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FDA takes hands-on approach to upskill workforce under data modernization action plan – Federal News Network

The Food and Drug Administration is giving employees a chance to learn data skills through hands-on projects, with less emphasis on traditional classrooms and coursework.

The FDA is launching a data modernization action plan focused in part on developing data skills within the existing FDA workforce and recruiting new hires with these in-demand skills.

FDA Chief Data Officer Ram Iyer, speaking Oct. 21 at an ACT-IAC event, said the data strategy looks to improve data capacity through driver projects, which add value to the agency but also help the workforce develop data skills through these hands-on tasks.

Lets say an example is a supply chain project: We should deliver the supply chain needs and forecasting for the agency. But we should also take this as an opportunity to improve our master data management, or data cataloging or pulling external data into the system. If you dont do that, then we are just becoming an episodic organization that maybe just develops use cases and use cases, and then we are not learning anything from that.

The strategy also focuses on data practices, which provide a reusable framework for how the agency should curate, use and share key data sets across multiple projects.

The data modernization strategy builds off momentum from a technology modernization plan the FDA launched in September 2019. Last month, the FDA also reorganized its IT, data and cybersecurity office to create the Office of Digital Transformation, which reports directly to the FDA commissioner.

Iyer said data modernization requires the agency to adopt a product mindset, that leads the agency to think about the benefits to end-users within FDA and across its partner agencies.

There is a very big emphasis that we are doing all of this not in isolation, but to help us collect better data and make better decisions, Iyer said.

Iyer said that this approach allows FDA to pull the plug on projects that dont show results.

The approach we take here is not to spend millions of dollars on the products and services, [but] what is the minimum product that we can actually respond to that, rather than making them wait six, nine, 12 months before we deliver something?

For example, Iyer said he recently pulled back on a project the agency already spent $150,000 on, after deciding it wasnt a good fit for its architecture.

Some people might think of that as, Hey, you promised something and it was not delivered. We look at it in a completely different direction. We say put something in the market and see how it works, and if it doesnt work, we pull it back or we pivot, Iyer said.

Iyer leads a data modernization steering committee of senior agency executives and subject-matter experts that oversees the rollout of the data strategy at FDA.

Their role is also to engage and act as ambassadors, and because of their senior positions, they have [been] putting it in their town halls, putting it in their newsletters and communication has really helped us to run this, Iyer said.

FDA has stood up four working groups dedicated to the workforce, data drivers and practices plus an additional group focused on stakeholder engagement. Iyer said about 60 employees across the agency are currently participating in the working groups.

The workforce working group sees remote work as an opportunity to recruit prospective employees from across the country. Meanwhile, Iyer said hes rolling out a 70/20/10 model to train the workforce on data skills.

We dont want to just roll out training after training to our team members. We want to have them learn 70% of their needs through projects, so were going to identify the right projects, get the team engaged. We want about 20% to come from peer mentoring and coaching on certain skills whether its data mining skills or its visualization techniques or storytelling, Iyer said.

For the remaining 10%, the FDA will focus on traditional classroom or online training.

Launched a data science 101 program last month called Data Forward. Iyer said more than 1,400 employees signed up for the initial lunch and learn. About 33% of attendees said they were intimidated by data science at the beginning of the presentation, but nearly all participants said they had a better appreciation for data science by the end of the presentation.

Small wins that we think will help us to deliver the larger impact for the agency, Iyer said.

Iyer said FDAs centers became increasingly cross-dependent on each others data during the pandemic from tracking infection rates of farm workers to tracking the status of the supply chain for pharmaceutical drugs.

But amid that increased demand for data-sharing, Iyer said they found FDAs data and technology assets to be highly fragmented and not easily sharable within different parts of the agency.

The agency also encountered challenges sharing data externally with other agencies. Iyer said FDA went through a different process, to share data with the Department of Veterans Affairs, for example, compared to the process of sharing data with the Centers for Disease Control and Prevention.

This was really becoming clear, that in a normal state, we could deal with these differentiated methods, but when you are in a crisis, you cant be creating [or] recreating these processes, Iyer said.

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Global Data Science Platform Market (2021 to 2027) – by Component, Deployment, Organization Size, Function, Industry Vertical and Geography -…

DUBLIN, October 22, 2021--(BUSINESS WIRE)--The "Global Data Science Platform Market (2021-2027) by Component, Deployment, Organization Size, Function, Industry Vertical, and Geography, Competitive Analysis, Impact of Covid-19, Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Data Science Platform Market is estimated to be USD 43.3 Bn in 2021 and is expected to reach USD 81.43 Bn by 2027, growing at a CAGR of 11.1%.

Key factors such as a massive increase in data volume due to increasing digitalization and automation of processes have been a crucial driver in the growth of data science platform. Besides, the enterprises are increasingly focusing on analytical tools for deriving insights into consumer behavior and purchasing patterns. This, in turn, has been shaping their business decisions and strategies to compete in the market. Besides, the adoption of data science platforms has found its way in various industry verticals such as manufacturing, IT, BFSI, retail, etc. All these factors have helped in contributing to the growth of the data science platform market.

However, the costs attached to the deployment of these platforms, along with less workforce with domain expertise capabilities and threats to data privacy, has been a hindrance in the growth of the market.

The global data science platform market is segmented based on Component, Deployment, Organization Size, Function, Industry Vertical, and Geography.

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Story continues

Why buy this report?

The report offers a comprehensive evaluation of the Global Data Science Platform Market. The report includes in-depth qualitative analysis, verifiable data from authentic sources, and projections about market size. The projections are calculated using proven research methodologies.

The report has been compiled through extensive primary and secondary research. The primary research is done through interviews, surveys, and observation of renowned personnel in the industry.

The report includes in-depth market analysis using Porter's 5 force model and the Ansoff Matrix. The impact of Covid-19 on the market is also featured in the report.

The report also contains the competitive analysis using Competitive Quadrant, Infogence's Proprietary competitive positioning tool.

Report Highlights:

A complete analysis of the market including parent industry

Important market dynamics and trends

Market segmentation

Historical, current, and projected size of the market based on value and volume

Market shares and strategies of key players

Recommendations to companies for strengthening their foothold in the market

Market Dynamics

Drivers

High Generation of Data Volumes

Rising Focus On Data-Driven Decisions

Increasing Adoption of Data Science Platforms Across Diversified Industry Verticals

Restraints

Opportunities

Increasing Adoption of Data-Driven Technologies by Enterprises

Increasing Demand for Public Cloud

Investments and Funding in Development of Big Data and Related Technologies by Public and Private Sectors

Challenges

Companies Mentioned

Microsoft Corporation

IBM Corporation

Google, Inc

Wolfram

DataRobot Inc.

Sense Inc.

RapidMiner Inc.

Domino Data Lab

Dataiku SAS

Alteryx, Inc.

Oracle

Tibco Software Inc.

SAS Institute Inc.

SAP SE

The Mathworks

For more information about this report visit https://www.researchandmarkets.com/r/c2taz0

View source version on businesswire.com: https://www.businesswire.com/news/home/20211022005351/en/

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ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com

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Planning to study Data Science after Plus-Two? Heres what is on offer – Telegraph India

Summary

Data Science combines technical, analytical and communication skills

You can work in a variety of industries ranging from e-commerce to fossil fuel and construction

What is the one thing that connects technologies like artificial intelligence (AI), Internet of Things (IoT), big data, blockchain and quantum computing? Its data, something that all industries need to grow their businesses and ensure better products and services.

But just acquiring data isnt much help. Data needs to be turned into information that one can use, and thats where Data Science comes in.

What is Data Science

Data Science analyses data to gather insights by using various tools, algorithms, processes and systems. Both structured and unstructured data are converted into information that can be used in a wide range of areas.

The three main pillars of Data Science are mathematics, statistics and computer science.

Why study Data Science

Skills needed in Data Science

To excel in the field of Data Science, a combination of technical, analytical and communication skills is needed.

Studying Data Science

If you have Physics, Chemistry and Maths in Class XI-XII, you can keep Data Science as a career option.

After a bachelors degree in a related field such as Statistics, Computer Science, Information Technology or Maths, you can go for a specialisation in Data Science.

Going for a BSc in Data Science is another option, though only a few institutes offer this course currently.

BSc in Data Science

This 3-year course combines programming knowledge, maths expertise and an introduction to business communication through data.

Eligibility Criteria:

Institutes offering BSc in Data Science:

IIT Madras runs two online courses on Data Science:

You can check it out here.

Allied bachelors degree: Some universities are developing one-of-a-kind courses that combine data with other fields where it can be applied.

Career opportunities in Data Science

Since Data Science comprises programming, product development, analysis and statistics, a variety of jobs are available. Those from engineering, business and management backgrounds are also needed to play key roles in this field.

Last updated on 24 Oct 2021

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Consumer-facing Companies Still Have Few Incentives to Stop Data Breaches, and Thats a National Security Concern. – Council on Foreign Relations

In August, personal information belonging tofiftymillion prospective, current, and former T-Mobile customers wasstolen, marking the mobile carriers third customer data breach in two years.

T-Mobile isnt unique: dozens of well-known brands, as well as hundreds of lesser-known companies, have experienced data breaches in recent years. Althoughthesebreaches are embarrassing, T-Mobile and its peersappear toconsiderthemlittle more than a cost of doing business.

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However, the consequences of leaving data vulnerable are more serious than most companies realize. In addition to exposing consumers to potential fraud and identity theft, data breaches are deeplyinjurioustonationalsecurity.

Net Politics

CFR experts investigate the impact of information and communication technologies on security, privacy, and international affairs.2-4 times weekly.

MatthewPottinger, former Deputy U.S. National Security Advisor,warnedin August that China is now able to compile a dossier on everyAmerican adult.In 2015, Chinahackedhealth insurance provider Anthem, exfiltrating data belonging to almosteightymillion people.China alsoaccessedthe Office of Personal Managementdatabases,seizing sensitive data includingthesecurityclearanceformsbelonging to current and former federal employees.About 150 million records werestolen when ChinahackedEquifax in 2017, and an additional 500 million records were compromised following a Marriothackin 2018. China hassincemade a habit ofobtainingincreasingly personal data, such as DNA information, from healthcare providers, biotechnology firms, and pharmaceutical companies.Intelligence officials haveestimatedthat80percentofAmericans have hadalltheir personal data stolenperhaps an exaggeration, but likely not far from the truth.

The potential usesfor the stolen consumer data extend far beyond counterintelligence and research purposes.Thestolen data couldbe (or, more likely, already has been) used to informspearphishingattacks, aid the coercion of intelligence personnel, or help identify potential spies. Such sinister use cases arent without precedent.Foreign Policyreportedlast year that, almost a decade ago, Chinese intelligence used its vast collection of stolen datasets to identify undercover American operatives entering Europe and Africa.

Chinas cyber capabilities have strengthened significantly over the last decade.The Chinese governmenthas spent years and billions of dollars developing some of the most advanced data synthesis and analysis technologies and methodologies in the worldto surveil its own citizens.Thesetechniquesareuseful not only for evaluatingdata gathereddomestically, but alsodatastolen from the United States.When geopolitical adversaries have both large amounts of personal data and sophisticated analysis tools, the impact on national security can be particularly acute. This month,The New York Timessuggestedthat artificial intelligence and facial recognition are partially responsible for the recent loss of dozens of C.I.A. informants.

In theUnited States, by contrast, data is held by private entities such as Google, Amazon, Facebook, and other major consumer-facing companies. The U.S.government,constrained bystrong civil liberties protections provided by the Constitution, hasengagedless oftenin the kind of wholesaleacquisitionof personal data that is common in authoritariancountries.

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State and Local Governments (U.S.)

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These asymmetries, combined with the U.S. governments history of patchy and often inconsistent cyber strategy, and exacerbated by the frequent intelligence community leadership and policy changes that accompany each new presidential administration, mean that America isgivingadversariesasignificanteconomic and militaryadvantage.As data science continues to advance,thisdisparitywillonlybecomemoreprominent.

So, how can the national security risks of consumer data exposures be mitigated? Unfortunately, the gatekeepers of consumer datacompanieshave little incentive to increase investments in their own resiliency.It is not clear that falling victim to a breach ismeaningfully more expensivethan paying for the additional cybersecurity that would have prevented it. Thus, theres an argument to be madethat finesfor cyber breachesshouldbe more consequential to companies bottom line.Greater fines, though,not onlyencouragecompanies to be lessforthcoming about databreaches butarealsofruitlessifreporting and disclosure requirementsremainweak.

At thenationallevel, there is an evolving and confusingpatchworkof disclosure laws, as states adopt different standards. This lack ofcoherence not only disadvantages consumers, who are confused and exhausted by often vague and unhelpful breach notifications, but also constitutes a key weakness inU.S.cybersecurity strategy.

Thereisalsocurrentlyno federal cybersecurity breach disclosure law, meaning that the UnitedStates struggles toidentify the scope, frequency, and severity of data breaches.A bill that would require disclosure of cyber incidents at federal agencies, government contractors, and critical infrastructure owners (like T-Mobile), theCyber Incident Notification Act of 2021, was introduced earlier this year. Related provisions passed recently by the House as part of theNational Defense Authorization Actwould have similar consequences.While these bills would be a good first step,manyof the companies that hold vast troves of consumer data would be outside the scope of either law, andtherefore continue to have no federally-imposed obligation to disclosedata breaches.

U.S.cyber policy continues to focus on critical infrastructure and other traditional sectors with obvious cyber vulnerabilities, while overlooking breaches with the greatest potential for consumer data theft. Although important, suchanarrow focus is insufficient. National cyber policy needs to reflect the reality thatintrusions can be damaging no matter where they happen.

Maya Villasenor is a computer science student at Columbia University and a former intern in the Digital and Cyberspace Policy program.

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Consumer-facing Companies Still Have Few Incentives to Stop Data Breaches, and Thats a National Security Concern. - Council on Foreign Relations

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Skills and online courses to become a Data Scientist, the top job role in the world by 2025 – India Today

Data analyst and data scientist job roles have been predicted to be the top on the world by 2025. Here's a list of skills you need and online courses you can try out to crack this sector.

According to the World Economic Forums 'Future of Jobs' Report published in 2020, the top job role in the world -- with the highest demand -- will be that of a Data Analyst and Scientist by 2025. The pandemic-driven digital transformation has led to greater demand for data science skills, where employers are willing to pay a premium, along with other benefits and long-term incentives, in order to attract and retain data scientists and data engineers.

The demand for data scientists outstrips supply worldwide, and India is no exception. The domain offers tremendous opportunities to take one's career to the next level, opening up access to various job roles such as -- Data Scientist, Data Architect, Data Engineer, Data Analyst, Business Analyst, Analytics Manager, and Business Analytics Specialist.

The data science skill is not restricted to learners and professionals from STEM fields. It is not uncommon for companies globally to build data science teams with talent from a broader choice of fields including social sciences alongside traditional hires like computer scientists, creating opportunities for a diverse set of professionals to get data science jobs.

According to Courseras Global Skills Report 2021, learners can prepare for an entry-level role of a Data Analyst with just around 64 hours of online learning sessions.

Here's a list of essential skills and online courses in the field of Data Science that learners can choose from:

Read: 5 tips to begin your career in the field of Data Science

Read: Over 93,500 data science jobs vacant in India: Study

Read: Career as a Data Scientist: Scope, skills needed, job profiles and other details

Click here for IndiaToday.ins complete coverage of the coronavirus pandemic.

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Skills and online courses to become a Data Scientist, the top job role in the world by 2025 - India Today

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Which degree is right for me: data science or digital health? – News – The University of Sydney

Our Master of Data Science will develop your analytical and technical skills to use data science to guide strategic decisions. Youll explore the latest in data mining, machine learning and data visualisation, and learn to communicate data insights to key stakeholders.

You will study principles of data science, machine learning, data mining, visual analytics, and computational statistical methods, and can further enhance your knowledge in areas such as AI or data science for business.

In our Master of Digital Health and Data Science, youll work with academics from both the School of Computer Science in the Faculty of Engineering, and the Faculty of Medicine and Health.

Youll study tailored content which focuses on the needs and expectations of the health industry, and have the opportunity to apply your skills through capstone projects that offer real health data problems.

Topics you'll cover include how to analyse health data and use it to aid preventative care, the role of AI in diagnosing and improving patient outcomes, and what the design process looks like in a healthcare setting.

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Which degree is right for me: data science or digital health? - News - The University of Sydney

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Data Science Leaders are Ruling the Corporate Industry for their Technical Talent as well as Management Expertise – Analytics Insight

Data Science Leaders are Ruling the Corporate Industry for their Technical Talent as well as Management Expertise

Data is running through the veins of advanced technology these days. The whole concept of algorithms and artificial intelligence is standing upon the concept of feeding a machine with stacks of data like the information stacked in our brain and then using that data for various operations, just how we use our brain to operate. The idea is very simple to grasp but it has a complex functioning structure that comes under the subject of data science.

The data science industry of 2021 is witnessing some of the biggest data science leaders of the decade. 2021 will be a pivotal year for data scientists as organizations are increasingly relying on insights derived from big data for making key decisions. More applications are being created with Python and theres an increased demand for end-to-end AI solutions. There are a lot of jobs available in the field, however, not enough data scientists. According to an ASA Report, nearly 70% of business owners prefer job applicants with specializations in data science, and the number of job openings is projected to grow to 2.72 million by 2021.

We see colossal impacts of data science across businesses; however, some are more developed than others, especially in finance. We see enormous improvement being made and this is to a great extent is in light of the fact that these organizations have a ton of data as of now. Like finance has a long history of making data helpful, thus there is now a culture of being reasonably data-driven set up in a large number of these organizations, and theyre additionally keen on stretching out those capabilities to new sorts of information.

Data science leaders are the senior executives with the aptitude to harness data science and business analytic insights to inform business decisions, strategy, execution, and more effective organizational leadership. Executives need to possess this critical and evolving knowledge foundation to optimally leverage the information produced for and by their data science teams in delivering business solutions based on analytic insights.

Data science is working pretty intensely in the media also. That is things like understanding your crowd, helping them discover content theyll cherish, helping them draw in with that content, ensuring its shared ideally across various platforms. Its one spot, however extremely truly distributed. The exponential growth in data we have seen since the start of our digital period will back off at any point soon. Truth be told, we have most likely just observed a hint of something larger. The coming years will realize a consistently expanding downpour of information. The new information will work as rocket fuel for our data science models, offering rise to better models as well as new and imaginative use cases.

Most organizations hiring data science leaders generally look for a Ph.D. in a related field or with significant experience with machine learning models, and they often drop the management experience required to obtain technical talent and which may hamper the operation of a data science leader. The management experience would help the leader connect with the non-technical staff of his company.

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Winners and losers in the fulfilment of national artificial intelligence aspirations – Brookings Institution

The quest for national AI success has electrified the worldat last count, 44 countries have entered the race by creating their own national AI strategic plan. While the inclusion of countries like China, India, and the U.S. are expected, unexpected countries, including Uganda, Armenia, and Latvia, have also drafted national plans in hopes of realizing the promise. Our earlier posts, entitled How different countries view artificial intelligence and Analyzing artificial intelligence plans in 34 countries detailed how countries are approaching national AI plans, as well as how to interpret those plans. In this piece, we go a step further by examining indicators of future AI needs.

Clearly, having a national AI plan is a necessary but not sufficient condition to achieve the goals of the various AI plans circulating around the world; 44 countries currently have such plans. In previous posts, we noted how AI plans were largely aspirational, and that moving from this aspiration to successful implementation required substantial public-private investments and efforts.

In order to analyze the implementation to-date of countries national AI objectives, we assembled a country-level dataset containing: the number and size of supercomputers in the country as a measure of technological infrastructure, the amount of public and private spending on AI initiatives, the number of AI startups in the country, the number of AI patents and conference papers the countrys scholars produced, and the number of people with STEM backgrounds in the country. Taken together, these elements provide valuable insights as to how far along a country is in implementing its plan.

As analyzing each of the data elements individually presented some data challenges, we conducted a factor analysis to determine if there was a logical grouping of the data elements. Factor analysis reveals the underlying structure of data; that is, the technique mathematically determines how many groups (or factors) of data exist by analyzing which data elements are most closely related to other elements.

Given that our data included five distinct dimensions (i.e., technology infrastructure, AI startups, spending, patents and conference papers, and people), we expected that five factors would emerge, particularly since the dimensions appear to be relatively separate and distinct. But the data showed otherwise. In all, this factor analysis revealed all of the data elements fall under two factorspeople-related and technology-related.

The first factor is the set of AI hiring, STEM graduates, and technology skill penetration data points, which are all associated with the people side of AI. Without qualified people, AI implementations are unlikely to be effective.

The second factor is comprised of all the non-people data elements of AI, which include computing power, AI startups, investment, conference and journal papers, and AI patent submission data points. In looking at these data elements, we realized that all of the data elements in this factor were technology-related, either from a hardware or a thought-leadership standpoint.

Given these findings, we can treat the data as containing two distinct categories: people and technology. Figure 1 shows where a select set of countries sit along these dimensions.

The countries that are in the upper right-hand corner we dub Leaders; they have both the people (factor 1) and the technology (factor 2) to meet their goals. Countries in the lower right quadrant we dub Technically Prepared, and because they are higher on the technology dimensions (factor 2) but lower on the people dimensions (factor 1). Those countries in the upper left quadrant we dub the People Prepared, and largely due to their factors higher on the people dimension (factor 1), but lower on the technology dimension (factor 2). The final quadrantthe lower left quadrantwe dub the Aspirational quadrant since those countries have not yet substantially moved forward in either the people or technology dimension (factor 1 and 2 respectively) in achieving their national AI strategy.

China is unmistakably closer to achieving its national AI strategy goals. It is both a leader in the technical dimension and a leader in the people dimension. Of note is that, while China is strongly positioned in both dimensions, it is not highest in either dimension; the U.S. is higher in the technical dimension, and India, Singapore, and Germany are all higher on the people dimension. Given the population of China and its overall investment in AI-related spending, it is not surprising that China has an early and commanding lead over other countries.

The U.S., while a leader in the technology dimension, particularly in the sub-dimensions of investments and patents, ranks a relatively dismal 15th place after such countries as Russia, Portugal, and Sweden in the people dimension. This is especially clear in the sub-dimension of STEM graduates, where it ranks near the bottom. While the vast U.S. spending advantage has given it an early lead in the technology dimensions, we suspect that the overall lack of STEM-qualified individuals is likely to significantly constrain the U.S. in achieving its strategic goals in the future.

By contrast, India holds a small but measurable lead over other countries in the people dimension, but is noticeably lagging in the technology dimension, particularly in the investment sub-dimension. This is not surprising, as India has long been known for its education prowess but has not invested equally with leaders in the technology dimension.

Our focus on China, the U.S., and India is not to suggest that these are the only countries that can achieve their national AI objectives. Other countries, notably South Korea, Germany, and the United Kingdom are just outside of top positions, and, by virtue of generally being well-balanced between the people and the technology dimensions, have an excellent chance to close the gap

At present, China, the U.S., and India are leading the way in implementing national AI plans. Yet China has already hit on a balanced strategy that has thus far eluded the U.S. and India. This suggests that China needs to merely continue its strategy. However, strategy refinement is necessary for the U.S. and India to keep pace. These leaders are closely followed by South Korea, Germany, and the United Kingdom.

In future posts, we will dive deeper into both the people and technology dimensions, and will dissect specific shortfalls for each country, as well as what can be done to address these shortfalls. Anything short of a substantial national commitment to AI achievement is likely to relegate the country to the status of a second-tier player in the space. If the U.S. wants to dominate this space, it needs to improve the people dimension of technology innovation and make sure it has the STEM graduates required to push its AI innovation to new heights.

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Artificial Intelligence Has Found an Unknown ‘Ghost’ Ancestor in The Human Genome – ScienceAlert

Nobody knows who she was, just that she was different: a teenage girl from over 50,000 years ago of such strange uniqueness she looked to be a 'hybrid' ancestor to modern humans that scientists had never seen before.

Only recently, researchers have uncovered evidence she wasn't alone. In a 2019 study analysing the complex mess of humanity's prehistory, scientists used artificial intelligence (AI) to identify an unknown human ancestor species that modern humans encountered and shared dalliances with on the long trek out of Africa millennia ago.

"About 80,000 years ago, the so-called Out of Africa occurred, when part of the human population, which already consisted of modern humans, abandoned the African continent and migrated to other continents, giving rise to all the current populations", explainedevolutionary biologist Jaume Bertranpetit from the Universitat Pompeu Fabra in Spain.

As modern humans forged this path into the landmass of Eurasia, they forged some other things too breeding with ancient and extinct hominids from other species.

Up until recently, these occasional sexual partners were thought to include Neanderthals and Denisovans, the latter of which were unknown until 2010.

But in this study, a third ex from long ago was isolated in Eurasian DNA, thanks to deep learning algorithms sifting through a complex mass of ancient and modern human genetic code.

Using a statistical technique called Bayesian inference, the researchers found evidence of what they call a "third introgression" a 'ghost' archaic population that modern humans interbred with during the African exodus.

"This population is either related to the Neanderthal-Denisova clade or diverged early from the Denisova lineage," the researchers wrote in their paper, meaning that it's possible this third population in humanity's sexual history was possibly a mix themselves of Neanderthals and Denisovans.

In a sense, from the vantage point of deep learning, it's a hypothetical corroboration of sorts of the teenage girl 'hybrid fossil' identified in 2018;although there's still more work to be done, and the research projects themselves aren't directly linked.

"Our theory coincides with the hybrid specimen discovered recently in Denisova, although as yet we cannot rule out other possibilities", one of the team, genomicist Mayukh Mondal from the University of Tartu in Estonia, said in a press statement at the time of discovery.

That being said, the discoveries being made in this area of science are coming thick and fast.

Also in 2018, another team of researchers identified evidence of what they called a "definite third interbreeding event" alongside Denisovans and Neanderthals, and a pair of papers published in early 2019 traced the timeline of how those extinct species intersected and interbred in clearer detail than ever before.

There's a lot more research to be done here yet. Applying this kind of AI analysis is a decidedly new technique in the field of human ancestry, and the known fossil evidence we're dealing with is amazingly scant.

But according to the research, what the team has found explains not only a long-forgotten process of introgression it's a dalliance that, in its own way, informs part of who we are today.

"We thought we'd try to find these places of high divergence in the genome, see which are Neanderthal and which are Denisovan, and then see whether these explain the whole picture," Bertranpetit told Smithsonian.

"As it happens, if you subtract the Neanderthal and Denisovan parts, there is still something in the genome that is highly divergent."

The findings were published in Nature Communications.

A version of this article was originally published in February 2019.

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China beats the USA in Artificial Intelligence and international awards – Modern Diplomacy

There is no doubt that the return of Huaweis CFO Meng Wanzhou to Beijing marks a historic event for the entire country that made every Chinese person incredibly proud, especially bearing in mind its timing, as the National Day celebrations took place on October 1.

Where there is a five-star red flag, there is a beacon of faith. If faith has a color, it must be China red, Ms. Meng said to the cheering crowd at Shenzhen airport after returning home from Canada. She also added that All the frustration and difficulties, gratitude and emotion, steadfastness and responsibility will transform into momentum for moving us forward, into courage for our all-out fight.

Regardless of how encouraging the Chinese tech giant heiresss words may sound, the fact remains that the company remains a target of U.S. prosecution and sanctionssomething that is not about to change anytime soon.

When the Sanctions Bite

It was former U.S. President Donald Trump who in May 2019 signed an order that allowed the then-Commerce Secretary Wilbur Ross to halt any transactions concerning information or communications technology posing an unacceptable risk to the countrys national security. As a result, the same month, Huawei and its non-U.S. affiliates were added to the Bureau of Industry and Security Entity List, which meant that any American companies wishing to sell or transfer technology to the company would have to obtain a licence issued by the BIS.

In May 2020, the U.S. Department of Commerce decided to expand the FPDP Rule by restricting the Chinese tech giant from acquiring foreign-made semiconductors produced or developed from certain U.S. technology or software and went even further in August the same year by issuing the Final Rule that prohibits the re-export, export from abroad or transfer (in-country) of (i) certain foreign-produced items controlled under the amended footnote 1 to the Entity List (New Footnote 1) when there is (ii) knowledge of certain circumstances, the scope of which were also expanded.

Moreover, the decision also removed the Temporary General License (TGL) previously authorizing certain transactions with Huawei and added thirty-eight additional affiliates of the Chinese company to the Entity List.

In these particular circumstances, despite the initial predictions made by Bloomberg early in 2020 that Trumps decision to blacklist Huawei fails to stop its growth, the current reality seems to be slightly changing for onceand brieflythe worlds largest smartphone vendor.

The impact of the U.S. sanctions has already resulted in a drop in sales in the smartphone business by more than 47% in the first half of 2021, and the total revenue fell by almost 30% if we compare it with the same period in 2020. As is estimated by rotating Chairman Eric Xu, the companys revenue concerning its smartphone sales will drop by at least $30-40 billion this year.

For the record, Huaweis smartphone sales accounted for $50 billion in revenue last year. The company has generated $49.57 billion in revenue in total so far, which is said to be the most significant drop in its history.

In Search of Alternative Income Streams

Despite finding itself in dire straits, the company is in constant search for new sources of income with a recent decision to charge patent royalties from other smartphone makers for the use of its 5G technologies, with a per unit royalty cap at $2.50 for every multimode mobile device capable of connections to 5G and previous generations of mobile networks. Huaweis price is lower than the one charged by Nokia ($3.58 per device) and Ericsson ($2.50-$5 per device).

Notably, according to data from the intellectual property research organization GreyB, Huawei has 3,007 declared 5G patent families and over 130,000 5G active patents worldwide, making the Chinese company the largest patent holder globally.

Jason Ding, who is head of Huaweis intellectual property rights department, informed early this year that the company would collect about $1.2-$1.3 billion in revenue from patent licensing between 2019 and 2021. But royalties will not be the only revenue source for the company.

Investing in the Future: Cloud Services and Smart Cars

Apart from digitizing native companies in sectors like coal mining and port operations that increased its revenue by 23% last year and 18% in the first part of 2021, Huawei looks far into the future, slowly steering away from its dependency on foreign chip supplies by setting its sight on cloud services and software for smart cars.

Seizing an opportunity to improve the currently not-so-perfect cloud service environment, the Chinese tech giant is swiftly moving to have its share in the sector by creating new cloud services targeting companies and government departments. For this purpose, it plans to inject $100 million over three years period into SMEs to expand on Huawei Cloud.

As of today, Huaweis cloud business is said to grow by 116% in the first quarter of 2021, with a 20% share of a $6 billion market in China, as Canalys reports.

Huawei Clouds results have been boosted by Internet customers and government projects, as well as key wins in the automotive sector. It is a growing part of Huaweis overall business, said a chief analyst at the company, Matthew Ball. He also added that although 90% of this business is based in China, Huawei Cloud has a more substantial footprint in Latin America and Europe, the Middle East and Africa as compared with Alibaba Cloud and Tencent Cloud.

Another area where Huawei is trying its luck is electric and autonomous vehicles, where the company is planning to invest $1 billion alone this year. Although the company has repeatedly made it clear that it is unwilling to build cars, Huawei wants to help the car connect and make it more intelligent, as its official noted.

While during the 2021 Shanghai Auto Show, Huawei and Arcfox Polar Fox released a brand new Polar Fox Alpha S Huawei Hi and Chinas GAC revealed a plan to roll out a car with the Chinese tech company after 2024, Huawei is already selling the Cyrus SF5, a smart Chinese car from Chongqing Xiaokang, equipped with Huawei DriveONE electric drive system, from its experience store for the first time in the companys history. Whats more, the car is also on sale online.

R&D and International Talent as Crucial Ingredients to Become Tech Pioneer

There is a visible emphasis put on investing in high-quality research and development to innovate both in Huawei and China as a whole.

According to the companys data, the Chinese technology giant invested $19.3 billion in R&D in 2019, which accounted for 13.9% of its total business revenue and $22 billion last year, which was around 16% of its revenue. Interestingly, if Huawei was treated as a provincial administrative region, its R&D expenditure would rank seventh nationwide.

As reported by Chinas National Bureau of Statistics, the total R&D spending in China last year was 2.44 trillion yuan, up 10.6% year-on-year growth, and 2.21 trillion yuan in 2019, with 12.3% year-on-year growth.

As far as activities are concerned, the most were spent on experimental development in 2020 (2.02 trillion yuan, which is 82.7% of total spending), applied research (275.72 billion yuan, which gives 11.3%) and basic research (146.7 billion yuan, accounting for 6%). While the most money was spent by enterprises (1.87 trillion yuan, which gives up 10.4% year-on-year), governmental research institutions spent 340.88 billion yuan (up 10.6% year-on-year), and universities and colleges spent 188.25 billion yuan (up 4.8% year-on-year).

As far as industries go, it is also worth mentioning that high-tech manufacturing spending accounted for 464.91 billion yuan, with equipment manufacturing standing at 913.03 billion yuan. The state science and tech spending accounted for 1.01 trillion yuan, which is 0.06 trillion yuan less than in 2019.

As Huawei raises the budget for overseas R&D, the company also plans to invest human resources by attracting the brightest foreign minds into its business, which is in some way a by-product of the Trump-era visa limitations imposed on Chinese students.

So far, concentrating on bringing Chinese talent educated abroad, Huawei is determined to broader its talent pool by tall noses, as the mainland Chinese sometimes refer to people of non-Chinese origin.

Now we need to focus on bringing in talent with tall noses and allocate a bigger budget for our overseas research centres, said the companys founder Ren Zhengfei in a speech made in August. We need to turn Huaweis research center in North America into a talent recruitment hub, Ren added.

While Huawei wants to scout for those who have experience working in the U.S. and Europe, it wants to meet the salary standards comparable to the U.S. market to make their offer attractive enough.

What seems to be extraordinary and crucial by looking at China through Huawei lens is that it is, to the detriment of its critics, indeed opening to the outside world by aiming at replenishing all facets of its business.

We need to further liberate our thoughts and open our arms to welcome the best talent in the world, to quote Ren, in an attempt to help the company become more assimilated in overseas markets as a global enterprise in three to five years.

The Chinese tech giant aims to attract international talent to its new 1.6 million square meter research campus in Qingpu, Shanghai, which will house 30,000 to 40,000 research staff primarily concerned with developing handset and IoT chips. The Google-like campus is said to be completed in 2023.

The best sign of Huaweis slow embrace of the start-up mentality, as the companys head of research and development in the UK, Henk Koopmans, put it, is the acquiring of the Center for Integrated Photonics based in Ipswich (UK) in 2012, which has recently developed a laser on a chip that can direct light into a fibre-optic cable.

This breakthrough discovery, in creating an alternative to the mainstream silicon-based semiconductors, provides Huawei with its product based on Indium Phosphide technology to create a situation where the company no longer needs to rely on the U.S. know-how.

As for high-profile foreign recruitments, Huawei has recently managed to hire a renowned French mathematician Laurent Lafforgue, a winner of the 2002 Fields Medal, dubbed as the Nobel Prize of mathematics, who will work at the companys research center in Paris, and appointed the former head of BBC news programmes Gavin Allen as its executive editor in chief to improve its messaging strategy in the West.

According to Huaweis annual report published in 2020, the Shenzhen-based company had 197,000 employees worldwide, including employees from 162 different countries and regions. Moreover, it increased its headcount by 3,000 people between the end of 2019 and 2020, with 53.4% of its employees in the R&D sector.

The main objective of the developments mentioned above is to lead the world in both 5G and 6G to dominate global standards of the future.

We will not only lead the world in 5G, more importantly, we will aim to lead the world in wider domains, said Huaweis Ren Zhengfei in August. We research 6G as a precaution, to seize the patent front, to make sure that when 6G one day really comes into use, we will not depend on others, Ren added.

Discussing the potential uses of 6G technology, Huaweis CEO told his employees that it might be able to detect and sense beyond higher data transmission capabilities in the current technologies, with a potential to be utilized in healthcare and surveillance.

Does the U.S. Strategy Towards Huawei Work?

As we can see, the Chinese tech giant has not only proved to be resilient through the years of being threatened by the harmful U.S. sanctions, but it also has made significant steps to become independent and, therefore, entirely out of Washingtons punishment reach.

Although under the intense pressure from the Republicans the U.S. Commerce Secretary Gina Raimondo promised that the Biden administration will take further steps against Huawei if need be, it seems that there is nothing much that the U.S. can do to stop the Chinese company from moving ahead without any U.S. permission to develop in the sectors of the future, while still making a crucial contribution to the existing ones.

At the same time, continuing with the Trump-era policies aimed at Huawei is not only hurting American companies but, according to a report from the National Foundation for American Policy published in August 2021, it also might deal a significant blow to innovation and scientific research in the country.

Restricting Huawei from doing business in the U.S. will not make the U.S. more secure or stronger; instead, this will only serve to limit the U.S. to inferior yet more expensive alternatives, leaving the U.S. lagging behind in 5G deployment, and eventually harming the interests of U.S. companies and consumers, Huawei said in, what now appears to be, prophetic statement to CNBC in 2019.

On that note, perhaps instead of making meaningless promises to the Republicans that the Biden administration wouldnt be soft on the Chinese tech giant, Raimondo would make the U.S. better off by engaging with Huawei, or at least rethinking the current policies, which visibly are not bringing the desired results, yet effectively working to undermine the U.S. national interest in the long run.

From our partner RIAC

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