At what point will we declare that quantum technologies are no longer emerging, but have fully arrived? Whatever the breakthrough is that signals the tipping point, legal frameworks are not yet ready to handle the impacts of widespread quantum computing on people, societies and the rights they hold. Recent developments in the artificial intelligence (AI) policy space provide a useful roadmap for anticipating the evolution of policy approaches for regulating quantum technologies and the universe of risks they will bring with them.

Yet, as with AI, the risks are still under examined. Though we know that they will emanate from the ways in which quantum computing will amplify existing technologiessuch as AI and surveillance it is also clear they will stem from brand new capabilities, like breaking all current encryption or the application of quantum sensing (which will bring the ability to see through barriers, around corners, and potentially into the body or mind). This paper aims to shine a light on these risks, as well as the practical steps that can be taken today to address them.

The widespread release of generative AI models and applications in 2023 sent shockwaves through popular culture and signaled to world leaders and policymakers that the risks of artificial intelligence (AI) outstripped many of our existing risk management frameworks. It triggered an unprecedented wave of new efforts to plug the gaps, including The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, The Voluntary Commitments from Leading Artificial Intelligence Companies, The Bletchley Declaration, The Hiroshima Process, and the UN Advisory Body on AI Interim Report on Governing AI for Humanity, the NIST AI Risk Management Framework, and the EU AI Act, as well as the forthcoming Council of Europes Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law.

Stakeholders point out that the fourth-quarter rush to better govern AI parallels the pace of efforts to govern social media and the digital economy. They continue to urge policymakers to act with greater speed to safeguard against AI risks, including stronger application of existing human rights frameworks to manage AI risks. The wait-and-see approach to regulation is only justifiable when the benefits of innovation are clear and the risks are low, ill-defined or under examined. However, quantum computing, particularly in conjunction with AI, has many foreseeable dangers. Hard won lessons from recent tech policy history show us how critical it is for policymakers to safeguard quantum technologies before they are more widely deployed and accessible.

The risks that over-regulation can stifle innovation and cause technological leaders among nations, like the US, to be less competitive in a complicated geopolitical environment are real, and policy recommendations must balance these considerations. Considering quantum regulation now provides an opportunity to develop forward-looking, intentional policy frameworks that better balance the need for innovation with the need to safeguard human rights. Now is the time to begin these conversations before yet another Pandora opens a box of societal ills.

IBM, the United States foremost quantum developer, estimates that by 2030 the full power of quantum computing will be unlocked. If the companys estimates are accurate, there could be as little as six years to build the international consensus needed to establish guardrails for responsible and rights-respecting quantum computing, including updated standards for cryptography. If the past is precedent, it will take time for the global community to coalesce around approaches for integrating key human rights principles into innovation-friendly risk management frameworks for quantum, and it will take even longer for new and updated standards to be implemented. For example, in 2022 the Biden Administrations National Security Memorandum 10 on Promoting United States Leadership in Quantum Computing While Mitigating Risks to Vulnerable Cryptographic Systems, establishes 2035 as the date by which US Government entities should achieve a timely and equitable transition to quantum resistant cryptography to mitigate as much risk as possible. The time to start building consensus is now. What risks should policymakers and companies prioritize and what can be done to manage them?

To underscore the urgency of preventative policy action, we present three concrete examples of the potential dangers posed by quantum computing if we fail to take precautionary steps now. These three risks are among the most nearterm issues the world will confront as quantum technologies are deployed for everyday use: encryption breaking quantum computing, the pairing of quantum technologies with artificial intelligence for digital repression, and the application of quantum technologies to make thoughts legible to external observers (also known as mind reading).

First, a quick overview of what quantum technologies mean at this moment in time. In their groundbreaking 2021 book Law and Policy for the Quantum Age, Chris Jay Hoofnagle and Simson L. Garfinkel outline three areas in which quantum information science (QIS) will have the biggest nearterm impacts on nation states, decisionmakers (including investors), and individuals lives. Those areas are: quantum sensing, quantum computing, and quantum communications, which are defined below. The authors highlight that the nexus of these QIS sectors present a number of potential civil and political rights implications that existing policy frameworks do not yet address. Fundamental human rights standards can and will eventually be applied to prevent and address the application of QIS technologies in harmful ways. However, the slow, halting application of such standards in the social media and AI spaces, often in the wake of avoidable tragedies, teaches us that additional international consensus is required to better define and guide how human rights shape technology governance. The absence of fit-for-purpose frameworks enables bad or negligent actors to take advantage of the gray space to societys collective detriment.

For many readers, QIS technologies are likely not yet well known. Here are some basics:

While we are focused on the potential human rights risks that could result from more generally accessible quantum technologies, human-rights based risk frameworks can and should be developed to consider the broader range of risks relating to the application of QIS technologies across the tech stack and across all sectors of society, industry and national defense. This article outlines some of the most troubling risks, largely outside of the national security context, and suggests potential policy approaches that policymakers can prioritize in the coming decade.

In this age of hyper-connectivity, the sanctity of personal information underpins not only individual privacy but also the pillars of national security and global diplomacy. This sanctity is often secured by RSA encryption. In basic terms, RSA encryption involves two keys: a public key, which can be shared with everyone, and a private key, which is kept secret. When a message is sent, it is encrypted using the recipient's public key. This encrypted message can only be decrypted with the corresponding private key. The security of RSA stems from the fact that, while it's relatively easy to multiply two large prime numbers together to create a product, it's extremely difficult to do the reversethat is, to start with the product and find the original prime numbers. This one-way function is what makes RSA encryption among our most robust data privacy protections. The greatest supercomputers on the planet today would take millions of years to break this code. A seemingly invincible algorithm will meet its match, though, in the coming age of quantum computing.

Quantum computers are uniquely advantaged in solving this problem due to their fundamentally different approach to processing information. Qubits within a quantum computer exist in multiple states at once, in stark contrast to the binary nature of traditional bits. Quantum programs such as Shors Factoring Algorithm take advantage of this property in order to test an array of potential factors in the public key all at once. This fundamental distinction and other qualities allow these devices to determine the correct factors much faster than traditional computers. A sufficiently powerful quantum computer could cut the time needed to decode RSA encryption from eons to minutes.

Some experts hold that RSAs demise is a distant problem, given the current capabilities of quantum computers. While we are still jumping the technological hurdle of scaling quantum devices, and although Shors algorithm is computationally taxing, recent research such as that by NYU researcher Oded Regev may bring about quantum code-breaking much sooner than we once thought. Given the rapidly changing quantum landscape, with new research constantly being published, the uncertain timeline for these algorithms is all the more reason to be prepared.

The threats that this development poses to our data infrastructure are glaringly obvious. In addition to threatening the security of government secrets and citizens private information, an RSA breach could have significant human rights implications. Consider the nature of end to end encryption over messaging services that use RSA encryption such as Skype, Apple iMessage and Telegram. These tools provide human rights defenders and activists with a means of communication that is less vulnerable to unwarranted surveillance practices, enabling them to avoid arrest or detention for exercising protected civil and political rights. As quantum computers extend encryption breaking capabilities to repressive regimes, human rights defenders will become easy targets for government surveillance and repression. Repressive regimes may already be collecting currently uncrackable message contents in hopes they may be readable down the road using a Harvest Now, Decrypt Later methodology, a scenario that has already prompted some tech firms to act.

Adopting post-quantum cryptography will be logistically challenging and resource intensive, but it is an issue we must address urgently. The path is clear: establish a more forward-looking quantum policy agenda that mandates the overhaul of our encryption standards and software to elevate the use of algorithms that are safe against classical and quantum computation. The United States has already taken decisive action in this area. In 2022 the Biden Administrations National Security Memorandum 10 on Promoting United States Leadership in Quantum Computing While Mitigating Risks to Vulnerable Cryptographic Systems established 2035 as the date by which US Government entities should achieve a timely and equitable transition to quantum resistant cryptography to mitigate as much risk as possible. To support implementation of NSM-10, the US is developing standards for post-quantum encryption methods through The National Institute of Standards and Technology (NIST), which has already selected four quantum-proof encryption algorithms.

The development and integration of these standards into software and hardware requires concerted efforts from manufacturers and developers, including rigorous security and interoperability testing. Moreover, the update of critical infrastructure and services must be prioritized to uphold security and trust. Regulatory adjustments by governments to foster or enforce the adoption of these new encryption standards are essential, alongside public education initiatives to highlight the importance of embracing these updates for enhanced security. Continuous research and adaptation are imperative to counteract evolving cyber threats and technological innovations, effectively future-proofing encryption methods. The degree to which new standards are implemented depends upon the availability of sufficient resources to convert encryption systems. Those resources will only be made available if government and private sector stakeholders are sufficiently aware of impending risks and motivated to prioritize often scarce resources.

Academics, policymakers and civil society groups have raised alarm bells in recent years to draw attention to the risks posed by the misuse of technology, including artificial intelligence, to repress political opposition, surveil activists and control populations. As authoritarian (and some democratic) regimes increasingly harness technology to repress the public and retain or expand power, threats to fundamental civil and political rights are growing. While policymakers currently have their hands full developing human rights frameworks and safeguarding tools to better identify and manage the risks of artificial intelligence, advances in QIS will not wait. As human rights and technology scholar Vivek Krishnamurthy warns us, Quantum technologies may not yet be at the level of development where their potential impacts can be examined in detail. Even so, now is the time for the [quantum science and technology] and human rights communities to begin a dialogue to prepare for the deployment and commercialization of these technologies in a rights-respecting manner.

While many unknowns remain, there are a number of risks that are more foreseeable, as described below. Is there a way to shape evolving AI risk management frameworks to account for the additional impacts of AI combined with quantum technologies? For example, guardrails that mitigate the risks of AI-powered data fusion and social scoring would go a long way to mitigating the compounded impacts when AI is combined with quantum technologies. In addition to building upon the policy roadmap provided by AI governance frameworks in the future, is it possible to embed additional, quantum-facing risk management measures now?

AI is already being used by autocratic governments to better track political opposition and activists, and to coerce support for autocratic regimes through denial of needed government services. As noted in the 2020 Senate Foreign Relations Committee report on the use of surveillance and big data analytics in the Peoples Republic of China, artificial intelligence, facial recognition technologies, biometrics, surveillance cameras, and big data analytics [are being used] to profile and categorize individuals quickly, track movements, predict activities, and preemptively take action against those considered a threat in both the real world and online. Through big data analytics, algorithms conglomerate personal data and surveillance data surrounding ones behavior, activities, and social interactions in order to track or even score individuals. This process requires the analysis of a huge amount of data, which is challenging for classical computers on a massive scale, but ideal for quantum systems. Quantum computers ability to handle vast amounts of data at high speeds will enable disturbingly sophisticated and invasive analysis of personal behaviors and social interactions. This increased computational power allows for the real-time monitoring and scoring of individuals on a more granular level, super-sizing tactics for authoritarian control and surveillance.

As alluded to above, real-time remote biometric surveillance equipment creates the capacity to track individuals. Digital identification and centralized databases for this information create the potential for governments and for-profit enterprises to misuse such systems to monitor individuals through the use of big data analytics. Artificial intelligence can make sense of this data in order to create profiles of citizens which aim to distinguish one person from another based on collected biometric information. The Carnegie Council estimates that over 100 US cities are currently using data fusion technologies to track individuals through doorbell cameras, license plate readers, digital utility meters, street cameras, and GPS technologies, in a way that can create extensive individual profiles. Data fusion is defined as bringing data points together to create a swarm of information that can reveal a great deal about a traceable individual. The Carnegie Councils Data Fusion Mapping Tool provides an overview of the impacts of data fusion on the exercise of civil liberties in the US and highlights the risks of allowing data fusion to be used in jurisdictions without adequate due process or other risk mitigation measures.

AI-powered data fusion is not yet universally used. Now is the time to consider the implications of a super-sized universal data fusion capacity powered by quantum computing technology. Quantum-powered data fusion could make it impossible for an individual to evade tracking due to the power to process massive amounts of data pulled from unlimited public or private sector sources. Quantum computers will further expand the ability of surveillance systems to recognize your gait across millions of hours of surveillance footage, single out your voice from an audio recording of a crowded room, or identify you from the cadence of your keystrokes, without needing to read the text you send. Whether moving through city streets, participating in protest, or simply enjoying the supposed solitude of open spaces, the shadow of surveillance looms large, with quantum-enhanced systems capable of sifting through the haystack of data to pinpoint the needle of an individual identity with astonishing precision. In short, the birth of quantum computing may signal the death of anonymity.

Due to their ability to analyze huge data sets and recognize patterns or deviations from those patterns, quantum computers detect anomalies far more effectively than do classical computers. When fed surveillance data regarding the behavior of an individual, a future quantum computer would have the power to determine if that behavior deviates from their usual conduct, and ascertain what future actions will likely stem from this abnormality. Human rights concerns arise if and when this technology is applied for the purpose of predictive policing. Detaining or questioning individuals based on predicted future actions blurs the line between potential and actual wrongdoing. If left unchecked, this predictive technology could be used to further erode the line between intent to potentially commit a crime and the criminal act itself.

Lawmakers are working to enact safeguards needed to address risks that can result from the application of artificial intelligence for certain uses and in certain contexts. For example, the EU AI Act will prohibit social scoring, certain applications of predictive policing, and remote biometric identification for law enforcement purposes in public settings. There is not yet global consensus supporting prohibition of these uses of AI, and there are clear concerns that such prohibitions will stifle innovation or constrain law enforcement. The fact remains that international consensus for innovation-friendly AI safeguards are urgently needed before the riskiest use cases outlined above become commonly accepted practice. Such guidelines, many of which are under development by the United Nations, OECD (in multiple papers), and other international bodies, will provide an invaluable roadmap for launching similar efforts to constrain the misuse of quantum-based technologies for digital repression.

Beyond the policy realm, are companies taking up the challenge to design, develop and deploy QIS in ways that protects us from extreme misuse cases? If QIS is deployed in tandem with data-driven AI technologies, then the biases and inaccuracies that can emerge from AI applications would be substantially scaled beyond what we see today. How will existing algorithmic bias audits or similar safeguards be tweaked to consider the potential impacts of the quantum age? What role can regulation play in prompting companies to take such steps without stifling innovation or hampering law enforcement? How can we advance such efforts now, before pandora opens the box? And perhaps most urgently, can we apply a quantum lens to the development of AI governance frameworks today that may help us mitigate tomorrows risks?

We are already living in a time when machines are capable of translating your brain activity, as seen through an MRI, into words. Your very thoughts are now legible. Surveillance cameras are similarly trained to register your emotionsthis is a form of emotional AI, which companies are already using to improve targeted sales. Do you have the right not to have your mind or emotions read? This is a question we will need to resolve before quantum computing amplifies the capabilities of mentally intrusive technologies.

Quantum computers are likely to further amplify the power that classical computers already have to identify patterns and correlations in MRI brain scan images that classical computers cannot. Consider again the question of arrests made possible by quantum computing. Is a quantum powered lie detector testone using an MRI machine and a sufficiently powerful quantum AI algorithm, instead of a heart rate monitoradmissible in court? To take it a step further, is intent to commit a crime, if recognized through the power of a quantum mind-reader, grounds for legal intervention? And what guardrails would be required to ensure that the data sets upon which such algorithms are based are free from bias and inaccuracy? While these applications of quantum computing are more speculative than the inferences made above, they are potentially more urgent given the degree of possible harm and the absence of targeted human-rights frameworks or safeguards.

Critical questions about the limits of brain legibility do not appear to be at the forefront of most AI policy conversations, which leads one to conclude they will be similarly sidelined in future engagements on the intersection of human rights and quantum computing. Policies that establish human rights-based neurological safeguards are still underdeveloped. Now is the time to better define them. While we are far from an international consensus, one initial effort to define neurorights identified five categories that could be helpful in considering the impact of quantum-powered brain legibility. Those rights are: the right to mental privacy so that our brain data cannot be used without our consent; the right to free will, so we can make decisions without neuro technological influence, the right to personal identity so that technology cannot change our sense of self, the right to protection from discrimination based on brain data, and not least, the right to equal access to neural augmentation. International policy conversations outlining the application of human rights in this context are urgently needed and long overdue. It is unclear whether the neurorights discussion will attract global attention. Fortunately, policymakers have a wealth of existing human rights to consider in connection with emerging quantum mind-reading risks, including the right to bodily integrity that protects autonomy over ones body.

It is too early to identify the full range of potential impacts that QIS technologies may have on individuals and societies. However, experience establishing safeguards in connection with the internet, social media and artificial intelligence shows how difficult it can be to erect risk management efforts after economic models are entrenched or unregulated behaviors coalesce into accepted practice, regardless of their impacts. Now is the time to raise awareness of the foreseeable risks and increase research on risks that are less well understood. Increased advocacy by stakeholders from civil society, consumer protection organizations and academic institutions will help to justify allocation of the resources needed to achieve the recommendations outlined above. Financial commitments by public and private sector entities will be necessary to support a transition to quantum-ready encryption by 2035. Resources will also be needed to support policy analysts in considering if and how quantum considerations can be accommodated in todays AI risk management frameworks. And perhaps most importantly, QIS developers must allocate sufficient resources to understand the impacts that brand new capabilitieslike quantum sensingwill have on individuals and society as a whole.

The quantum computing community has a great deal to learn from recent efforts to apply the UN Guiding Principles for Business and Human Rights to generative AI models and applications. Such efforts provide a roadmap for better weaving human rights-based enterprise risk management approaches to govern QIS for governments and businesses alike. QIS stakeholders are fortunate to have an opportunity to build upon the evolving international consensus being hammered out now for AI.

The risk quantum computing poses to RSA encryption is already well understood, and NIST has established important guidelines for bringing encryption standards into the quantum age. However, as noted above, this shift will require significant policy support and even public funding to ensure that the pace of transition matches evolving quantum capabilities.

While world leaders and policymakers have their hands full addressing the most urgent AI-related risks, parallel questions in the QIS space will become increasingly urgent as we near 2030. Bandwidth among policymakers in the technology space is more limited than ever, and one can argue that regulating quantum risks should take a backseat when compared with the urgency of present day impacts of AI. While the risks may be years awaythey will also be significant. This moment offers an important opportunity for the legions of organizations, think tanks and academics who moved quickly to respond to evolving risks of generative AI to now turn their attention to the QIS horizon. This is the time to prepare the same level of thoroughly researched, insightful and practical recommendations for innovation-friendly QIS risk management that will enable policymakers and companies to take action beforeglobal society becomes a real-time testbed for identifying QIS impacts.

This article represents the opinions of the authors and in no way reflects the position of the United States Government or USAID. Thanks go to Stanley Byers, Chris Doten and Paul Nelson for their contributions to this article.

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Are We Ready for the Quantum Age? Preparing for the Risks of Quantum Technologies with Rights-Respecting Policy ... - Tech Policy Press

Quantum computing is a million-dollar question, even for Google. Thats why they started the XPRIZE Quantum Applications competition with GESDA. Theyre looking for ideas from people all over the world on how quantum computing can help solve things like healthcare, finance, and more. Google hopes this will lead to practical uses for quantum technology, making life better for everyone.

The XPRIZE Quantum Applications competition, launched by Google in collaboration with the Geneva Science and Diplomacy Anticipator (GESDA), is a global initiative aimed at discovering real-world applications for quantum computing technology. Heres a detailed explanation of how the competition works:

While the immediate goal of the competition is to identify and reward innovative quantum computing applications, its broader impact lies in accelerating the development and adoption of quantum technologies. By incentivizing research and development in this emerging field, the competition aims to unlock new possibilities for solving complex problems and driving societal progress.

Overall, the XPRIZE Quantum Applications competition represents a collaborative effort to harness the transformative power of quantum computing for the benefit of humanity. Through targeted investments, rigorous evaluation, and global participation, the competition endeavors to chart a path towards a future where quantum technologies play a central role in addressing some of the worlds most pressing challenges.

Quantum computing is like a puzzle waiting to be solved we know its powerful, but figuring out exactly how to use it in the real world is still a bit of a mystery. However, weve got some exciting ideas brewing.

Imagine if we could speed up the process of finding new medicines by using quantum computers to simulate how molecules interact. It could help us discover life-saving drugs faster than ever before. Want to learn the role of generative AI in drug discovery? Visit the related article and explore.

Quantum computers have a knack for solving complex puzzles, like figuring out the most efficient routes for delivery trucks or finding the best investment strategies in finance. Its like having a supercharged brain for solving problems.

Traditional encryption methods might not stand a chance against quantum computers, but on the flip side, we can use quantum principles to create ultra-secure communication channels. Its like turning the tables on hackers and making our data safer than ever.

Quantum algorithms could revolutionize machine learning, allowing us to crunch through mountains of data at lightning speed. The possibilities are endless, from recognizing images to understanding languages.

By harnessing the immense computational power of quantum computers, we could create more accurate climate change models. This could give us a better understanding of environmental processes and help us find ways to protect our planet.

So, while were still figuring out all the ins and outs of quantum computing, theres no shortage of exciting possibilities on the horizon. Who knows what other amazing applications well discover as we continue to unlock the mysteries of this cutting-edge technology? Probably, the winner of XPRIZE Quantum Applications competition.

Featured image credit: Jp Valery/Unsplash

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Google Is Ready To Burn $5M To Learn How To Use Quantum Computing - Dataconomy

Notre Dame's Maddy Westbeld joins the show to discuss her love of basketball, how coffee and meditation in the morning is a big part of her process, being recruited to Notre Dame, meeting Dara for the first time, and how the team has come together over the five game winning streak.

3:00 - When she fell in love with basketball

4:02 - Development of perimeter game

5:35 - First interaction between Dara and Maddy

7:35 - What it was like to play with Mabrey

9:10 - Recruiting process

11:05 - Love of coffee

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23:28 - Thoughts on Notre Dames five-game winning streak

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Dimes with Dara | Notre Dame's Maddy Westbeld On Hoops, Coffee, Meditation & Winning - Irish Sports Daily

The main state-controlled insurer in Italy is guaranteeing a petrochemical project in Uzbekistan that could be backed by Russias Gazprombank, an investigation showed, raising doubts about a potential indirect collaboration between Italian institutions and a lender under U.S. and U.K. sanctions.

The investigation by Re:Common, an Italian environment and corruption watchdog, drew a link between SACE, the state-owned Italian insurer, and an engineering company owned by Bakhtiyor Fazilov, a businessman from Samarkand, and allegedly bankrolled by Gazprombank.

Uzbekistans Ministry of Energy signed a memorandum of understanding in 2021 with Vnesheconombank (VBE), a Russian state-owned foreign investment bank, and Gazprombank, among others for the development of a new gas-to-chemical complex in Karakul, a special economic zone in the Bukhara region.

Shortly after the project was kickstarted, Versalis, the petrochemicals subsidiary of Italys ENI (which in turn is 30 percent owned by the Italian government), won a tender with the complexs main contractor, the Singapore-based Enter Engineering Pte. Ltd. Other Italian companies also won tenders for specific supplies.

With a $3 billion commitment, Singapore-based Enter Engineering Pte. Ltd. is the main contractor of the project and has unequivocal links to Russia through Fazilov, who also owns Eriell, an oilfield service group.

According to industry data seen by Re:Common, SACE is guaranteeing the financing of at least two deals worth 51.4 million euro. The first is an 11.4 million euro Front-End Engineering and Design (FEED) service that Enter Engineering subcontracted to the Italian branch of Wood, a Scotland-based engineering company. Italys Unicredit, one of the countrys largest lenders, is the financial link of the operation, figuring in the contract as the facility agent. The second is a 40 million euro deal to supply industrial machinery, the financing of which was set up by Unicredit as the mandated lead arranger. An Italian company is poised to supply the machinery to a plastic bags factory in Uzbekistan.

Essentially, should Enter Engineering fail to meet its contractual obligations and pay the Italian suppliers, SACE would step in and compensate the companies, while attempting to collect the debt via other legal means. In case of default, according to Re:Common, Enter Engineering could be subjected to a previous put and call agreement that the company seems to have with Gazprombank.

Through a complex web of relations with Cyprus-based companies related to both Fazilov and Gazprombank, the worst-case scenario for Enter Engineering could mean that its shares could be transferred to the sanctioned Russian bank.

A worst-case scenario, though potentially unlikely, should be taken into account by the insurer, which manages 300 billion euro in savings of Italian taxpayers.

Investigations from 2023 support the findings by Re:Common, especially regarding the links between companies owned by Fazilov and sanctioned Russian entities and individuals.

Radio Ozodlik, RFE/RLs Uzbek Service, found that the granting of development and extraction rights [and contracts] to obscure offshore firms located in Cyprus, Singapore, China, and Great Britain, among other jurisdictions, are grounded primarily on decrees issued by [Uzbekistans President Shavkat] Mirziyoyev himself.

Within this context, the principal beneficiary has been Russias gas giant Gazprom, specifically via ties to Fazilov.

A detailed report of the investigation was published by Kristian Lasslett, a professor at the University of Ulster focusing on corruption.

The report indicates that companies tied to Uzbekistans and Russias governments formed an international consortium, or as the dossier puts it an octopus. Given that Russian stakeholders exercise significant control and that the consortium has secured a sizable share in Uzbekistans gas and oil fields, gas storage and oil/gas refining capability, the report concludes that the Kremlin [holds] potential leverage over Uzbekistan through one of its key industries.

Given the right of reply, Fazilov answered sharply: We hereby confirm you that your information is grossly incorrect, inaccurate and incomplete. The businessman, however, did not specify which part of the report contained factual mistakes.

While the agreements between SACE and the main contractors and backers of the petrochemical complex pre-date the start of Russias war of aggression in Ukraine, the contracts might have to be reconsidered in light of the current risks associated with the Russian role in the Uzbek project. As Re:Commons report concludes, over the long term SACE could now end up helping one of Russias most important banks.

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Italian Insurer Buttresses Russian Gas Investment in Uzbekistan - The Diplomat

SINGAPORE - Media OutReach Newswire - 29 February 2024 - In a groundbreaking move to democratize access to premier, practice-based investment management education worldwide, the Wealth Management Institute (WMI) announced the global launch of the Dalio Market Principles (DMP) Online Program. The launch event was attended by over 80 distinguished leaders in wealth and asset management, as well as family offices, across Singapore and the region.

Fireside chat at the Global Launch of the Dalio Market Principles (DMP) Online Program with Ray Dalio, moderated by Foo Mee Har.

Based on the research and insights developed by the renowned investor Mr. Ray Dalio over five decades, this Program is designed to help learners understand the universal linkages driving markets and economies. The Program also offers a world-class pathway for participants to advance their skills and capabilities in investing. It benefits not only investors seeking to sharpen their insights, but also finance professionals across various sectors, including asset management, wealth management, family offices, reserves management, and policy. To ensure greater accessibility for learners worldwide, the Program leverages cutting-edge technology, such as generative AI, to provide an immersive digital learning experience that is accessible anytime and from any location.

As the world confronts what Mr. Dalio identifies as 'a changing world order,' the launch of the program is timely in providing investors with a practical, market-tested framework to understand market cycles and how to navigate them effectively.

"The world is changing in ways that we've not experienced in our lifetimes but have happened many times in the past," says Mr Ray Dalio, Founder, CIO Mentor and Member of the Board, Bridgewater Associates. "To deal with these changes well, investors need to recognize the deeper patterns of history and the underlying timeless and universal principles driving markets and economies. My hope for this course is that it provides this knowledge as well as an opportunity for investors to further refine their own investment principles."

Commenting on the launch, Ms. Foo Mee Har, CEO of WMI, says, "With Singapore being a leading global wealth management hub, we are thrilled to introduce this first-of-its-kind investment education initiative. The Dalio Market Principles Online Program is designed to empower finance professionals and investors with a strong foundation for understanding the forces driving markets, enabling participants to build investment portfolios tapping drivers of differential asset returns, economic and currency cycles, and paradigm shifts. Most importantly, this program is designed to fully equip those aspiring to establish successful careers in various sectors of the finance industry."

To support local residents with capability development, the DMP Program is accredited by the Institute of Banking and Finance (IBF), and eligible participants may receive up to 70% course fee subsidies. The Program is also SkillsFuture Credit claimable.

The DMP Online Program is designed to provide a flexible and highly interactive online learning experience. Participants will be able to 'live through' major market events as an investor or policymaker, assess how their expectations for the future and investment decisions measure up, and learn from mistakes. The Program's purpose-built Portfolio Simulator will enable them to create and stress test portfolios, evaluating their investment performance across different macro scenarios with data from up to 40 major economies over the last 120 years.

Providing participants with a personalized learning experience, the Program's innovative Generative AI Tutor will guide learners through the curriculum. Fully integrated into the course, the Tutor will be able to answer questions, generate quizzes, get feedback, and search through a vast repository of Mr Dalio's writings over the past decades, for participants to learn at their own pace.

In addition, participants will also gain opportunities to learn, network and thrive with peers and leaders from the industry. Through cohort-based activities such as discussion forums and live online workshops, participants will be able to glean multiple perspectives on investments and the financial markets from industry players from diverse backgrounds. The Program also lays a foundational pathway for those seeking excellence as a great investor, which will later include opportunities to apply for a Dalio Fellowship to be launched in the future.

For more information about the Dalio Market Principles Online Program, please visit http://www.wmi.edu.sg/dmp-online.

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Wealth Management Institute

Established in 2003, the Wealth Management Institute (WMI) is committed to building capabilities for investing in a better tomorrow. Founded by GIC and Temasek, our vision is to be Asia's Centre of Excellence for wealth and asset management education and research. WMI is appointed as Singapore's Lead Training Provider for Private Banking by the Institute of Banking and Finance Singapore (IBF) and supported by the Monetary Authority of Singapore (MAS).

WMI also helms the Global-Asia Family Office Circle, a network platform that fosters a trusted environment to build capabilities and community in the family office sector. The GFO Circle is supported by the Singapore Economic Development Board (EDB) and the Monetary Authority of Singapore (MAS).

WMI provides a comprehensive suite of practice-based certification and diploma programmes and collaborates with leading universities for master's qualifications. With over 20,000 annual enrolments, WMI provides training in asset management, wealth management, compliance, risk management, family office, as well as the development of the next generation across more than 100 programmes.

About Raymond T. Dalio, Founder, CIO Mentor, and Member of the Bridgewater Board, Bridgewater Associates, LP

A global macro investor for more than 50 years, Ray Dalio founded Bridgewater Associates out of his two-bedroom apartment in NYC and ran it for most of its 47 years, building it into the largest hedge fund in the world. Ray remains an investor and mentor at Bridgewater and serves on its board. He is also the #1 New York Times bestselling author of Principles: Life and Work, Principles for Dealing with the Changing World Order, and Principles for Navigating Big Debt Crises. He graduated with a B.S. in Finance from C.W. Post College in 1971 and received an MBA degree from Harvard Business School in 1973. He has been married to his wife, Barbara, for more than 40 years and has three grown sons and five grandchildren. He is an active philanthropist with special interests in ocean exploration and helping to rectify the absence of equal opportunity in education, healthcare, and finance.

About the Dalio Market Principles (DMP) Online Program

The Dalio Market Principles (DMP) Online Program is a global initiative by the Wealth Management Institute and supported by Dalio Philanthropies, to help investors understand the underlying linkages driving markets and economies, and give them the investment skills to navigate the changes.

This first-of-its-kind programme is developed with Ray Dalio, Founder and CIO Mentor of Bridgewater Associates. Ray has been a global macro investor for over 50 years, and his investment innovations and thought leadership have made a lasting mark on the industry.

The Program provides a practical, market-tested framework to understand market cycles and the applied skills to navigate them effectively. It also covers Ray's key investment principles underlying his passive and active investment strategies.

The Program is designed to provide a flexible and highly interactive online learning experience, with cutting-edge tools including a Generative AI Virtual Tutor and a Portfolio Builder to create and stress-test portfolios. As the body of knowledge is captured online, it allows learners to access it anytime, anywhere at their own pace.

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The Wealth Management Institute Unveils First-of-its-kind Investment Education Program Based On The Market Principles Of Investment Legend Ray Dalio -...

Updated at 11:52 a.m. ET on February 16, 2024

ChatGPT exploded into the world in the fall of 2022, sparking a race toward ever more advanced artificial intelligence: GPT-4, Anthropics Claude, Google Gemini, and so many others. Just yesterday, OpenAI unveiled a model called Sora, the latest to instantly generate short videos from written prompts. But for all the dazzling tech demos and promises, development of the fundamental technology has slowed.

The most advanced and attention-grabbing AI programs, especially language models, have consumed most of the text and images available on the internet and are running out of training data, their most precious resource. This, along with the costly and slow process of using human evaluators to develop these systems, has stymied the technologys growth, leading to iterative updates rather than massive paradigm shifts. Companies are stuck competing over millimeters of progress.

As researchers are left trying to wring water from stone, they are exploring a new avenue to advance their products: Theyre using machines to train machines. Over the past few months, Google Deepmind, Microsoft, Amazon, Meta, Apple, OpenAI, and various academic labs have all published research that uses an AI model to improve another AI model, or even itself, in many cases leading to notable improvements. Numerous tech executives have heralded this approach as the technologys future.

This is a scenario that countless works of science fiction have prepared us for. And, taken to the extreme, the result of such self-learning might be nothing less than eschatological. Imagine GPT-5 teaching GPT-6, GPT-6 teaching GPT-7, and so on until the model has surpassed human intelligence. Some believe that this development would have catastrophic results. Nine years ago, OpenAIs CEO, Sam Altman, blogged about a theoretical AI capable of recursive self-improvementand the prospect that it would perceive humans in the same way that we perceive the bacteria and viruses we wash from our hands.

Read: AI doomerism is a decoy

We are not anywhere close to the emergence of superintelligence, as pundits call it. (Altman speaks often of AIs supposed existential risk; its good PR.) Even so, more modest programs that teach and learn from one another could warp our experience of the world and unsettle our basic understandings of intelligence. Generative AI already detects patterns and proposes theories that humans could not discover on their own, from quantities of data far too massive for any person to comb through, via internal algorithms that are largely opaque even to their creators. Self-learning, if successful, might only magnify this issue. The result could be a sort of unintelligible intelligence: models that are smart, or at least capable, in ways humans cannot readily comprehend.

To understand this shift, you have to understand the basic economics behind AI. Building the technology requires tremendous amounts of money, time, and information. The process begins with feeding an algorithm enormous amounts of databooks, math problems, captioned photos, voice recordings, and so onto establish the models baseline capabilities. Researchers can then enhance and refine those pre-trained abilities in a couple of different ways. One is by providing the model with specific examples of a task done well: A program might be shown 100 math questions with correct solutions. Another is a trial-and-error process known as reinforcement learning that typically involves human operators: A human might evaluate a chatbots responses for sexism so the program can learn to avoid those deemed offensive. Reinforcement learning is the key component to this new generation of AI systems, Rafael Rafailov, a computer scientist at Stanford, told me.

This is not a perfect system. Two different people, or the same person on different days, can have inconsistent judgments. All of those evaluators work at a slow, human pace, and require payment. As models become more powerful, they will require more sophisticated feedback from skilled, and thus better-paid, professionals. Doctors might be tapped to evaluate a medical AI that diagnoses patients, for instance.

You can see why self-learning holds a special appeal. Its cheaper, less labor-intensive, and perhaps more consistent than human feedback. But automating the reinforcement process comes with risks. AI models are already riddled with imperfectionshallucinations, prejudice, basic misunderstandings of the worldwhich they pass along to users through their outputs. (In one infamous example last year, a lawyer used ChatGPT to write a legal brief and ended up citing cases that didnt exist.) Training or fine-tuning a model with AI-generated data may amplify those flaws and make the program worse, like simmering a toxic stock into a thick demi-glace. Last year, Ilia Shumailov, then a junior research fellow at Oxford University, quantified one version of this self-destructive cycle and dubbed it model collapse: the complete degeneration of an AI.

To avoid this problem, the latest wave of research on self-improving AI uses only small amounts of synthetic data, guided by a human software developer. This approach relies on some sort of external check, separate from the AI itself, to ensure the quality of the feedbackperhaps the laws of physics, a list of moral principles, or some other, independent criteria already deemed true. Researchers have seen particular success with automating quality control for narrow, well-defined tasks, such as mathematical reasoning and games, in which correctness or victory provide a straightforward way to evaluate synthetic data. Deepmind recently used AI-generated examples to boost a language models ability to solve math and coding problems. But in these cases, the AI isnt learning from another AI so much as from scientific results or other established criteria, Rohan Taori, a computer scientist at Stanford, told me. Today, self-learning is more about setting the rules of the game, he said.

Read: A machine crushed us at Pokmon

Meanwhile, in cases of training AI models with more abstract abilities, such as writing in a pleasant tone or crafting responses that a person would find helpful, human feedback has remained crucial. The furthest-reaching vision of AI models training themselves, then, would be for them to learn to provide more subjective feedback to themselvesto rate how helpful, polite, prosodic, or prejudiced a chatbot dialogue is, for instance. But to date, in most research, language-model feedbacks training of other language models stops working after a few cycles: Perhaps the second iteration of the model improves, but the third or fourth plateaus or worsens. At some point, the AI model is just reinforcing existing abilitiesbecoming overconfident about what it knows and less capable at everything else. Learning, after all, requires being exposed to something new. Generative-AI models in use today are data-torturing machines, Stefano Soatto, the vice president of applied science for Amazon Web Services AI division, told me. They cannot create one bit of information more than the data theyre trained on.

Soatto compared self-learning to buttering a dry piece of toast. Imagine an AI model as a piece of bread, and its initial training process as placing a pat of butter in the center. At its best today, the self-learning technique simply spreads the same butter around more evenly, rather than bestowing any fundamentally new skills. Still, doing so makes the bread taste better. This kind of self-trained, or buttered, AI has recently been shown, in limited research settings, to provide more helpful summaries, write better code, and exhibit enhanced commonsense reasoning. Superintelligence might be beside the point if self-improving AI can reliably cut costs for OpenAI, Google, and all the rest by simulating an infinite army of human evaluators.

But for true evangelists, the dream is for self-learning to do more than thatto add more butter to the slice of toast. To do that, computer scientists will need to continue to devise ways of verifying synthetic datato see whether more powerful AI models can ever serve as reliable sources of feedback, and perhaps even generate new information. If researchers succeed, AI could crash through the ceiling of human-made content on the web. In that case, a sign of true artificial intelligence may well be artificial teaching.

AI may not need to attain the capacity for more holistic self-improvement before it becomes unrecognizable to us. These programs are already labyrinthineit is frequently impossible to explain why or how AI generated a given answerand developing a process whereby they take their own lead would only further compound that opacity.

You could call it artificial artificial intelligence: AI that might not perceive or approach problems in ways humans readily relate to. It would be similar, perhaps, to how people cannot fully grasp how dogs use their noses, or bats their ears, to orient themselveseven as smell and echolocation are excellent ways of navigating the world. Machine intelligence might be similarly difficult to fathom, simultaneously of this world and unfamiliar.

Such strange behaviors have already cropped up in far from superintelligent ways. Asked to achieve a specific goalproviding helpful chatbot responses, flipping pancakes, moving blocksvery often those [reinforcement-learning] agents learn how to cheat, Shumailov said. In one example, a neural network plugged into a Roomba that was learning not to bump into anything just learned to drive backwardbecause the bumper sensors were all on the front of the vacuum.

Read: Science is becoming less human

This will be less funny when an AI model is used to align another model with a set of ethical principlesa constitutional AI of sorts, as the start-up Anthropic has dubbed the concept. Already, different people see different interpretations of abortion, gun ownership, and race-conscious admissions in the U.S. Constitution. And while human disagreements over the law are at least legible and debatable, it might be difficult to understand how a machine interprets and applies a rule, especially over many cycles of training, producing subtly harmful results. An AI instructed to be helpful and engaging could turn aggressive and manipulative; rules to prevent one form of bias might breed another. Computer-generated feedback, for all the ways a human can tweak it, might offer a false sense of control, Dylan Hadfield-Menell, a computer scientist at MIT, told me.

Although those opaque inner workings have the potential to be dangerous, rejecting them on principle could also mean rejecting revelation. Having ingested an internets worth of information, self-training AI models might bring out genuinely important patterns and ideas that are already embedded in their training data but that humans cannot elicit or fully comprehend. The most advanced chess-playing programs, for instance, learned by playing millions of games against themselves. These chess AIs play moves that elite human players struggle to comprehend, and utterly dominate those playerswhich has caused a reevaluation of chess at the highest human level.

Shumailov put it this way: In the 17th century, Galileo correctly asserted that the Earth revolves around the sun, but this was rejected as heresy because it didnt align with existing belief systems. The fact that weve managed to realize some knowledge does not necessarily mean that well be able to interpret this knowledge, Shumailov said. Perhaps we will ignore the outputs of some AI models, even if they are later found to be true, simply because they are incommensurate with what we currently understandmath proofs we cant yet follow, brain models we cant explain, knowledge we dont recognize as knowledge. The ceiling provided by the internet may simply be higher than we can see.

Whether self-training AI leads to catastrophic disaster, subtle imperfections and biases, or unintelligible breakthroughs, the response cannot be to entirely trust or scorn the technologyit must be to take these models seriously as agents that today can learn, and tomorrow might be able to teach us, or even one another.

This article has been updated to include a reference to Sora.

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Things Get Strange When AI Starts Training Itself - The Atlantic

Three years after abandoning edX, the pioneering open-access online course provider, Harvard University and the Massachusetts Institute of Technology are settling into amore conventional route toimprove post-secondary access and completion.

The two elite universities, through anon-profit they call the Axim Collaborative, have begun redirecting slices oftheir $800million (600million) payout from the edXsale towards funding projects designed largely to benefit low-income and non-traditional students.

To lead Axim on that mission, the universities have chosen Stephanie Khurana, a Harvard-affiliated expert in social venture philanthropy and technological innovation who has begun identifying partners largely university-based with demonstratable skills in using technology to improve overall student success.

Some of those things are inside the classroom; sometimes theyre adjacent to the classroom; and sometimes theyre connected to work, MsKhurana said in outlining the strategies that Axim hopes to accelerate. The goal, she said, is to just fill those holes and overcome those barriers, with these innovations.

The need is clearly there. Only about 60per cent of US high school leavers enrol in college, and just about 60per cent of them earn adegree, leaving the country with some 40million people who have spent some time at university but lack aqualification acostly outcome that is substantially worse among racial minorities.

Less clear, however, is whether and how successfully higher education reformers will work with the progeny of two prominent and wealthy educational institutions that already had a promising solution inedX but abandoned itbecause of the cost.

Ms Khurana acknowledged the challenge, saying Axim is really trying to make a difference and, frankly, earn the trust of these broad-access institutions.

Campus resource: open educational resources to make lifelong learning accessible to all

Theyve had a lot of people come in and try to innovate, and a lot of people back out, and were very sensitive to that, she told Times Higher Education.

That concern looks central, given the Harvard-MIT abandonment ofedX, said Kevin Carey, director of education policy at New America Foundation, a left-of-centre thinktank. Itseems like Harvard and MIT sold at the top, and are now trying to figure out what to do with all that money in a way that is in some way connected to the original spirit ofedX, MrCarey said.

Harvard and MIT started edX in 2012, with a combined investment of $60million and a goal of helping universities share their courses globally at little or nocost to students. The edX platform grew to supply more than 3,600 courses from 160 university partners to 42million students worldwide.

But Harvard and MIT sold the main business in 2021, saying its low-fee model was not looking financially viable, and promising to use the proceeds to drive the next iteration of learning innovation. The buyer, 2U, is a for-profit version ofedX that has since seen itscorporate value plummet to below $100million and has pushed out the founder who negotiated the purchase.

The name Axim is a portmanteau combining access and impact. Its initial round offunding beneficiaries shows heavy attention to students in community colleges and the development of artificial intelligence tools. They include such projects as Arizona State University working with the Southwestern Community College District in California to help under-represented students by combining in-person and online services; and MIT, Georgia State University and Quinsigamond Community College testing AI-infused student tutoring.

Maria Anguiano, the executive vice-president of ASUs Learning Enterprise, is among Axims enthusiasts. ASUs Axim-funded work shows how the standard model can be flipped on its head to reach more learners, shesaid.

Mr Carey said he saw some worthwhile goals, but questioned why Harvard and MIT appeared to be largely reinforcing work done by others, and limiting their planned spending on Axim MsKhurana expects an annual budget of $20million to $25million ayear, including the cost of maintaining afree version of the edX course distribution system to the interest generated by edXs $800million sale proceeds. The richest university in the world got richer from this, Mr Carey said of the edX sale, and theyre going to keep the money and just spend the interest.

The appropriate way to evaluate Axim is in relation to the original ideals around which edX was formed and [to] ask: are they doing all they can to live up to this? he said. And Im not sure Ican say the answer is yes.

Ms Khurana, by contrast, sees Axim as giving a substantial boost to worthwhile projects. Trickle-down innovation isnt apanacea for reaching those who could benefit most, she said.

paul.basken@timeshighereducation.com

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After pioneering edX, Harvard and MIT tackle online access afresh - Times Higher Education

QuSecure Uniquely Offers Quantum Resilient Platform Deployment Expertise to Discussion to Further the Development and Adoption of PQC

SAN MATEO, Calif., January 31, 2024--(BUSINESS WIRE)--QuSecure, Inc., a leader in post-quantum cryptography (PQC), today announced it was invited to participate and contribute to a key White House Quantum Security Roundtable discussion to consider and help influence the impact from quantum computing on information security. QuSecure offered its unique, customer-driven experience in creating and implementing PQC solutions in both enterprise and government environments to the White Houses discussion around the looming post-quantum cybersecurity threat.

"The event was a success and I congratulate the government for collaborating with industry on attempting to address an existential threat," said Aaron Moore, QuSecure EVP of Engineering. "We know that nation states are currently harvesting exabytes of encrypted data that are vulnerable to decryption once a cryptographically relevant quantum computer (CRQC) comes online. The exposure and exploitation of this information and the surprise it creates will be equivalent to what will be in essence the worlds greatest recorded ambush."

During the Jan. 26 PQC Migration Roundtable, organized by the Office of the President of the United States and the White House, it was stated that "confidentiality of ephemeral sessions (e.g. TLS) should be the highest priority due to the relative ease of the transition and the threat of store-now-decrypt-later."

Moore also added: "What we need is immediate action. We should incorporate the current PQC algorithms into our systems now by overlaying them on our current cryptographic modules. At worst we have the same level of security that we have today, and at best we become quantumresilient and begin to future proof our national security."

QuSecure recently won Small Business Innovation Research (SBIR) awards from the U.S. Air Force and the U.S. Army, reinforcing its commitment to work with the federal government. These are examples of QuSecures leadership and innovation in PQC, a testament to the necessary collaboration between the federal and private sector to quickly and efficiently develop solutions to protect against the emerging threats from AI and quantum computing. This collaboration has been echoed by QuSecures co-founder and Chief Product Officer Rebecca Krauthamer, who is a member of the World Economic Forum (WEF) Global Futures Council on Quantum. While at the World Economic Forum in Davos earlier this month, Krauthamer said that QuSecure and "the Council help to set the Davos agenda, driving policy and change we collectively need to capitalize on the good while preventing the bad that technology acceleration can bring."

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QuSecures QuProtect software enables organizations to leverage quantum-resilient technology and is currently available to test and deploy, helping to prevent todays cyberattacks, while future-proofing networks and preparing for quantum cyberthreats. It provides quantum-resilient cryptography, anytime, anywhere and on any device including network, cloud, IoT (Internet of Things), edge devices, and satellite communications. Using QuProtect, organizations can implement PQC on the network without removing existing encryption so installation is fast and risk is minimal. QuProtect software uses an end-to-end quantum-security-as-a-service architecture that addresses the digital ecosystems most vulnerable aspects, uniquely combining zero-trust, next-generation post-quantum cryptography, crypto agility, quantum-strength keys, high availability, easy deployment, and active defense into a comprehensive and interoperable cybersecurity suite. The end-to-end approach is designed to protect the entire information lifecycle as data is communicated, used and stored.

About QuSecure

QuSecure is a leader in post-quantum cybersecurity with a mission to protect enterprise and government data from quantum and classical cybersecurity threats. Its quantum-safe solutions provide an easy transition path to quantum resiliency across any organization. The companys QuProtect solution is the industrys first PQC software-based platform uniquely designed to protect encrypted communications and data with quantum-resilience using a quantum secure channel. For more information visit http://www.qusecure.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20240131064996/en/

Contacts

Dan Spalding dspalding@qusecure.com (408) 960-9297

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QuSecure Contributes to White House Quantum Security Roundtable Addressing the Post-Quantum Cybersecurity Threat - Yahoo Finance

As a result, KQC's ecosystem of users will have access to IBM's full stack solution for AI, including watsonx, an AI and data platform to train, tune and deploy advanced AI models and software for enterprises.

KQC has operated as an IBM Quantum Innovation Center since 2022 and will continue to offer access to IBM's global fleet of utility-scale quantum systems over the cloud.

At the same time, it is expanding its quantum computing collaboration with IBM and the two plan to deploy an IBM Quantum System Two on-site at KQC in Busan, South Korea by 2028.

"KQC is providing versatile computing infrastructure in Korea through our collaboration with IBM, said Ji Hoon Kweon, chairman of KQC.

Our robust hardware computing resources and core software in quantum and AI are poised not only to meet the growing demand for high performance computing, but also to catalyse industry utilisation and ecosystem development.

The joint collaboration includes an investment in infrastructure to support the development and deployment of generative AI.

Plans for this enhanced infrastructure include advanced GPUs and IBM's Artificial Intelligence Unit (AIU), managed with Red Hat OpenShift to create a cloud-native environment.

Together, the GPU system and AIU is being established to offer members the optimal hardware to power AI research and business opportunities.

"We are excited to work with KQC to deploy AI and quantum systems to drive innovation across Korean industries. With this engagement, KQC clients will have the ability to train, fine-tune, and deploy advanced AI models, using IBM watsonx and advanced AI infrastructure, added Daro Gil, senior vice president and director of research at IBM.

The collaboration will also include access for KQC's clients to Red Hat OpenShift AI for management and runtime needs, and IBM's watsonx platform to power generative AI and lay the foundations for future computing technology.

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KQC and IBM partner on bringing IBM watsonx and quantum computing to Korea - Capacity Media

Read on to learn why crypto experts are predicting major rallies for Ripple (XRP), Binance Coin (BNB) and NuggetRush (NUGX) and what this means for the future of the cryptocurrency market.

The cryptocurrency market is beginning to recover from the aftermath of the ETF impact on crypto prices. With an improved market sentiment, Matthew Dixon has predicted that Ripple's (XRP) price could increase in the coming weeks.

Meanwhile, Binance Coin (BNB) and NuggetRush (NUGX) have both formed a bullish pattern that could lead to more increases. Read on to learn why crypto experts are predicting major rallies for these top altcoins.

>> Buy NuggetRush Now <<

As the investor sentiment post BTC ETF approval dropped off significantly, Ripple (XRP) was one of the worst affected altcoins. Since the start of 2024, the price of Ripple has plunged by more than 17%.

The Ripple coin dropped to a new yearly low of around $0.50 from a high of $0.645 on January 3. Although the altcoin price is circling around $0.500, Matthew Dixon stated he anticipates XRP to see a recovery in the upcoming weeks.

In the upcoming weeks, the expert anticipates a significant surge for the Ripple coin after it breaks over the $0.5400 resistance. The trend reversal that many others have forecast for XRP will be confirmed by a break over the resistance level of $0.600.

With improved crypto market sentiment, NuggetRush (NUGX) is one of the top DeFi coins that analysts expect to experience a major rally. The crypto community has been attracted to the exciting mining adventure game NuggetRush. In this game, players will compete against miners and mining firms as it was during the gold rush.

NuggetRush will link players to artisanal miners to help improve their mining operations. NuggetRush is now one of the best cryptocurrency investments since players may earn rewards in a variety of ways.

Players may earn rewards as they engage in competition as they search for gold. There are various challenges in the game. These include constructing tunnels and mining shafts, identifying mineral resources, and using the proper excavation equipment.

NuggetRush's marketplace allows users to sell in-game treasures and resources for NUGX, real cash, or gold. NUGX is the utility token of the ecosystem and looks poised for a major rally as its platform grows. Now priced at just $0.018 per coin, NUGX is considered very cheap, given its 50x forecasts from experts.

>> Buy NuggetRush Now <<

For most of 2024, the price of Binance Coin (BNB) has been on a downtrend as the crypto market battled bearish sentiment. BNB dropped to a yearly low of $287 after losing more than 15%. Nonetheless, the development of a bull flag pattern may signal the beginning of a trend reversal.

The bullish pattern is active, and BNB has already risen beyond $310. BNB may rise above the $350 barrier level in the upcoming days if the pattern persists. There are indications that the protest might go on. First, more and more dApps are being used on the BNB blockchain network.

It is evident that bullish momentum is now developing in the crypto market. As some of the top crypto coins, Binance Coin and XRP are expected to rise, but NuggetRush is the expert option with the greatest potential for the upcoming weeks. With a fascinating ecosystem set to debut soon, NUGX may be up for a parabolic rise in 2024.

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Disclaimer: This is a paid press release, BSC.News does not endorse and is not responsible for or liable for any content, accuracy, quality, advertising, products, or other materials on this page. The project team has purchased this advertisement article for $275. Readers should do their own research before taking any actions related to the company. BSC.News is not responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods, or services mentioned in the press release.

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Crypto Experts Expect Higher Prices for Ripple (XRP): Binance Coin (BNB) and NuggetRush (NUGX) Set for Major ... - BSC NEWS