yoshi Sodeoka

The first wave of a new class of anti-aging drugs have begun human testing. These drugs wont let you live longer (yet) but aim to treat specific ailments by slowing or reversing a fundamental process of aging.

The drugs are called senolyticsthey work by removing certain cells that accumulate as we age. Known as senescent cells, they can create low-level inflammation that suppresses normal mechanisms of cellular repair and creates a toxic environment for neighboring cells.

In June, San Franciscobased Unity Biotechnology reported initial results in patients with mild to severe osteoarthritis of the knee. Results from a larger clinical trial are expected in the second half of 2020. The company is also developing similar drugs to treat age-related diseases of the eyes and lungs, among other conditions.

Senolytics are now in human tests, along with a number of other promising approaches targeting the biological processes that lie at the root of aging and various diseases.

A company called Alkahest injects patients with components found in young peoples blood and says it hopes to halt cognitive and functional decline in patients suffering from mild to moderate Alzheimers disease. The company also has drugs for Parkinsons and dementia in human testing.

And in December, researchers at Drexel University College of Medicine even tried to see if a cream including the immune-suppressing drug rapamycin could slow aging in human skin.

The tests reflect researchers expanding efforts to learn if the many diseases associated with getting oldersuch as heart diseases, arthritis, cancer, and dementiacan be hacked to delay their onset.

Adam Piore

Scientists have used AI to discover promising drug-like compounds.

The universe of molecules that could be turned into potentially life-saving drugs is mind-boggling in size: researchers estimate the number at around 1060. Thats more than all the atoms in the solar system, offering virtually unlimited chemical possibilitiesif only chemists could find the worthwhile ones.

Now machine-learning tools can explore large databases of existing molecules and their properties, using the information to generate new possibilities. This could make it faster and cheaper to discover new drug candidates.

In September, a team of researchers at Hong Kongbased Insilico Medicine and the University of Toronto took a convincing step toward showing that the strategy works by synthesizing several drug candidates found by AI algorithms.

Using techniques like deep learning and generative models similar to the ones that allowed a computer to beat the world champion at the ancient game of Go, the researchers identified some 30,000 novel molecules with desirable properties. They selected six to synthesize and test. One was particularly active and proved promising in animal tests.

Chemists in drug discovery often dream up new moleculesan art honed by years of experience and, among the best drug hunters, by a keen intuition. Now these scientists have a new tool to expand their imaginations.

David Rotman

We can now affordably build, launch, and operate tens of thousands of satellites in orbit at once.

Julia Dufoss

Satellites that can beam a broadband connection to internet terminals. As long as these terminals have a clear view of the sky, they can deliver internet to any nearby devices. SpaceX alone wants to send more than 4.5 times more satellites into orbit this decade than humans have ever launched since Sputnik.

These mega-constellations are feasible because we have learned how to build smaller satellites and launch them more cheaply. During the space shuttle era, launching a satellite into space cost roughly $24,800 per pound. A small communications satellite that weighed four tons cost nearly $200 million to fly up.

Today a SpaceX Starlink satellite weighs about 500 pounds (227 kilograms). Reusable architecture and cheaper manufacturing mean we can strap dozens of them onto rockets to greatly lower the cost; a SpaceX Falcon 9 launch today costs about $1,240 per pound.

The first 120 Starlink satellites went up last year, and the company planned to launch batches of 60 every two weeks starting in January 2020. OneWeb will launch over 30 satellites later this year. We could soon see thousands of satellites working in tandem to supply internet access for even the poorest and most remote populations on the planet.

But thats only if things work out. Some researchers are livid because they fear these objects will disrupt astronomy research. Worse is the prospect of a collision that could cascade into a catastrophe of millions of pieces of space debris, making satellite services and future space exploration next to impossible. Starlinks near-miss with an ESA weather satellite in September was a jolting reminder that the world is woefully unprepared to manage this much orbital traffic. What happens with these mega-constellations this decade will define the future of orbital space.

Neel V. Patel

Google has provided the first clear proof of a quantum computer outperforming a classical one.

Yoshi Sodeoka

Quantum computers store and process data in a way completely differently from the ones were all used to. In theory, they could tackle certain classes of problems that even the most powerful classical supercomputer imaginable would take millennia to solve, like breaking todays cryptographic codes or simulating the precise behavior of molecules to help discover new drugs and materials.

There have been working quantum computers for several years, but its only under certain conditions that they outperform classical ones, and in October Google claimed the first such demonstration of quantum supremacy. A computer with 53 qubitsthe basic unit of quantum computationdid a calculation in a little over three minutes that, by Googles reckoning, would have taken the worlds biggest supercomputer 10,000 years, or 1.5 billion times as long. IBM challenged Googles claim, saying the speedup would be a thousandfold at best; even so, it was a milestone, and each additional qubit will make the computer twice as fast.

However, Googles demo was strictly a proof of conceptthe equivalent of doing random sums on a calculator and showing that the answers are right. The goal now is to build machines with enough qubits to solve useful problems. This is a formidable challenge: the more qubits you have, the harder it is to maintain their delicate quantum state. Googles engineers believe the approach theyre using can get them to somewhere between 100 and 1,000 qubits, which may be enough to do something usefulbut nobody is quite sure what.

And beyond that? Machines that can crack todays cryptography will require millions of qubits; it will probably take decades to get there. But one that can model molecules should be easier to build.

Gideon Lichfield

We can now run powerful AI algorithms on our phones.

Julia Dufoss

AI has a problem: in the quest to build more powerful algorithms, researchers are using ever greater amounts of data and computing power, and relying on centralized cloud services. This not only generates alarming amounts of carbon emissions but also limits the speed and privacy of AI applications.

But a countertrend of tiny AI is changing that. Tech giants and academic researchers are working on new algorithms to shrink existing deep-learning models without losing their capabilities. Meanwhile, an emerging generation of specialized AI chips promises to pack more computational power into tighter physical spaces, and train and run AI on far less energy.

These advances are just starting to become available to consumers. Last May, Google announced that it can now run Google Assistant on users phones without sending requests to a remote server. As of iOS 13, Apple runs Siris speech recognition capabilities and its QuickType keyboard locally on the iPhone. IBM and Amazon now also offer developer platforms for making and deploying tiny AI.

All this could bring about many benefits. Existing services like voice assistants, autocorrect, and digital cameras will get better and faster without having to ping the cloud every time they need access to a deep-learning model. Tiny AI will also make new applications possible, like mobile-based medical-image analysis or self-driving cars with faster reaction times. Finally, localized AI is better for privacy, since your data no longer needs to leave your device to improve a service or a feature.

But as the benefits of AI become distributed, so will all its challenges. It could become harder to combat surveillance systems or deepfake videos, for example, and discriminatory algorithms could also proliferate. Researchers, engineers, and policymakers need to work together now to develop technical and policy checks on these potential harms.

Karen Hao

A technique to measure the privacy of a crucial data set.

In 2020, the US government has a big task: collect data on the countrys 330 million residents while keeping their identities private. The data is released in statistical tables that policymakers and academics analyze when writing legislation or conducting research. By law, the Census Bureau must make sure that it cant lead back to any individuals.

But there are tricks to de-anonymize individuals, especially if the census data is combined with other public statistics.

So the Census Bureau injects inaccuracies, or noise, into the data. It might make some people younger and others older, or label some white people as black and vice versa, while keeping the totals of each age or ethnic group the same. The more noise you inject, the harder de-anonymization becomes.

Differential privacy is a mathematical technique that makes this process rigorous by measuring how much privacy increases when noise is added. The method is already used by Apple and Facebook to collect aggregate data without identifying particular users.

But too much noise can render the data useless. One analysis showed that a differentially private version of the 2010 Census included households that supposedly had 90 people.

If all goes well, the method will likely be used by other federal agencies. Countries like Canada and the UK are watching too.

Angela Chen

Researchers can now spot climate changes role in extreme weather.

Yoshi Sodeoka

Ten days after Tropical Storm Imelda began flooding neighborhoods across the Houston area last September, a rapid-response research team announced that climate change almost certainly played a role.

The group, World Weather Attribution, had compared high-resolution computer simulations of worlds where climate change did and didnt occur. In the former, the world we live in, the severe storm was as much as 2.6 times more likelyand up to 28% more intense.

Earlier this decade, scientists were reluctant to link any specific event to climate change. But many more extreme-weather attribution studies have been done in the last few years, and rapidly improving tools and techniques have made them more reliable and convincing.

This has been made possible by a combination of advances. For one, the lengthening record of detailed satellite data is helping us understand natural systems. Also, increased computing power means scientists can create higher-resolution simulations and conduct many more virtual experiments.

These and other improvements have allowed scientists to state with increasing statistical certainty that yes, global warming is often fueling more dangerous weather events.

By disentangling the role of climate change from other factors, the studies are telling us what kinds of risks we need to prepare for, including how much flooding to expect and how severe heat waves will get as global warming becomes worse. If we choose to listen, they can help us understand how to rebuild our cities and infrastructure for a climate-changed world.

James Temple

Continued here:

10 Breakthrough Technologies 2020 - MIT Technology Review

Labs test artificial intelligence, virtual reality and other innovations that could improve learning and lower costs for Generation Z and beyond.-- The New York Times, "How Technology Is Changing the Future of Higher Education," Jon Marcus, Feb. 20, 2020

We should celebrate the efforts of colleges and universities to experiment with new technologies to drive down costs and advance learning.

From the perspective of recognizing the role that universities play in driving educational technologies, The New York Times article "How Technology Is Changing the Future of Higher Education" is a welcome development.

It is fun to read about technology advances in higher education. As original members of the HAIL Storm -- Harvesting Academic Innovation for Learners -- it is particularly gratifying to see this work called out in The New York Times. And many of the educators profiled in the piece are our friends and colleagues.

There is only one problem.

Technology, on its own, does not drive higher education change.

We are excited, like many, about AI, VR, AR and all the other emerging technologies that colleges and universities are experimenting with. In our roles on campus, we participate in and support this experimentation.

We invest in AI, AR, VR and other technologies with the understanding, however, that their development will not solve the pressing issues facing our sector. On their own, new technologies will not drive down costs, increase access or advance the quality of learning.

Instead, advances in technology need to be considered within the context of an interlocking web of trends and forces, from demographic drivers (not good) to levels of public funding (even worse). Demographic headwinds and funding shortfalls will swamp any impact of new technologies.

What also matters more than technology is ideas (and people to support them). The permanent scarcity that is now endemic to the postsecondary system has caused many to look to technology for economic answers. The vision of your teacher is a robot as the solution to higher eds cost disease is alive in those who wish to disrupt the postsecondary ecosystem.

This vision of lowering instructional costs with AI, however, only appeals if you think of learning as a transaction. In this model of learning, there are inputs (instruction) and outputs (assessments), all of which can be measured and tracked.

This conception of the learning process, however, is in reality deeply impoverished. Learning as a transaction takes the least impactful practices of higher education and elevates them to the primary goal of our colleges and universities.

An alternative view is that learning is relational, iterative, recursive and constructed. This nuanced, complicated and human-centric conception of learning requires the active participation of educators.

We can look forward to a time when well-supported (and economically secure) faculty (including adjuncts) are given new AI and AR tools to aid in their teaching. These tools, however, will complement, not substitute for, the professors.

The narrative that technology drives higher education change, and that if we just get the technology right that we will finally fix what is wrong in higher ed, is a story with widespread appeal. We want to believe this story. Each article in publications like The New York Times adds to this belief of how higher education changes.

The question is, how can we shift this technology-first narrative of higher ed change while simultaneously making investments in educational technology research and development?

How do we celebrate technological experimentation in higher ed without succumbing to the belief that the solutions to our challenges will be found in technology?

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How Technology Is Not Changing the Future of Higher Education - Inside Higher Ed

Once viewed as purely futuristic, immersive technologies like virtual reality (VR), augmented reality (AR) and mixed reality (MR) are transforming manufacturing workflows as business leaders recognize the value of visualizing complex data dimensionally and supporting more information-rich experiences.

When talking about these three technologies, it is important to understand the differences between them. Virtual reality is a fully immersive technology in which the user only sees the digital content displayed by the device. With augmented reality, the physical and virtual worlds are combined by overlaying digital information into the users environment, usually through a 2D display. Mixed reality blends the digital and physical worlds and allows the digital content to interact with the real-world in 3D.

All of these technology innovations provide benefits such as increased productivity and reduced risk of error; however, they have mostly been used in the manufacturing world for visualization and communication scenarios. But that is changing.

Paradigm Shift

While the use of AR, VR and MR for communication and visualization is still important, the way manufacturers are taking advantage of these solutions has started to shift. Now more than ever, manufacturers are using AR and MR technology in production environments and bringing it to frontline workers to be used for more sophisticated and demanding tasks, such as quality control, production control and actual manufacturing in assembly lines.

This is especially true when it comes to mixed reality, where improved hardware and software capabilities enable hands free interaction and compliance with safety requirements. The main improvements on the hardware side compared to previous-generation devices include a wider field of view, better performance and better calibrated optics for improved alignment accuracy--a critical aspect when it comes to production. In addition, a specialized, production-targeted mixed reality device can address safety requirements like hardhat integration and bone-conduction headsets that keep the user aware of the surroundings.

Significant improvements on the software side include two-hands interaction as a natural, more efficient interface, eye tracking, and advanced features such as sequencing for improved context-based interaction.

The market has also matured to the point where technology is being more widely adopted and is expanding to the production floor. Until now, most digital transformation has consisted of changes in the office. Engineers have embraced 3D modeling solutions and mixed reality for the design and evaluation of projects and products in the office, but workers in the manufacturing facility were still using paper drawings or interacting with models or 2D representations on a computer screen for production. This painful gap motivated companies to look for solutions to improve efficiency by extending the use of mixed reality on the production floor.

Mixed reality can directly translate the time invested in the 3D digital content during the design stage to the production side to improve efficiency. Manufacturers can justify their investment by using the digital datasuch as 3D modelsnot just for the design phase, but also for the manufacturing phase.

New Use Cases

The combination of market maturity and technology innovation has enabled a new set of use cases for the manufacturing industry. including:

Employee Training: Rather than reading a set of drawings or flipping through a manual for passive learning, workers can immerse themselves in the training material while physically interacting with the assets needed for the job. This is especially useful with spatially complex products were traditional training methods take time due to the high cognitive load.

Visual Sequencing: Visual sequencing provides step-by-step instructions of an assembly process delivered to the line worker during assembly. For complex assemblies, 3D views of each step with accompanying instructions help even experienced workers to avoid potential errors. As an added benefit, the wearable devices allow them to keep their hands free while working. A concrete example for such a scenario is an assembly of rebar cages in a prefab factory. The ability to filter the displayed information based on production sequence helps both novice and experienced team members avoid costly errors.

Quality Assurance: Blending digital information into real world views enables on-site teams to validate the quality of their work, identify discrepancies in real-time, and benefit from a short communication loop with the office team. Mixed reality can help embed the quality assurance in the production process by enabling real-time visual analysis of the assembled product.

Production Control: Prefabricated construction elements are becoming more complex. Mixed reality provides manufacturers with the ability to monitor the production process in the context of the physical assembly and determine whether production is ahead, according, or behind schedule.

Look for integration of mixed reality with advanced technologies such as artificial intelligence, computer vision and machine learning to further increase its value.

Aviad Almagor is senior director of mixed reality and BCI at Trimble.

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Can Your Technology Weather the Storm? - IndustryWeek

The Transportation Security Administration at Boston's Logan Airport has installed new technology in preparation for REAL IDs which will be required of all passengers starting in October.

The 25 Credential Authentication Technology scanners, which were installed last fall and debuted to the public on Wednesday, will improve checkpoint screening capabilities at the busy airport, TSA officials said.

Logan was one of the first airports to roll out the technology, which TSA officials say can detect fraud in roughly 8 to 10 seconds.

"The technology enhances detection capabilities for identifying fraudulent documents such as driver's licenses and passports at checkpoints and increases efficiency by automatically verifying passenger identification," TSA's Massachusetts Federal Security Director Bob Allison said in a statement. "The system will also confirm the passenger's flight status in near real-time through a secured connection."

The law requires airline passengers to present a Real ID-compliant driver's license or ID card at Transportation Security Administration checkpoints in airports as of Oct. 1.

Those licenses require more proof of identification than regular licenses and are generally marked with a star on the top.

The IDs, which are required as part of a law passed by Congress in 2005 in response to the 9/11 attacks, haven't been available in all states until more recently. The TSA is now urging people to get them as the deadline is quickly approaching.

"If you're going to wait until later this month into the spring, into the summer, go to the local DMV to get your compliant license my tip would be bring a snack because there will be a line and you will have a wait. So get it now, make that appointment," TSA spokesperson Lisa Farbstein said.

Since Massachusetts started to implement the IDs in 2018, 1.5 million people have obtained them but millions more have not.

"It's been a law since 2005 and states have a had significant amount of time to comply," Farbstein said.

While passengers can still travel domestically using a passport or military ID, the REAL ID will eventually replace the standard license at security checkpoints.

Some travelers at Logan Airport say they are not looking forward to making that appointment to get the new ID.

"It's just one of those things - it's a hassle," said one man at the airport Wednesday.

"I will need to do it. I am going to have to do it. I just need to commit - find time," added frequent flyer Lisa Kirlick.

Information on how to get a REAL ID can be found on the Massachusetts Registry of Motor Vehicles website.

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New Technology at Logan Airport Will Improve REAL ID Screening - NBC10 Boston

Tech pros want opportunities for growth and a work/life balance, while employers say they face challenges finding skilled workers, according to the staffing firm LaSalle Network.

Technology professionals and the companies that employ them all have their own unique needs, goals, and challenges. Workers often face the question of whether they should stay with their current employer or explore opportunities elsewhere. And companies are often in need of skilled and qualified workers that they may not be able to find. Two new reports from LaSalle Network shed some light on what tech pros and employers both want in the year ahead.

For its report "What Do Technologists Really Want," LaSalle Network surveyed more than 6,000 tech professionals to find out what they're seeking in a company and career in 2020. Among the respondents, 83% said they're open to new opportunities, while 53% said they've received anywhere from one to three job offers over the past year.

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Asked why they're open to new offers away from their present company, those surveyed cited more opportunity for growth, higher compensation, and better benefits. The top benefits that tech pros want are a 401(k) match, paid time off, and an option to work from home. While respondents pointed to growth as the top motivation for seeking new opportunities, they cited a work/life balance as the main reason for staying with their current employer.

Next, the certifications that professionals most frequently seek are CompTIA A+, Network+, and Project Management Professional. Tech pros are also looking to beef up their skills in some specific areas. With cloud computing such a hot topic, 55% of the respondents said they want to improve their skills in Amazon Web Services (AWS), while 14% are looking at Microsoft Azure. Programming languages is another popular field as 38% said they want to improve their prowess with SQL, 25% with Java, and 25% with Python. Looking at software development, 66% said they want to enhance their skills in Scrum and 11% in Kanban.

On the flip side, employers have their own laundry list of needs. For its "2020 Hiring Challenges report," LaSalle Network surveyed 100 leaders in finance, operations, and human resources to ascertain their goals ahead. The economy and job market remain strong as 80% of the respondents said that the talk last year of a possible recession hasn't affected their hiring plans. A full 70% of those surveyed plan to add headcount this year, with 40% looking to fill IT jobs.

Beyond hiring more workers, employers are aiming to invest in other areas this year. Almost 40% of the respondents said that their company would make investments in technology in the next 12 months. Another area for investment will be training and development as companies realize that retaining skilled staff requires investing in them.

Automation is expected to expand further into the business world. Around 33% of the respondents said they automated parts of their workforce last year, a number that will rise over the next decade. And among the companies that have automated, 60% said they provided retraining programs to help employees stay with the organization.

Employers also face their own set of challenges. Among those surveyed, 71% cited the ability to find skilled workers as their biggest hiring challenge.

More specifically, the Society of Human Resources Management listed the top three missing soft skills in many job candidates as: 1) Problem solving, critical thinking, innovation, and creativity; 2) An ability to deal with complexity and ambiguity; and 3) Communication. Some of the hard skills missing in candidates include data analysis, science, medical, and engineering.

Identifying candidates who fit the company culture was another challenge, cited as a top one by 62% of those surveyed, up dramatically from 36% in last year's survey.

Finally, compensation requirements was the third-largest challenge among employers, listed by 58% of the respondents. Some 60% said they plan to increase salaries this year to remain competitive, while around 33% plan to add benefits.

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What technology professionals and employers are looking for in 2020 - TechRepublic

Amazon Go Grocery in Seattle will bring the e-tail titan's autonomous checkout technology, which had previously been used in over 20 convenience-size stores, to a grocery store format, perThe Wall Street Journal.

Business Insider Intelligence

Customers check in via app upon entry, pick their items throughout the store, which is outfitted with cameras, sensors, and computer vision, and leave without physically checking out instead their account is automatically charged. Amazon Go Grocery appears to be a separate effort from both Whole Foods and the e-tailer's forthcomingrumored grocery store in Los Angeles.

With the new Amazon Go Grocery store, Amazon may have successfully handled two key obstacles faced by its Go technology:

These advancements could mean that Amazon is poised to license its Go technology to other stores, but it still has one major hurdle to overcome retrofitting existing stores.Amazon isreportedlyinterested in licensing its technology to outside retailers, which could prove lucrative since it could capture fees or a percentage of revenue from thousands of existing stores if they use its technology, bolstering its physical retailsegment.

But it's still hasn't retrofit an existing store instead it has built stores so they could deploy its Go technology. Learning to retrofit stores may slow any licensing plans since it will need to make its technology work for stores, rather than the other way around.

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Amazon opened a new grocery store that uses its Go technology - Business Insider Nordic

Last year, researchers at Fermilab received over $3.5 million for projects that delve into the burgeoning field of quantum information science. Research funded by the grant runs the gamut, from building and modeling devices for possible use in the development of quantum computers to using ultracold atoms to look for dark matter.

For their quantum computer project, Fermilab particle physicist Adam Lyon and computer scientist Jim Kowalkowski are collaborating with researchers at Argonne National Laboratory, where theyll be running simulations on high-performance computers. Their work will help determine whether instruments called superconducting radio-frequency cavities, also used in particle accelerators, can solve one of the biggest problems facing the successful development of a quantum computer: the decoherence of qubits.

Fermilab has pioneered making superconducting cavities that can accelerate particles to an extremely high degree in a short amount of space, said Lyon, one of the lead scientists on the project. It turns out this is directly applicable to a qubit.

Researchers in the field have worked on developing successful quantum computing devices for the last several decades; so far, its been difficult. This is primarily because quantum computers have to maintain very stable conditions to keep qubits in a quantum state called superposition.

Superconducting radio-frequency cavities, such as the one seen here, are used in particle accelerators. They can also solve one of the biggest problems facing the successful development of a quantum computer: the decoherence of qubits. Photo: Reidar Hahn, Fermilab

Superposition

Classical computers use a binary system of 0s and 1s called bits to store and analyze data. Eight bits combined make one byte of data, which can be strung together to encode even more information. (There are about 31.8 million bytes in the average three-minute digital song.) In contrast, quantum computers arent constrained by a strict binary system. Rather, they operate on a system of qubits, each of which can take on a continuous range of states during computation. Just as an electron orbiting an atomic nucleus doesnt have a discrete location but rather occupies all positions in its orbit at once in an electron cloud, a qubit can be maintained in a superposition of both 0 and 1

Since there are two possible states for any given qubit, a pair doubles the amount of information that can be manipulated: 22 = 4. Use four qubits, and that amount of information grows to 24 = 16. With this exponential increase, it would take only 300 entangled qubits to encode more information than there is matter in the universe.

Qubits can be in a superposition of 0 and 1, while classical bits can be only one or the other. Image: Jerald Pinson

Parallel positions

Qubits dont represent data in the same way as bits. Because qubits in superposition are both 0 and 1 at the same time, they can similarly represent all possible answers to a given problem simultaneously. This is called quantum parallelism, and its one of the properties that makes quantum computers so much faster than classical systems.

The difference between classical computers and their quantum counterparts could be compared to a situation in which there is a book with some pages randomly printed in blue ink instead of black. The two computers are given the task of determining how many pages were printed in each color.

A classical computer would go through every page, Lyon said. Each page would be marked, one at a time, as either being printed in black or in blue. A quantum computer, instead of going through the pages sequentially, would go through them all at once.

Once the computation was complete, a classical computer would give you a definite, discrete answer. If the book had three pages printed in blue, thats the answer youd get.

But a quantum computer is inherently probabilistic, Kowalkowski said.

This means the data you get back isnt definite. In a book with 100 pages, the data from a quantum computer wouldnt be just three. It also could give you, for example, a 1 percent chance of having three blue pages or a 1 percent chance of 50 blue pages.

An obvious problem arises when trying to interpret this data. A quantum computer can perform incredibly fast calculations using parallel qubits, but it spits out only probabilities, which, of course, isnt very helpful unless, that is, the right answer could somehow be given a higher probability.

Interference

Consider two water waves that approach each other. As they meet, they may constructively interfere, producing one wave with a higher crest. Or they may destructively interfere, canceling each other so that theres no longer any wave to speak of. Qubit states can also act as waves, exhibiting the same patterns of interference, a property researchers can exploit to identify the most likely answer to the problem theyre given.

If you can set up interference between the right answers and the wrong answers, you can increase the likelihood that the right answers pop up more than the wrong answers, Lyon said. Youre trying to find a quantum way to make the correct answers constructively interfere and the wrong answers destructively interfere.

When a calculation is run on a quantum computer, the same calculation is run multiple times, and the qubits are allowed to interfere with one another. The result is a distribution curve in which the correct answer is the most frequent response.

When waves meet, they may constructively interfere, producing one wave with a higher crest. Image: Jerald Pinson

As waves, they may also destructively interfere, canceling each other so that theres no longer any wave to speak of. Image: Jerald Pinson

Listening for signals above the noise

In the last five years, researchers at universities, government facilities and large companies have made encouraging advancements toward the development of a useful quantum computer. Last year, Google announced that it had performed calculations on their quantum processor called Sycamore in a fraction of the time it would have taken the worlds largest supercomputer to complete the same task.

Yet the quantum devices that we have today are still prototypes, akin to the first large vacuum tube computers of the 1940s.

The machines we have now dont scale up much at all, Lyon said.

Theres still a few hurdles researchers have to overcome before quantum computers become viable and competitive. One of the largest is finding a way to keep delicate qubit states isolated long enough for them to perform calculations.

If a stray photon a particle of light from outside the system were to interact with a qubit, its wave would interfere with the qubits superposition, essentially turning the calculations into a jumbled mess a process called decoherence. While the refrigerators do a moderately good job at keeping unwanted interactions to a minimum, they can do so only for a fraction of a second.

Quantum systems like to be isolated, Lyon said, and theres just no easy way to do that.

When a quantum computer is operating, it needs to be placed in a large refrigerator, like the one pictured here, to cool the device to less than a degree above absolute zero. This is done to keep energy from the surrounding environment from entering the machine. Photo: Reidar Hahn, Fermilab

Which is where Lyon and Kowalkowskis simulation work comes in. If the qubits cant be kept cold enough to maintain an entangled superposition of states, perhaps the devices themselves can be constructed in a way that makes them less susceptible to noise.

It turns out that superconducting cavities made of niobium, normally used to propel particle beams in accelerators, could be the solution. These cavities need to be constructed very precisely and operate at very low temperatures to efficiently propagate the radio waves that accelerate particle beams. Researchers theorize that by placing quantum processors in these cavities, the qubits will be able to interact undisturbed for seconds rather than the current record of milliseconds, giving them enough time to perform complex calculations.

Qubits come in several different varieties. They can be created by trapping ions within a magnetic field or by using nitrogen atoms surrounded by the carbon lattice formed naturally in crystals. The research at Fermilab and Argonne will be focused on qubits made from photons.

Lyon and his team have taken on the job of simulating how well radio-frequency cavities are expected to perform. By carrying out their simulations on high-performance computers, known as HPCs, at Argonne National Laboratory, they can predict how long photon qubits can interact in this ultralow-noise environment and account for any unexpected interactions.

Researchers around the world have used open-source software for desktop computers to simulate different applications of quantum mechanics, providing developers with blueprints for how to incorporate the results into technology. The scope of these programs, however, is limited by the amount of memory available on personal computers. In order to simulate the exponential scaling of multiple qubits, researchers have to use HPCs.

Going from one desktop to an HPC, you might be 10,000 times faster, said Matthew Otten, a fellow at Argonne National Laboratory and collaborator on the project.

Once the team has completed their simulations, the results will be used by Fermilab researchers to help improve and test the cavities for acting as computational devices.

If we set up a simulation framework, we can ask very targeted questions on the best way to store quantum information and the best way to manipulate it, said Eric Holland, the deputy head of quantum technology at Fermilab. We can use that to guide what we develop for quantum technologies.

This work is supported by the Department of Energy Office of Science.

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Particle accelerator technology could solve one of the most vexing problems in building quantum computers - Fermi National Accelerator Laboratory

(WWLP) The National Weather Service is making some major computer upgrades which will help improve weather computer models here in the United States.

The 22News Storm Team relies on a number of different forecast models from all over the world to help us forecast the weather.

NOAA just announced that they will be making a significant upgrade to their Weather and Climate Operational Super Computing System.

Other computer models in other countries have been getting faster than ours in the United States, Storm Team Meteorologist Nick Bannin explained. So by investing in higher resolution computer models, improved technology, better speed, [and] more accuracy, in the long term, well have a better idea what the forecast is going to be without having to look at some of those other computer models from other countries.

The upgrade will triple the computing capacity and allow for better, high-resolution forecast model guidance.

NOAA plans to start implementing the new computer model upgrades by early 2022.

See more here:

National Weather Service announces upgrades to weather forecasting technology - WWLP.com

Find all our Lessons of the Day here.

Featured Article: How Technology Is Changing the Future of Higher Education by Jon Marcus.

Labs across the country are testing artificial intelligence, virtual reality and other innovations that could improve learning and lower costs for Generation Z and beyond.

In this lesson, you will learn how they are trying to solve problems in higher education with solutions that sound straight out of a science-fiction novel. Then, youll come up with creative ideas of your own for improving your schooling experience.

Imagine you have the opportunity to join a team of innovators to shape the future of education. Your job is to dream up what school could look like in five, 10 or 50 years from now.

To do this, youll follow the process of design thinking:

Step 1: Empathize. What do students need? Make a list of all the things that help young people learn and set them up for success. And try to think beyond your own needs. What could be helpful for people of all backgrounds, places and abilities?

Step 2: Define. Next, youll have to define the problems in the current system that might prevent students from being successful in school. These might have to do with the structure of school. For example, some students might have a hard time just getting to the building every day or learning in a classroom with 30 other students. Or, you might identify problems with the curriculum, expectations or anything else. Make a list of at least three that you think are important.

Now, youll read an article about how one organization is trying to come up with creative solutions for how to solve problems like these in higher education.

Read the article, then answer the following questions:

1. What is the Sandbox ColLABorative? How does it imagine the future of education?

2. What does the current structure of higher education look like? Why do innovators see a need to disrupt that system?

3. William Zemp, chief strategy and innovation officer at Southern New Hampshire University, says one of the most important things the Sandbox ColLABorative does is disprove and dismantle ideas. What does he mean by this? Why do you think its so important to the work?

4. What problem does the college by subscription model aim to solve? How does the proposed model address this problem?

5. How might a virtual teaching assistant improve students educational experiences? What gaps in the current system is it trying to fill?

6. What are the issues with traditional academic transcripts? How is the interoperable learning record trying to address them?

7. Which of the innovations mentioned in the article are most intriguing to you? Why? Could you see potential challenges with adopting any of them?

Now that youve read about some of the ways innovators are trying to solve problems in higher education, youll continue the process of design thinking to come up with some of your own:

Step 3: Ideate. This is the fun part. What creative, out-of-the-box solutions can you come up with to solve the problems you identified at the beginning of this lesson? What would your ideal schooling experience look like? How could you restructure the school day, reimagine the curriculum or use virtual reality, artificial intelligence or even technology that hasnt been invented yet to improve education? Brainstorm a list of as many ideas as you can think of, no matter how impossible they may seem.

Steps 4-5: Prototype and Test. If you have time, you might imagine how you could produce a scaled-down version of one of these ideas. What steps would you take to test it out with real students? How might your proposal make education better for everyone? What potential challenges might you encounter when putting it into effect?

Originally posted here:

Lesson of the Day: How Technology Is Changing the Future of Higher Education - The New York Times