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Jordan Peterson says Joe Rogan beats ratings of legacy media because ‘he doesn’t lie’ – Washington Examiner

Canadian psychology professor and author Jordan Peterson responded to ratings numbers showing that Joe Rogan's podcast has more than triple the number of views of legacy media prime-time shows such as The Rachel Maddow Show and Tucker Carlson Tonight.

In response to Q3 media ratings posted to Twitter, Peterson argued that the reason Rogan's show beats cable talk shows in the ratings is because he "doesn't lie."


"That's because he doesn't lie. Or talk down to his audience. Or manipulate for his own narrow advantage," Peterson tweeted. "Go @joerogan. See you in three weeks in Austin."


The ratings chart, based on data from Nielsen Holdings and Spotify, shows that Rogan's show is ranked No. 1 with an average of 11 million viewers per show, followed by Tucker Carlson Tonight, with 3.24 million per show, and The Five, with 2.98 million viewers per show.


Rogan often has controversial guests on his show. He recently had the embattled Dr. Robert Malone, a contributor to the research that created mRNA vaccine technology, on his show to discuss the potential risk involved with the coronavirus vaccine as well as alleged malpractice by the Centers for Disease Control and Prevention, Pfizer, and other health authorities in relation to the COVID-19 response.

Malone was banned from Twitter after making posts that could have promoted vaccine hesitancy.

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Data Science Is the Future. Let’s Start Teaching It (Opinion) – Education Week

As the coronavirus has infected millions of Americans, the news media have become saturated with numbers: new infection cases, hospitalization rates, death tolls, and vaccine trial results. Many Americans have been overwhelmed, and in part because too few of us are comfortable with data, we have been susceptible to a plague of misinformation.

Most Americans dont have the skills and knowledge to work with data, despite their critical importance to understanding our world and making informed decisions. This data illiteracy must change, and our education system needs to prioritize data-science education for all students.

Technically speaking, data science is nothing new. Scientists, businesses, and governments have long collected and interpreted data and used it as a basis for decisionmaking. But two recent changes have made data science much more relevant to all of us. The first is an explosion in the availability of data, fed by smartphones and the internet. The second is a dramatic improvement in the quality of software tools for analyzing that data.

Despite being commonly misunderstood as a skill relevant only to technical roles, the rise of data science has had huge impacts in almost every field, from football to art history. This sea change presents many opportunities, and skills in analyzing and interpreting data can give young people access to new career opportunities. The employment-information website Glassdoor, for example, ranked data scientist as the second best job for 2021 based on openings, compensation, and job satisfaction. Even for those who dont pursue data science as a career, many, many working adultsnurses, salespeople, journalistsneed data skills.

More importantly, data use is a practical skill that makes education more relevant. When I wrote Freakonomics, I employed data to explore topics as diverse as sumo wrestling, real estate, and the drug trade. Similarly, educators can engage students by having them analyze data on topics that interest them like crime, the border crisis, global development, or climate change.

In many ways, this is a plea for educational pragmatism. Our world has been revolutionized by information technology, yet our K-12 curriculum is still trapped in the industrial age. Instead of teaching our young people obscure trigonometric techniques, lets help them learn how to interpret the huge amounts of data being produced every day in our hyperconnected world.

So what needs to be done? Reforms should continue along a number of tracks. First, education policymakers at the state and district levels can modernize the curriculum in mathematics and other disciplines, especially in high schools, to stress data science and computational fluency; a dozen states are already starting that work. Second, universities need to change their admissions policies to accept data-science coursework as evidence of rigorous mathematics preparation. Third, federal and state policymakers should increase funding to equip educators with the tools and training necessary to teach this material effectively.

There are already signs of progress. Some organizations, like CourseKata and Bootstrap, are exposing students to powerful tools for data analysis and equipping them with the skills to do real analysis and report on their findings. CourseKata has developed a full data-science course curriculum, and Bootstrap offers flexible modules that can be incorporated across disciplines. The U.S. Department of Educations Institute of Education Sciences is helping to spur change by including data-science efforts in its grantmaking.

But much more needs to be done, which is why my team at the University of Chicagos Center for Radical Innovation for Social Change launched the Data Science for Everyone Coalition to mobilize and convene an active community, spark policy reform, and expand access to resources that will catalyze the expansion of data-science education in K-12 schools. In the next two years, the coalition expects to grow to 3,000 members, including teachers, parents, administrators, and policymakers.

At its heart, this is a grassroots campaign. That means engaging parents at the local level about the importance of learning data scienceand connecting with educators in schools across the country, who need more support in teaching data science. So far, members of the coalition have posted some important victories.

Coalition members are already increasing access to high-quality data-science education. For instance, the San Diego school district has committed to rolling out data-science education across P-12 by 2023, which will impact 120,000 students across 168 schools. The District of Columbia school system is partnering with American University to offer teacher training at the undergraduate and graduate levels. The Stanford Graduate School of Educations teacher education program (known as STEP) is launching a new preservice teacher education course on teaching high school data science that is responsive to multiple disciplines. And companies like DataCamp, which provides data-science instruction online, and Tableau, an analytics platform, are offering their software for free to teachers and students.

These organizations are bravely pioneering a new data-based math future, and we all need to fully commit the resources required to make it happen. Lets build a math curriculum together that engages students more fully, prepares them for successful careers, and equips them to be good citizens.

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Data Science is Helping the World Recover from Covid Loss – Analytics Insight

Data science has been improving in many ways in order to help recover the world from covid loss

Data science has become a powerful weapon that is helping the world to recover from covid losses. COVID forced companies to make a full model jump to match the dramatic shift in daily life. Models had to be rapidly retrained and redeployed to try to make sense of a world that changed overnight. Many organizations ran into a wall in data collection but others were able to create new data science processes that could be put into production much faster and easier than what they had before. From this perspective, data science processes have become more flexible. On the flip side, there is another segment of the population that experienced (and continues to experience) economic difficulties as a result of the pandemic. This skews economics, as millions of people are attempting to climb back up to the standard of where they were pre-COVID. People who previously would have played a sizable role in economic models are effectively removed from the equation for the time being.

Data science, in principle, is a very powerful source of technologies that help each of the businesses. Data scientists are big data wranglers, gathering and analysing large sets of structured and unstructured data.

Data science technology, on a basic level, is an extremely strong wellspring of advances that help every one of the organizations. Information researchers are enormous information wranglers, assembling and examining huge arrangements of organized and unstructured information.

Various Approaches to Recover World from Covid Losses:

1. Convey a computerized operational hub: Digital operational hubs that go about as a basic connection between digitalized tasks, cycles, and resources, momentary functional proficiency, and long-haul system have turned into a vital ability during COVID-19. They permit organizations to assemble assets, for example, new information sources and investigation frameworks, to empower business groups to examine arising patterns all the more rapidly, abbreviate input cycles, and acquire knowledge into potential results.

2. Embrace constant information: Monitoring ongoing information from sites, online media, clickstreams, and portable applications has become progressively significant lately. A pioneer no longer has the advantage of sitting tight days and weeks for the most recent data. Different advancements, including informing stages and stream-handling capacities, empower continuous information handling and investigation; the utilization of the half and half cloud permits chiefs to react in hours rather than days or weeks.

3. Focus on social moves: The pandemic showed numerous pioneers that their associations could be more dexterous than they understood they were during an emergency. A developing number of interdisciplinary groups, nimble working techniques, and information-driven mentalities have grown, for the time being, making profoundly designated and productive investigation capacities. Keeping the force going will require developing these movements , for example, reskilling laborers. Such work is as yet conceivable while representatives work from a distance. As a feature of its groundwork for the future, one monetary administrations organization utilized Zoom video preparing to show senior leaders AI ideas, ways of utilizing the innovation, and ways to execute change. Associations can be more precise and quicker at anticipating the changing requirements of their client networks by having an assorted labour force.

4. Embrace an agreeable plan: Analytical advancement: groups can upgrade hazard the board and identification with different exercises and devices, permitting them to incorporate basic oversight into the interaction. For instance, reported rules, agendas, and preparing materials are accessible to set up assorted groups, use hazard measurements, and keep steady over changes, like changes in approaches, laws, and guidelines. Exercises remember putting for place techniques and information apparatuses for distinguishing and moderating danger in information and observing models.

There is no ideal opportunity for carelessness or sentimentality in this new world. What was once ordinary cant be re-established; neither danger nor opportunity is little in this new time. To manage consistent vulnerability, interruption, and steadily evolving conditions, pioneers should get ready associations to flourish in this new climate.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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CIOs and CDOs Strengthen Their Relationship – CMSWire

PHOTO:Jan Antonin Kolar | unsplash

It's taken some time, but CIOs and CDOs (chief data officers) now understand the need for a strong working relationship. Data has become the driver behind digital transformation and better business decision-making. For CDOs and CIOs to succeed, they must succeed together. To get this partnership right will in part require CDO reporting and chartering and the creation of shared goals, objectives and key results (OKRs) and key performance indicators (KPIs).

CDOs have been around nearly 10 years. Half of the Global 500 have a CDO and they are found in more than half of state governments in the United States as well as key elements of the Federal Government including being mandated by the Department of Defense. As the CDO function has promulgated across organizations, it has become clear CDOs have skills unique to those of the CIO. This includes skills around what Tom Davenport has labeled data defense and data offense. CDOs have been willing to take on topics like data governance, which CIOs historically shied away from, or in some cases, unfortunately led from an IT or top-down perspective.

CIOs have historically viewed CDOs as an additional bureaucrat who would gum up the works. CIOs also claimed CDOs would not be accountable for a total solution, but instead would leave it to the CIOs and architects to do all the work. However, this attitude has changed as the twin force of self-service business intelligence and digital transformation have brought data to the forefront of the enterprise agenda.

Related Article: Does Your Organization Need a Chief Data Officer? Probably

Recent discussions with CIOs show that for the most part, they have positive views towards the emergence of the CDO.

But CIOs appear to be splitting between two camps. Camp one includes CIOs like Anthony McMahon, who see CDOs as a good thing as long as they report to the CIO. Camp two also believes the CDO role is a great thing, but think it should be a separate function from the CIO. Both groups think the exact CDO charter depends on the CIO and whether they are well versed in data and analytics. Yet CIOs also believe the chartering of a CDO organization is a strong indicator there is a mandate for data and for sharing data across business organizations.

Camp one's argument for CIO ownership of the CDO function is that it's challenging to define responsibilities and expect collaboration between CIOs and CDOs when each function has different reporting structures. However, former CIO Isaac Sacolick suggests this thinking could be a trap: Unfortunately, there are still too many CIOs that know more about the boxes databases run on than the underlying data models and how the business drives competitive value from their data assets.

Camp two's perspective suggests CDOs should not have the same reporting line. CIO Paige Francis said, "I think a CDO role is a great thing and should be separate from the CIO. The data officer should focus more on defining what data to collect, how to best use it and then continuously improve that process in line with business fluidity." So who should CDOs report to in that case? Francis said it depends on, whoever will empower CDOs to best do their job. The CEO, CIO or COO Id think. So many leaders are at different maturity levels in our wildly different organizations. If they report to the wrong person, the value even having a CDO function may be lost. Regardless of reporting structure, CDOs need advocates at the executive leadership and board levels. CIOs and CDOs need to debate and decide a common vision, and the CIO should back the CDO's solutions.

Related Article: Chief Data Officer Enters v4.0: What Does That Mean?

What should be the division of labor? CIOs believe they should provide the infrastructure, the data stores, and the operations and support for data-centric IT. CIOs should be custodians of the data. The CDO on the other hand, should lead the stewards of the data, which often includes the CIO for IT data.

CIOs believe a CDO's prime directives should include governance, citizen data science, cloud data management, DataOps, data quality, data security and data science. CIOs are hoping CDOs provide perspectives on how to build great data and support for data protection (security, retention and compliance). And since CIOs do not typically have data science or operations research backgrounds, they are looking to CDOs to know how to get value out of the corporate investment in data scientists.

CIOs want CDOs to have a broad mandate across organizations and functions to identify, gather and help place value on data. This is about more than getting more from existing data, it is about determining options for new data collection.

CIOs think it's a bad idea for them to back away completely from the data agenda there is a difference between managing data and engineering data. Data modeling is hard to do without great data and it's hard to unify how people access and use data resources without a well-designed data architecture. This is where CIOs and enterprise architects can help.

Given this, CIOs should focus on technical data while CDOs focus on business data. However, CIOs want CDOs to have broad and sometimes overlapping data expertise. This includes management, analytics and higher-level data management. They want CDOs to have the time to be able to focus on all things data. CIOs say they would also like CDOs to have a strategic perspective on how to change data culture, how data can be used to move an organization forward, and how to implement privacy and ethics.

Related Article: The Rise of the Chief Data Officer

For CDOs to be successful, coordination is essential. CIOs hope CDOs become the ultimate intermediary to assure that new data silos are not created, and data is used for insight, innovation and revenue. Additionally, CIO have always worn too many hats, so having someone managing the data agenda is good for their businesses. Francis said, "The CIO and CDO should bolster each other." CIO David Seidl added, They should plan strategy together, like other key connections at this level, it is critical. Trust and shared investment in efforts, as well as leveraging governance, so what each does is aligned is also critical. In this process, Seidl says, there needs to be integration and shared sprints and planning. When this doesnt occur, everyone loses.

Enterprise strategies today depend upon CDOs and CIOs working together. Data is essential to timely decision-making and represents the fuel that enables the automation from digital transformation. For many established business franchises, getting data right represents a do or die moment. According to MIT-CISR research, 51% of companies still have their data locked away in silos. And 21% have their data integrated with the digital equivalent of duck tape and band aids. Only 28% have transitioned to being truly digital competitors.

For organizations not in that 28%, successful collaboration between CIOs and CDO is essential. But how do they do this? Here are five ideas on how CIOs and CDOs can work together and ensure their teams build the basis for being digital winners.

CIOs today have a big agenda in digitizing and integrating their companies. Having an expert data leader to take on the data agenda allows the CIO to become a critical change agent for digitalization. Meanwhile, CDOs need to ensure that AI and data are adopted. Doing this right creates a business advantage. So, CIOs should take on the transformation agenda and allow CDOs to manage the creation of, access to, and automation of data.

Myles Suer, according to LeadTail, is the No. 1 leading influencer of CIOs. Myles is director of solutions marketing at Alation and he's also the facilitator for the #CIOChat.

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Geoscience expert to study why continents break apart where magma is missing – EurekAlert

image:D. Sarah Stamps view more

Credit: Mike Lee for Virginia Tech

The Earths surface is ever incrementally moving and changing shape, breaking apart and forming new land masses and oceans. In the billions of years of history of planet Earth there have been 10 supercontinents, the most famous and recent being Pangaea breaking apart about 175 million years ago.

Africa itself is slowly separating into several large and small tectonic blocks along the diverging East African Rift System, which includes Madagascar the long island just off the coast of Southeast Africa that itself will also break apart into smaller islands. The culprit is the regions rich and deep intrusions of magma. Yet, Africa is also seeing continental rifts, the separations, in areas where there is no evidence of magma intrusions. These types of continent rifts are known as magma-poor or dry rifts. In short, if this were a mystery the culprits identity is unknown.

D. Sarah Stamps, an associate professor in the Department of Geosciences, part of the Virginia Tech College of Science wants to put her expertise in continental rifting to find the villain. Stamps recently was awarded a $3 million National Science Foundation grant for the DRIAR project (thats short for Dry Rifting In the Albertine-Rhino Graben, Uganda) to help spur her efforts.

You can think of the breakup of eastern Africa as the continuation of the breakup of Pangaea, said Stamps, leader of the Geodesy and Tectonophysics Laboratory. Eastern Africa is actively breaking up, and if it continues, well see new oceans forming. In the northern parts of East Africa, like in Ethiopia and the Afar region, its already extended to the point of forming baby oceanic areas. The spreading has already created new oceanic crust. The land is subsiding, and the first stages of new ocean basin formation is underway.

Further south in the central East African Rift System, the breakup of the continent is less intense. This is where Stamps has spent much of her research career. For this effort, Stamps is leading a large team of experts. From the U.S., her collaborators come from Woods Hole Oceanographic Institute, the University of Kansas, Northwestern University, the University of California, Davis, and Midwestern State University in Texas. In Uganda, the team is working directly with the governments Ministry of Energy and Mineral Development and with Makerere University in Uganda.

This team and I are very interested in understanding the physics of how a continent can break apart when there's no surface expression of magma as volcanoes, Stamps said.The team will focus on the Northern Western Branch of the East African Rift System located in Uganda, East Africa where magma-poor rifting is taking place. A wide range of geophysical, geological, and geochemical observations will be collected, and numerical modeling of the region will be performed to understand how the magma-poor rifts form and evolve.Among the answers Stamps and her collaborators seek to answer: In magma-rich rifts, is strain accommodated through lithospheric weakening from melt?; In magma-poor rifts, is melt present below the surface weakening the lithosphere such that strain is accommodated during upper crustal extension?; And in magma-poor rifts, what if there is no melt at depth and strain is accommodated along fluid-filled faults or pre-existing structures such as inherited compositional, structural, and rheological lithospheric heterogeneities?I hope there will definitely be impacts on our understanding of the physics of continental rifting, Stamps said. But we also have a lot of broader impacts with respect to capacity building in Uganda. So, were going to conduct field schools in Uganda to teach people how to use the equipment and analyze the data.

Working with Stamps are three scientists, a Ph.D. student in geosciences and a native of Uganda, Asenath Kwagalakwe, and two undergraduate students from the Academy of Data Sciences computational modeling and data analytics program, Esha Islam, a third-year student, and third-year student Crystal Lee. The Academy of Data Science is also part of the College of Science.I am working on the Albertine-Rhino Graben, which is the northernmost rift in the Western branch of the East African Rift System. My research interests are in investigating the physics of strain accommodation in the magma-poor Albertine-Rhino Graben of the East African Rift System using geodynamic modeling and GNSS [Global Navigation Satellite System] geodesy, said Kwagalakwe.Islam, for her part, took an elective geosciences course, and greatly enjoyed Stamps presence as a professor in the classroom. Islam asked Stamps about research opportunities. Data science is very flexible in what it can be applied to and coding is used in most STEM-related fields, so even though I didnt have any notable geoscience background, Dr. Stamps was willing to offer me a spot, she said.Currently, my job is to rerun test models of other graduate students to determine that we all get the same results.

Added Lee, I was brought into the project through my friend, Esha Islam, who has been working with Dr. Stamps for some time and is also a peer in my major. I was interested in joining the project when she talked to me about it because I wanted to expand upon my experience with data processing and modeling. Lee will be analyzing GNSS data collected in Uganda.Among the benefits from the study, in addition to better understanding continental rifting, Stamps points to improving estimates of carbon dioxide transfer into the atmosphere that occurs during continental rifting, advancing rifting models used for exploring natural resources, and creating new insights into seismic hazards associated with active faulting.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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68% of CTOs have Implemented Machine Learning at their Organization – insideBIGDATA

55% of businesses now employ at least one team member dedicated to AI/ML solutions, although only 15% have their own separate AI division

Research fromSTX Next, Europes largest software development company specializing in the Python programming language, has found that 68% of chief technical officers (CTOs) have implemented machine learning at their company. This makes it overwhelmingly the most popular subset of AI, with others such as natural language processing (NLP), pattern recognition and deep learning also showing considerable growth.

Despite the popularity of AI and its various subsets, its also clear that AI implementation is still in its early phases and theres progress to be made in recruiting the talent needed for its development. In fact, 63% of CTOs reported that they arent actively hiring AI talent and of those that are, over 50% report facing recruitment challenges.

The findings were taken from STX Nexts 2021 Global CTO Survey, which gathered insights from 500 global CTOs about their organizations tech stack and what theyre looking to add to it in the future. Other key findings from the research included:

ukasz Grzybowski, Head of Machine Learning & Data Engineering at STX Next, said: The implementation of AI and its subsets in many companies is still in its early stages, as evidenced by the prevalence of small AI teams.

Its unsurprising to see machine learning as a definite leader when it comes to future technologies as its applications are becoming more widespread every day. Whats less obvious is the skills that people will need to take full advantage of its growth and face the challenges that will arise alongside it. Its important that CTOs and other leaders are wise to these challenges, and are willing to take the steps to increase their AI expertise in order to maintain their innovative edge.

Deep learning is a good example of where there is plenty of room for progress to be made. It is one of the fastest developing areas of AI, in particular when it comes to its application in natural language processing, natural language understanding, chatbots, and computer vision. Many innovative companies are trying to use deep learning to process unstructured data such as images, sounds, and text.

However, AI is still most commonly used to process structured data, which is evidenced by the high popularity of classical machine learning methods such as linear or logistic regression and decision trees.

Grzybowski concluded: To adapt AI to unstructured data, the technology will need to mature further. This is why initiatives such as MLOps have a major role to play, as long-term success will only be achieved when data scientists and operations professionals are all on the same page and fully committed to making AI and machine learning work for everyone.

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The best job in America pays up to $125,000 a year and has 10,000 job openings – MarketWatch

Most people want to find a job that keeps up with inflation and provides some level of work-life flexibility, but they also want to be happy. After all, most Americans spend at least eight hours a day working and often without paid time off.

Its the $125,000 question in an increasingly unpredictable labor market: How can you have it all? Is there a career that comes with the prospect of a six-figure income, high job satisfaction and has enough job openings to make it a real possibility?

Workers quit their jobs at record pace in November, suggesting that people are fed up. The number of quits increased by 370,000 to a record 4.5 million in November.The quits rate rose to 3% from 2.8% in October.

At the same time, job openings fell by 529,000 to 10.6 million on the last day of November,the Labor Department said this week. Economists polled by The Wall Street Journal had forecast a gain to 11.1 million vacancies.

Companies are always keen to use intel to improve efficiency and learn more about their customers and, so, computer scientists are in high demand. They are also one of the privileged professions to have the opportunity to work remotely.

Java developers were No. 1 on Glassdoors 50 Best Jobs in America for 2021. They typically work at startups focused on the creation of web applications to market and to fill existing customer orders, the careers website said.

They boast a salary range of $69,000 to $125,000 and have a median base annual salary of more than $93,000. They had a 4.2 out of 5.0 job rating, and there are approximately 10,103 job openings for Java developers.

Java developers ideally have abachelors degree in computer science with a professional IT certification, and are required to have expert level Java programming, plus experience in database management and computer architecture.

They were followed by data scientists at No. 2 ($113,736 median annual base salary) and product managers at No. 3 ($121,107), enterprise architects at No. 4 ($131,361) and devops engineers at No. 5 ($110,003).

Data scientists and software developers use programming language such as Python, followed by R, SQL, Hadoop and the more well-known Java. Product managers are responsible for the strategy and blueprint of a product or product line.

An enterprise architect is responsible for a companys entire IT infrastructure, while a devops engineer is proficient in both engineering and coding, and creates and implements systems software, and improves existing systems.

These computer scientist positions are in high demand across all industries, career consultants say, particularly at Silicon Valley companies such as Meta FB, -0.20%, Alphabet GOOG, -0.40% GOOGL, -0.53% and Microsoft MSFT, +0.05%, among many others.

The Glassdoor Job Score is determined by weighing three factors equally: Earning potential (median annual base salary), overall job satisfaction rating and number of job openings. C-suite and intern level jobs were excluded from this report.

For a job title to be considered, it must receive at least 100 salary reports and at least 100 job satisfaction ratings shared by U.S.-based employees in one year. Results represent job titles that rate highly among all of those three categories.

Computer scientists have competition. A separate report by theU.S. News & World Reporton the best jobs of the year lists physician assistant as No. 1 ($112,260 median salary and 39,300 openings). Software developers were No. 2.

That reports definition of best jobs may sound familiar: They pay well, challenge us year after year, match our talents and skills, arent too stressful, offer room to advance throughout our careers, and provide a satisfying work-life balance.

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Is the omicron variant less severe? And what are its symptoms? : Shots – Health News – NPR

A commuter masks up for a bus ride in Liverpool, England. The omicron variant of the coronavirus has surged in the U.K. and is now dominant in the U.S. as well. There's now data indicating just how severe its symptoms might be. Paul Ellis/AFP via Getty Images hide caption

A commuter masks up for a bus ride in Liverpool, England. The omicron variant of the coronavirus has surged in the U.K. and is now dominant in the U.S. as well. There's now data indicating just how severe its symptoms might be.

When it was discovered, omicron alarmed scientists.

The variant looked wildly different from earlier versions of the coronavirus and it quickly became clear that these mutations gave omicron an uncanny ability to sidestep our vaccines and spread very rapidly.

But it has taken longer to untangle what, if anything, sets an omicron illness apart from that of its predecessors. And most of all, does this variant cause less severe disease than the variants that have come before it?

With infections at all-time highs in the U.S., the clinical picture is now coming together and starting to confirm what other countries have found a typical case of omicron not only presents slightly differently but also likely carries a lower chance of getting seriously ill.

Scientists at Case Western Reserve University have preliminary evidence that the risk of being admitted to the hospital or the intensive care unit during the omicron surge in the U.S. is about half of the risk observed during the delta surge. And this reflects what doctors across the country are now seeing firsthand with their patients.

"This is a pretty different surge," says Dr. Brendan Carr, chair of emergency medicine for the Mount Sinai Health System where the emergency rooms are busier than ever but many of the COVID-19 patients are not sick enough to be admitted.

But as with any variant of SARS-CoV-2, your absolute risk depends on many factors, including whether you're vaccinated and boosted, your age, your overall health and your economic situation.

"In the older age group, it's still a nasty disease, even if it's less [nasty] than the delta variant," says Dr. Pamela Davis, who's a pulmonologist at Case Western Reserve University and a senior author on the new study. "You don't get off scot-free just because you happen to be infected in the time of omicron."

"While omicron does appear to be less severe compared to delta, especially in those vaccinated, it does not mean it should be categorized as 'mild,' " said the World Health Organization's director-general, Tedros Adhanom Ghebreyesus, on Thursday. "Just like previous variants, omicron is hospitalizing people and it is killing people."

Indeed, hospitalizations across the U.S. now stand at more than 126,000, and more than 1 in every 4 ICU beds is filled with a COVID-19 patient, according to the latest data from the Department of Health and Human Services.

What those hospital numbers don't tell us is what a typical case looks like.

As with previous variants, the vast majority of people infected with omicron have a mix of symptoms that resolve relatively quickly and don't require hospital care.

And doctors are finding many of these cases tend to look like an ordinary upper respiratory infection. In other words, what you think of as the common cold.

"It's mostly that runny nose, sore throat and nasal congestion," says Dr. John Vanchiere, the associate director of the Center for Emerging Viral Threats at LSU Health Shreveport. "The cough is milder [than previous variants], if there's any cough at all, and fever seems to be a little less common."

This fits with early data from the U.K. showing that fever and cough are not as prevalent with omicron cases there and that the five top symptoms are runny nose, headache, fatigue, sneezing and sore throat.

With omicron, the symptoms also come on more quickly once you're infected. Several studies have found that the incubation period the time it takes to develop symptoms after being exposed is about three days. In contrast, delta took about four days, and the original variant took more than five.

Another difference doctors are noticing: Loss of smell and taste considered a telltale sign of COVID-19 is not nearly as common with omicron infections. And fewer patients have symptoms related to lower respiratory problems, such as shortness of breath, says Vanchiere, including older patients.

At the same time, it appears anecdotally at least that certain symptoms show up more with omicron than they did with delta. Three that have gained attention are nausea, night sweats and lower back pain.

But it's very possible that doctors and patients are simply paying more attention to these symptoms than they did with earlier variants, says Dr. Scott Roberts, an assistant professor of infectious diseases at the Yale School of Medicine.

"A lot of this is probably magnifying these symptoms under a microscope instead of clear changes," he says. "Omicron versus delta are really more similar than they are different."

And just like earlier variants, omicron can't be defined as causing only a narrow group of symptoms. As at earlier stages in the pandemic, many patients are still having some combination of fever, gastrointestinal problems, aches and pains, brain fog, weakness and, less often, trouble breathing, says Mount Sinai's Carr.

"Omicron can present in a myriad of different ways," he says.

It's also still not clear how much vaccines and prior infections are responsible for some of these early clinical impressions that omicron is causing a milder constellation of symptoms, says Dr. Daniel Griffin, who's chief of infectious diseases at ProHEALTH in New York and an instructor at Columbia University.

"It just seems that people who have been vaccinated ahead of time are getting much milder symptoms across the board," he says.

This was the case even before omicron: People who had breakthrough infections tended to have fewer symptoms and milder ones than those who were unvaccinated.

With SARS-CoV-2, the big danger is that a mild illness will turn into a life-threatening one. Although that could definitely still happen with omicron, the risk appears to be lower than it was with delta.

A study published online on Jan. 2 provides some of the first compelling evidence from the U.S. that the chance of ending up in the hospital is lower with omicron compared with the delta variant.

Scientists at Case Western Reserve University analyzed health records from more than a half-million people infected with SARS-CoV-2 across the country, including 14,000 people possibly infected with omicron from Dec. 15 to 24, after the variant became dominant.

"In this period, we still have delta circulating in the community. But you're pushing more and more and more toward the omicron variant," says Davis, who contributed to the study.

Then the researchers looked to see if there was a difference between people infected during the end of the delta wave and those infected during the early stage of the omicron wave. "The difference was huge," says data scientist Rong Xu, who led the study and is also at Case Western Reserve University. "We didn't need to do any complicated statistics to see the difference."

Xu and her colleagues found that the risk of needing to go to the ER dropped from about 15% during the delta surge to 5% during the early omicron surge (about a 70% decrease) and the risk of being hospitalized dropped from 4% to 2% (or by 50%).

If a person did end up in the hospital, the person's risk of being admitted to the ICU or being put on a ventilator also decreased substantially at the end of December compared with during the delta surge. Specifically, the risk of being admitted to the ICU fell from 0.8% to 0.4% (or by 50%) and the chance of being put on a ventilator fell from 0.4% to 0.1%.

This lower risk with omicron is also consistent with what scientists have observed in South Africa and the United Kingdom.

Xu and her team estimate that, in their study, about 60% of the people were vaccinated. So some of this lower risk could be because of vaccination, but the data altogether suggests that there is a reduced risk for hospitalization with the omicron variant compared with the delta variant.

In particular, Xu and her team observed a similar reduction in risk across all age groups, including children under age 5, who are not eligible for vaccination, and children ages 5 to 15, who may have been vaccinated but haven't been boosted.

That consistency, Xu says, suggests the reduction in severity is due, in part, to something inherent with omicron itself and not simply because of changes in vaccination status.

"So this is really something that's different between omicron and delta," Xu says. That all said, this reduction in risk doesn't mean omicron will be mild for everyone. For people who are at high risk for severe disease, such as older people or those with underlying health issues, the chance of being hospitalized is still quite significant. For example, if you're over age 65, your risk of being hospitalized with COVID-19 is still 5% with the omicron variant, which means 1 in 20 people infected in this age group will end up in the hospital. (By contrast, with the original version of the virus, the rate was 1 in 10.)

"The risk is not zero," says Xu's colleague Davis, speaking of omicron. "Many people are still going to be admitted to the ICU, and some people are still going to need to have mechanical ventilation."

That's why, she says, everyone should be vaccinated and boosted. As with previous variants, being vaccinated greatly protects you from severe disease with omicron. A study from the U.K. government, published last week, found that three doses of vaccine cuts the risk of hospitalization due to omicron by about 80% compared with a person who's not vaccinated at all.

Even though early data shows that omicron is milder than delta, many hospitals are packed because the sheer number of people getting infected is enormous.

And doctors are finding a key difference among their patients who are ending up in the ER or being admitted: Many are neither struggling to breath nor dealing with perilously low oxygen levels.

Those two conditions were "a hallmark of the first disease and of delta and not nearly as prominent in omicron," says Mount Sinai's Carr.

In the past, it was basically a given that a severe case of COVID-19 would wreak havoc on the lungs, at times leading to pneumonia and uncontrolled inflammation. But this apparent change in the disease that a severe infection in the lungs doesn't seem as common means fewer people need supplemental oxygen or intubation.

"They're not short of breath, and really the lungs are OK," says Roberts, of Yale. These observations also line up with lab research that shows omicron does not replicate in lung tissue as well as delta.

Many of the patients who are being hospitalized often have some underlying health condition, or they're older and more vulnerable to a viral infection. "What we're seeing is something really tips these patients over the edge," says Roberts. For example, an omicron infection may lead to complications of an existing condition such as diabetes or heart failure.

But Roberts says it's still quite rare for people who are vaccinated and boosted to get seriously ill from omicron. About 80% of the patients at Yale New Haven Hospital are unvaccinated. And among those who are vaccinated, almost all have not received a booster shot.

While it's welcome news that omicron is easier on the lungs, ProHEALTH's Griffin says it's not that way for some of his patients. And among unvaccinated people, he says, an omicron infection can feel like the same unforgiving disease to him.

"If we have a patient who's younger, if we have a patient who's vaccinated, if we have a patient who recently recovered from delta, we're tending to see very mild disease with omicron," says Griffin. "But people who are fresh, with no preexisting immunity, it's hard to see that the virus is milder."

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New Opportunities: Top Deep Learning Jobs to Apply in Jan 2022 – Analytics Insight

Deep learning jobs are preferred highly in terms of salary, growth, and exploration

After artificial intelligence came into existence, many sub-technologies started emerging. Machine learning and deep learning are two important branches of AI that are invading every industry and serving their purpose to the best. Deep learning is a subset of machine learning that helps in analyzing datasets to improve real-time decision-making. In particular, deep learning works with unstructured data and builds effective AI models. Owing to its increasing usage, deep learning jobs are also put under the spotlight. According to a report, deep learning jobs are preferred highly in terms of salary, growth, and exploration. If you are interested in handling data, have a special talent in automation and machine learning, then deep learning jobs are for you. By gaining deep learning knowledge, aspirants can apply it for a variety of professions like machine learning engineering, data scientists, business intelligence developers, etc.

Analytics Insight has listed the top deep learning jobs that interested candidates should apply for in January 2022.

Locations: Bengaluru

Roles and Responsibilities: As an edge AI deep learning software engineer at Intel, the candidate should conduct design and development to build and optimize deep learning software. He/she should implement various distributed algorithms such as model/data-parallel frameworks, parameter asynchronous data communication in deep learning frameworks. They should transform the computational graph representation of neural network model. The candidate should develop deep learning primitives in math libraries.

Apply here for the role.

Locations: Bengaluru

Roles and Responsibilities: At Qualcomm Technologies, the deep learning compiler specialist will be working on researching and developing an in-house framework and also open-source compiler frameworks with TVM. He/she is responsible for optimizing the deep learning models from various frameworks like TensorFlow, PyTorch, Onnx, etc to Aderno, GPU. They should do researches to enhance the performance of these compiler frameworks by enhancing them with Qualcomm proprietary extensions. The candidate should be contributing to open-source communities.

Apply here for the job.

Locations: Bengaluru

Roles and Responsibilities: As a technical expert at Siemens Limited, the candidate can move beyond theoretical models and build innovative, practical, and robust real-world solutions for computer vision-based applications in smart mobility, intelligent infrastructure, and autonomous systems. He/she should develop strategic concepts and engage in technical business development to address new markets with the companys business units using video-analytics-based technologies. They should demonstrate the ability to drive innovation and research in the form of patents and publishing at top-tier conferences/journals.

Apply here for the job.

Locations: Bengaluru

Roles and Responsibilities: As a senior systems software engineer, the candidate will be responsible for developing and maintaining software drivers for next-generation NVIDIA hardware. He/she along with other specialists in the team will help advance the companys leadership in applying deep learning to tackle real-world problems in the autonomous driving domain. As a member of the team, the candidate should architect, design, and implement a user-mode compiler for the deep learning accelerator for both Tegra and open-source DLA.

Apply here for the job.

Locations: Bengaluru

Roles and Responsibilities: The deep learning researcher is expected to research and develop DL models from the ground up for new use cases, based on set business objectives. The candidate should identify datasets for the problem, data cleansing, analysis, and visualization. He/she should evaluate different machine learning algorithms to solve a given problem.

Apply here for the job.

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PhD Candidate in Data-driven Load Disaggregation and Building Load Profile Classification job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY -…

About the position

The Department of Electric Power Engineering (IEL), at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), invites applications for a PhD Candidate position in Data-driven load disaggregation and building load profile classification. This PhD position is associated with the Electricity Markets and Energy System Planning (EMESP) research group at IEL and a part ofCOFACTORproject funded by the Research Council of Norway.

Electrification of all enduse sectors, including transportation and buildings, is identified as a key enabler in the transition to a sustainable energy system. This will lead to increased electricity demand, increased peak load and modified consumption patterns, challenging traditional grid planning practices.

COFACTOR project will provide new knowledge on current and future load profiles for typical Norwegian buildings and establish a methodology for calculating their peak load considering coincidence factors of different energy services. The project will collect a comprehensive data set of detailed energy measurements for approximately 400 buildings. However, most Norwegian buildings have meter values for hourly total net energy use, but detailed measurements of energy use separated on different energy services behind the meter are usually not available.

COFACTOR will develop new data-driven methods for load disaggregation. Detailed knowledge on disaggregated load profiles behind the meter provides the basis to evaluate how these technologies interact and how buildings can become active nodes in the electricity grid, thus playing a key role in the transition to a sustainable energy system.

In this context, the main task of the PhD candidate in the doctoral work will be two-fold. First, to develop a modeling framework and methodologies for load disaggregation to specially account for energy demand services and energy technologies (domestic hot water, space heating, EV charging, PV panels, other appliances, etc.) using advanced statistics and machine learning. Second, to use disaggregated load data for classifying building load profiles and providing insights into the relationship between different buildings categories and their energy demand services.

The prospective candidate will be part of theElectricity Markets and Energy System Planning (EMESP) research group.The candidate will also cooperate closely with the Cofaktor research team. The PhD candidate will have the opportunity to collaborate with researchers in project partner institutions and benefit from collaborative research and education activities.

The duration of the PhD employment is 3 years.

Main supervisor: Associate ProfessorJayaprakash Rajasekharan- NTNUCo-supervisor: Associate ProfessorKaren Byskov Lindberg- NTNUThe position's working place is NTNU campus in Trondheim.

The candidate will report to the Head of the Department at IEL.

We are looking for PhD candidates from all nationalities, who want to contribute to our quest to create knowledge for a better world.

Duties of the position

The candidate is expected to work towards

Required selection criteria

In addition, the candidate must have:

Expertise in following technical areas is an added advantage

The appointment is to be made in accordance with the regulations in force concerningState Employees and Civil ServantsandRegulations concerning the degrees ofPhilosophiaeDoctor (PhD)andPhilosodophiaeDoctor (PhD) in artistic researchnational guidelines for appointment as PhD, post doctor and research assistant

Preferred selection criteria

Personal characteristics

We offer

Salary and conditions

PhD candidates are remunerated in code 1017 and are normally remunerated at gross from NOK 491 200 per annum before tax, however it may be negotiable (increased) depending on high level of qualifications and research experience of the candidate. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is3years.

Appointment to a PhD position requires that the candidate is admitted to the PhD programme inElectric Power Engineeringwithin three months of employment, and that candidate participates in an organized PhD programme during the employment period.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

It is a prerequisite you can be present at and accessible to the institution daily.

About the application

The application and supporting documentation to be used as the basis for the assessment must be in English. Please note that applications are only evaluated based on the information available on the application deadline. Application must clearly highlight the candidates skills and experience that meet the criteria set out above.

Please submit your application electronically via Jobbnorge website. Applications submitted elsewhere/incomplete applications will not be considered. Applicants must upload the following documents within the closing date:

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability. Incomplete applications will not be assessed.

NTNU is committed to following evaluation criteria for research quality according toThe San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background.

The city of Trondheimis a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.

As an employeeatNTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

According to the Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.If you have any questions about the position, please contact XXX, telephone XXX, email xxx. If you have any questions about the recruitment process, please contact xxx, e-mail: XXX

Please submit your application electronically via with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma fromChina Credentials Verification (CHSI).

If you have any questions about the position, please contact Associate Prof.Jayaprakash Rajasekharan( If you have any questions about the recruitment process, please contactBodil Wold(

Application deadline: 04.02.2022

NTNU - knowledge for a better world

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Electric Power Engineering

The Department of Electric Power Engineering is one of the seven departments at the Faculty of Information Technology and Electrical Engineering. Our department is Norways leading in the field, and our vision is to be at the centre of the digital, green shift. We have excellent collaboration with business and industry as well as other universities and research organizations internationally. This gives us outstanding opportunities for interdisciplinary research with high relevance for the society, addressing industrial needs and global challenges.

Deadline4th February 2022EmployerNTNU - Norwegian University of Science and TechnologyMunicipalityTrondheimScopeFulltimeDurationTemporaryPlace of serviceNTNU Campus Trondheim

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PhD Candidate in Data-driven Load Disaggregation and Building Load Profile Classification job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY -...

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