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At LI hospitals, the artificial intelligence revolution has already begun – Newsday

The words "artificial intelligence" evoke a futuristic world, but at certain Long Island hospitals, the future is here and now.

At some hospitals, nurses track the severity of patients symptoms with help from artificial intelligence, a broad term that encompasses computer programs that can be fed huge volumes of data and trained to analyze new data.

Others use A.I. to predict which patients are at risk of becoming ill again because they dont follow instructions after theyre discharged, or those who are healthy enough to be allowed to sleep through the night instead of being awakened to have their vital signs checked. Still others use the technology to speed the analysis of sleep studies that help diagnose conditions such as sleep apnea.

The ventures vary widely in their origins, scope and funding. One is a new company called Truveta, formed in an unusual alliance between New Hyde Park-based Northwell Health and 19 other health systems across the country. The company, which recently announced $200 million in new private funding, pulls information from millions of the networks patient records anonymized to protect confidentiality and provides real-time analysis to health care providers.

Northwell Healthhas joined forces with 19 other health systems to start acompany called Truveta, which recently announced $200 million in new private funding from its member networks and its CEO, Terry Myerson. Using information from millions of the networks anonymized patient records, the company provides real-time analysis to health-care providers.

NYU Langone Hospital-Long Islandin Mineola has launched an A.I.-powered program that tracks COVID-19 patients vital signs, lab results and other information, recording17 data points every 30 minutes to detect signs of potential deterioration.

Mount Sinai South Nassau in Oceansideuses A.I. to detect patients' risks of falling or becoming severely ill, and to predict how much nursing care they will need.

Stony Brook University'sDepartment of Biomedical Informaticshas received more than $5 million in federal grants to research the potential use of A.I. in diagnosing and treating cancer.

Catholic Health uses A.I.to analyze patients' brain waves, breath patterns, cardiac signals, leg movements and other data points recorded during sleep studies, speeding up the completion of reports that are reviewed by board-certified physicians.

Sources: Northwell Health, NYU Langone Health,Mount Sinai South Nassau,Stony Brook University,Catholic Health

Northwell sees "revolutionary potential" in A.I., Dr. Martin Doerfler, Northwells senior vice president of clinical strategy and development, said in an interview, "and we wanted to be part of it."

On a different scale, another new program got its start on a local nurses laptop during the coronavirus surge last year. After months of research and development, it evolved into an A.I. tool that flags COVID-19 patients at NYU Langone Hospital-Long Island who are at high risk of becoming severely ill in the next 12 hours.

The A.I. program "doesn't take over your decision-making and it never should," said Jeanmarie Moorehead, senior director of operations at the Mineola hospital. "But it is definitely value-added, tremendous value-added to the clinician."

What the A.I. efforts have in common is an ambitious effort to use specialized computer programs to comb through columns of data too vast to be understood by a human being, detect patterns and use that information to guide health care providers in diagnosing and treating patients.

The use of A.I. in health care is on the rise, with global funding in the sector reaching $8.5 billion from January through September nearly double the amount in all of 2019, according to CB Insights, a company that tracks A.I. investments. The United States was the biggest spender, with investments in A.I. in health care totaling $5.45 billion from January through September, the company reported.

Health care technology, including A.I., "is clearly seeing an increased level of investment," especially over the last year and a half, said Peter Micca, a partner and national health tech leader with Deloitte & Touche LLP in Manhattan. "COVID has only accelerated the awareness around the importance of technology in health care."

One hurdle is that, in contrast with industries such as finance and social media, health care data "is completely fragmented," Doerfler said. "We need to know the answers that are hidden inside the fragmented data, and you don't get the answers until you get the data sets large enough that you can find the answers quickly."

Incomplete data sets often lack diversity of race, gender, socioeconomic status and other characteristics, and overrepresent middle-aged white men with health insurance, Doerfler said. By contrast, said Terry Myerson, Truveta's CEO, the data set drawn from its 20 networks represents 16% of all clinical care provided in the United States and reflects "the diversity of our country."

The goal of Truveta, Myerson said, is to "empower our clinicians to be experts" and "help families make the most informed decisions about their care."

Some industry analysts warn of potential pitfalls in the adoption of A.I. At the annual conference of Stony Brook University's Center of Excellence in Wireless and Information Technology this month, Daniel Holewienko, executive director, big data and business intelligence at Henry Schein in Melville, said failing to embrace A.I. would put health care companies "at a competitive disadvantage."

Still, he said, those adopting the new technology can face high costs and difficulties integrating it into their current systems, among other challenges. Protecting privacy, preventing bias and making sure clinicians do not place excessive faith in the machines are among the other concerns, health care providers say.

Dr. Joel Saltz, founding chair of the Department of Biomedical Informatics at Stony Brook University, said the industry has proceeded cautiously in adopting A.I. The advanced technology has become more widely used in the last five years or so, he said.

"These things are incremental, especially in health care, because you've got to make sure they're safe and effective," said Saltz, who is working with colleagues on a project led by the federal Food and Drug Administration, focusing on the use of A.I. in digital pathology. Such tools, he said, are used for "decision support," to aid doctors and nurses rather than replace their work.

Stony Brook's biomedical informatics department is working on three projects funded by more than $5 million in federal grants to research the potential use of A.I. in diagnosing and treating cancer. An A.I. program can examine hundreds of slides and analyze millions of cells, complementing doctors' ability to visually classify tumors, Saltz said. "Think about the difference between a paper map and Google Earth," Saltz said. "It really opens up a whole new way of doing things."

It's possible that some of the research could be put into clinical practice within 10 years, he said.

In some cases, the COVID-19 crisis has sparked innovation by doctors, researchers and nurses as they raced to understand the new virus and find ways to save patients lives. Nurses have been key players in using and, in at least one case, helping to develop the new technology.

At NYU Langone Hospital-Long Island in Mineola, for instance, computers are running a new A.I.-powered program that keeps an eye on COVID-19 patients vital signs, lab results and other information, using patients' electronic medical records to monitor 17 data points every 30 minutes and detect signs of impending danger.

A paper version of the program was born of necessity during the first COVID surge in early 2020. At the time, nurse clinician Cathrine Abbate was seeking a rapid, consistent way to communicate with her fellow nurses and doctors about the severely ill patients suffering from a new and brutal virus.

On video conference calls before and after their shifts, Abbate and other nurses brainstormed about the warning signs that tended to precede a rapid decline in patients condition, such as needing large amounts of oxygen or not being able to eat or move. With that information, she used Microsoft Word to create a blank grid that she printed out at her home in Huntington Station. The grid included seven columns, tracking information about the patients condition. In the hospital, using copies of the grid made it easier for nurses to quickly rank the severity of each symptom and give an overall rating from 1 to 10, with 10 being the worst, she said.

"We needed to be able to fluidly communicate with each other about how the patients were doing," Abbate recalled. "It was just a way to create a language for ourselves."

Nurse manager Sarojini Seemungal helped implement the new system on the 30-bed unit, and alerted her own managers. Moorehead brought it to the attention of researchers at NYU Langone in Manhattan who specialize in analyzing data.

The researchers spent months meeting weekly with nurses and developing an A.I. program that provides information to a rapid response team of critical care nurses at the Long Island hospital who give special attention to the highest-risk patients, said Dr. Yindalon Aphinyanaphongs, director of operational data science and machine learning at NYU Langone Health.

The program acts as a "tireless monitor," taking information about thousands of previous patients including many whose conditions deteriorated and using it to predict whether current patients are likely to decline, he said.

Theres a lot of "hype" about A.I. and its subset machine learning, a term that refers to computers learning from examples, Aphinyanaphongs said.

"A lot of times when people think of artificial intelligence, they think of, you know, WALL-E," he said, in a reference to the 2008 animated movie about a lonely robot. But in fact, "the value in some of these models has to do with, not doing something better than humans, but doing things faster than humans can do," and more consistently, he said.

A tool like the one developed by the nurses and researchers, he said, can take a health care provider who has little experience with COVID, and it "can help elevate their experience and their expertise to the point where they're functioning at the same sort of assessment level as someone who has seen a lot of COVID patients."

The program can be downloaded for free by other hospitals that use the Epic electronic medical records system, Aphinyanaphongs said.

At Mount Sinai South Nassau in Oceanside, computers use A.I. to make sure patients receive precise, personalized care, taking into account the severity of their illnesses and other factors, said Stacey Conklin, chief nursing officer and senior vice president of patient care services. Those at higher risk of falls, for example, get extra help moving around if needed, she said.

A.I. "takes a lot of the subjectivity away from staffing, and allows us to really put the resources where they're needed most," Conklin said. "If I as a manager am trying to figure out where to put all of my resources, it's very helpful for me to be able to look broadly across the unit and see what's going on with all the patients so that I can ensure that the patients are getting the best care."

At the Catholic Health systems six sleep labs, A.I. is used to analyze the sleep studies of patients who spend the night hooked up to machines that record brain waves, breath patterns, cardiac signals, leg movements and other data points to diagnose conditions such as sleep apnea, said Brendan Duffy, director of sleep services at the network.

The data can fill hundreds of pages, and analyzing the information is "a very time-consuming, very meticulous" process that used to take one to two hours for each report, Duffy said.

Once the health system started using the A.I. program about three months ago, he said, that time was reduced to about 20 minutes, he said.

The new system means the sleep labs can get patients on the calendar for follow-up appointments more quickly, so patients spend less time driving while drowsy or suffering compromised immune systems due to sleep deprivation, he said.

But despite their remarkable efficiency, he said, the computers cant have the last word.

A board-certified physician reviews the sleep reports "each and every time, and that's nonnegotiable," he said.

At Northwells Feinstein Institutes for Medical Research in Manhasset, researchers used A.I. to analyze 24 million patient vital sign measurements. The results helped them predict which patients were low-risk enough to sleep through the night with a nurse looking in on them periodically, instead of being awakened to have their vitals checked, according to an article published last year in the journal Nature Partner Journals Digital Medicine.

The health system also is using A.I. to identify certain high-risk patients, said Dr. Jamie Hirsch, director of Northwells data science program.

In presentations about A.I., Hirsch tells his fellow physicians the technology can help identify people such as a fictional patient he has dubbed "Ethel," a sprightly 87-year-old grandmother who is "fiercely independent," but who feels overwhelmed in the hospital, lives alone and might need more assistance than she realizes.

In a busy hospital filled with hundreds of patients, a patient like Ethel might not get the hand-holding she needs, he said.

But when an A.I. program is trained to flag patients who are older, live alone and are coping with a bewildering array of medications and discharge instructions, he said, "now you have a patient experience specialist that's going to come in and say, How are you? Let's sit down, let's talk, you know, how can we make your experience better . How do we get you home, so you can continue living that independent life that you so value?"

He said, "It allows us to focus our energies in the right way, to the right person, at the right time."

Maura McDermott covers health care and other business news on Long Island.

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Email Encryption Market Research Report by Type, by Component, by Industry, by Deployment, by Region – Global Forecast to 2026 – Cumulative Impact of…

Email Encryption Market Research Report by Type (Boundary Email Encryption, Client Plugins, and End-to-End Email Encryption), by Component (Services and Solution), by Industry, by Deployment, by Region (Americas, Asia-Pacific, and Europe, Middle East & Africa) - Global Forecast to 2026 - Cumulative Impact of COVID-19

New York, Nov. 24, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Email Encryption Market Research Report by Type, by Component, by Industry, by Deployment, by Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p06169055/?utm_source=GNW

The Global Email Encryption Market size was estimated at USD 2,696.15 million in 2020 and expected to reach USD 3,055.98 million in 2021, at a CAGR 13.68% to reach USD 5,820.33 million by 2026.

Market Statistics:The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period.

Market Segmentation & Coverage:This research report categorizes the Email Encryption to forecast the revenues and analyze the trends in each of the following sub-markets:

Based on Type, the market was studied across Boundary Email Encryption, Client Plugins, End-to-End Email Encryption, Gateway Email Encryption, and Hybrid Email Encryption.

Based on Component, the market was studied across Services and Solution.

Based on Industry, the market was studied across Aerospace & Defense, Automotive & Transportation, Banking, Financial Services & Insurance, Building, Construction & Real Estate, Consumer Goods & Retail, Education, Energy & Utilities, Government & Public Sector, Healthcare & Life Sciences, Information Technology, Manufacturing, Media & Entertainment, Telecommunication, and Travel & Hospitality.

Based on Deployment, the market was studied across On-Cloud and On-Premises.

Based on Region, the market was studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. The Europe, Middle East & Africa is further studied across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom.

Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, and the long-term effects are projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlying COVID-19 issues and potential paths forward. The report delivers insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecasts, considering the COVID-19 impact on the market.

Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Email Encryption Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Competitive Scenario:The Competitive Scenario provides an outlook analysis of the various business growth strategies adopted by the vendors. The news covered in this section deliver valuable thoughts at the different stage while keeping up-to-date with the business and engage stakeholders in the economic debate. The competitive scenario represents press releases or news of the companies categorized into Merger & Acquisition, Agreement, Collaboration, & Partnership, New Product Launch & Enhancement, Investment & Funding, and Award, Recognition, & Expansion. All the news collected help vendor to understand the gaps in the marketplace and competitors strength and weakness thereby, providing insights to enhance product and service.

Company Usability Profiles:The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Email Encryption Market, including BAE Systems, Cryptzone, Echoworx, Egress Software, Entrust Datacard, Intemedia, Lux Sci, Micro Focus International PLC, Mimecast, Proofpoint, Inc., Sophos Group PLC, Symantec Corporation, Trend Micro Incorporated, Virtru, and Zix.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:1. What is the market size and forecast of the Global Email Encryption Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Email Encryption Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Email Encryption Market?4. What is the competitive strategic window for opportunities in the Global Email Encryption Market?5. What are the technology trends and regulatory frameworks in the Global Email Encryption Market?6. What is the market share of the leading vendors in the Global Email Encryption Market?7. What modes and strategic moves are considered suitable for entering the Global Email Encryption Market?Read the full report: https://www.reportlinker.com/p06169055/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Sunnybrook launches innovative new artificial intelligence research lab with $1-million gift from TD Bank Group – Canada NewsWire

TORONTO, Nov. 25, 2021 /CNW/ - TD Bank Group has donated a $1-million gift to establish the Augmented Precision Medicine Lab at Sunnybrook Health Sciences Centre. The Augmented Precision Medicine Lab will develop cutting-edge artificial intelligence (AI) systems to help improve the clinical care that patients receive in the fields of cardiology, cancer and other chronic diseases. Sunnybrook's rich and complex data stores will be harnessed to develop clinical risk prediction models that will enable physicians to provide personalized care to patients and potentially improve outcomes.

With this investment, Sunnybrook will have the resources it needs to build technological infrastructure, attract more talent, and accelerate a number of innovative projects either planned or underway.

"This generous gift will unite medical experts, computer scientists and industry partners to harness the power of big data and machine learning to drive personalized approaches to medicine," says Kelly Cole, President and CEO, Sunnybrook Foundation. "TD has long been a dedicated supporter of innovation at Sunnybrook and we are delighted to take this next step together."

The Augmented Precision Medicine Lab will work closely with industry partners to develop powerful new diagnostic tools, bring them to communities across Canada, and ultimately improve health outcomes.

"AI in medicine will undoubtedly improve the quality of care that patients receive, and, perhaps more importantly, it will improve health-care equity by dramatically widening access to underserved communities and populations," says Dr. Alexander Bilbily, a physician and computer scientist at Sunnybrook who will serve as the director of the new lab. "And by recognizing the essential role that industry plays in health care, we create a clear path from the lab to the patient where these tools can have a real impact on the patient journey."

The Augmented Precision Medicine Lab's first project aims to leverage Sunnybrook's extensive experience with patients with COVID-19 to create AI tools that can identify which patients are more likely to deteriorate. As a result, doctors will be empowered to closely monitor and improve care for these patients. The tool is being developed for use in smaller community hospitals, which demonstrates how AI can extend the reach of medical knowledge to smaller centres with less experience, thereby improving health-care equity for patients in underserved areas.

"The funding announced today will help Sunnybrook enhance its research and develop AI technologies to advance quality health care for patients who need it most," says Janice Farrell Jones, Senior Vice President, Sustainability and Corporate Citizenship, TD Bank Group. "Through the TD Ready Commitment, the Bank's corporate citizenship platform, we are proud to support this important initiative that will ultimately help patients living with cardiac conditions, cancer and other chronic diseases access equitable and personalized care."

Together, Sunnybrook and TD Bank Group are inventing the future of health care.

About Sunnybrook

Sunnybrook Health Sciences Centre is inventing the future of health care for the 1.3 million patients the hospital cares for each year through the dedication of its more than 10,000 staff and volunteers. An internationally recognized leader in research and education and a full affiliation with the University of Toronto distinguishes Sunnybrook as one of Canada's premier academic health sciences centres. Sunnybrook specializes in caring for high-risk pregnancies, critically ill newborns and adults, offering specialized rehabilitation, and treating and preventing cancer, cardiovascular disease, neurological and psychiatric disorders, orthopaedic and arthritic conditions and traumatic injuries. The hospital also has a unique and national leading program for the care of Canada's war veterans.

SOURCE Sunnybrook Health Sciences Centre

For further information: Media contact: Samantha Sexton, Sunnybrook Health Sciences Centre, 416.480.4040, [emailprotected]

http://www.sunnybrook.ca/foundation

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6 positive AI visions for the future of work – World Economic Forum

Current trends in AI are nothing if not remarkable. Day after day, we hear stories about systems and machines taking on tasks that, until very recently, we saw as the exclusive and permanent preserve of humankind: making medical diagnoses, drafting legal documents, designing buildings, and even composing music.

Our concern here, though, is with something even more striking: the prospect of high-level machine intelligence systems that outperform human beings at essentially every task. This is not science fiction. In a recent survey the median estimate among leading computer scientists reported a 50% chance that this technology would arrive within 45 years.

Importantly, that survey also revealed considerable disagreement. Some see high-level machine intelligence arriving much more quickly, others far more slowly, if at all. Such differences of opinion abound in the recent literature on the future of AI, from popular commentary to more expert analysis.

Yet despite these conflicting views, one thing is clear: if we think this kind of outcome might be possible, then it ought to demand our attention. Continued progress in these technologies could have extraordinarily disruptive effects it would exacerbate recent trends in inequality, undermine work as a force for social integration, and weaken a source of purpose and fulfilment for many people.

In April 2020, an ambitious initiative called Positive AI Economic Futures was launched by Stuart Russell and Charles-Edouard Boue, both members of the World Economic Forums Global AI Council (GAIC). In a series of workshops and interviews, over 150 experts from a wide variety of backgrounds gathered virtually to discuss these challenges, as well as possible positive Artificial Intelligence visions and their implications for policymakers.

Those included Madeline Ashby (science fiction author and expert in strategic foresight), Ken Liu (Hugo Award-winning science fiction and fantasy author), and economists Daron Acemoglu (MIT) and Anna Salomons (Utrecht), among many others. What follows is a summary of these conversations, developed in the Forum's report Positive AI Economic Futures.

Participants were divided on this question. One camp thought that, freed from the shackles of traditional work, humans could use their new freedom to engage in exploration, self-improvement, volunteering, or whatever else they find satisfying. Proponents of this view usually supported some form of universal basic income (UBI), while acknowledging that our current system of education hardly prepares people to fashion their own lives, free of any economic constraints.

The second camp in our workshops and interviews believed the opposite: traditional work might still be essential. To them, UBI is an admission of failure it assumes that most people will have nothing of economic value to contribute to society. They can be fed, housed, and entertained mostly by machines but otherwise left to their own devices.

People will be engaged in supplying interpersonal services that can be provided or which we prefer to be provided only by humans. These include therapy, tutoring, life coaching, and community-building. That is, if we can no longer supply routine physical labour and routine mental labour, we can still supply our humanity. For these kinds of jobs to generate real value, we will need to be much better at being human an area where our education system and scientific research base is notoriously weak.

So, whether we think that the end of traditional work would be a good thing or a bad thing, it seems that we need a radical redirection of education and science to equip individuals to live fulfilling lives or to support an economy based largely on high-value-added interpersonal services. We also need to ensure that the economic gains born of AI-enabled automation will be fairly distributed in society.

One of the greatest obstacles to action is that, at present, there is no consensus on what future we should target, perhaps because there is hardly any conversation about what might be desirable. This lack of vision is a problem because, if high-level machine intelligence does arrive, we could quickly find ourselves overwhelmed by unprecedented technological change and implacable economic forces. This would be a vast opportunity squandered.

For this reason, the workshop attendees and interview participants, from science-fiction writers to economists and AI experts, attempted to articulate positive visions of a future where Artificial Intelligence can do most of what we currently call work.

These scenarios represent possible trajectories for humanity. None of them, though, is unambiguously achievable or desirable. And while there are elements of important agreement and consensus among the visions, there are often revealing clashes, too.

The economic benefits of technological progress are widely shared around the world. The global economy is 10 times larger because AI has massively boosted productivity. Humans can do more and achieve more by sharing this prosperity. This vision could be pursued by adopting various interventions, from introducing a global tax regime to improving insurance against unemployment.

Large companies focus on developing AI that benefits humanity, and they do so without holding excessive economic or political power. This could be pursued by changing corporate ownership structures and updating antitrust policies.

Human creativity and hands-on support give people time to find new roles. People adapt to technological change and find work in newly created professions. Policies would focus on improving educational and retraining opportunities, as well as strengthening social safety nets for those who would otherwise be worse off due to automation.

The World Economic Forums Centre for the Fourth Industrial Revolution, in partnership with the UK government, has developed guidelines for more ethical and efficient government procurement of artificial intelligence (AI) technology. Governments across Europe, Latin America and the Middle East are piloting these guidelines to improve their AI procurement processes.

Our guidelines not only serve as a handy reference tool for governments looking to adopt AI technology, but also set baseline standards for effective, responsible public procurement and deployment of AI standards that can be eventually adopted by industries.

We invite organizations that are interested in the future of AI and machine learning to get involved in this initiative. Read more about our impact.

Society decides against excessive automation. Business leaders, computer scientists, and policymakers choose to develop technologies that increase rather than decrease the demand for workers. Incentives to develop human-centric AI would be strengthened and automation taxed where necessary.

New jobs are more fulfilling than those that came before. Machines handle unsafe and boring tasks, while humans move into more productive, fulfilling, and flexible jobs with greater human interaction. Policies to achieve this include strengthening labour unions and increasing worker involvement on corporate boards.

In a world with less need to work and basic needs met by UBI, well-being increasingly comes from meaningful unpaid activities. People can engage in exploration, self-improvement, volunteering or whatever else they find satisfying. Greater social engagement would be supported.

The intention is that this report starts a broader discussion about what sort of future we want and the challenges that will have to be confronted to achieve it. If technological progress continues its relentless advance, the world will look very different for our children and grandchildren. Far more debate, research, and policy engagement are needed on these questions they are now too important for us to ignore.

Written by

Stuart Russell, Professor of Computer Science and Director of the Center for Human-Compatible AI, University of California, Berkeley

Daniel Susskind, Fellow in Economics, Oxford University, and Visiting Professor, Kings College, London

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Yes, ransomware is your number one security nightmare. But heres how to sleep easy – The Register

Advertorial It may have escaped your notice, but last month was Cybersecurity Awareness month, and this years theme is Do Your Part. Be #CyberSmart.

That might sound slightly simplistic, but it does remind us that everyone has a role to play in keeping what is arguably the biggest security nightmare at bay. And yes, were talking ransomware.

And, just as over the last year washing our hands became something we do for the common good, the first step to blocking ransomware is some basic cyber hygiene, as detailed by Thales chief product security officer, Robert Burns, here. This runs from acknowledging and preparing for the threat, hardening your systems, segmenting your network, and making sure all your data is encrypted ultimately making the ransomware merchants own encryption, or exfiltration, plans pointless.

Thats a start, but if youd like a deeper dive into the implications of ransomware, check out this whitepaper.

Just in case you hadnt fully grasped the nature of the ransomware threat, it recaps the vital statistics such as a victim every 11 seconds and a $20bn total cost to business this year as well as highlighting some of the most egregious attacks so far.

But it also takes you deep into the anatomy of a ransomware attack, walking you through the seven stage Cyber Kill Chain, and highlighting why traditional baseline security falls woefully short in countering these threats, right along the line.

Which will no doubt leave you asking what, exactly, you can do beyond following basic hygiene and hoping for the best. Thankfully, the whitepaper also delivers you a detailed recipe for blocking ransomware with robust data access policies, and yes, encryption on your part is part of the mix.

You also get a detailed walk through of the sort of rule making process you should expect a data access platform to enable.

Needless to say, the remorseless nature of cyber attacks makes it hard to trust anyone. But you can trust us when we say youll finish up that little lot better informed than when you started. And that is the first step to protecting your systems. This month, next month, and beyond.

Sponsored by Thales

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Relief Therapeutics and InveniAI Sign a Strategic Collaboration Agreement to Identify New Product Development Opportunities using Artificial…

GENEVA, SWITZERLAND / ACCESSWIRE / November 24, 2021 / RELIEF THERAPEUTICS Holding SA (SIX:RLF, OTCQB:RLFTF, RLFTY) (" Relief "), announced today that it has signed a collaboration agreement (the " Collaboration ") with InveniAI LLC (" InveniAI "), a U.S. based company that has pioneered the application of artificial intelligence and machine learning across biopharma and other industries, in order to identify promising drug candidates to treat rare and specialty diseases.

Under the terms of the Collaboration, InveniAI will use its proprietary platform for the identification of potential pharmaceutical product opportunities using its Pharma Big Data Innovation Lab (" Platform "), consisting of (i) its proprietary AlphaMeld(R) platform, a cloud-based Artificial Intelligence (" AI ") platform that utilizes proprietary machine learning and deep learning based neural networks to identify product opportunities in therapeutic areas, (ii) its cross-functional teams at its Integrated Center of Excellence, and (iii) domain expertise, to generate novel pharmaceutical opportunities and the related development pathway for the development of such concepts.

In the Collaboration, it is expected that InveniAI will utilize its Platform to navigate the volume of data for all regulatory agency approved drugs and their associated active ingredients (Active Pharmaceutical Ingredient (" API ")) to identify potential rare and specialty disease indications for development and commercialization by Relief (" Product Concepts "). InveniAI will seek to prioritize top Product Concepts, associated diseases, scientific packages and evidence to support the potential drug development opportunities by Relief. Relief anticipates InveniAI's Platform will complement its wholly owned subsidiary APR Applied Pharma Research SA's existing capabilities in research and development and drug reformulation. Based on product leads developed by InveniAI, Relief hopes to develop proprietary versions of existing drugs, and to protect those drugs with long-lived intellectual property and defensible patent claims.

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Under the terms of the Collaboration, Relief will pay InveniAI an initial up-front fee, success milestones and commercialization royalties for the full development program. Additional financial details were not disclosed.

"We believe that the addition of InveniAI's AI-powered capabilities will meaningfully complement our existing drug development efforts. AI is becoming an increasingly important tool in identifying and screening new drug projects and Relief intends to fully leverage this promising technology," stated Raghuram (Ram) Selvaraju, Chairman of Relief. "In partnering with InveniAI, we are accessing decades' worth of expertise which has already led to successful drug re-innovation (e.g., vilazodone for treatment of depression and dexmedetomidine for treatment of agitation) and a proven platform that has been the basis of multiple partnerships with established companies. We believe that our work with InveniAI could generate multiple promising additions to our pipeline that may represent capital-efficient, cost-effective and risk-mitigated approaches to product development. In focusing on the optimization of existing approved APIs, we hope to ensure well-established clinical safety and tolerability for the product concepts identified at inception, giving us a running start in pursuing development of novel uses for these drugs. In our view, this approach will enable us to rapidly and efficiently execute innovation that brings relief to patients suffering from severe and debilitating conditions."

ABOUT RELIEF

Relief focuses primarily on clinical-stage programs based on molecules with a history of clinical testing and use in human patients or a strong scientific rationale. Relief's drug candidate, RLF-100(TM) (aviptadil), a synthetic form of Vasoactive Intestinal Peptide (VIP), is in late-stage clinical testing in the U.S. for the treatment of respiratory deficiency due to COVID-19. As part of its pipeline diversification strategy, in March 2021, Relief entered into a Collaboration and License Agreement with Acer Therapeutics for the worldwide development and commercialization of ACER-001. ACER-001 is a taste-masked and immediate release proprietary powder formulation of sodium phenylbutyrate (NaPB) for the treatment of Urea Cycle Disorders and Maple Syrup Urine Disease. In addition, Relief's recently completed acquisitions of APR Applied Pharma Research SA and AdVita Lifescience GmbH bring to Relief a diverse pipeline of marketed and development-stage programs.

RELIEF THERAPEUTICS Holding SA is listed on the SIX Swiss Exchange under the symbol RLF and quoted in the U.S. on OTCQB under the symbol RLFTF. For more information, visit http://www.relieftherapeutics.com . Follow us on LinkedIn.

CONTACT:RELIEF THERAPEUTICS Holding SAJack WeinsteinChief Financial Officer and Treasurercontact@relieftherapeutics.com

FOR MEDIA/INVESTOR INQUIRIES:Rx Communications GroupMichael Miller+1-917-633-6086mmiller@rxir.com

Disclaimer: This communication expressly or implicitly contains certain forward-looking statements concerning RELIEF THERAPEUTICS Holding SA. Such statements involve certain known and unknown risks, uncertainties and other factors, including (i) whether InveniAI will bring to RELIEF THERAPEUTICS Holding SA drug candidates that can be successfully developed by RELIEF THERAPEUTICS Holding SA, (ii) whether RELIEF THERAPEUTICS Holding SA will successfully develop and ultimately market any drug candidate identified by InveniAI, and (iii) those risks discussed in RELIEF THERAPEUTICS Holding SA's press releases and filings with the SIX, which could cause the actual results, financial condition, performance or achievements of RELIEF THERAPEUTICS Holding SA to be materially different from any future results, performance or achievements expressed or implied by such forward-looking statements. RELIEF THERAPEUTICS Holding SA is providing this communication as of this date and does not undertake to update any forward-looking statements contained herein as a result of new information, future events or otherwise.

SOURCE: Relief Therapeutics Holdings AG

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Now Isnt the Time to Give Users Control of Their Data – WIRED

The testimony of Facebook whistleblower Frances Haugen sparked the latest flare-up in a never-ending series of revelations on how companies and governments mine and commercialize our personal data. In an attempt to put consumers back in the drivers seat, recent updates to data protection regulations such as the GDPR in the European Union and the CCPA in California have mandated transparency and control as critical pillars of privacy protection. In the words of the European Commission: Its your datatake control!

Empowering consumers by giving them a say is a noble goal that certainly has a lot of appeal. Yet, in the current data ecosystem, control is far less of a right than it is a responsibilityone that most of us are not equipped to take on. Even if our brains were to magically catch up with the rapidly changing technology landscape, protecting and managing ones personal data would still be a full-time job.

Think of it this way: Being in charge of your sailing boat is absolutely wonderful if you are drifting along the Mediterranean coast on a beautiful day. You can decide which of the many cute little towns to steer toward, and there are really no wrong choices. Now lets imagine being in charge of the same sailing boat in the middle of a raging thunderstorm. You have no idea which direction to go in, and none of your options seem particularly promising. Having the right to control your own ship under these circumstances might not be very appealing, and could very easily end in disaster.

And yet, thats exactly what we do: Current regulations drop people in the middle of a raging technology sea and bless them with the right to control their personal data. Instead of forcing the tech industry to make systemic changes that would create a safer and more amenable ecosystem, we put the burden of safeguarding personal data on consumers. Taking this step is protecting the creators of the storm more than the sailors.

For users to be able to exercise control over their personal data successfully, regulators need to first create the right environment that guarantees basic protection, in the same way the Securities and Exchange Commission regulates the investment world and protects individuals from making bad decisions. Under the proper conditions, individuals can choose among a series of desirable outcomes, rather than a mix of undesirable ones. In other words, we first need to tame the sea before handing individuals more control over their boats. There are a few steps that regulators can take immediately to calm the waters.

First, we need to make it costly for companies to collect and use personal data by taxing companies for the data they collect. If they have to pay a price for every piece of data they gather, they will think twice about whether they really need it.

Regulators also need to mandate that defaults are set to sufficient levels of protection. Users data should be guarded unless they choose otherwise, a concept termed privacy by design. Nobody has time to make privacy protecting their full-time job. Safeguarding information needs to be easy. Privacy by design reduces the friction on the path to privacy, and guarantees that basic rights are automatically protected.

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What is The Role of Chief Data Officers (CDOs) – TechFunnel

Digital transformations promise organizations faster innovation and increased competitiveness. As investments in data and analytics continue to grow, they are quickly becoming a cornerstone of digital enterprise strategies.

The challenges that many enterprises face in achieving the full value of their investments have more to do with human behavior than with data and technologies.

Data-driven companies are becoming more prevalent. With technology, we can now track unlimited amounts of data, and that data can now be used to drive everything from product innovation to employee engagement.

Theres no surprise that more and more companies are introducing Chief Data Officers (CDOs)(1) to their executive teams since data is playing a bigger role in driving strategy and business success.

In the early 2000s, the Chief Data Officer (CDO) was first introduced. Originally, the CDO was responsible for data governance and compliance, but these days, the role uses data to drive business outcomes.

Chief Data Officers (not to be confused with Chief Digital Officers) have many responsibilities in relation to data, including:

Data governance and usage are the responsibilities of the CDO. CDOs understand both strategy and how to use data to drive a business in the right direction. Investors and stakeholders can then justify this direction to the best of CDOs.

Good data operations, or DataOps, are supported by the Chief Data Officer. Process-oriented, automated, and collaborative DataOps take a structured approach to design, implement, and manage data workflows and distributed data architectures.

In every case, however, the Chief Data Officer must use the companys data to achieve broader business goals. Examples include:

To perform these tasks, the CDO uses a variety of technology systems, such as business intelligence platforms and advanced analytics programs.

It is equally important for the CDO to build a team of data professionals who know the companys industry, the company itself, and its objectives, so that they can apply data analysis to resolve any concerns, challenges, risks, or opportunities.

As part of the position, the CDO is also responsible for developing and maintaining the organizations data governance policy and procedures, as well as managing data across its lifecycle.

For as long as organizations have existed, they have gathered and compiled data. With the rise of computers in the second half of the 20th century, however, data volumes and opportunities for analysis grew exponentially.

Data processing managers and others in the IT department initially handled the data and data analysis, and CIOs were typically responsible and overseen the data programs within their companies.

However, it wasnt until the first few years of the 21st century that the chief data officer role was created to manage and oversee data.

After the great recession of 2007, compliance regulations increased and the CDO position evolved further. A large part of a CDOs job at that time was to assist organizations in developing data governance policies to minimize compliance burdens.

Currently, the position focuses on assisting organizations in recognizing the benefits of big data in identifying revenue opportunities and reducing operating costs.

In recent decades, there has been an explosion of data generated by society, which provided organizations of all types with the opportunity to mine both structured and unstructured data for insights about their business, their industry, their customers, market dynamics, and consumers in general.

Leaders realized that these insights could be used to boost productivity and efficiency in the organization, as well as better connect with customers and create new products and services.

Data mining and analytics have recently become critical to driving digital transformation and to competing in the digital marketplace, according to enterprise leaders.

A CDO can be beneficial to any organization that has evolved to the point where it views data as an asset.

Due to the importance of data to organizational success, leadership has begun to see the need for data strategy to be owned by an executive.

An increasing number of organizations have taken on the position attests to this. In 2012, only 12% of large companies had a CDO. As of 2018, this figure was 63%, up from 56% in 2017. By the end of 2019, most large global companies will have established CDOs.

The Chief Information Officers (CIOs) job is to select, implement, and manage a companys technology systems so that it can function at its best.

Some believe that CDOs should report to the CIO, while others see the two roles as a kind of partnership, with the CIO being responsible for the technology infrastructure, and the CDO is in charge of connecting the data and insights collected by the infrastructure to the business priorities.

In the early 2000s, Chief Digital Officers became popular. In various roles, they are responsible for driving digital innovation in the business leading the charge in digital transformation or running the digital side of the company. By 2023, Gartner predicts 50% of chief digital officers without a chief data officer counterpart will have to assume those responsibilities.

According to whom you ask and the nature of your company, there is no difference between a Chief Analytics Officer and a Chief Data Officer. Many consider the CDO role to be a natural evolution of the CAO role, which traditionally has focused on data analysis and business intelligence.

However, some organizations still differentiate between the two, having the CDO focus on higher-level business objectives based on insights gleaned from the CAO.

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Machine Learning Clarifies Stress-Based Degradation of Biosimilars – The Center for Biosimilars

Machine learning shows promise as a complementary approach to chromatographic (mixture separation) techniques for assessing biosimilarity and stability, according to a recent study.

Investigators evaluated machine learning vs chromatographic analysis in the study of 3 trastuzumab biosimilars and their reference product (Herceptin) under control and stress conditions. They concluded the machine learning results correlated with the chromatographic data and revealed patterns elucidating the effects of pH and thermal stress conditions.

Trastuzumab, a monoclonal antibody to human epidermal growth factor receptor 2 (HER2), is approved as a treatment for metastatic breast cancer, early breast cancer, and metastatic gastric cancer. The investigators found that the biosimilars showed high similarity under control conditions, but differences in degradation patterns were detected underforced degradation conditions in the study.

First, physicochemical characteristics of the reference product and biosimilar trastuzumab products (approved for use in Egypt; and referred to as B1, B2, and B3 in the study) were determined by size exclusion chromatography, cation exchange chromatography, and peptide mapping. The biologics were evaluated under control conditions and under pH and thermal stress. The investigators then used unsupervised machine learning techniques to find patterns in the chromatographic data.

Chromatographic Analysis

The authors said primary structure and size and charge variants are quality attributes expected to affect the quality, safety, and efficacy of biologic drugs including trastuzumab. These attributes were similar in the biosimilars and reference product under control conditions, the authors found.

Thermal and pH stress, the authors noted, are among the most studied stress conditions in forced degradation studies due to their direct effect on the size and charge variant profiles of [monoclonal antibodies] mAbs through deamidation and oxidation. Under thermal and pH stress, the investigators did find differences in the degradation of the different products.

Size variants

Based on size exclusion chromatography, B2 and B3 showed a tendency to form high- and low-molecular weight variants under acidic and basic stress, and B2 showed 83% degradation by the 2-week time point under acidic stress. Under thermal stress, B3 showed the greatest degradation, 39% after 2 weeks.

Charge variants

Under acidic stress, the products varied from 19.9% degradation of the main variant of the reference product at 2 weeks to 93% for B2. Under basic stress, all samples showed a comparable increase in abundance of acidic variants. Under thermal stress, the charge variant distribution of B2 and B3 were similar to charge variant distribution for the reference product, while B1 showed a greater abundance of acidic variants.

Principal Component Analysis

The investigators used unsupervised machine learning techniques, which find patterns in data with no prior training or predefined subcategories. Principal component analysis (PCA) is a method for reducing complexity in high-dimensional data to a small number of components that explain the greatest percentage of the variance in the data set.

The authors plotted size exclusion chromatography and cation exchange chromatography data on 2-dimensional coordinates representing the 2 components (PC1 and PC2) that explained the most variance to identify patterns in the data. Primary component analysis of chromatographic and peptide mapping data of the control samples showed no outliers, which the authors said supports biosimilarity of the products.

The plot of control and acidic stressed samples showed that the control samples were separated along the primary component 1 (PC1) axis, while the stressed samples were distributed along the PC2 axis. Samples of the same product were clustered relevantly close to each other, the authors said, and their PCA results on control and acidic-stressed samples suggested 41% of the variance in the data was due to the applied stress, and 25% was due to inherent differences in the chromatographic profiles of the products.

Clustering Analysis

The investigators also used 2 clustering techniques, k-means and density-based spatial clustering of applications with noise (DBSCAN), on the data from the top 2 PCs from their primary component analysis. According to the authors, cluster analysis is an unsupervised exploratory technique aiming to find natural grouping in data so that items in the same cluster are more similar to each other than to those from different clusters.

Due to the inherent variability and large number of possible structural variants of monoclonal antibodies, the authors said, machine learningaided approaches have great value for assessing their critical quality attributes. They cited previous research using PCA to reveal patterns in the data on biosimilarity and stability of other biologics, recombinant human growth hormone and infliximab.

K-means clustering of the unstressed samples segregated the products into 3 clusters, with the reference product and B2 each forming their own cluster, and B1 and B3 allocated to the same cluster. DBSCAN segregated each product to its own cluster.

K-means clustering was able to separate control and pH-stressed samples into different clusters, although B2 control samples were clustered with the stressed reference product and B3 samples. Cluster analysis suggested B3 was most similar to the reference product under acidic stress, while B2 was most similar under thermal stress, and all products had a similar response to basic pH stress. The greatest variability between control samples was between the reference product and B2.

Finally, application of principal component and clustering analyses to the collective data set from all the applied chromatographic techniques supported biosimilarity of the products, the authors said. This principal component analysis identified no samples that were significantly different from the others; k-means identified 3 clusters (reference product, B1 + B3, and B2), and DBSCAN identified 4 clusters, one containing each product.

The authors concluded their results supported the biosimilarity of the products analyzed, and highlighted that regarding the charge and size profiles of the studied products, B2 showed higher variability (than B1 and B3) compared to HC under both control and stress conditions. They said that the chromatographic fingerprints and machine learning results were correlated and were able to reveal patterns related to the effect of different stress conditions on the different investigated products. They recommended future studies explore other machine learning tools to interpret physicochemical data on biologic products.

For Further Reading

The European Medicines Authority reports on a pilot experiment in tailoring development of biosimilars, or eliminating unnecessary testing, and the World Health Organization develops guidelines to support the tailoring concept.

Reference

Shatat SM, Al-Ghobashy MA, Fathalla FA, Abbas SS, Eltanany BM. Coupling of trastuzumab chromatographic profiling with machine learning tools: a complementary approach for biosimilarity and stability assessment. J Chromatogr B Analyt Technol Biomed Life Sci. 2021;1184:122976. doi:10.1016/j.jchromb.2021.122976

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Top 10 AI Jobs Available in Government Agencies Across the World – Analytics Insight

The government and public sector stand to gain exceptional benefits from the integration of AI in its daily operations. As artificial intelligence and machine learning gain momentum, an increasing number of government agencies have also started to use AI tools to improve decision-making and national security. The use of AI in government must take into account privacy and security, compatibility with legacy systems, and evolving workloads. The heart of AI in government services involves techniques like deep learning, computer vision, speech recognition, and robotics. Also, cyber anomaly detection can revolutionize cyber strategies in defense systems. To utilize these advanced technologies, government agencies from around the world are employing skilled and experienced AI professionals. In this article, we have listed 10 AI jobs that are available right now in these agencies.

Offered by: Canadian Security Intelligence Service

Location: Ottowa, ON

The applicants should possess an undergraduate degree in either mathematics, statistics, computer science, or computer engineering. The chosen candidates will have to define, develop, and lead data science programs to identify opportunities and provide solutions and capabilities to address them. They are required to autonomously find, transform, interpret, and leverage the collected data.

Offered by: Canadian Security Intelligence Service

Location: Ottowa, ON

This organization is looking for a Business Intelligence Specialist to transform its business solutions as a part of a mission to ensure the safety of all Canadians. Their core responsibilities would include the installation, maintenance, and configuration of business intelligence platforms, and ensuring that all the issues are identified, tracked, and reported promptly.

Offered by: Department of the City of Grand Junction

Location: Colorado

The organization is searching for an analytical expert to join their team as a data scientist, who will be responsible for collecting, converting, and integrating, raw data from various sources and implementing the analytics into meaningful insights to market Grand Junction as a destination of choice. The candidates have to be proficient in data mining and modeling, programming languages, and data visualization.

Offered by: Government of Alberta

Location: Alberta, Remote

The candidates will be required to support the RPA team and stakeholders by providing RPA process analysis and improvement recommendations for automation projects. They will also be responsible for researching, reviewing, and analyzing the effectiveness and efficiency of the existing business process and developing strategies for enhancing and automating them.

Offered by: Pixuate (Cocolabs Innovative Solutions Pvt Ltd)

Location: Bangalore

As embedded ML engineers, the applicants will be required to work on building next-generation products based on ML-based edge devices. They will have to research and develop continual learning and personalize ML algorithms, innovate new methods to develop AI on-device performance. Pixuate is a cutting-edge video analytics company, whose innovations are even recognized by the Government of Indias TDB as the top 6 innovations that helped India fight Covid-19.

Offered by: National Security Agency

Location: Fort Meade, MD

NSA is looking for a data scientist who is a qualified computer science professional and can solve complex problems, test innovative approaches, and research new solutions for storing, manipulating, and presenting information. The candidates who are interested in being a part of developing cutting-edge tools and technologies can apply for this role.

Offered by: Par Government Systems Corporation

Location: Annapolis Junction, MD

The qualified candidate will work with a group of peers to work in areas like image processing, digital forensics, and data stewardship. In addition, they will also have the opportunity to be involved in the company-funded research and white paper development to DOD and IC research and development.

Offered by: World Economic Forum LLC

Location: Mumbai, Maharashtra, India

The project lead will be developing and facilitating the engagement across the Forums communities for industry partners, government institutions, research institutes, media, and other organizations. His or her responsibility will be to lead the project team across project management, content management, and coordination.

Offered by: SAIC

Location: Virginia, United States, Remote

In this role, the candidates will be conducting competitive analysis on NSSS highest priority bids, working closely with capture managers, and other business development personnel. They will have to successfully work independently and collaboratively not just with the CI team, but with other SAIC teams as well.

Offered by: NLUD

Location: New Delhi, India

NLUD is looking for a data analyst who can successfully become the gatekeeper for the organizations data so that the professionals and stakeholders involved can make use of the data and make strategic business decisions. Degrees in computer science, analytics, or maths would be considered a bonus.

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Top 10 AI Jobs Available in Government Agencies Across the World - Analytics Insight

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