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Dear Editor,

Education is set to undergo a major transformation thanks to the rise of artificial intelligence (AI) and quantum computing technologies. These new technologies will revolutionize the way we learn, communicate, and interact with each other, and will open up a whole new world of possibilities for educators and students alike. AI and quantum computing technologies will allow us to create intelligent learning systems that can adapt to the individual needs of each student. By analyzing data on student performance, these systems can identify areas where students need more help and provide personalized recommendations to help them improve. One of the key benefits of AI-powered learning systems is that they can help to reduce educational disparities. With personalized learning systems, students who are struggling can receive the extra help they need, while advanced learners can be challenged with more complex material. This can help to level the playing field and ensure that all students have an equal opportunity to succeed. In addition to personalized learning, AI and quantum computing technologies will also transform the way we interact with each other. Virtual and augmented reality technologies will allow us to create immersive educational experiences that can transport us to different parts of the world and even beyond. For example, students could use virtual reality to explore the Great Barrier Reef, or to experience life in a different time period. These immersive experiences can help to make learning more engaging and exciting, and can help to create a deeper understanding of complex concepts. However, with these new technologies comes a need for educators to stay up-to-date with the latest developments and ensure that students are equipped with the necessary skills to thrive in an AI-powered world. As AI and quantum computing technologies continue to evolve, educators will need to be prepared to adapt their teaching methods and curricula to keep up with the latest advancements. In conclusion, the rise of AI and quantum computing technologies will bring about a transformation in education that has the potential to level the playing field and provide students with personalized learning experiences that meet their individual needs. Educators who are prepared to embrace these new technologies and adapt their teaching methods accordingly will be at the forefront of this transformation, and will help to shape the future of education for generations to come.

The paragraphs above were written for me by ChatGPT with the request Write a newspaper article on how education will be transformed by AI and quantum computing technologies. As an educator, an engineer, and a science enthusiast, the above is remarkable. The question that arises is How should a country transform its current education system for AI and quantum computing technologies? This is what I asked ChatGPT and here is the amazing response. Transforming a countrys education system to incorporate AI and quantum computing technologies can be a complex and challenging process, but it is essential to prepare future generations for the evolving job market and technological landscape. Here are some steps a country can take to transform its current education system:

1. Develop a comprehensive AI and quantum computing education plan: This plan should include the specific skills and knowledge that students will need to be successful in an AI-driven economy, as well as strategies for incorporating new technologies into the classroom.

2. Invest in teacher training: Teachers are the key to transforming education systems, so its important to invest in training programs that will help them learn new technologies and teaching methods.

3. Create partnerships with industry: Collaboration with industry leaders can help to provide educators with the latest technology and insights on how to best prepare students for the workforce.

4. Introduce AI and quantum computing into the curriculum: This can include introducing courses on these topics at all levels of education, from elementary school to university.

5. Encourage creativity and innovation: AI and quantum computing technologies require creativity and innovation, so its important to foster these skills in students through hands-on projects and other experiential learning opportunities.

6. Develop cross-disciplinary programs: AI and quantum computing are multidisciplinary fields, so its important to create programs that incorporate different fields of study, such as computer

science, mathematics, physics, and engineering.

7. Encourage lifelong learning: The rapid pace of technological change means that students will need to continue learning throughout their careers. Education systems should therefore encourage lifelong learning and provide opportunities for upskilling and reskilling.

Transforming an education system for AI and quantum computing technologies is a long-term process that requires collaboration between policymakers, educators, and industry leaders. By investing in the development of AI and quantum computing education programs, training teachers, and encouraging creativity and innovation, countries can prepare their citizens for a future that is increasingly reliant on these emerging technologies.

The writer spent several months researching the two questions posed to ChatGPT and he confesses that ChatGPT responses, in a few seconds, were mind-blowing. Services such as medicine, engineering, law, accounting that involve processes and procedures will undergo paradigm shifts. Indeed, AI and quantum computing technologies will quickly change 21st century living. The policy makers in Guyana should heed the recommendations of ChatGPT and setup a committee to commence the transformation and be among the leaders rather than as a follower.

Sincerely,

Muniram Budhu

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An AI and quantum computing technologies based education ... - Stabroek News

Quantum AI has emerged as a powerful tool in portfolio management, offering unprecedented potential to optimize investment strategies and generate superior returns. By leveraging quantum mechanical principles to process complex data and analyze patterns, quantum AI can unlock hidden value and generate insights that traditional investment techniques simply cant match. In this article, well explore the evolution of quantum AI, its key components, and how it intersects with portfolio management. Well also examine the benefits and challenges it presents for investors, and consider some real-world examples of quantum AI in action.

What is Quantum AI?

At its core, quantum AI is an interdisciplinary field that combines the principles of quantum mechanics and artificial intelligence to solve complex problems. Like traditional AI, quantum AI uses machine learning algorithms to analyze data and identify patterns. However, unlike classical computers, which process information using binary bits, quantum computers use quantum bits (qubits) to represent data. This allows quantum AI to process immense volumes of data simultaneously, making it ideal for applications in finance, healthcare, and other data-intensive fields.

Quantum AI is not just a theoretical concept; it has already been implemented in various industries. For example, in finance, quantum AI is used to identify patterns in financial data, which can help traders make better investment decisions. In healthcare, quantum AI is used to analyze medical images, such as MRIs and CT scans, to detect early signs of diseases.

The Evolution of Quantum Computing

Quantum computing has been an area of research for several decades, but it wasnt until the late 20th century that scientists were able to build functional quantum computers. The first quantum computer was built in 1994 by Peter Shor, a mathematician at MIT, and since then, scientists have made significant strides in developing more powerful quantum processors.

One of the biggest challenges in developing quantum computers is maintaining the delicate quantum state of the qubits. Even the slightest disturbance, such as a stray photon or a vibration, can cause the qubits to lose their quantum state, which can lead to errors in calculations. To overcome this challenge, scientists are developing new materials and technologies, such as superconducting qubits and ion traps, that can better isolate the qubits from their environment.

Key Components of Quantum AI

Quantum AI is built on several key components, including quantum processors, quantum algorithms, and classical computers. Quantum processors are the heart of any quantum computer, and they are responsible for manipulating the qubits that represent data. Quantum algorithms, on the other hand, are the software programs that run on quantum processors, and they are designed to perform specific tasks, such as optimization or pattern recognition.

Classical computers are used to control and monitor quantum processors, as well as to process the output data generated by quantum algorithms. However, because quantum and classical computers operate on different principles, it can be challenging to integrate them seamlessly. To address this issue, researchers are developing new software tools and programming languages that can bridge the gap between quantum and classical computing.

Overall, quantum AI has enormous potential to revolutionize many fields, from finance and healthcare to energy and transportation. As quantum computers become more powerful and more widely available, we can expect to see even more exciting applications of quantum AI in the years to come.

Traditional Portfolio Management Techniques

Portfolio management is the process of constructing and managing a portfolio of assets to achieve a specific financial goal, such as maximizing returns or minimizing risk. Traditional portfolio management techniques rely on a combination of fundamental analysis, technical analysis, and market data to select investments and optimize allocation. While these techniques have been successful in generating modest returns, they are limited by their reliance on historical data and predetermined models.

How Quantum AI Enhances Portfolio Management

QuantumAItrading.net offers several benefits for portfolio management, including enhanced risk assessment and mitigation, optimized asset allocation and diversification, and increased efficiency and cost savings. By leveraging quantum algorithms to analyze complex market data, quantum AI can identify patterns and generate insights that traditional techniques cant match. Additionally, quantum AIs ability to process massive amounts of data simultaneously allows investors to perform real-time risk assessment, adjust portfolios to changing market conditions, and optimize asset allocation to achieve specific financial goals.

Real-World Examples of Quantum AI in Finance

Several financial institutions have already started to incorporate quantum AI into their investment strategies. For example, JPMorgan Chase launched a quantum AI platform in 2019 that uses quantum algorithms to optimize asset trading and portfolio management. Similarly, Goldman Sachs has partnered with QC Ware, a quantum computing startup, to explore the potential of quantum AI in finance. Other companies, such as Cambridge Quantum Computing, are developing quantum algorithms specifically designed for asset pricing and portfolio optimization.

Benefits of Quantum AI in Portfolio Management

Improved Risk Assessment and Mitigation

One of the key benefits of quantum AI in portfolio management is its ability to perform real-time risk assessment and mitigation. By analyzing complex market data in real-time, quantum AI can identify patterns and correlations that traditional techniques cannot, allowing investors to adjust their portfolios to changing market conditions and minimize risk.

Enhanced Decision-Making Capabilities

Quantum AIs ability to process massive amounts of data simultaneously also means that investors can make more informed and accurate investment decisions. By analyzing vast datasets with unparalleled speed and accuracy, quantum AI can generate insights that traditional techniques simply cant match, helping investors make better-informed investment decisions.

Optimized Asset Allocation and Diversification

Quantum AI also offers significant potential for optimizing asset allocation and diversification. By analyzing vast quantities of market data, quantum AI can identify correlations and relationships between assets that traditional techniques cannot. This allows investors to construct portfolios that are optimized for specific financial goals, such as maximizing returns or minimizing risk.

Increased Efficiency and Cost Savings

Finally, quantum AI presents significant potential for increasing efficiency and reducing costs in portfolio management. By automating many of the tasks traditionally performed by humans, such as data analysis and portfolio optimization, quantum AI can reduce the workload of portfolio managers and free them up to focus on higher-level tasks. This can lead to significant cost savings for investors, as well as increased efficiency and productivity.

Challenges and Limitations of Quantum AI in Portfolio Management

Technological Barriers and Scalability

While quantum AI presents significant potential for portfolio management, there are also several significant challenges and limitations that must be addressed. One of the primary challenges is technological, as quantum computers are still in the experimental phase and are not yet powerful enough to handle many of the complex tasks required in portfolio management. Additionally, scalability remains a significant challenge, as quantum algorithms and hardware are still in their infancy.

Data Privacy and Security Concerns

Another significant challenge is data privacy and security. As quantum AI algorithms become more sophisticated, they may be able to crack many of the encryption techniques currently used to protect sensitive financial data. This presents significant risks for investors, as well as regulatory and ethical concerns around data privacy and security.

Ethical Considerations and Bias in AI

Finally, there are ethical considerations around the use of AI in finance, particularly around bias and discrimination. As AI algorithms become more sophisticated, they may inadvertently introduce biases that perpetuate existing inequalities in the financial system. Additionally, the lack of transparency in AI algorithms presents challenges for regulators and investors alike, as it can be difficult to audit AI decision-making and ensure that it is fair and unbiased.

Conclusion

Despite these challenges, quantum AI presents significant potential for portfolio management, offering enhanced risk assessment and mitigation, optimized asset allocation and diversification, and increased efficiency and cost savings. As quantum computing technology continues to evolve and become more powerful, we can expect to see increased adoption of quantum AI in finance and other data-intensive fields. However, addressing the challenges and limitations of quantum AI will be critical to ensuring that it is used ethically and responsibly, and that it benefits investors and society as a whole.

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Unlocking the Potential of Quantum AI in Portfolio Management ... - NNN NEWS NIGERIA

The proposed technique is expected to enable quantum sampling algorithms to scale to high precision, paving the way for quantum advantage on practical problems

COLLEGE PARK, Md., May 04, 2023--(BUSINESS WIRE)--IonQ (NYSE: IONQ), an industry leader in quantum computing, in collaboration with the Fidelity Center for Applied Technology (FCAT), today announced an efficient and reliable design as a critical first step in the application of quantum computing to Monte Carlo methods. The first-of-its-kind state preparation technique is scalable and has been demonstrated on IonQ hardware for up to 20 qubits. The achievement may not only benefit financial institutions in tasks like portfolio management, but also other industries including science and engineering, where Monte Carlo algorithms are used. A research paper outlining the new technique is available here for viewing.

State preparation is a necessary component of many quantum algorithms and is fundamental in expediting Monte Carlo methods, which use randomness to simulate outcomes of complex problems. Financial institutions use Monte Carlo algorithms to understand the relationship between an outcome and multiple variables in complex systems, but their precision is frequently limited by the length of time needed to run the same algorithm repeatedly with different values of the variables. IonQ and FCAT believe that when run on large and accurate quantum computers, this state preparation technique will help these institutions achieve faster results.

"The Fidelity Center for Applied Technology team were early believers in the power of quantum to reshape the field of finance, and we are pleased to announce the development of a first-of-its-kind state preparation technique with them," said Peter Chapman, CEO, IonQ. "In finance, accuracy and speed can mean the difference between profit or loss. We believe this technique can provide financial institutions a tool they need to integrate quantum into their workflow and explore novel ways to inform portfolio engineering, retirement planning, and risk management in even the most complex of scenarios."

Story continues

Todays announcement is an extension of IonQs project with the FCAT team, during which the two groups issued a paper describing how certain generative quantum machine learning algorithms may provide an advantage over their classical counterparts. Additional information about IonQs work with FCAT will be presented at the American Physical Society (APS) Annual Meeting 2023, taking place March 5-10, 2023, in Las Vegas, Nevada.

"The Monte Carlo protocol is an integral component of financial planning, as it helps us understand how several correlated variables interact with each other when one element is changed," said Adam Schouela, Head of Emerging Technology, Fidelity Center for Applied Technology. "However, current state preparation techniques are either theoretical or have some type of deficiency when scaling. Today, were proud to announce alongside IonQ a state preparation algorithm that we believe is scalable and executable on NISQ hardware."

Todays announcement follows recent news that IonQ has acquired quantum software compiler Entangled Networks, with the goal of building large-scale quantum computers by enabling computation across multiple distributed quantum processors. Additionally, in 2022 IonQ entered into a multi-million-dollar contract with the U.S. Air Force Research Lab, as well as partnerships with companies like Dell Technologies, Hyundai Motors, Airbus, and GE Research, among others. For more information, visit investors.ionq.com/news.

About IonQ

IonQ, Inc. is a leader in quantum computing, with a proven track record of innovation and deployment. IonQ Aria is the latest in a line of cutting-edge commercial quantum systems, boasting industry-leading 25 algorithmic qubits. Along with record performance, IonQ has defined what it believes is the best path forward to scale.

IonQ is the only company with its quantum systems available through the cloud on Amazon Braket, Microsoft Azure, and Google Cloud, as well as through direct API access. IonQ was founded in 2015 by Dr. Christopher Monroe and Dr. Jungsang Kim based on 25 years of pioneering research. To learn more, visit http://www.ionq.com.

About the Fidelity Center for Applied Technology

Since 1999, the Fidelity Center for Applied Technology has served as an innovative technology resource for Fidelity Investments. FCAT research and development teams evaluate and track important trends in technology, identifying those that will matter most to Fidelity over the next five years. FCAT is a catalyst for breakthrough achievements, developing and bringing new capabilities and products to scale and building effective tools and systems that support millions of individual and institutional customers. Every day, FCAT teams lay the foundation for Fidelitys future, one experiment at a time.IONQ and Fidelity Investments are not affiliated.

IonQ Forward-Looking Statements

This press release contains certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Some of the forward-looking statements can be identified by the use of forward-looking words. Statements that are not historical in nature, including the words "anticipate," "expect," "suggests," "plan," "believe," "intend," "estimates," "targets," "projects," "should," "could," "would," "may," "will," "forecast" and other similar expressions are intended to identify forward-looking statements. These statements include those related to the application of quantum computing to Monte Carlo methods and its benefit to financial institutions and other industries where the Monte Carlo method is used; the ability of the state preparation technique to help institutions achieve faster results with Monte Carlo algorithms; the power of quantum computing to reshape the field of finance; the ability of financial institutions to integrate quantum computing into their workflow; and IonQs ability to build large-scale quantum computers by enabling computation across multiple distributed quantum processors. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this press release, including but not limited to: market adoption of quantum computing solutions and IonQs products, services and solutions; the ability of IonQ to protect its intellectual property; changes in the competitive industries in which IonQ operates; changes in laws and regulations affecting IonQs business; IonQs ability to implement its business plans, forecasts and other expectations, and identify and realize additional partnerships and opportunities; and the risk of downturns in the market and the technology industry. The foregoing list of factors is not exhaustive. You should carefully consider the foregoing factors and the other risks and uncertainties described in the "Risk Factors" section of IonQs Quarterly Report on Form 10-Q for the quarter ended September 30, 2022 and other documents filed by IonQ from time to time with the Securities and Exchange Commission. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and IonQ assumes no obligation and does not intend to update or revise these forward-looking statements, whether as a result of new information, future events, or otherwise. IonQ does not give any assurance that it will achieve its expectations.

View source version on businesswire.com: https://www.businesswire.com/news/home/20230504005416/en/

Contacts

IonQ Media contact:Tyler Ogoshipress@ionq.com

IonQ Investor Contact:investors@ionq.com

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IonQ and Fidelity Center for Applied Technology Announce Development of Scalable Quantum State Preparation for Monte Carlo Algorithms - Yahoo Finance

The U.S. Air Force Research Laboratory/Information Directorate (AFRL) is expected to have $500 million in anticipate funding over a five year period to support quantum research in five focus areas including:

This new solicitation is described in a recently issued government Broad Agency Announcement (BAA). They will be making multiple awards with individual awards expected to normally range from $500 thousand to $27 million in funding with a potential to make awards as high as $99.9 million. The timeframe for an individual project should not exceed 36 months.

The first step to receive funding is to submit a 2-4 white paper including a title, contact information, estimated cost, task objectives and technical summary and proposed deliverables. There are yearly recommended deadlines for submitting a white paper based upon the fiscal year. The next upcoming recommended deadline is September 30, 2023 for FY24. Upon receipt of a white paper, the AFRL will review it for:

White papers found to be consistent with the research areas of interest and expected results will be invited to submit a full technical and cost proposal for funding consideration..

For those interested in submitting a white paper you can download BAA FA8750-23-S-7001 with all the associated terms and conditions from the SAM.gov website here.

May 5, 2023

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AFRL Issues Broad BAA to Solicit Quantum Research Proposals with $500 Million in Potential Funding - Quantum Computing Report

NASHVILLE Gilbert Herrera was accustomed to never seeing the fruits of his labor.

After spending almost 40 years at Sandia National Laboratories, Herrera was appointed in 2021 as the head of the National Security Agencys Research Directorate, the largest research and development organization in the U.S. clandestine community.

One of the reasons why I decided to come to NSA is because I spent a career engaged in research and technology deployment in a field that you hope your research would never be tested, Herrera told The Record on Thursday during the Vanderbilt University Summit on Modern Conflict and Emerging Threats. Sandia, based in Albuquerque, New Mexico, is one of the federal governments most important nuclear science laboratories.

But at NSA, research eventually makes its way into mission, helping anyone ranging from lower-level agency analysts all the way to policymakers who seek better information.

The Record sat down with Herrera before his appearance at the summit to discuss the directorate, how it works to be relevant and the outlook for some of todays top emerging technologies. This transcript has been edited for length and clarity.

The Record: What is the job of the Research Directorate?

Gilbert Herrera: The role of the Research Directorate is really twofold.

One is, it needs to be the eyes and ears of the agency in terms of what's happening in research, in academia and in industry; to kind of have this outward-facing look. That's why we have a number of facilities that are unclassified, like universities and whatnot.

The other is to help prevent technology surprise on the inside. We help advise the agency, make sure we're prepared for that. And part of that is developing tools and techniques that help in a mission.

TR: How does the directorate stay operationally relevant? Do you keep an ear out for whats happening in the private sector or academia? Are you tinkering in a lab? Are you receiving orders from leadership to explore certain technologies?

GH: We have a lot of people who are outward-facing, but we also have people who are inward-facing and people that do both.

So having people go forward to work with the mission customers, so they could better understand what their needs are, so they could feel the pain. That helps inform the research. It's this virtuous cycle of gaining an understanding of the mission needs; understanding in part through practice and in part through observation of what the best opportunities are in the research regime. Then conducting tailored and focused research so you can create tools that will make the life of the analyst easier is really what an effective research organization and a mission organization does.

It's that virtuous cycle of understanding, awareness, creation and deployment.

TR: What's changed at the directorate under your tenure and where do you want to put emphasis?

GH: It's easy to get caught up in the moment of wanting to solve today's problem, but what I'm trying to do is make sure we have an adequate balance of today, tomorrow, and then the future transformational problems.

Because we need to do all three. An example of the today is, we actually have an email distribution list where if an analyst has a scientific question that they need answered, then it goes to this [distribution list] of a bunch of scientists called Scientist on Call and then they can answer it. That's an important thing for us to do. That's probably not research.

When the [Ukraine] war broke out, there were a number of things we did where we deployed researchers in order to help in the prompt.

The other is that as I came in, it was right around the time of changing focus away from looking at the war on terror. We also had China and Russia and Iran and others as priorities and more focusing on the realities of today. Our mission pivoted at the time that I came in, and so I'm working with my leadership team to make sure that we're following that pivot.

TR: Lets talk about some emerging technologies. Artificial intelligence. What excites you about it? What concerns you?

GH: What excites me is that it's developing at a level much faster than I ever expected. I've been involved in AI for a while and I never thought it could actually help research, per se.

But I'm beginning to change my mind on that.

I'm beginning to believe now that AI might be able to actually support science. What it's done relative to writing is beyond belief. These models are so big. I see that there's a lot more near-term opportunity.

Now from a threat perspective the most immediate one is that AI can now help the infamous Nigerian prince and other phishers to make more credible English-sounding attacks.

But it's much more than that in terms of potential for reverse engineering.

Right now, the big companies have said that they have put protections on it. You can't tell DALL-E to make child pornography or write stuff like that, but innovative people can find loops around that. The LLaMA model that Facebook had done has gotten into the outside world and people are already modifying that.

Bad actors will pursue ways to get around it and do bad things like child porn, like finding zero-day vulnerabilities and all these other terrible things.

The challenge we have with AI is similar to the challenge we had in 1968 when the protocols were developed for TCP IP. If you develop these technologies without envisioning how you integrate security into them, then that's a problem. I don't know how well we've done with AI. I don't have an opinion at this point.

TR: Lightning round time. Your take on quantum computing, encryption and a non-obvious threat we should be talking about today?

GH: Let me do the reverse order.

What are the energy ramifications of AI? These models take massive amounts of energy to train and to update [and utilize]. AI is going to help revolutionize things but at what energy cost? I don't think weve fully thought through the ramifications. It could be that in the final analysis, we save energy through AI, but I'm not sure that's obvious.

Now, relative to quantum, you really said two questions in one. The first one relative to the encryption part. NSM-10 came out. People need to take it seriously. There's guidance in moving to quantum-resistant encryption. NIST has published a bunch of standards. Something the government rarely does in my reading, they published some standards a year early, and more will come out this summer.

Now, back to computing. I've been involved in quantum computing since 2006. I am on the National Quantum Initiative Advisory Committee, and, over time, my optimism has diminished. I still am a believer in quantum computing. We'll get there someday, but it's a really tough problem.

We need the best minds working on it. The Laboratory for Physical Sciences, which is the physical science research arm of my organization, is funding academics all over the world in pursuit of dealing with the underlying problems for quantum computing because there are still a lot of unanswered questions.

What I hope we can do in quantum computing space is get through the quantum winter, because a winter is coming.

Remember, AI was coined in 1956. Then it ran into the reality of the compute they had. Then there was another revolution in the early 80s when the microprocessor came out and they realized you had neither enough information or compute power. The next one came towards the end of the 90s with the dawn of the internet, but the infrastructure wasn't there. We're finally successful now where we have sufficient compute power and information and new technologies like the GPU, so we can train models. There were, in my counting, three winters before we got to the final goal.

What I tell people is my own personal estimate is somewhere between and I stole this from somebody else 10 years and never.

Martin Matishak is a senior cybersecurity reporter for The Record. He spent the last five years at Politico, where he covered Congress, the Pentagon and the U.S. intelligence community and was a driving force behind the publication's cybersecurity newsletter.

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The NSA's research chief on emerging tech including 'beyond ... - The Record by Recorded Future