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

Firewall secures funding from North Island Ventures, Breyer Capital, and Hack VC to bulletproof smart contract networks.

San Francisco, USA / California, 7 March, Chainwire Firewall, a blockchain infrastructure startup, announced its $3.7M pre-seed round, co-led by North Island Ventures, Breyer Capital, and Hack VC. Firewall transforms the usability of smart contract technology through an innovative finality consensus mechanism that eliminates smart contract exploits.

The founders of Firewall, previously the first and sixth employees at Staked a staking company acquired by Kraken in a landmark crypto deal have helped breathe life into the eras of proof-of-stake and decentralized finance over the last six years. In that time, the founders served institutional clients with infrastructure that handled billions of dollars, and now building on their experience, are addressing what most perceive as the final major hurdle to a full embrace of digital assets by the traditional financial system.

Firewall is building the safety rails that enable the everyday person to use the next era of the Internet, stated Devan Purhar, Co-Founder of Firewall. Today, billions of dollars are stolen from users, through irreversible transactions that are classifiable as theft. Theres a parallel between the current state of crypto-networks and the early internet, with a similar lack of essential security infrastructure. Our focus is not on marginal improvements; rather, we bring a required paradigm shift in the usability of blockchains. We designed a solution from first principles, and created programmable finality. Fundamentally, we make exploits a concept of the past.

Akin to a digital version of a traditional networks firewall, Firewalls technology introduces "programmable finality. It extends rollups to use programmable transaction finalization rules, which act as automated checkpoints that block harmful transactions, inserted before later stages when the data is finalized by a DA layer such as EigenDA or Celestia. The founders envision Firewall as a part of every smart contract network, acting as an embedded security system that intelligently guards against threats.

"Firewall uses real-time algorithms to pre-filter exploits from being included in blocks," shared Sam Mitchell, Firewall Co-Founder. "Then, by using programmable finality we automatically recover from any exploits that bypass the pre-filter checks. Detection at this stage can involve AI models or social consensus, which may take longer. Mitchell emphasized that institutions, managing trillions in assets, are interested in the benefits of smart contracts but require a secure environment to deploy capital. Creating comfort for institutional clients to use smart contracts will be the pivotal point for the widespread adoption of digital assets.

Past the founders, the core team is credited with successfully pioneering AI use in crypto threat detection at OpenZeppelin and Forta, and is set to revolutionize the field with Firewall's all-encompassing security approach. The startups initial focus is on the rollup ecosystem, and prides itself on alignment with building non-custodial and trustless solutions. The funding will help expand the team and create the community to firewall the EVM. Longer-term plans include developing coordination mechanisms to integrate the social layer directly into the Firewall.

Travis Scher, Managing Partner at North Island Ventures, said:

We believe the primary impediment to cryptos mainstream adoption is the current security paradigm, in which a single bug can lead to a total loss of user funds. Firewall's solution can prevent such losses, and we are thrilled to support such an important company from the outset.

The funding round was co-led by North Island Ventures, Breyer Capital, and Hack VC, with participation from Finality Capital, and angels including Tim Ogilvie of Staked, Kain Warwick and Jordan Momtazi of Synthetix, Nathan McCauley of Anchorage, and Yaoqi Jia of AltLayer.

Firewall is making blockchains safer for users, developers, and institutions, said Ted Breyer of Breyer Capital. We see this catalyzing a new era of smart contract utility, and were delighted to support the team.

With the growing global adoption of crypto and regulatory spotlight, catalyzed by the BTC ETF and anticipated ETH ETF, the time for crypto-networks to become bulletproof is now. Trillions of dollars remain on the sidelines, scared to use smart contracts. Firewall's programmable finality which effectively neutralizes exploits, offers the security assurance needed to unlock these assets, paving the way for crypto to revolutionize the global financial system.

Firewall is dedicated to making smart contract technology safe to use in everyday life, by eliminating smart contract exploits. Their solution is akin to a robust network firewall, applied to the modular blockchain ecosystem.

Co-Founder

Devan Purhar

Firewall

devan@usefirewall.com

This publication is provided by the client. Cointelegraph does not endorse and is not responsible for or liable for any content, accuracy, quality, advertising, products, or other materials on this page. Readers should do their own research before taking any actions related to the company. Cointelegraph 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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Firewall raises $3.7M to take smart contracts mainstream with programmable finality - Cointelegraph

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

Q-Day is when a quantum computer so powerful is built it could break the public encryption systems. How concerned should we be?

There may come a day known as Q-Day, which will shatter global security as we know it.

It could be in a few years from now, or in 10 years or more. But scientists, mathematicians, and governments are not waiting idly by for the quantum threat to happen.

Q-Day is when a quantum computer so powerful is built, it could break the public encryption systems that protect our online conversations, bank accounts, and most vital infrastructure, wreaking havoc on governments and businesses.

How this digital doomsday would happen comes down to simple maths.

Since the beginning of the Internet, cryptography has protected our online data and conversations by hiding or coding information that only the person receiving the message can read on traditional computers.

In the 1970s, mathematicians built encryption methods that consisted of numbers hundreds of digits long. The difficulty of mathematical problems was such that it could take at hundreds of years to solve if using the right parameter size and numbers.

To break the encryption, the numbers need to be split into their prime factors, but this could take hundreds if not thousands of years with traditional computers.

The threat of codes being cracked was therefore not a big worry.

That was until 1994 when the American mathematician Peter Shor showed how it could be done with an algorithm using a then hypothetical quantum computer that could split large numbers into their factors much quicker than a traditional computer.

The quantum threat was still not a significant concern back then but it started to become an issue four years later when the first quantum computer was built.

Though that quantum computer - and those currently being built - are still not powerful enough to use Shors algorithm to decrypt the numbers, in 2015, intelligence agencies determined that the advancement in quantum computing is happening at such a speed that it poses a threat to cyber security.

At the moment, qubits, the processing units of quantum computers, are not stable for long enough to decrypt large amounts of data.

But tech companies such as IBM and Google have slowly but steadily started making progress in building machines strong enough to deliver the benefits of quantum, which include pharmaceutical research, subatomic physics, and logistics.

Its a matter of time and it's a matter of how long does it take until we have a large quantum computer to go, Dr Jan Goetz, CEO and co-founder of IQM Quantum Computers, a start-up that builds quantum computers, told Euronews Next.

If it takes 30 years to build a strong enough computer, there would be less reason to panic as most of the encrypted data might no longer be relevant.

But if someone comes up with a very clever idea and can already, do the code-breaking in 3 to 5 years, the whole situation also looks different, Goetz said.

Individuals should not be concerned by Q-Day as there are probably few people who have data that is very sensitive and will still be relevant in years to come.

Goetz said once the new technology comes, encryption codes will be updated on all computers and phones and you should not be too concerned about this because the industry will take care of this.

But governments, organisations, and businesses should be concerned by the quantum threat.

There is a concept called store now, decrypt later. It means someone could be storing the data and waiting for a quantum computer strong enough to come along and decrypt it.

Governments in particular are harvesting data from the Internet, said Dr Ali El Kaafarani, founder and CEO of quantum-safe cryptography company PQShield.

They are storing data that they can't access or read at the moment, but they can keep them there until the cryptography layer becomes weaker until they know of a way to attack it and then they break it and they read those communications, he told Euronews Next.

Governments are not standing by for that to happen and the cryptographic community are building encryption methods that can withstand the quantum threat, known as post-quantum cryptography (PQC).

This year, sometime between May and June, the final standardisation of PQC will be released by the US National Institute of Standards and Technology.

This will be a game-changer as it will be on the market for all industries.

The US legislation has mandated that the timeline to change to PQC will be from 2025 until 2033, by which time the cyber secure supply chain will have to have transitioned to using PQC by default.

In 2025, web browsers and software updates will have to become post-quantum secure by default if they are sold to the US, said El Kaafarani.

This is why some companies, such as Google Chrome and Cloudflare, have already started using PQC.

The USs PQC standards are international standards, but every country has their own guidelines governments do collaborate.

The US, UK, French government, German, and Dutch governments, among others, have all weighed in and produced whitepapers and guidelines for the industry to push them to start the transition phase to post-quantum cryptography as they understand that it is a process that will take time.

Governments take care of standardising the algorithms so that we all speak the same language, said El Kaafarani, but it is the cryptographic community that comes up with the new encryption methods that are not vulnerable against quantum computers.

Most of the cryptographic standards are developed in Europe by European cryptographers, he added, whose UK-based company had four encryption methods selected to be in the USs PQC standards.

Once developed, the encryption methods are ruthlessly scrutinised by the wider cryptographic community, governments, and everyone else who is interested in cracking the encryption methods.

Some get broken along the way. And that's the whole point of the process, is to root out the weak ones and keep them the strong ones, said El Kaafarani.

But there is no perfect encryption method or security method that can ensure that everything will stay secure forever.

Therefore cryptography is naturally an evolving field and that's why we need to keep ahead and keep an eye on how things are evolving, he said.

Read the rest here:

What is the quantum threat and what has simple maths got to do with protecting global security? - Euronews

Going vegan in 1987, advised by Dick Gregory, may have almost doubled Dexter Kings lifespan in long fight against cancer

MALIBU, CaliforniaDexter Scott King, 62, second son of Martin Luther King Jr., influential for more than 35 years in boosting vegetarianism and veganism among African-Americans, on the morning of January 22, 2024 transitioned peacefully in his sleep at home with me in Malibu, his wife Leah Weber King said in a media statement distributed by the Atlanta-based Martin Luther King Jr. Center for Social Change.

Dexter Scott King and Leah Weber King had been married since 2013.

Dexter King died from prostate cancer, a disease he apparently fought for most of his life.

He gave it everything and battled this terrible disease until the end. As with all the challenges in his life, he faced this hurdle with bravery and might, Leah Weber King said.

According to a 1997 profile by Kevin Sack of the Tampa Bay Times, Dexter King dropped out of his fathers alma mater, Morehouse College, because of an illness he will not discuss. He said the condition became manageable after he adopted a vegan diet and took a journey of self-discovery.

In 1987 Dexter King visited a health spa that athlete, comedian, and activist Dick Gregory founded in the Bahamas.

(See Dick Gregory, 50 years a vegan activist, dies at 84.)

Influenced by Gregory, On January 30, 1988, my twenty-seventh birthday, I became a strict vegetarian. I developed a passion for health and nutrition, Dexter King testified in 2003. My diet consists of fruits, vegetables, grains, nuts and legumes only, and has for the past 15 years now.

His family mother Coretta Scott King, sisters Bernice and Yolanda, and brother Martin Luther III greeted his new regimen with curiosity, wrote Jill Howard Church in 1995 for Vegetarian Times.

My family has always been very open-minded, said Dexter King, but certainly [veganism] was not their orientation. They were not sure what to think.

When I first became a vegetarian, I was very self-righteous about it, Dexter King added. As Ive aged and become more seasoned with time, Ive mellowed. The best testimonial is the proof in the pudding.

Part of that proof was that Dexter Kings mother, Coretta Scott King (1927-2006), also persuaded by her lifelong friend Barbara Reynolds, became vegan in 1995 and remained vegan for the last 12 years of her life, as did several of her other friends.

Among them was Rosa Parks (1913-2005), whose 1955 refusal to give up her seat on a bus to a white man in Montgomery, Alabama, led to her arrest and touched off a boycott of the city-owned bus company led by Martin Luther King Jr., then a young local minister.

This led to the November 1955 U.S. Supreme Court decision that abolished segregation in public transportation, was among the first major victories of the 20th century civil rights movement, and projected Martin Luther King Jr. to national prominence.

I was not in the practice of eating a lot of meat, Rosa Parks explained.

In childhood, she said, We had peach, apple, plums. We would go into the woods and eat blackberries. It was not hard at all for me to not eat meat.

Adds the Vegetarians of Washington website, Among her favorite vegetables were broccoli, greens, sweet potatoes and string beans.

Deeply involved in the affairs of the Martin Luther King Jr. Center for Social Change, and quiet by nature, Dexter King except in that 1995 Vegetarian Times article relatively seldom spoke in public about veganism and animal advocacy.

Though noted in passing seven times in Dexter Kings 2004 memoir Growing Up King, his beliefs about animals and food were usually mentioned by others almost as a footnote to articles focused on the legacies of his father, Martin Luther King Jr., and, sometimes, Dick Gregory.

But Dexter King made his views clear to Jill Howard Church.

Veganism has given me a higher level of awareness and spirituality, Dexter King said, primarily because the energy associated with eating has shifted to other areas.

If you are violent to yourself by putting [harmful] things into your body that violate its spirit, it will be difficult not to perpetuate that [violence] onto someone else, Dexter King added.

Dexter King also observed that, Women in general are probably more sensitive to their health needs and sensitive to what they eat. Men generally are not as concerned.

I dont know a heck of a lot of African-Americans who are vegetarian, Dexter King admitted, but I know more who are becoming aware.

That was 28 years before his death.

By then the downtown Atlanta neighborhood surrounding the Martin Luther King Jr. Center for Social Change had become one of the national hubs of the fast-growing African-American vegan/vegetarian movement.

Traci Thomas, who founded the Black Vegetarian Society of Georgia in 2002, the first of an international string of Black Vegetarian Societies, credited Dick Gregory rather than Dexter King with inspiring her to give up meat in 1994, but when in Atlanta, Dexter King was a regular customer at the tiny Black Vegetarian Society of Georgia restaurant.

Thomas was among the first vegansof any ethnicityto win national media notice as a vegan teacher and advocate without initially achieving celebrity as an athlete, entertainer, or spiritual leader. Her 2002 recommendation of corn on the cob as a simple vegan focal food for summer picnics won extensive notice in Midwestern small town newspapers that might never before have published the word vegan.

Thomas followed up by popularizing vegan recipes consisting of five ingredients or fewer, to appeal to anyone whose time for shopping and cooking is limited.

Later, fellow Atlanta resident Pinky Cole founded her Slutty Vegan burger counter in the neighborhood.

Slutty Vegan became the place to be seen waiting, especially if youre an African-American celebrity, observed New York Timesreporter Kim Severson on July 1, 2019.

Since then, the vegan burger restaurant has expanded successfully to five locations serving majority African-American neighborhoods around Atlanta; Athens and Columbus, Georgia; Birmingham, Alabama; and Brooklyn and Harlem in New York City.

Dexter King meanwhile established himself as a businessman on another front.

After succeeding his mother as both the head of the King Center for Social Change and executor of Dr. Kings estate, Dexter King quickly consolidated control over the familys social agenda and financial affairs, recounted Kevin Sack of the Tampa Bay Times.

Dexter Kings first tenure heading the King Center, in 1989, was brief, as his initial attempts to exercise leadership met intense opposition from within.

When Dexter King returned, in 1994, the King Center was reportedly almost bankrupt.

Since then, with halting, often awkward steps, Sack wrote in his 1997 profile, Dexter King has cobbled together a vision for preserving his fathers legacy that relies more on the Internet and intellectual property rights than on the cause-oriented mission that Mrs. King established for the King center in 1968.

In many ways, Sack observed, the transition from mother to son has highlighted the generational differences between the marchers and dreamers of the civil rights era and the deal makers and realists of today.

An early bizarre misstep was a March 1997 televised prison meeting with the terminally ill confessed Martin Luther King Jr. assassin James Earl Raywho later recanted his own testimony.

(See MLK assassin ex-wife Anna Ray busted for dog hoardingagain!)

Without any showing of evidence, summarized Sack, Dexter King declared that his family believed Ray innocent of any knowing involvement in the killing.

Dexter King later implicated President Lyndon B. Johnson in a government conspiracy, Sack continued, a theory promoted by Rays lawyer, William Pepper.

I have never seen myself the way the media has portrayed me, as a leader, Dexter King told Sack. Im not trying to have a constituency. Im not trying to be preachy or be on a pedestal. Im not trying to effect change on that level, not because its not something that should be done, but thats just not my best destiny.

Sack noted intense opposition or, at the very least, befuddlement, from civil rights veterans who marched at Dr. Kings side, from board members of the King Center, from the pulpit of the church where Dr. King, his father, and his maternal grandfather had been pastor, and from the liberal black editorial page editor of the Atlanta Constitution.

Lawsuits filed against Dexter King in 2008 by his sisterBernice Kingand brotherMartin Luther King III followed, including a case filed by Bernice King on behalf of the estate ofCoretta Scott King. All three lawsuits were settled out of court in 2009.

The Dexter King legacy as regards the legacy of Martin Luther King Jr. may remain controversial for years to come.

His best destiny, meanwhile, may be a statistic: when Dexter King became vegan in 1988, only about 3% of Americans of European descent were vegans and vegetarians, and barely 1% of Americans of African descent.

Today the percentage of Americans of European descent who are vegans or vegetarians is still only about 3%, but the percentage of Americans of African descent who are vegans or vegetarians is at 8% and rapidly growing.

Continued here:

Vegan advocate Dexter King, son of Martin Luther King Jr., dies at 62 - Animals 24-7

Tiffany Barnard was born in the UK but at the age of just six months the family moved to Tanzania where she spent the best part of forty years.

She studied in England and then at Buckingham University before returning to Tanzania and eventually founded the highly successful business Mansons Mines Logistics (Tanzania) Limited. This is still growing strong today in the hands of her business partner, which enabled her to return to Europe for family reasons and then move to Soller where she now lives.

As an Associate Certified Coach, she is officially based in Barcelona, where she has just been rated one of the top 15 coaches. But by conducting her sessions via Zoom, she is able to care for and help her clients from all over the world remotely.

Clarity and confidence Tiffany is a Certified Leadership Growth and Communication Coach, committed to guiding people to the clarity and confidence to make powerful decisions. With multicultural experience, she integrates diversity and equity. Her business focus and leadership skills combine to provide ethical and professional service. She uses a persuasive and motivational coaching style, challenging people to step out of their comfort zone.

Life throws you a curve ball from time to time and, as a result of that, 12 years ago my family moved to France. And then ten years ago, weirdly my brother, who was going through his own curve ball, suddenly had a message from somebody who he had met literally in another time and another world, offering him a job if he was interested in Soller. He had no idea where it was but decided to give it a go about ten years and came out to Mallorca. So he came out, said it was lovely and amazing and decided to stay. Then he got me to come out with the kids for a holiday and I agreed. It is gorgeous and thought why not be here with my brother.

Moving to Mallorca In the meanwhile my parents were living in Oman and they had had enough after some ten years and suddenly announced they were going to come and live with us in France to be with the kids. I said that was cool but that the following year I might be moving to Mallorca.

So, my mother decided one morning to come and join us for the summer and then asked - why dont we just move now? But I had two kids, two dogs, a full apartment. Nevertheless, three weeks later we all moved to Soller - the whole family, and we all moved here nine years ago.

I guess that was the beginning of my new personal journey. Mallorca has been amazing for my children, its enabled them to put down some strong roots and I too realised there was a lot going on. It healed me too and then I came into the coaching world around five years ago now.

Human behaviour What sparked that? Well Ive always been working since I finished university aged 20. But I never really enjoyed it, it was something that paid the bills and thats what you had to do; you never really questioned it. I got very frustrated, there was something definitely missing. The business was ticking over, Ive got an amazing business partner who didnt really need me on the ground and I was bored, looking for something. So I started studying for the sake of studying. Ive always loved human behaviour, psychology, how humans think and how people are and I kind of fell upon these coaching courses and that was where my new journey started.

When you start to train as a coach you go through that process yourself. You go though the whole thing and so I did several courses and ended up taking and completing some of the most prestigious and highly valued courses in the world and eventually set up my own business. I had finally found something that fulfilled me and spoke to my soul. I get up every morning and I have something to look forward to do, something I like, being able to spend the day working with people and helping them on their journey. For me there really isnt anything better and that frustrated feeling I had before has definitely gone, Tiffany explained. What is interesting is that everybody talks about your coaching niche - business coaches, leadership coaches etc., but it comes down to the same things.

Self awareness Ive coached people who have come to me for things from looking for a promotion or a career change to having problems with their wife or partner and what you end up coaching them on is themselves, because anything we want to change or achieve in our life has to come from us. Its not going to fall into our laps. It always comes down to self awareness, personal development and communication, she said.

If you want to know what my specialty is it would be that. It would be personal development and communication. Now when you look at the different places that you can find on the internet, it can be personal development, leadership development or business communication development, but it really is just semantics. Its talking to the people who would be needing particular things, but it really does come down to getting clarity, understanding that you have choice and affecting the change. For me, those are the three Cs as the main factors in coaching.

Expat coaching For example, here in Mallorca, what are peoples pain points? I look about and see that a lot of people come to Mallorca. The summer is fabulous, its amazing, they buy a holiday home, perhaps eventually move the family out, but the winter is a different story. Being an expat anywhere has its problems and challenges and so I could speak to them as a sort of expat coach. But the coaching I would do would be exactly the same as if I was coaching you how to handle all your pain, whatever; those three Cs apply to everything and everyone whatever the situation. It is about getting clarity, understanding what the problem is because a lot of people know there is something wrong, but they dont know what. And that problem is always with you.

People like to think that somebody made me feel like this but nobody makes you feel like anything. Were always blaming the outside, especially in the world we live in today. The problem is that people know whats wrong with them but they expect other people to navigate around them.

There is a lot of I want to win the lottery but Im not going to buy a ticket kind of thing, So people come in and theyre like I want to get this promotion, but its never going to happen for somebody like me because I dont have the right qualifications. I dont have this or that. Thats not true, she stressed.

It doesnt matter if you have not been to university or have no A levels, it does not matter and I have experience with this. Lets look at what youve got. You may, will have amazing attributes and you would have gone past the stage where they are going to be looking at qualifications. People get to a certain age when businesses dont care any more about those documents.

Once you get rid of the blocks of what you think are the things that are going to stop you, it sets you free to take a chance. What are you going to lose? she explained. Ive helped people get rid of those blocks, get to know what they want, obtain that clarity. Thats when you can actually start moving forward, taking the steps, putting the plan into action. Strip away the social stigmas so many of us are worried or obsessed about.

There are four things which stop us from achieving anything or what we really want. One is interpretation - were sure we know what everyone is thinking, theres no point me even trying because its not going to happen. Assumptions, I tried once, so its never going to happen again, so theres no point.

Stereotypes Then youve got limiting beliefs which is that societal stereotype system we grow up with which dont bear any truth; theres no reason behind them. We just believe them because weve been told them without questioning. And the last one is the real kicker, that gremlin, that little voice which tells you youre not good enough; thats your internal voice. Its something that everybody has and it takes some work to quieten down. We need to retrain the brain to actually believe that we are good enough.

It sounds very woo woo, if you put your mind to it, you can do it. But you really can. If you want to do something, you want to achieve something and you visualise it, you know what you are doing and youre going to take the steps to get there, you will do it, Tiffany underlined. Its all down to you at the end of the day.

Tiffany Barnard, an Associate Certified Coach (ACC) which was awarded by the International Coaching Federation (ICF), the leading global organisation dedicated to advancing the coaching profession by setting high standards. Tiffany trained at the Institute of Professional Excellence in Coaching (iPEC). Tiffany lives in Soller and is also an Energy Leadership Index Master Practitioner (ELI-MP) and a COR.E Transitions Dynamics Specialist (CTDS).

For more information: tiffanybarnard.com

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The Mallorca-based British life coach rated one of the best in spain - Majorca Daily Bulletin