Bare trust reporting is a new requirement by the CRA this year, but most Canadians are likely unaware of its existence — or penalties.

Did you co-sign your kid’s mortgage? There are new tax reporting rules to know - National | Globalnews.ca

After what&#x27;s become known in some quarters at the &quot;Kate Middleton photo scandal,&quot; questions loom over what this latest episode might mean more broadly for the Royal Family as it tries to find ways to bolster its relevance for the future.

ANALYSIS | The Kate photo scandal has tested the public's trust. How do the Royals get it back? | CBC News

If you&#x27;ve tried to use Google to research a product recently, you&#x27;ve probably encountered a whole lot of extremely similar ‘Best Of’ lists.

Can you still trust Google's search results?

Truts

truts

This is Part 9 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Implications for Research Existing research is often biased towards engineering and optimization, lacking deeper insights from a social science perspective. The time for a new sociology of humans and machines is critical, before AI becomes more sophisticated: generative AI exhibits emergent behavior that itself requires explanation[220,221], complicating the understanding of system dynamics. Researchers would benefit from an agent-based modeling framework that outlines distinctions between human and bot agents: utility function, optimization ability, access to information, learning, innovation/creativity, accuracy, etc. The framework could borrow concepts from other two-agent systems, such as predator–prey, principal–agent, and common pool resource models. Controlled experiments should explicitly compare human-machine, human-only and machine-only networks, and known bots against covert bots. Experiments could manipulate participants’ perceptions of algorithms’ technical specifications, agenthood[222], emotional capability, and biases. Field interventions in online communities with endemic bot populations present another promising direction. Existing examples include social bots that gain influence by engaging human users [223,224,225,226], trading bots that manipulate prices in cryptocurrency markets [227], political bots that promote opposing political views to decrease polarization[228], and “drifters” to measure platform bias [94]. Expanding on the cases reported here, we need observational research on additional human-machine communities and contexts such as traffic systems with human-driven and driverless vehicles, online multiplayer games comprising human players, non-player characters, and cheating code, and dating markets with AI-driven chatbots [229]. Finally, research with artificial agents introduces ethical problems demanding careful elaboration and mitigation. Research protocols should minimize interventions [230], possibly deploying covert bots only where they already exist, ensuring their actions are not unusual or harmful [93]. Even then, bots may still face opposition from users due to privacy concerns [223]. Do people perceive certain bots as inherently deceptive? Could knowledge of the bot owner and algorithm mitigate this perception? Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 9 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 9 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Implications for Research Existing research is often biased towards engineering and optimization, lacking deeper insights from a social science perspective. The time for a new sociology of humans and machines is critical, before AI becomes more sophisticated: generative AI exhibits emergent behavior that itself requires explanation[220,221], complicating the understanding of system dynamics. Researchers would benefit from an agent-based modeling framework that outlines distinctions between human and bot agents: utility function, optimization ability, access to information, learning, innovation/creativity, accuracy, etc. The framework could borrow concepts from other two-agent systems, such as predator–prey, principal–agent, and common pool resource models. Controlled experiments should explicitly compare human-machine, human-only and machine-only networks, and known bots against covert bots. Experiments could manipulate participants’ perceptions of algorithms’ technical specifications, agenthood[222], emotional capability, and biases. Field interventions in online communities with endemic bot populations present another promising direction. Existing examples include social bots that gain influence by engaging human users [223,224,225,226], trading bots that manipulate prices in cryptocurrency markets [227], political bots that promote opposing political views to decrease polarization[228], and “drifters” to measure platform bias [94]. Expanding on the cases reported here, we need observational research on additional human-machine communities and contexts such as traffic systems with human-driven and driverless vehicles, online multiplayer games comprising human players, non-player characters, and cheating code, and dating markets with AI-driven chatbots [229]. Finally, research with artificial agents introduces ethical problems demanding careful elaboration and mitigation. Research protocols should minimize interventions [230], possibly deploying covert bots only where they already exist, ensuring their actions are not unusual or harmful [93]. Even then, bots may still face opposition from users due to privacy concerns [223]. Do people perceive certain bots as inherently deceptive? Could knowledge of the bot owner and algorithm mitigate this perception? Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AI's Complexity Demands Urgent Study of Trust, Bias, and Behaviors in Diverse Contexts

This is Part 10 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Implications for Design Humans are resilient and successful when they exhibit high levels of efficient communication, context awareness, emotional recognition and response, and ethical and cultural sensitivities. These features should be encouraged when designing social machines to build trust, ensure legal compliance, promote social harmony, enhance user satisfaction, and achieve long-term sustainability [231,232]. Since H-H, H-M, and M-M interactions differ, machines should be specifically designed for each scenario, with separate training for each interaction. This could avoid market underperformance when bargaining algorithms trained on human-only markets adapt poorly to human-machine negotiations[64], or traffic jams when driverless vehicles trained on human driving fail to properly interact with each other [233]. AI design should also adopt a hierarchy of behavioral rules and conventions guiding H-M and MM interactions in the context of H-H interactions. Isaac Asimov’s famous Three Laws of Robotics [234] which regulate M-H interactions and self-preservation—1) not harming humans, 2) obeying humans, and 3) protecting own existence, with priority given to higher order rules—could be adapted for M-M interactions, considering specific contexts, implications, and unintended consequences [235]. Further, cultural context is crucial for AI design. People’s perceptions of machines vary by age, environment, personality, and geography [236,60]. Machines reflect their developers’ culture and operate in settings with specific organizational and community norms [36]. Thus, AI design for self-driving cars and assistant bots should consider local driving culture and attitudes toward domestic assistants during training. Finally, the systems under review exhibit complexity in that their emergent behavior transcends a mere aggregation of individual components, displaying what is known as “network effects.” While natural complex systems often demonstrate remarkable resilience and adaptivity [237], the human-designed complex systems discussed here, albeit to varying degrees, are not inherently adaptive. To enhance resilience and robustness, AI designers should incorporate complex adaptive system principles, like negative feedback, modularity, and hierarchical organization. For example, dense networks lacking diversity and modularity are susceptible to systemic failures [238,239,240] and are easy to control [241]. Introducing bots that increase network diversity, introduce resistance, build resilience, or incorporate negative feedback could enhance adaptability and stability. Such configurations have improved group outcomes in forecasting [242], exploration/exploitation[243], and general knowledge tasks [151]. Literature on monitoring and steering complex systems can help predict critical transitions [244,245,15], designing safer human-machine systems across different environments and perturbations. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 10 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 10 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Implications for Design Humans are resilient and successful when they exhibit high levels of efficient communication, context awareness, emotional recognition and response, and ethical and cultural sensitivities. These features should be encouraged when designing social machines to build trust, ensure legal compliance, promote social harmony, enhance user satisfaction, and achieve long-term sustainability [231,232]. Since H-H, H-M, and M-M interactions differ, machines should be specifically designed for each scenario, with separate training for each interaction. This could avoid market underperformance when bargaining algorithms trained on human-only markets adapt poorly to human-machine negotiations[64], or traffic jams when driverless vehicles trained on human driving fail to properly interact with each other [233]. AI design should also adopt a hierarchy of behavioral rules and conventions guiding H-M and MM interactions in the context of H-H interactions. Isaac Asimov’s famous Three Laws of Robotics [234] which regulate M-H interactions and self-preservation—1) not harming humans, 2) obeying humans, and 3) protecting own existence, with priority given to higher order rules—could be adapted for M-M interactions, considering specific contexts, implications, and unintended consequences [235]. Further, cultural context is crucial for AI design. People’s perceptions of machines vary by age, environment, personality, and geography [236,60]. Machines reflect their developers’ culture and operate in settings with specific organizational and community norms [36]. Thus, AI design for self-driving cars and assistant bots should consider local driving culture and attitudes toward domestic assistants during training. Finally, the systems under review exhibit complexity in that their emergent behavior transcends a mere aggregation of individual components, displaying what is known as “network effects.” While natural complex systems often demonstrate remarkable resilience and adaptivity [237], the human-designed complex systems discussed here, albeit to varying degrees, are not inherently adaptive. To enhance resilience and robustness, AI designers should incorporate complex adaptive system principles, like negative feedback, modularity, and hierarchical organization. For example, dense networks lacking diversity and modularity are susceptible to systemic failures [238,239,240] and are easy to control [241]. Introducing bots that increase network diversity, introduce resistance, build resilience, or incorporate negative feedback could enhance adaptability and stability. Such configurations have improved group outcomes in forecasting [242], exploration/exploitation[243], and general knowledge tasks [151]. Literature on monitoring and steering complex systems can help predict critical transitions [244,245,15], designing safer human-machine systems across different environments and perturbations. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AI Design Must Prioritize Adaptation to Human Culture, Trust, and Ethics 

How to Set Up a Trustless Escrow Smart Contract on Rootstock for Secure Transactions

Imagine receiving an alert that your credit card has been maxed out with purchases you didn’t make. To make matters worse, your social security number is being used to open fraudulent loans. Unfortunately, this troubling scenario is far from rare. In just the past year, identity theft drained a whopping $10 billion from victims in the U.S., leaving countless lives disrupted and trust in digital systems shaken. The problem lies in centralized identity systems, where vast amounts of personal data are stored on isolated servers. A single breach can expose your most sensitive information. The impact on businesses is staggering: the average cost of a data breach in 2024 reached $4.88 million. But what if there was a better solution? Decentralized Digital Identities (DDIs) offer a groundbreaking approach that empowers individuals to control their own data while decentralizing trust. Built on blockchain technology, DDIs remove the middleman, minimize vulnerabilities, and allow you to choose who can access your information. Could this new vision of identity systems be the answer to our escalating digital trust issues? Let’s explore further. The Issue with Centralized Identity Systems In our digital-first world, we’ve come to accept a risky trade-off: convenience at the cost of security. Centralized identity systems store vast amounts of sensitive user information in one place, making them prime targets for hackers. Each breach, whether it’s Facebook or Equifax, highlights the weaknesses of this approach. The consequences are deeply personal as well, as when identity theft happens, one has to deal with: Financial Ruin: Fraudulent loans, damaged credit scores, and countless hours spent trying to restore your reputation.
Reputational Harm: Your online presence can be exploited by malicious individuals.
Time Loss: Victims frequently invest years in the recovery process. The Takeaway is Clear: Conventional identity systems cannot keep up with the growing sophistication of cyber threats. Decentralized Digital Identities: How Do They Work? Decentralized Digital Identities (DDIs) present a fundamentally different approach. Rather than depending on centralized databases, they utilize three essential principles: Blockchain for Trustless Security A blockchain acts as an unchangeable ledger, allowing verification without a central authority. Decentralized nodes authenticate identity records, ensuring there is no single point of failure. This eliminates the weakest link found in centralized systems: dependence on a central entity. Selective Disclosure via Cryptography Technologies such as Zero-Knowledge Proofs (ZKPs) enable users to share only the information necessary for specific interactions. For example, you can prove you’re over 21 without disclosing your date of birth. With cryptography, privacy is inherently built into the system. Self-Sovereign Identity (SSI) With SSI, users have control over their credentials stored in digital wallets. You determine who can access your data, when, and for how long. This reduces reliance on third parties and empowers users to take charge of their digital identity. Real-World Transformation: Where DDIs Are Making an Impact From finance to healthcare, DDIs are reshaping industries: Finance: Identity verification presents a $16 billion challenge for financial institutions. DDIs streamline this process, allowing for seamless onboarding and reducing the risk of fraud. This transition also cuts compliance costs by minimizing the need for repeated KYC checks.
Healthcare: Picture a future where patients can carry their medical records in secure digital wallets. DDIs make this possible, ensuring that sensitive information adheres to HIPAA regulations while granting patients remarkable control over their health history.
Supply Chain Transparency: Fraud results in a loss of 5% of revenue for companies each year. DDIs help guarantee product authenticity by connecting identities to goods and transactions, promoting trust and accountability. Case Studies: DDIs in Action Buenos Aires, Argentina: Buenos Aires is at the forefront of decentralized identity with its implementation of QuarkID. This platform empowers citizens to manage their credentials securely through blockchain technology, allowing them to access public services without depending on a centralized authority. Residents enjoy a system that is both private and efficient, serving as a potential model for other cities to emulate. Zug, Switzerland: Often referred to as 'Crypto Valley,' Zug has taken the lead in decentralized identity initiatives. In collaboration with uPort, the city introduced a self-sovereign identity program in 2017, enabling residents to register their IDs on the Ethereum blockchain. This system facilitates secure access to municipal services, including online voting, showcasing how blockchain can revolutionize public administration. China’s RealDID System: In December 2023, China unveiled the Real-Name Decentralized Identifier System (RealDID). Created by the First Research Institute of the Ministry of Public Security, this platform combines real-name verification with blockchain technology. It provides encrypted data protection, secure private logins, and adherence to national regulations. RealDID marks a significant advancement in the use of decentralized identity systems on a national level, merging privacy with regulatory compliance. Barriers to Adoption: Challenges in the Road Ahead Despite their promise, DDIs face hurdles that cannot be ignored: Technological Complexity: Building decentralized systems requires expertise in blockchain and cryptography, making implementation a high-cost endeavor.
Interoperability Issues: DDIs must work seamlessly across platforms. Without global standards, fragmentation could stifle adoption.
Regulatory Compliance: Balancing decentralization with laws like GDPR and HIPAA is no small feat.
User Education: For DDIs to succeed, users need to understand and trust them. Overcoming resistance to change is critical. What’s Next for Digital Identity? The path to widespread DDI adoption is challenging but necessary. For businesses, early adoption offers a competitive edge, signaling trust, security, and forward-thinking innovation. For users, DDIs promise a future where our identities are no longer liabilities but tools of empowerment. This is our moment to redefine what identity means in our interconnected world. Let’s seize this chance to rethink identity. Together, we can build a digital world that prioritizes security, privacy, and trust - one decentralized identity at a time. Imagine receiving an alert that your credit card has been maxed out with purchases you didn’t make. To make matters worse, your social security number is being used to open fraudulent loans. Unfortunately, this troubling scenario is far from rare. In just the past year, identity theft drained a whopping $10 billion from victims in the U.S., leaving countless lives disrupted and trust in digital systems shaken. $10 billion $10 billion The problem lies in centralized identity systems, where vast amounts of personal data are stored on isolated servers. A single breach can expose your most sensitive information. The impact on businesses is staggering: the average cost of a data breach in 2024 reached $4.88 million. $4.88 million. $4.88 million. But what if there was a better solution? But what if there was a better solution? But what if there was a better solution? Decentralized Digital Identities (DDIs) offer a groundbreaking approach that empowers individuals to control their own data while decentralizing trust. Built on blockchain technology, DDIs remove the middleman, minimize vulnerabilities, and allow you to choose who can access your information. Could this new vision of identity systems be the answer to our escalating digital trust issues? Let’s explore further. The Issue with Centralized Identity Systems The Issue with Centralized Identity Systems In our digital-first world, we’ve come to accept a risky trade-off: convenience at the cost of security. Centralized identity systems store vast amounts of sensitive user information in one place, making them prime targets for hackers. Each breach, whether it’s Facebook or Equifax, highlights the weaknesses of this approach. The consequences are deeply personal as well, as when identity theft happens, one has to deal with: Financial Ruin: Fraudulent loans, damaged credit scores, and countless hours spent trying to restore your reputation. Reputational Harm: Your online presence can be exploited by malicious individuals. Time Loss: Victims frequently invest years in the recovery process. Financial Ruin: Fraudulent loans, damaged credit scores, and countless hours spent trying to restore your reputation. Financial Ruin: Reputational Harm: Your online presence can be exploited by malicious individuals. Reputational Harm: Time Loss: Victims frequently invest years in the recovery process. Time Loss: The Takeaway is Clear: Conventional identity systems cannot keep up with the growing sophistication of cyber threats. The Takeaway is Clear: Decentralized Digital Identities: How Do They Work? Decentralized Digital Identities: How Do They Work? Decentralized Digital Identities (DDIs) present a fundamentally different approach. Rather than depending on centralized databases, they utilize three essential principles: Blockchain for Trustless Security A blockchain acts as an unchangeable ledger, allowing verification without a central authority. Decentralized nodes authenticate identity records, ensuring there is no single point of failure. This eliminates the weakest link found in centralized systems: dependence on a central entity. Selective Disclosure via Cryptography Technologies such as Zero-Knowledge Proofs (ZKPs) enable users to share only the information necessary for specific interactions. For example, you can prove you’re over 21 without disclosing your date of birth. With cryptography, privacy is inherently built into the system. Self-Sovereign Identity (SSI) With SSI, users have control over their credentials stored in digital wallets. You determine who can access your data, when, and for how long. This reduces reliance on third parties and empowers users to take charge of their digital identity. Real-World Transformation: Where DDIs Are Making an Impact From finance to healthcare, DDIs are reshaping industries: Finance: Identity verification presents a $16 billion challenge for financial institutions. DDIs streamline this process, allowing for seamless onboarding and reducing the risk of fraud. This transition also cuts compliance costs by minimizing the need for repeated KYC checks. Healthcare: Picture a future where patients can carry their medical records in secure digital wallets. DDIs make this possible, ensuring that sensitive information adheres to HIPAA regulations while granting patients remarkable control over their health history. Supply Chain Transparency: Fraud results in a loss of 5% of revenue for companies each year. DDIs help guarantee product authenticity by connecting identities to goods and transactions, promoting trust and accountability. Finance : Identity verification presents a $16 billion challenge for financial institutions. DDIs streamline this process, allowing for seamless onboarding and reducing the risk of fraud. This transition also cuts compliance costs by minimizing the need for repeated KYC checks. Finance $16 billion $16 billion Healthcare : Picture a future where patients can carry their medical records in secure digital wallets. DDIs make this possible, ensuring that sensitive information adheres to HIPAA regulations while granting patients remarkable control over their health history. Healthcare Supply Chain Transparency : Fraud results in a loss of 5% of revenue for companies each year. DDIs help guarantee product authenticity by connecting identities to goods and transactions, promoting trust and accountability. Supply Chain Transparency 5% of revenue 5% of revenue Case Studies: DDIs in Action Buenos Aires, Argentina : Buenos Aires is at the forefront of decentralized identity with its implementation of QuarkID. This platform empowers citizens to manage their credentials securely through blockchain technology, allowing them to access public services without depending on a centralized authority. Residents enjoy a system that is both private and efficient, serving as a potential model for other cities to emulate. Buenos Aires, Argentina Buenos Aires, Argentina Zug, Switzerland : Often referred to as 'Crypto Valley,' Zug has taken the lead in decentralized identity initiatives. In collaboration with uPort, the city introduced a self-sovereign identity program in 2017, enabling residents to register their IDs on the Ethereum blockchain. This system facilitates secure access to municipal services, including online voting, showcasing how blockchain can revolutionize public administration. Zug, Switzerland Zug, Switzerland China’s RealDID System : In December 2023, China unveiled the Real-Name Decentralized Identifier System (RealDID). Created by the First Research Institute of the Ministry of Public Security, this platform combines real-name verification with blockchain technology. It provides encrypted data protection, secure private logins, and adherence to national regulations. RealDID marks a significant advancement in the use of decentralized identity systems on a national level, merging privacy with regulatory compliance. China’s RealDID System China’s RealDID System Barriers to Adoption: Challenges in the Road Ahead Despite their promise, DDIs face hurdles that cannot be ignored: Technological Complexity: Building decentralized systems requires expertise in blockchain and cryptography, making implementation a high-cost endeavor. Interoperability Issues: DDIs must work seamlessly across platforms. Without global standards, fragmentation could stifle adoption. Regulatory Compliance: Balancing decentralization with laws like GDPR and HIPAA is no small feat. User Education: For DDIs to succeed, users need to understand and trust them. Overcoming resistance to change is critical. Technological Complexity : Building decentralized systems requires expertise in blockchain and cryptography, making implementation a high-cost endeavor. Technological Complexity Interoperability Issues : DDIs must work seamlessly across platforms. Without global standards, fragmentation could stifle adoption. Interoperability Issues Regulatory Compliance : Balancing decentralization with laws like GDPR and HIPAA is no small feat. Regulatory Compliance User Education : For DDIs to succeed, users need to understand and trust them. Overcoming resistance to change is critical. User Education What’s Next for Digital Identity? The path to widespread DDI adoption is challenging but necessary. For businesses, early adoption offers a competitive edge, signaling trust, security, and forward-thinking innovation. For users, DDIs promise a future where our identities are no longer liabilities but tools of empowerment. This is our moment to redefine what identity means in our interconnected world. Let’s seize this chance to rethink identity. Together, we can build a digital world that prioritizes security, privacy, and trust - one decentralized identity at a time.

Are Decentralized Digital Identities the Future of Online Trust?

DUBAI, UAE, November 27th, 2024/Chainwire/--Trust Wallet, a leading self-custody Web3 wallet trusted by over 140 million users, has announced the third project under Trust Wallet Launchpool, featuring the reward token $WOD. $WOD is the native token of World of Dypians, a MMORPG available on Epic Games, set in a connected virtual world, featuring advanced AI, stunning graphics, and immersive gameplay. The dynamic ecosystem merges DeFi, NFTs, Gaming, and AI into a single immersive experience. For this launchpool campaign, World of Dypians will allocate 1% of the total $WOD supply to Trust Wallet Launchpool participants. How Trust Wallet Launchpool Works Trust Wallet Launchpool allows TWT holders, and other token holders who are Trust Wallet users, the opportunity to get rewards by discovering and engaging with promising projects in a secure and user-friendly way. By locking tokens like TWT or any partner tokens designated for each campaign, users may acquire new tokens or rewards from both pre-launch and launched token projects, diversifying their portfolios and supporting innovative ventures with reduced risk. Available in the app’s “Earn” section, Trust Wallet Launchpool is powered by secure, on-chain smart contracts and open-source protocols, setting it apart from centralized alternatives while offering a user-friendly experience to explore early-stage crypto opportunities. Launchpool 3: Featuring $WOD The Trust Wallet Launchpool campaign details are as follows: Reward Token: $WOD
Lock Token: $TWT
Blockchain: BNB Smart Chain
Total Reward Allocation: 1% of total supply (10 million $WOD)
Campaign Period: 6:00AM UTC, November 27 – 6:00AM UTC, December 4
Lock Duration: Tokens locked for 7 days (168 hours) starting from lock time.
Rewards Calculation: Based on locking start time and token amount. $WOD fuels the World of Dypians ecosystem, a leading BNB Chain gaming platform available on Epic Games. It is part of the BNB Chain Alliance Program and a DAU Incentive Winner, boasting: 895,000+ daily active users (DAUs)
174 million on-chain transactions
1.2 million community members $WOD is set to be listed on several cryptocurrency exchanges, including KuCoin, Gate.io, MEXC, and PancakeSwap, on November 27th at 11:00 AM UTC. Key Features of Trust Wallet Launchpool Trust Wallet Launchpool creates a collaborative ecosystem that supports both users and projects. For Users: A decentralized way to potentially earn project tokens while exploring the Web3 landscape and diversifying portfolios. For Projects: A unique platform to increase visibility and build community, by connecting with Trust Wallet’s extensive user base, desiged to drive growth in a competitive market. Trust Wallet’s third Launchpool featuring $WOD is live now. Users can Download Trust Wallet to participate and potentially gain rewards while supporting the evolution of blockchain gaming and DeFi. About World of Dypians World of Dypians is a revolutionary MMORPG that bridges the gap between traditional Web2 gaming and the decentralized future of Web3. By integrating blockchain technology with immersive gameplay, World of Dypians empowers players with true ownership of in-game assets through NFTs, decentralized economies, and a vibrant community-driven ecosystem. As a leading gaming ecosystem on every network it operates, World of Dypians continues to set new benchmarks for user engagement and innovation, offering players seamless access to interconnected virtual worlds. With stunning graphics, an intuitive user journey, and a focus on accessibility, the platform brings together the best of Web2 familiarity and Web3 innovation, creating an inclusive space for gamers worldwide. Since its inception, World of Dypians has been dedicated to redefining the gaming landscape by merging Gaming, DeFi, NFTs, and AI into one seamless platform, creating a groundbreaking experience for players. For business collaborations, users may contact: contact@worldofdypians.com About Trust Wallet Trust Wallet is a secure, self-custody Web3 wallet and gateway for people who want to fully own, control, and leverage the power of their digital assets. From beginners to experienced users, Trust Wallet aims to make it easier, safer, and convenient for millions of people around the world to experience Web3, access dApps securely, store and manage their crypto and NFTs, as well as buy, sell, and stake crypto to earn rewards — all in one place and without limits. Contact Head of Communications Dami Odufuwa Trust Wallet press@trustwallet.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here DUBAI, UAE, November 27th, 2024/Chainwire/--Trust Wallet, a leading self-custody Web3 wallet trusted by over 140 million users, has announced the third project under Trust Wallet Launchpool, featuring the reward token $WOD . $WOD $WOD $WOD is the native token of World of Dypians , a MMORPG available on Epic Games, set in a connected virtual world, featuring advanced AI, stunning graphics, and immersive gameplay. World of Dypians World of Dypians The dynamic ecosystem merges DeFi, NFTs, Gaming, and AI into a single immersive experience. For this launchpool campaign, World of Dypians will allocate 1% of the total $WOD supply to Trust Wallet Launchpool participants. How Trust Wallet Launchpool Works Trust Wallet Launchpool allows TWT holders, and other token holders who are Trust Wallet users, the opportunity to get rewards by discovering and engaging with promising projects in a secure and user-friendly way. By locking tokens like TWT or any partner tokens designated for each campaign, users may acquire new tokens or rewards from both pre-launch and launched token projects, diversifying their portfolios and supporting innovative ventures with reduced risk. Available in the app’s “Earn” section, Trust Wallet Launchpool is powered by secure, on-chain smart contracts and open-source protocols, setting it apart from centralized alternatives while offering a user-friendly experience to explore early-stage crypto opportunities. Launchpool 3: Featuring $WOD The Trust Wallet Launchpool campaign details are as follows: Reward Token: $WOD Lock Token: $TWT Blockchain: BNB Smart Chain Total Reward Allocation: 1% of total supply (10 million $WOD) Campaign Period: 6:00AM UTC, November 27 – 6:00AM UTC, December 4 Lock Duration: Tokens locked for 7 days (168 hours) starting from lock time. Rewards Calculation: Based on locking start time and token amount. Reward Token: $WOD Lock Token: $TWT Blockchain: BNB Smart Chain Total Reward Allocation: 1% of total supply (10 million $WOD) Campaign Period: 6:00AM UTC, November 27 – 6:00AM UTC, December 4 Lock Duration: Tokens locked for 7 days (168 hours) starting from lock time. Rewards Calculation: Based on locking start time and token amount. $WOD fuels the World of Dypians ecosystem, a leading BNB Chain gaming platform available on Epic Games. It is part of the BNB Chain Alliance Program and a DAU Incentive Winner, boasting: 895,000+ daily active users (DAUs) 174 million on-chain transactions 1.2 million community members 895,000+ daily active users (DAUs) 174 million on-chain transactions 1.2 million community members $WOD is set to be listed on several cryptocurrency exchanges, including KuCoin, Gate.io, MEXC, and PancakeSwap, on November 27th at 11:00 AM UTC. Key Features of Trust Wallet Launchpool Trust Wallet Launchpool creates a collaborative ecosystem that supports both users and projects. For Users: A decentralized way to potentially earn project tokens while exploring the Web3 landscape and diversifying portfolios. For Projects: A unique platform to increase visibility and build community, by connecting with Trust Wallet’s extensive user base, desiged to drive growth in a competitive market. Trust Wallet’s third Launchpool featuring $WOD is live now. Users can Download Trust Wallet to participate and potentially gain rewards while supporting the evolution of blockchain gaming and DeFi. Download Trust Wallet Download Trust Wallet About World of Dypians World of Dypians is a revolutionary MMORPG that bridges the gap between traditional Web2 gaming and the decentralized future of Web3. World of Dypians World of Dypians By integrating blockchain technology with immersive gameplay, World of Dypians empowers players with true ownership of in-game assets through NFTs, decentralized economies, and a vibrant community-driven ecosystem. As a leading gaming ecosystem on every network it operates, World of Dypians continues to set new benchmarks for user engagement and innovation, offering players seamless access to interconnected virtual worlds. With stunning graphics, an intuitive user journey, and a focus on accessibility, the platform brings together the best of Web2 familiarity and Web3 innovation, creating an inclusive space for gamers worldwide. Since its inception, World of Dypians has been dedicated to redefining the gaming landscape by merging Gaming, DeFi, NFTs, and AI into one seamless platform, creating a groundbreaking experience for players. For business collaborations, users may contact: contact@worldofdypians.com contact@worldofdypians.com contact@worldofdypians.com About Trust Wallet Trust Wallet is a secure, self-custody Web3 wallet and gateway for people who want to fully own, control, and leverage the power of their digital assets. From beginners to experienced users, Trust Wallet aims to make it easier, safer, and convenient for millions of people around the world to experience Web3, access dApps securely, store and manage their crypto and NFTs, as well as buy, sell, and stake crypto to earn rewards — all in one place and without limits. Trust Wallet Trust Wallet Contact Head of Communications Dami Odufuwa Trust Wallet press@trustwallet.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Trust Wallet Introduces World Of Dypians (WOD) On Trust Wallet Launchpool

Trust Wallet በታማኝነት Wallet ማስጀመሪያ ላይ የአለም የዳይፒያን (WOD) አስተዋውቋል

Το Trust Wallet παρουσιάζει το World Of Dypians (WOD) στο Trust Wallet Launchpool

Trust Wallet presenta World Of Dypians (WOD) en el Trust Wallet Launchpool

Trust Wallet-ek World Of Dypians (WOD) aurkezten du Trust Wallet Launchpool-en

Trust Wallet が Trust Wallet Launchpool で World Of Dypians (WOD) を導入

Trust Wallet Trust Wallet Launchpool боюнча Dypians дүйнөсүн (WOD) киргизет

Trust Wallet introduceert World Of Dypians (WOD) op Trust Wallet Launchpool

Trust Wallet විසින් Trust Wallet Launchpool මත Dypians වර්ල්ඩ් (WOD) හඳුන්වා දෙයි

Труст Валлет уводи Ворлд Оф Дипианс (ВОД) на Труст Валлет Лаунцхпоол

Trust Wallet introducerar World Of Dypians (WOD) på Trust Wallet Launchpool

Ipinakilala ng Trust Wallet ang World Of Dypians (WOD) Sa Trust Wallet Launchpool

Trust Wallet Yi Tivisa Misava Ya Dypians (WOD) Eka Trust Wallet Launchpool

Author:
(1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 8 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract
The Benefits of Simple Writing
The Current Work
Study 1a: Method
Study 1a: Results
Study 1b: Method
Study 1b: Results
Study 2: Method
Study 2: Results
General Discussion, Acknowledgements, and References Study 2: Method Participants in the US were recruited from Prolific and paid $4.00 for their time in a short study (median completion time < 7 minutes). People were told that they would read scientific summaries and make judgments about the authors of such texts. Participants and Power Based on this study’s preregistration (https://aspredicted.org/C3K_T31), a minimum of 164 participants were required to detect a small effect powered at 80% in a within-subjects study (f = 0.10, α = two-tailed, three measurements). A total of 274 participants were recruited to ensure enough participants were in the study. Most participants self-identified as men (n = 139; 50.7%; women n = 127, other n = 7), they were 36.74 years old on average (SD = 12.47 years), and were mostly White (n = 190; 69.3%). On a 7-point political ideology scale (1 = extremely liberal, 7 = extremely conservative), participants leaned liberal (M = 2.97, SD = 1.63). Procedure Five pairs of stimuli from Study 1b were selected for the experiment, having had the greatest difference in common words scores between the PNAS and GPT texts (Pair 1 GPT = 79.31%, Pair 1 PNAS = 48.65%; Pair 2 GPT = 76.64%, Pair 2 PNAS = 46.32%; Pair 3 GPT = 79.07%, Pair 3 PNAS = 52.14%; Pair 4 GPT = 85.00%, Pair 4 PNAS = 59.66%; Pair 5 GPT = 87.10%, Pair 5 PNAS = 62.81%). Participants were randomly assigned to read stimuli from three out of a possible five pairs (see the online supplement for the stimuli texts), and within these randomly selected pairs, participants were randomly assigned to the GPT (simple) or PNAS (complex) version of each pair. Participants were told to read each summary of a scientific paper and then answer questions below each summary. They were specifically told “we are not expecting you to be an expert in the topic discussed below. Instead, make your judgments based on how the summary is written.” Finally, participants made various perceptions of the author (e.g., intelligence, trustworthiness) based on prior work (14, 15, 34), judgments about the identity of who wrote the scientific summary (AI or human), and assessed the complexity in each text as a manipulation check. The order of these measures was randomized, and items within each block were randomized as well. This study was approved by the author’s university research ethics board. Measures Manipulation Check Based on prior work (15, 34), three questions asked participants to rate how clear (“How clear was the writing in the summary you just read?”), complex (“How complex was the writing in the summary you just read?”), and how well they understood each scientific summary (“How much of this writing did you understand?”). Ratings for the first two questions were made on 7- point Likert-type scales from 1 = Not at all to 7 = Extremely. The third question ranged from 1 = Not at all to 7 = An enormous amount. Author Perceptions Participants made three ratings about the author of each scientific summary: (1) “How intelligent is the scientist who wrote this summary?”, (2) How credible is the scientist who wrote this summary?”, and (3) “How trustworthy is the scientist who wrote this summary?” As a collection, these dimensions were highly reliable (Cronbach’s α = 0.88) and therefore, they were averaged to create a general author perceptions index, while also being evaluated individually. All items were measured on 7-point Likert-type scales from 1 = Not at all to 7 = Extremely. Author Identity Perceptions Participants were asked for their agreement with two questions: (1) This summary was written by a human, and (2) This summary was written by Artificial Intelligence. All items were measured on 7-point Likert-type scales from 1 = Strongly disagree to 7 = Strongly agree. Demographics Basic demographic data were obtained from each participant, including their age, gender, ethnicity, and political ideology. Analytic Plan Since there were multiple observations per participant, linear mixed models with random intercepts for participant and stimulus were constructed (45, 46). This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Author: Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 8 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 8 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 8 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract The Benefits of Simple Writing The Current Work Study 1a: Method Study 1a: Results Study 1b: Method Study 1b: Results Study 2: Method Study 2: Results General Discussion, Acknowledgements, and References Abstract Abstract The Benefits of Simple Writing The Benefits of Simple Writing The Current Work The Current Work Study 1a: Method Study 1a: Method Study 1a: Results Study 1a: Results Study 1b: Method Study 1b: Method Study 1b: Results Study 1b: Results Study 2: Method Study 2: Method Study 2: Results Study 2: Results General Discussion, Acknowledgements, and References General Discussion, Acknowledgements, and References Study 2: Method Participants in the US were recruited from Prolific and paid $4.00 for their time in a short study (median completion time < 7 minutes). People were told that they would read scientific summaries and make judgments about the authors of such texts. Participants and Power Participants and Power Based on this study’s preregistration (https://aspredicted.org/C3K_T31), a minimum of 164 participants were required to detect a small effect powered at 80% in a within-subjects study (f = 0.10, α = two-tailed, three measurements). A total of 274 participants were recruited to ensure enough participants were in the study. Most participants self-identified as men (n = 139; 50.7%; women n = 127, other n = 7), they were 36.74 years old on average (SD = 12.47 years), and were mostly White (n = 190; 69.3%). On a 7-point political ideology scale (1 = extremely liberal, 7 = extremely conservative), participants leaned liberal (M = 2.97, SD = 1.63). Procedure Procedure Five pairs of stimuli from Study 1b were selected for the experiment, having had the greatest difference in common words scores between the PNAS and GPT texts (Pair 1 GPT = 79.31%, Pair 1 PNAS = 48.65%; Pair 2 GPT = 76.64%, Pair 2 PNAS = 46.32%; Pair 3 GPT = 79.07%, Pair 3 PNAS = 52.14%; Pair 4 GPT = 85.00%, Pair 4 PNAS = 59.66%; Pair 5 GPT = 87.10%, Pair 5 PNAS = 62.81%). Participants were randomly assigned to read stimuli from three out of a possible five pairs (see the online supplement for the stimuli texts), and within these randomly selected pairs, participants were randomly assigned to the GPT (simple) or PNAS (complex) version of each pair. Participants were told to read each summary of a scientific paper and then answer questions below each summary. They were specifically told “we are not expecting you to be an expert in the topic discussed below. Instead, make your judgments based on how the summary is written.” Finally, participants made various perceptions of the author (e.g., intelligence, trustworthiness) based on prior work (14, 15, 34), judgments about the identity of who wrote the scientific summary (AI or human), and assessed the complexity in each text as a manipulation check. The order of these measures was randomized, and items within each block were randomized as well. This study was approved by the author’s university research ethics board. Measures Measures Manipulation Check Manipulation Check Manipulation Check Based on prior work (15, 34), three questions asked participants to rate how clear (“How clear was the writing in the summary you just read?”), complex (“How complex was the writing in the summary you just read?”), and how well they understood each scientific summary (“How much of this writing did you understand?”). Ratings for the first two questions were made on 7- point Likert-type scales from 1 = Not at all to 7 = Extremely. The third question ranged from 1 = Not at all to 7 = An enormous amount. Author Perceptions Author Perceptions Author Perceptions Participants made three ratings about the author of each scientific summary: (1) “How intelligent is the scientist who wrote this summary?”, (2) How credible is the scientist who wrote this summary?”, and (3) “How trustworthy is the scientist who wrote this summary?” As a collection, these dimensions were highly reliable (Cronbach’s α = 0.88) and therefore, they were averaged to create a general author perceptions index, while also being evaluated individually. All items were measured on 7-point Likert-type scales from 1 = Not at all to 7 = Extremely. Author Identity Perceptions Author Identity Perceptions Author Identity Perceptions Participants were asked for their agreement with two questions: (1) This summary was written by a human, and (2) This summary was written by Artificial Intelligence. All items were measured on 7-point Likert-type scales from 1 = Strongly disagree to 7 = Strongly agree. Demographics Demographics Demographics Basic demographic data were obtained from each participant, including their age, gender, ethnicity, and political ideology. Analytic Plan Analytic Plan Since there were multiple observations per participant, linear mixed models with random intercepts for participant and stimulus were constructed (45, 46). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Are AI or Humans More Trustworthy? A Study Puts Readers to the Test

Author:
(1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 2 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract
The Benefits of Simple Writing
The Current Work
Study 1a: Method
Study 1a: Results
Study 1b: Method
Study 1b: Results
Study 2: Method
Study 2: Results
General Discussion, Acknowledgements, and References The Benefits of Simple Writing The idea that simple language patterns can improve perceptions of scientists is supported by decades of processing fluency research (13–15). This literature suggests people tend to use their feelings when consuming information (16, 17), and simple (fluent) information feels better to most people than complex (disfluent) information. Support for this contention suggests people engage with, approach, and prefer content that is written in simple versus complex terms (e.g., simple synonyms of the same concept compared to complex synonyms) (18–20). Indeed, much of this research supports the simpler-is-better hypothesis, which claims that people will engage with content that is communicated in simple versus complex terms, absent some instrumental goal being activated (18). The most common linguistic fluency dimension is lexical fluency, which considers the degree to which people use common and everyday terms in the communication. People perceive scientists to be more intelligent if their work is written with simple words (e.g., the word job) compared to complex words (e.g., the word occupation) (15). In most cases, people prefer simple synonyms for a concept compared to complex synonyms of the same concept because it is more of a challenge to interpret and comprehend complexity, and people are economical with their effort and attention (21, 22). Another fluency dimension is analytic writing fluency (23). This dimension considers one’s communication style and how people communicate, instead of what they are communicating about (24, 25). According to prior work, a simple communication style is informal and reflects a story (e.g., it contains more pronouns, adverbs) compared to a complex communication style, which is formal and contains high rates of articles and prepositions (26–28). Finally, another relevant fluency concept is structural fluency, which considers the length of words and sentences. Longer words (e.g., occupation vs. job) and sentences with more words tend to require more effort to process (29). Therefore, the final marker of fluency relevant to the current work is operationalized by readability, which considers verbal simplicity/complexity in terms of word and sentence length. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Author: Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 2 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 2 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 2 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract The Benefits of Simple Writing The Current Work Study 1a: Method Study 1a: Results Study 1b: Method Study 1b: Results Study 2: Method Study 2: Results General Discussion, Acknowledgements, and References Abstract Abstract The Benefits of Simple Writing The Benefits of Simple Writing The Current Work The Current Work Study 1a: Method Study 1a: Method Study 1a: Results Study 1a: Results Study 1b: Method Study 1b: Method Study 1b: Results Study 1b: Results Study 2: Method Study 2: Method Study 2: Results Study 2: Results General Discussion, Acknowledgements, and References General Discussion, Acknowledgements, and References The Benefits of Simple Writing The idea that simple language patterns can improve perceptions of scientists is supported by decades of processing fluency research (13–15). This literature suggests people tend to use their feelings when consuming information (16, 17), and simple (fluent) information feels better to most people than complex (disfluent) information. Support for this contention suggests people engage with, approach, and prefer content that is written in simple versus complex terms (e.g., simple synonyms of the same concept compared to complex synonyms) (18–20). Indeed, much of this research supports the simpler-is-better hypothesis, which claims that people will engage with content that is communicated in simple versus complex terms, absent some instrumental goal being activated (18). The most common linguistic fluency dimension is lexical fluency, which considers the degree to which people use common and everyday terms in the communication. People perceive scientists to be more intelligent if their work is written with simple words (e.g., the word job) compared to complex words (e.g., the word occupation) (15). In most cases, people prefer simple synonyms for a concept compared to complex synonyms of the same concept because it is more of a challenge to interpret and comprehend complexity, and people are economical with their effort and attention (21, 22). Another fluency dimension is analytic writing fluency (23). This dimension considers one’s communication style and how people communicate, instead of what they are communicating about (24, 25). According to prior work, a simple communication style is informal and reflects a story (e.g., it contains more pronouns, adverbs) compared to a complex communication style, which is formal and contains high rates of articles and prepositions (26–28). Finally, another relevant fluency concept is structural fluency, which considers the length of words and sentences. Longer words (e.g., occupation vs. job) and sentences with more words tend to require more effort to process (29). Therefore, the final marker of fluency relevant to the current work is operationalized by readability, which considers verbal simplicity/complexity in terms of word and sentence length. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How Simple Writing Enhances the Public's Trust in Science

Before we begin, if you haven’t read <Part 1> and <Part 2> of The Reality of Digital Nomads, make sure to check it out! <Part 1> The Reality of Digital Nomads: Chasing Adventure, Finding What? <Part 2> The Reality of Digital Nomads: Defying Sight and Sound Rediscovering Home The best part of being a digital nomad didn’t strike me until it was over.. I returned home like Leonardo DiCaprio after surviving an attack from a grizzly bear. As the bus carried me from Incheon Airport to Gangnam, I gazed at Seoul’s cityscape through the window. We passed the park where I once cried after an unprecedented fight with my ex, the luxurious hotel where I’d had a staycation with my high school best friend, and the tall building where I’d faced my first nerve-wracking job interview. All my history that I had forgotten during my travels came back as once, as something that I was so proud to have endured. And when we finally arrived in my old neighborhood, I felt the familiar comfort it always offered, quietly welcoming me back. As I mentioned in Part 1, the idea of being “digitally homeless” reminds us that a home is more than just a roof over our heads. It includes family, friends, familiar neighborhood faces—like the couple I awkwardly greet internally every morning at the gym. Being away and facing challenges brought a deep sense of gratitude for these overlooked constants that have shaped me. And simply feeling this gratitude for something so familiar is, in itself, a profound joy. Growth Through Challenges Finding myself while away from home hasn’t made me dislike the culture and community that shaped me. With a greater understanding of who I uniquely am, I realize that the customs formed by my culture and community will remain a lifelong part of my identity. Back in my home community, my shyness still existed.  Before, I was the type of girl who loved popcorn but would skip it when at the theater alone, worried about what strangers might think if they saw me in line by myself. I’d even wait until the theater emptied out to shyly grab a bag on my way out, and it didn’t magically wash away from being a digital nomad. But it got better as I learned about my incredible adaptability and the kindness that emerges when it needs to. The whole experience let me rediscover my strengths and cultivate new ones. Yet, the downsides of being a digital nomad are hard to ignore. The immense mental stress of constant relocation while balancing a full-time job was the toughest. While traveling, I was often overwhelmed by the fear of losing my job due to poor performance. Ironically, I ended up losing my job four months after finishing my travels—not because of poor performance while traveling, but due to something entirely out of my control: restructuring. (Though, if I’m honest, it had more to do with a new marketing manager who didn’t seem to like me, but getting fired is a story for another post.) Having returned home after surviving and overcoming massive stress on the road, I wasn’t even fazed by the bad news. Instead, it served as a double reminder not to stress over things beyond my control. Pushing my limits under intense pressure had made me mentally stronger. This newfound ability to handle stress with composure might just have become my greatest superpower. Possibly in the future.. (Never Mind) I’m now working remotely for another blockchain company, and I still have a desire to live as a digital nomad again. However, if I set off again, I’d like to stay in each city for at least three months. Living through Airbnb rates, even in Southeast Asia, ended up costing quite a bit. As a young woman who likes nice things, I stayed in spacious apartments with pools and gyms in the most developed neighborhoods. When I got sick of Airbnbs, I headed for hotels. My monthly rent alone was at least $1,600. If I go again, I’d prefer to reduce costs with a short-term rental contract instead of Airbnbs. Moreover, with a longer stay, I want to try blending in with the local community rather than just being a foreigner. Now that I have a clearer sense of who I am, I’d like to confidently introduce myself to new friends and share in detail what parts of me come from my culture and what parts are intrinsically me. I’d also like to reduce the stress of constantly moving from place to place and really take in my surroundings at a more relaxed pace. (Never Mind) The Complexities of Immersion Actually, while the last paragraph might have made for a neat conclusion to this article, it wouldn’t be honest. I enjoy being alone; that’s why I write these blogs. As an introvert, it’s the most entertaining activity for me. Initially, I intended this piece to present slowmads as the next big trend, aiming to craft a buzzworthy article. But the truth is, traveling slowly—staying in one place for two to six months—might strip away the convenience, thrill, and lessons of the fast-paced digital nomad lifestyle. Settling down for an extended period has its downsides. You begin to see the cracks of a culture. When I stayed in Tokyo for the seventh time—accumulating a total of two months—I realized that its meticulous, rule-based culture came with challenges. There seemed to be a “right” way to do everything, and I quickly learned that I was often doing things “wrong.” Something as innocuous as receiving a wet towel with one hand was a faux pas. I only became aware of this because of a friendship I formed during my stay. She was half-Japanese, half-American, and would quietly correct me under her breath: “You’re not supposed to do it that way.” What began as harmless ignorance soon made me feel increasingly self-conscious. Japan, my culinary haven of udon, tempura, and yakiniku, started to feel less comforting and more judgmental. I no longer felt at ease there. That’s when I started longing for home—not just the physical space, but the feeling of being somewhere where I was noticed yet inherently accepted. This journey, ironically, led me to a newfound appreciation for home and all the ways it raised me and allowed me to exist in my most natural form. With already a lot of stress that comes from balancing full-time work and adapting to a new environment, I wouldn’t prefer to also have to experience the downsides of the different culture that makes me feel inferior. But this is all about perspective - maybe I’ve just learned that Japan isn’t the right place to settle long-term, as a new home was what I was looking for in the beginning anyways. Why Fast-Paced Travel Still Appeals From my experience, being a digital nomad is the perfect opportunity to become a borderline workaholic, driven by loneliness and isolation from any sense of community. Yet, paradoxically, it also offers profound opportunities for spiritual growth. Experiencing diverse cultures and unfamiliar landscapes pushes you out of your comfort zone, uncovering parts of yourself long buried beneath habitual ways of thinking. During my time as a digital nomad, I found the space to love myself again as I turned 28. I advanced in my career, diving headfirst into work while enduring immense psychological pressure. If you’re considering taking the leap into this lifestyle, I’d highly recommend a fast-paced approach. It provides the illusion of starting over—a fresh, history-free existence—and keeps you moving so quickly that you’re not weighed down by the need to create one. Above all, traveling alone allows you to amplify that small, inner voice until it’s finally loud enough to be heard. Final Thoughts To summarize, fast-paced digital nomading delivers life in its rawest form. The frantic juggling act of adapting to new environments while maintaining a full-time workload builds resilience and sharpens your ability to manage stress. The exposure to various cultures, coupled with the freedom from the labels of your past, nurtures spiritual growth. And the solitude—no friends, no usual yoga or fitness studios—creates a unique opportunity to focus intensely on your work and yourself. When the journey inevitably leads you back home, you’ll find a newfound sense of gratitude. You’ll see your old environment with fresh eyes and uncover unexpected joys in the familiar. I recognize that everyone’s journey as a digital nomad is unique. I’d love to hear about yours in the comments—how you traveled, managed the logistics, and what you gained or perhaps lost along the way. Thank you for reading my stories, and thank you to those who reached out after. I hope it provided good insight to everyone of you :) Before we begin, i f you haven’t read <Part 1> and <Part 2> of The Reality of Digital Nomads, make sure to check it out! Before we begin, f you haven’t read <Part 1> and <Part 2> of The Reality of Digital Nomads, make sure to check it out! <Part 1> The Reality of Digital Nomads: Chasing Adventure, Finding What? <Part 1> The Reality of Digital Nomads: Chasing Adventure, Finding What? <Part 2> The Reality of Digital Nomads: Defying Sight and Sound <Part 2> The Reality of Digital Nomads: Defying Sight and Sound Rediscovering Home Rediscovering Home The best part of being a digital nomad didn’t strike me until it was over.. I returned home like Leonardo DiCaprio after surviving an attack from a grizzly bear. As the bus carried me from Incheon Airport to Gangnam, I gazed at Seoul’s cityscape through the window. We passed the park where I once cried after an unprecedented fight with my ex, the luxurious hotel where I’d had a staycation with my high school best friend, and the tall building where I’d faced my first nerve-wracking job interview. All my history that I had forgotten during my travels came back as once, as something that I was so proud to have endured. And when we finally arrived in my old neighborhood, I felt the familiar comfort it always offered, quietly welcoming me back. As I mentioned in Part 1, the idea of being “digitally homeless” reminds us that a home is more than just a roof over our heads. It includes family, friends, familiar neighborhood faces—like the couple I awkwardly greet internally every morning at the gym. Being away and facing challenges brought a deep sense of gratitude for these overlooked constants that have shaped me. And simply feeling this gratitude for something so familiar is, in itself, a profound joy. Growth Through Challenges Growth Through Challenges Finding myself while away from home hasn’t made me dislike the culture and community that shaped me. With a greater understanding of who I uniquely am, I realize that the customs formed by my culture and community will remain a lifelong part of my identity. Back in my home community, my shyness still existed.  Before, I was the type of girl who loved popcorn but would skip it when at the theater alone, worried about what strangers might think if they saw me in line by myself. I’d even wait until the theater emptied out to shyly grab a bag on my way out, and it didn’t magically wash away from being a digital nomad. But it got better as I learned about my incredible adaptability and the kindness that emerges when it needs to. The whole experience let me rediscover my strengths and cultivate new ones. Yet, the downsides of being a digital nomad are hard to ignore. The immense mental stress of constant relocation while balancing a full-time job was the toughest. While traveling, I was often overwhelmed by the fear of losing my job due to poor performance. Ironically, I ended up losing my job four months after finishing my travels—not because of poor performance while traveling, but due to something entirely out of my control: restructuring. (Though, if I’m honest, it had more to do with a new marketing manager who didn’t seem to like me, but getting fired is a story for another post.) Having returned home after surviving and overcoming massive stress on the road, I wasn’t even fazed by the bad news. Instead, it served as a double reminder not to stress over things beyond my control. Pushing my limits under intense pressure had made me mentally stronger. This newfound ability to handle stress with composure might just have become my greatest superpower. Possibly in the future.. (Never Mind) I’m now working remotely for another blockchain company, and I still have a desire to live as a digital nomad again. However, if I set off again, I’d like to stay in each city for at least three months . Living through Airbnb rates, even in Southeast Asia, ended up costing quite a bit. As a young woman who likes nice things, I stayed in spacious apartments with pools and gyms in the most developed neighborhoods. When I got sick of Airbnbs, I headed for hotels. My monthly rent alone was at least $1,600. If I go again, I’d prefer to reduce costs with a short-term rental contract instead of Airbnbs. at least three months Moreover, with a longer stay, I want to try blending in with the local community rather than just being a foreigner. Now that I have a clearer sense of who I am, I’d like to confidently introduce myself to new friends and share in detail what parts of me come from my culture and what parts are intrinsically me. I’d also like to reduce the stress of constantly moving from place to place and really take in my surroundings at a more relaxed pace. community (Never Mind) The Complexities of Immersion (Never Mind) The Complexities of Immersion Actually, while the last paragraph might have made for a neat conclusion to this article, it wouldn’t be honest. I enjoy being alone; that’s why I write these blogs. As an introvert, it’s the most entertaining activity for me. Initially, I intended this piece to present slowmads as the next big trend, aiming to craft a buzzworthy article. But the truth is, traveling slowly—staying in one place for two to six months—might strip away the convenience, thrill, and lessons of the fast-paced digital nomad lifestyle. Settling down for an extended period has its downsides. You begin to see the cracks of a culture. When I stayed in Tokyo for the seventh time—accumulating a total of two months—I realized that its meticulous, rule-based culture came with challenges. There seemed to be a “right” way to do everything, and I quickly learned that I was often doing things “wrong.” Something as innocuous as receiving a wet towel with one hand was a faux pas. I only became aware of this because of a friendship I formed during my stay. She was half-Japanese, half-American, and would quietly correct me under her breath: “You’re not supposed to do it that way.” What began as harmless ignorance soon made me feel increasingly self-conscious. Japan, my culinary haven of udon, tempura, and yakiniku, started to feel less comforting and more judgmental. I no longer felt at ease there. That’s when I started longing for home—not just the physical space, but the feeling of being somewhere where I was noticed yet inherently accepted. This journey, ironically, led me to a newfound appreciation for home and all the ways it raised me and allowed me to exist in my most natural form. With already a lot of stress that comes from balancing full-time work and adapting to a new environment, I wouldn’t prefer to also have to experience the downsides of the different culture that makes me feel inferior. But this is all about perspective - maybe I’ve just learned that Japan isn’t the right place to settle long-term, as a new home was what I was looking for in the beginning anyways. Why Fast-Paced Travel Still Appeals Why Fast-Paced Travel Still Appeals From my experience, being a digital nomad is the perfect opportunity to become a borderline workaholic, driven by loneliness and isolation from any sense of community. Yet, paradoxically, it also offers profound opportunities for spiritual growth. Experiencing diverse cultures and unfamiliar landscapes pushes you out of your comfort zone, uncovering parts of yourself long buried beneath habitual ways of thinking. During my time as a digital nomad, I found the space to love myself again as I turned 28. I advanced in my career, diving headfirst into work while enduring immense psychological pressure. If you’re considering taking the leap into this lifestyle, I’d highly recommend a fast-paced approach. It provides the illusion of starting over—a fresh, history-free existence—and keeps you moving so quickly that you’re not weighed down by the need to create one. Above all, traveling alone allows you to amplify that small, inner voice until it’s finally loud enough to be heard. If you’re considering taking the leap into this lifestyle, I’d highly recommend a fast-paced approach. Final Thoughts Final Thoughts To summarize, fast-paced digital nomading delivers life in its rawest form. The frantic juggling act of adapting to new environments while maintaining a full-time workload builds resilience and sharpens your ability to manage stress . The exposure to various cultures, coupled with the freedom from the labels of your past, nurtures spiritual growth . And the solitude—no friends, no usual yoga or fitness studios—creates a unique opportunity to focus intensely on your work and yourself . resilience ability to manage stress spiritual growth focus intensely on your work and yourself When the journey inevitably leads you back home, you’ll find a newfound sense of gratitude. You’ll see your old environment with fresh eyes and uncover unexpected joys in the familiar. I recognize that everyone’s journey as a digital nomad is unique. I’d love to hear about yours in the comments—how you traveled, managed the logistics, and what you gained or perhaps lost along the way. I recognize that everyone’s journey as a digital nomad is unique. I’d love to hear about yours in the comments—how you traveled, managed the logistics, and what you gained or perhaps lost along the way. Thank you for reading my stories, and thank you to those who reached out after. I hope it provided good insight to everyone of you :)

The Truth of Digital Nomads: The Long Way Home and What It Really Teaches Us

የዲጂታል ዘላኖች እውነት፡ የረዥም መንገድ ቤት እና በእውነት የሚያስተምረን

La verdad de los nómadas digitales: el largo camino a casa y lo que realmente nos enseña

حقیقت عشایر دیجیتال: راه طولانی به خانه و آنچه واقعاً به ما می آموزد

デジタル遊牧民の真実：長い道のりとそれが私たちに教えてくれること

Ny fahamarinan'ny mpifindrafindra monina nomerika: Ny lalana lavitra mody sy ny tena ampianariny antsika

Therešo ya Digital Nomads: Tsela e Telele ya Gae le Seo e Re Rutago Sona e le ka kgonthe

د ډیجیټل کوچیانو حقیقت: د کور اوږده لاره او هغه څه چې دا واقعیا موږ ته درس راکوي

Adevărul nomaților digitali: drumul lung spre casă și ceea ce ne învață cu adevărat

ඩිජිටල් නාමිකයන්ගේ සත්‍යය: දිගු මග නිවස සහ එය අපට සැබවින්ම උගන්වන දේ

Pravda digitálnych nomádov: dlhá cesta domov a čo nás v skutočnosti učí

E vërteta e nomadëve dixhitalë: Rruga e gjatë për në shtëpi dhe çfarë na mëson vërtet

ڈیجیٹل خانہ بدوشوں کی حقیقت: گھر کا طویل راستہ اور یہ واقعی ہمیں کیا سکھاتا ہے۔

Introduction Imagine sitting down for a two-hour conversation with an AI interviewer. You discuss your childhood, pivotal life moments, and even your views on social issues. Soon after, an eerily accurate digital version of yourself, a so-called "AI twin", is created. This clone mirrors your personality, preferences, and decision-making processes with up to 85% similarity. This isn’t science fiction. Groundbreaking research by Stanford University and Google DeepMind, as highlighted in James O’Donnell’s recent piece for MIT Technology Review “AI can now create a replica of your personality”, explores the fascinating and unsettling world of AI-powered replicas. This article dives into the research, its potential applications, and the ethical dilemmas of sharing your decision-making power with a machine. The Making of a Digital Twin Led by Joon Sung Park, the research team conducted interviews with 1,000 participants across a diverse demographic spectrum. The interviews covered personal histories, career journeys, and opinions on societal issues. From this data, the researchers created AI agents designed to mimic participants’ behaviors in personality tests, social surveys, and logic games. These digital doppelgängers achieved an impressive 85% similarity in their responses when compared to their human counterparts. Though not flawless, the results point to a near future where digital replicas could extend our influence into tasks we may not have time, or inclination, to handle. Park suggests these replicas might one day act as proxies in decision-making processes. “If you can have a bunch of small ‘yous’ running around and actually making the decisions that you would have made, that, I think, is ultimately the future,” he says. Applications Beyond Entertainment While the concept of having an AI clone may conjure images of personalized avatars in gaming or digital assistants, its implications extend far beyond entertainment. These simulation agents could become invaluable tools for research in social sciences, where ethical or logistical constraints often limit the scope of studies. John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” From analyzing how interventions combat misinformation to understanding the behavioral drivers of traffic jams, these agents promise to simulate complex human scenarios at scale, offering researchers a new lens for exploring societal challenges. Ethical Quandaries and Risks Despite its promise, this technology raises significant ethical concerns. Much like how image-generation AI has enabled deepfakes, personality replication tools could be misused. Imagine a digital twin endorsing products or political ideologies without your consent. The implications for privacy, consent, and misinformation are enormous. Additionally, critics have noted the limitations of current evaluation methods used in the study, such as the General Social Survey and Big Five personality assessments. While useful, these tools may fail to capture the intricacies of what makes us unique. For instance, AI agents struggled in behavioral tests like the “dictator game,” which explores fairness in decision-making. Reducing the Data Burden The research also introduces an efficient alternative to the vast datasets typically required to create digital twins. Current companies, such as Tavus, often rely on extensive datasets, emails, social media interactions, and more, to replicate personalities. This study, however, demonstrates that a focused interview might achieve similar results in less time and with fewer resources. Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” The Future of AI Replicas As AI advances, the line between human and machine behavior will continue to blur. Whether aiding researchers, enhancing productivity, or enabling creative exploration, simulation agents represent a powerful new frontier. However, their ethical implications cannot be overlooked. Would you trust an AI version of yourself to make decisions on your behalf? As technology like this becomes more accessible, the question isn’t just “if” but “when” we’ll confront these dilemmas in our daily lives. By leveraging the insights from James O’Donnell’s reporting and the latest AI research, we stand at the cusp of a transformative era, one where our digital selves may be as active as our physical ones. But as we move forward, a balanced approach will be critical, ensuring that these tools serve humanity without compromising the essence of individuality. Sources: James O’Donnell’s article in MIT Technology Review
Research from Stanford University and Google DeepMind What are your thoughts on living alongside your AI twin? Share them below! Introduction Introduction Imagine sitting down for a two-hour conversation with an AI interviewer. You discuss your childhood, pivotal life moments, and even your views on social issues. Soon after, an eerily accurate digital version of yourself, a so-called "AI twin", is created. This clone mirrors your personality, preferences, and decision-making processes with up to 85% similarity. This isn’t science fiction. Groundbreaking research by Stanford University and Google DeepMind, as highlighted in James O’Donnell’s recent piece for MIT Technology Review “AI can now create a replica of your personality” , explores the fascinating and unsettling world of AI-powered replicas. MIT Technology Review “AI can now create a replica of your personality” This article dives into the research, its potential applications, and the ethical dilemmas of sharing your decision-making power with a machine. The Making of a Digital Twin The Making of a Digital Twin Led by Joon Sung Park, the research team conducted interviews with 1,000 participants across a diverse demographic spectrum. The interviews covered personal histories, career journeys, and opinions on societal issues. From this data, the researchers created AI agents designed to mimic participants’ behaviors in personality tests, social surveys, and logic games. These digital doppelgängers achieved an impressive 85% similarity in their responses when compared to their human counterparts. Though not flawless, the results point to a near future where digital replicas could extend our influence into tasks we may not have time, or inclination, to handle. Park suggests these replicas might one day act as proxies in decision-making processes. “If you can have a bunch of small ‘yous’ running around and actually making the decisions that you would have made, that, I think, is ultimately the future,” he says. Applications Beyond Entertainment Applications Beyond Entertainment While the concept of having an AI clone may conjure images of personalized avatars in gaming or digital assistants, its implications extend far beyond entertainment. These simulation agents could become invaluable tools for research in social sciences, where ethical or logistical constraints often limit the scope of studies. John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” From analyzing how interventions combat misinformation to understanding the behavioral drivers of traffic jams, these agents promise to simulate complex human scenarios at scale, offering researchers a new lens for exploring societal challenges. Ethical Quandaries and Risks Ethical Quandaries and Risks Despite its promise, this technology raises significant ethical concerns. Much like how image-generation AI has enabled deepfakes, personality replication tools could be misused. Imagine a digital twin endorsing products or political ideologies without your consent. The implications for privacy, consent, and misinformation are enormous. Additionally, critics have noted the limitations of current evaluation methods used in the study, such as the General Social Survey and Big Five personality assessments. While useful, these tools may fail to capture the intricacies of what makes us unique. For instance, AI agents struggled in behavioral tests like the “dictator game,” which explores fairness in decision-making. Reducing the Data Burden Reducing the Data Burden The research also introduces an efficient alternative to the vast datasets typically required to create digital twins. Current companies, such as Tavus, often rely on extensive datasets, emails, social media interactions, and more, to replicate personalities. This study, however, demonstrates that a focused interview might achieve similar results in less time and with fewer resources. Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” The Future of AI Replicas The Future of AI Replicas As AI advances, the line between human and machine behavior will continue to blur. Whether aiding researchers, enhancing productivity, or enabling creative exploration, simulation agents represent a powerful new frontier. However, their ethical implications cannot be overlooked. Would you trust an AI version of yourself to make decisions on your behalf? As technology like this becomes more accessible, the question isn’t just “if” but “when” we’ll confront these dilemmas in our daily lives. By leveraging the insights from James O’Donnell’s reporting and the latest AI research, we stand at the cusp of a transformative era, one where our digital selves may be as active as our physical ones. But as we move forward, a balanced approach will be critical, ensuring that these tools serve humanity without compromising the essence of individuality. Sources : Sources James O’Donnell’s article in MIT Technology Review Research from Stanford University and Google DeepMind James O’Donnell’s article in MIT Technology Review MIT Technology Review Research from Stanford University and Google DeepMind What are your thoughts on living alongside your AI twin? Share them below!

Can You Trust Your AI Twin to Think Like You?

Here we go again. It was supposed to be the shining beacon of academic research, a tool that could sift through mountains of data, offer accurate citations, and provide credible sources to enhance scholarly work. Scholar GPT, the supposed breakthrough in AI-driven academic assistance, was meant to revolutionize the way we gather information. It was supposed to be a reliable resource that combined the power of cutting-edge technology with the expertise of years of research, right? WRONG. Once again, we are confronted with a harsh reality that we have all come to dread when it comes to AI: It can’t be trusted. In a shocking revelation, Scholar GPT—this supposed paragon of academic assistance. However, when I was using it I found it to be providing inaccurate information and statistics. The very thing it was supposed to excel at—accuracy—has turned out to be a total farce. This is not just a minor blip. This is a massive blow to the credibility of AI as a source of reliable academic content. Scholar GPT, which touts itself as being trained on the most valid and credible sources, was providing data that didn’t match up with reputable research and statistics And let’s not ignore the glaring fact that the platform claims to reference “credible” academic papers, yet several of these so-called references were completely fabricated or misquoted. At this point, anyone still believing that AI like CHATGPT, or in this case, Scholar GPT, is a trustworthy partner in academic research is either naive or seriously misinformed. I’m sorry, but when you trust a tool that generates fake statistics or pulls sources out of thin air, you are opening the door to disaster in your academic work. Sure, you might say, "But these AI models are constantly improving!" Really? Because the last time I checked, they’re still feeding you half-baked data that could ruin your credibility in a split second. It’s one thing for AI to make a mistake on trivial things, but when it comes to providing factual, verifiable information to the world of academia, there is no room for error. The repercussions of getting this wrong aren’t just some theoretical debate about technology—they can have real-world consequences. Imagine citing a non-existent study or quoting a statistic that’s completely fabricated—your academic integrity, your reputation, and your work could all go down the drain. How does AI expect to replace credible research when it doesn’t even know what reliability looks like? And don’t even get me started on how CHATGPT itself is still far from trustworthy. The promises of AI revolutionizing everything from writing to research have clearly been wildly overstated. Sure, it might be able to string together sentences that sound decent, but that’s about it. When it comes to fact-checking, cross-referencing, and providing legitimate citations, it falls flat on its face. Even after all the hype and supposed advancements, AI still struggles with the most basic and vital aspect of academic work: accuracy. It’s no longer just a matter of missing the mark on a few minor details—AI systems like Scholar GPT and CHATGPT are actively contributing to the spread of misinformation, and that is unacceptable. The reality is that we cannot afford to blindly rely on AI in fields that demand precision and scholarly integrity. So let’s all take a deep breath and stop pretending that these so-called "academic tools" are the holy grail of research. They’re not. They are not ready to replace the critical thinking, in-depth knowledge, and meticulous verification that true scholars have spent years honing. And, quite frankly, they may never be. Until AI can be trusted to consistently provide accurate, reliable, and verifiable information, it remains nothing more than a tool that could derail your academic efforts. If you’re still putting your faith in Scholar GPT or any other AI-driven platform for your academic work, I hate to break it to you—but you’re on shaky ground. The truth is, AI just isn’t ready for the world of academia yet. Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Here we go again. It was supposed to be the shining beacon of academic research, a tool that could sift through mountains of data, offer accurate citations, and provide credible sources to enhance scholarly work. Scholar GPT , the supposed breakthrough in AI-driven academic assistance, was meant to revolutionize the way we gather information. It was supposed to be a reliable resource that combined the power of cutting-edge technology with the expertise of years of research, right? Scholar GPT WRONG. WRONG. Once again, we are confronted with a harsh reality that we have all come to dread when it comes to AI: It can’t be trusted. It can’t be trusted. In a shocking revelation, Scholar GPT—this supposed paragon of academic assistance. However, when I was using it I found it to be providing inaccurate information and statistics. The very thing it was supposed to excel at—accuracy—has turned out to be a total farce. This is not just a minor blip. This is a massive blow to the credibility of AI as a source of reliable academic content. Scholar GPT, which touts itself as being trained on the most valid and credible sources, was providing data that didn’t match up with reputable research and statistics And let’s not ignore the glaring fact that the platform claims to reference “credible” academic papers, yet several of these so-called references were completely fabricated or misquoted. At this point, anyone still believing that AI like CHATGPT , or in this case, Scholar GPT, is a trustworthy partner in academic research is either naive or seriously misinformed. I’m sorry, but when you trust a tool that generates fake statistics or pulls sources out of thin air, you are opening the door to disaster in your academic work. CHATGPT Sure, you might say, "But these AI models are constantly improving!" Really? Because the last time I checked, they’re still feeding you half-baked data that could ruin your credibility in a split second. It’s one thing for AI to make a mistake on trivial things, but when it comes to providing factual, verifiable information to the world of academia, there is no room for error. no room for error. The repercussions of getting this wrong aren’t just some theoretical debate about technology—they can have real-world consequences. Imagine citing a non-existent study or quoting a statistic that’s completely fabricated—your academic integrity, your reputation, and your work could all go down the drain. How does AI expect to replace credible research when it doesn’t even know what reliability looks like? And don’t even get me started on how CHATGPT itself is still far from trustworthy. The promises of AI revolutionizing everything from writing to research have clearly been wildly overstated. Sure, it might be able to string together sentences that sound decent, but that’s about it. CHATGPT wildly overstated. When it comes to fact-checking, cross-referencing, and providing legitimate citations, it falls flat on its face. Even after all the hype and supposed advancements, AI still struggles with the most basic and vital aspect of academic work: accuracy. fact-checking, cross-referencing, and providing legitimate citations, It’s no longer just a matter of missing the mark on a few minor details—AI systems like Scholar GPT and CHATGPT are actively contributing to the spread of misinformation, and that is unacceptable. The reality is that we cannot afford to blindly rely on AI in fields that demand precision and scholarly integrity. we cannot afford to blindly rely on AI So let’s all take a deep breath and stop pretending that these so-called "academic tools" are the holy grail of research. They’re not. They are not ready to replace the critical thinking, in-depth knowledge, and meticulous verification that true scholars have spent years honing. And, quite frankly, they may never be. Until AI can be trusted to consistently provide accurate, reliable, and verifiable information, it remains nothing more than a tool that could derail your academic efforts. If you’re still putting your faith in Scholar GPT or any other AI-driven platform for your academic work, I hate to break it to you—but you’re on shaky ground. The truth is, AI just isn’t ready for the world of academia yet. Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: #DYOR

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