In a report released on October 30, Keith Hughes from Truist Financial maintained a Buy rating on Trex Company (TREX – Research Report), with a price target of $70.00. The company’s shares closed yesterday at $56.21. According to TipRanks, Hughes is a ...

Truist Financial Reaffirms Their Buy Rating on Trex Company (TREX)

Truist Financial analyst Terry Tillman maintained a Buy rating on Roper Technologies (ROP – Research Report) on October 30 and set a price target of $575.00. The company’s shares closed yesterday at $488.57. According to TipRanks, Tillman is a 5-star ...

Truist Financial Reaffirms Their Buy Rating on Roper Technologies (ROP)

Fintel reports that on October 31, 2023, Truist Securities reiterated coverage of Kinsale Capital Group (NASDAQ:KNSL) with a Buy recommendation. Analyst Price Forecast Suggests 25.90% Upside As of November 1,

Truist Securities Reiterates Kinsale Capital Group (KNSL) Buy Recommendation

Fintel reports that on October 31, 2023, Truist Securities maintained coverage of ON Semiconductor (NASDAQ:ON) with a Buy recommendation. Analyst Price Forecast Suggests 40.17% Upside As of October 31,

Truist Securities Maintains ON Semiconductor (ON) Buy Recommendation

J. Kempin/Getty Images for Extra Kroy Biermann and Kim Zolciak-Biermann are doing everything they can to prevent foreclosure on their shared Georgia mansion. The estranged couple each filed affidavits on Thursday,

Kim Zolciak and Kroy Biermann Seek to Avoid 'Devastating' House Foreclosure

Perhaps the biggest goal of the 10-year plan will be figuring out what to do with empty buildings, such as the BB&T Financial Center, which was left vacant after its single tenant, Truist, moved its headquarters to Charlotte between 2020 and 2021.

Downtown Winston-Salem will look to fill empty buildings, modernize parking in 2024

The bankruptcy filing allows Sunlight to continue operating while it seeks court approval to proceed with the acquisition.

Sunlight Financial to be bought by investors through Chapter 11 reorganization plan

“We would be left homeless, without financial resources, and unable to address the debts accumulated during our marriage. Among these debts are the Truist mortgage, a Home Equity Line of Credit (HELOC) on the property, and an IRS lien.” Biermann noted ...

Kim Zolciak and Kroy Biermann Say Losing Home Would Be ‘Devastating’ in Attempt to Avoid Foreclosure

Kim Zolciak and Kroy Biermann are putting aside their differences in order to prevent their Alpharetta, Georgia, mansion from going into foreclosure. Both Kim, 45, and Kroy, 38, filed affidavits on October 26,

Kim Zolciak and Kroy Biermann Join Forces in an Attempt to Avoid ‘Devastating’ Foreclosure

Truist Financial

truist

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

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

Scholar GPT Fails To Deliver – A Wake-Up Call For The Trustworthiness of AI in Academia

Samsung, the world's second largest technology company by revenue, recently unveiled its plan to build the first AI-powered home appliances to have blockchain security-grade architecture. In other words, the tech giant is planning to leverage its Knox Matrix security framework  to create a ‘Trust Chain”, in a bid to secure connected devices in smart homes. This, like many other instances, is clear proof of the growing intersection between AI and blockchain technology,  which is often marked by the pronounced interest of tech companies in blockchain solutions. It reinforces the theory that AI needs blockchain and vice versa.  (Put simply, intelligence needs trust to thrive). In the words of Peter Kyle, UK’s Secretary of State For Science And Technology: AI has the potential to improve our public services, boost productivity and rebuild our economy.  But to take full advantage,  we need to build trust in these systems. Moreover, AI needs not just trust but it also needs decentralization to thrive. With decentralized AI, we can securely and efficiently distribute AI data against the background of centralized rules, thus enabling us to break free from centralized AI platforms commonly referred to as ‘AI data silos”. Dismantling AI Ecosystem's Bottlenecks AI must be built on trust and decentralization. The reasons are straightforward. In traditional AI systems, large corporations wield significant control over the distribution and accessibility of AI data. This centralized control is a bottleneck to AI development because it practically limits the potential of AI systems to learn from a diverse range of comprehensive datasets, thus hindering innovation in various industries where AI is used. Additionally, the lack of user control over their sensitive data has brought the issue of users’ privacy to the fore, making it one of their core underlying issues in AI development. Given the tendency of giant tech companies to cross the line by breaching users' privacy, the need for a collaborative AI ecosystem built on users’ privacy and ownership has become more apparent than ever. Furthermore, the centralized storage of AI data also poses a significant challenge to its accessibility. A decentralized AI data storage, however, opens up access to multiple stakeholders working in the AI industry,  improving the quality of AI data and the sophistication of its end users. A general example of the collaborative approach to data storage and accessibility is DePIN platforms which allow users to rent out their computing resources in return for tokenized rewards. These platforms incentivize users to individually participate in the distribution and cloud storage of heavy data loads on a decentralized network. Filecoin, Storj, Airweave, and Zetta AI, for example, reward users for contributing to a decentralized data economy, enabling the coordination of a global network of contributors—from developers to enterprises—to build, share, and be fairly rewarded for high-quality AI asset contributions while maintaining ownership, privacy, and control. Benefits of Decentralized AI Decentralized AI offers numerous advantages when compared to traditional data storage systems, which are often criticized for their centralization, limited accessibility, and breach of users’ privacy. Some of these benefits include : Data Quality As Jeff Dean rightly put it: The quality of your data determines the quality of your AI. Enabling AI models to learn from a wider comprehensive dataset can significantly improve the quality and output of AI data, thus creating more sophisticated AI data repositories for various stakeholders in the industry. Decentralized Ownership The beauty of decentralized AI is its ability to foster decentralized ownership of data while preserving users’ privacy and security. Centralization is a far cry from these attributes because it allows few platforms with significant resources to determine how AI data is stored and used. Collaboration and Incentive Decentralized AI fosters open collaboration among the various participants in a decentralized network by rewarding collaborative data sharing and participants’ contributions to AI model development. Tokenized rewards (i.e. tokens) are fairly distributed to users as incentives. Increased Transparency and Trust Decentralized AI fosters transparency, which involves recording all transactions and decision-making processes on a blockchain. Blockchain has the ability to ensure that AI data is traced and verified.  The traceability and transparency of AI data is crucial in building trust with users as it enables them to understand how their data is used.  This is a stark contrast to centralized platforms where users have little or no knowledge of how their data are used. Democratized AI development Famous AI advocate, Cathy O Neil, once said: Decentralized AI is a powerful tool for democratizing access to AI technology. By breaking down the barriers of centralization, we can empower individuals and communities to develop and deploy AI solutions that address their specific needs. The development of AI is dependent on the diversity of its learning models. It's vital to train AI systems from multiple rather than a single data set to enhance their performance and the quality of data across various industries. Latest Development: ZettaBlock Unveils KiteAI To Democratize AI Development ZettaBlock recently unveiled KiteAI, a transformative AI platform that opens access to non-public data for AI developers, with a focus on ensuring that contributors are fairly compensated. The team has concentrated on building enhanced data pipelines, especially for blockchain data, supporting key players like SUI, Eigenlayer, Chainlink Labs,etc. This platform has processed tens of terabytes of data, making it accessible to organizations for AI model development while safeguarding the privacy of the original data sources. Through advanced data attribution and privacy-preserving technology, ZettaBlock’s platform uniquely supports a collaborative economy. It ensures that contributors are recognized and rewarded as their data directly contributes to model performance and value. ZettaBlock is set to showcase elements of this technology at the upcoming Devcon, where attendees will gain insights into how the platform enables secure, equitable access to data for AI advancements. Conclusion: Building a responsible, efficient, and inclusive AI ecosystem is at the heart of decentralized AI. Moreso, it is a paradigm shift from centralized control of vital AI data hindering the further development of AI models. By encouraging decentralized data sharing and storage, it increases accessibility and foresters a collaborative approach to improving the quality of AI data. Most importantly, decentralized AI is poised to help AI to unlock its full potential and serve the various needs of individuals and organizations. Samsung, the world's second largest technology company by revenue, recently unveiled its plan to build the first AI-powered home appliances to have blockchain security-grade architecture. In other words, the tech giant is planning to leverage its Knox Matrix security framework  to create a ‘Trust Chain”, in a bid to secure connected devices in smart homes. This, like many other instances, is clear proof of the growing intersection between AI and blockchain technology,  which is often marked by the pronounced interest of tech companies in blockchain solutions. It reinforces the theory that AI needs blockchain and vice versa.  (Put simply, intelligence needs trust to thrive). In the words of Peter Kyle, UK’s Secretary of State For Science And Technology: AI has the potential to improve our public services, boost productivity and rebuild our economy.  But to take full advantage,  we need to build trust in these systems. AI has the potential to improve our public services, boost productivity and rebuild our economy.  But to take full advantage,  we need to build trust in these systems. Moreover, AI needs not just trust but it also needs decentralization to thrive. With decentralized AI, we can securely and efficiently distribute AI data against the background of centralized rules, thus enabling us to break free from centralized AI platforms commonly referred to as ‘AI data silos”. Dismantling AI Ecosystem's Bottlenecks AI must be built on trust and decentralization. The reasons are straightforward. In traditional AI systems, large corporations wield significant control over the distribution and accessibility of AI data. This centralized control is a bottleneck to AI development because it practically limits the potential of AI systems to learn from a diverse range of comprehensive datasets, thus hindering innovation in various industries where AI is used. This centralized control is a bottleneck to AI development because it practically limits the potential of AI systems to learn from a diverse range of comprehensive datasets, thus hindering innovation in various industries where AI is used. This centralized control is a bottleneck to AI development because it practically limits the potential of AI systems to learn from a diverse range of comprehensive datasets, thus hindering innovation in various industries where AI is used. Additionally, the lack of user control over their sensitive data has brought the issue of users’ privacy to the fore, making it one of their core underlying issues in AI development. Given the tendency of giant tech companies to cross the line by breaching users' privacy, the need for a collaborative AI ecosystem built on users’ privacy and ownership has become more apparent than ever. Furthermore, the centralized storage of AI data also poses a significant challenge to its accessibility. A decentralized AI data storage, however, opens up access to multiple stakeholders working in the AI industry,  improving the quality of AI data and the sophistication of its end users. A general example of the collaborative approach to data storage and accessibility is DePIN platforms which allow users to rent out their computing resources in return for tokenized rewards. These platforms incentivize users to individually participate in the distribution and cloud storage of heavy data loads on a decentralized network. Filecoin, Storj, Airweave, and Zetta AI, for example, reward users for contributing to a decentralized data economy, enabling the coordination of a global network of contributors—from developers to enterprises—to build, share, and be fairly rewarded for high-quality AI asset contributions while maintaining ownership, privacy, and control. Benefits of Decentralized AI Decentralized AI offers numerous advantages when compared to traditional data storage systems, which are often criticized for their centralization, limited accessibility, and breach of users’ privacy. Some of these benefits include : Data Quality As Jeff Dean rightly put it: The quality of your data determines the quality of your AI. The quality of your data determines the quality of your AI. Enabling AI models to learn from a wider comprehensive dataset can significantly improve the quality and output of AI data, thus creating more sophisticated AI data repositories for various stakeholders in the industry. Decentralized Ownership The beauty of decentralized AI is its ability to foster decentralized ownership of data while preserving users’ privacy and security. Centralization is a far cry from these attributes because it allows few platforms with significant resources to determine how AI data is stored and used. Collaboration and Incentive Decentralized AI fosters open collaboration among the various participants in a decentralized network by rewarding collaborative data sharing and participants’ contributions to AI model development. Tokenized rewards (i.e. tokens) are fairly distributed to users as incentives. Increased Transparency and Trust Decentralized AI fosters transparency, which involves recording all transactions and decision-making processes on a blockchain. Blockchain has the ability to ensure that AI data is traced and verified.  The traceability and transparency of AI data is crucial in building trust with users as it enables them to understand how their data is used.  This is a stark contrast to centralized platforms where users have little or no knowledge of how their data are used. Democratized AI development Famous AI advocate, Cathy O Neil, once said: Decentralized AI is a powerful tool for democratizing access to AI technology. By breaking down the barriers of centralization, we can empower individuals and communities to develop and deploy AI solutions that address their specific needs. Decentralized AI is a powerful tool for democratizing access to AI technology. By breaking down the barriers of centralization, we can empower individuals and communities to develop and deploy AI solutions that address their specific needs. The development of AI is dependent on the diversity of its learning models. It's vital to train AI systems from multiple rather than a single data set to enhance their performance and the quality of data across various industries. Latest Development: ZettaBlock Unveils KiteAI To Democratize AI Development Latest Development: ZettaBlock Unveils KiteAI To Democratize AI Development ZettaBlock recently unveiled KiteAI, a transformative AI platform that opens access to non-public data for AI developers, with a focus on ensuring that contributors are fairly compensated. The team has concentrated on building enhanced data pipelines, especially for blockchain data, supporting key players like SUI, Eigenlayer, Chainlink Labs,etc. This platform has processed tens of terabytes of data, making it accessible to organizations for AI model development while safeguarding the privacy of the original data sources. Through advanced data attribution and privacy-preserving technology, ZettaBlock’s platform uniquely supports a collaborative economy. It ensures that contributors are recognized and rewarded as their data directly contributes to model performance and value. ZettaBlock is set to showcase elements of this technology at the upcoming Devcon, where attendees will gain insights into how the platform enables secure, equitable access to data for AI advancements. Conclusion: Conclusion: Building a responsible, efficient, and inclusive AI ecosystem is at the heart of decentralized AI. Moreso, it is a paradigm shift from centralized control of vital AI data hindering the further development of AI models. By encouraging decentralized data sharing and storage, it increases accessibility and foresters a collaborative approach to improving the quality of AI data. Most importantly, decentralized AI is poised to help AI to unlock its full potential and serve the various needs of individuals and organizations.

AI Needs Two Things To Thrive: Trust And Decentralization

Hello developers, let's talk about donations for a minute. Imagine a "hypothetical" world where the top 50% earners around the world donated only but just $1 monthly out of their annual income. Surprisingly, this small gesture could rake in a staggering $42 BILLION - money that could empower small NGOs to bring that "small" change that could bring about transformative change to people's lives across the globe. Yes, in the “hypothetical” world where this happens, there would be enough to meaningfully improve lives, reduce inequality, and address urgent needs. But, how did I come up with that number? Let's do the math together now, shall we? Who are the top 50% of earners? You may be thinking, “I’m not exactly wealthy, just an average person.” Well, the statistics would rather say otherwise. You might be surprised to learn that a lot of people do qualify as top earners by global standards. If you earn approximately $23,380 annually, you are already above the bottom half of the global income distribution -- alongside approximately 3.5 Billion others. This also means that almost half of the world's population is positioned to make a tangible impact on the other bottom half, even with very small contributions. How did the $42 Billion come about? Since we have now established some of the facts: There are approximately 3.5 BILLION people who are above the bottom half of the global income distribution which makes them 50% of the top earners around the world.
The top 50% earners on planet earth earn approximately $23,380 annually which can be said to be $1,948 per month. If each of these 3.5 billion people contributed just $1 per month which by the way is 0.0513% ($1/$1948 = x %100) of their monthly income -- the collective impact would be massive: 3.5 billion people × $1 × 12 months = $42 billion annually The need for Trustless Donation Platforms Currently, the charitable landscape is marred by significant trust issues between donors and non-governmental organizations (NGOs). High-profile scandals, like the cases of Oxfam and Kids Company, where funds raised through donations were misused and there were serious ethical breaches. Events like these have led to increased skepticism amongst potential donors, who fear their contributions would not be utilized as intended. According to the report on a study done by the World Economic Forum, trust in NGOs has rapidly declined due to bad perceptions that these organizations have become overly focused on fundraising at the expense of their core missions, further alienating the public they aim to serve. This rapid decline in trust is also further compounded by a lack of accountability and most importantly transparency in traditional donation methods. Since donors usually feel disconnected from the impact of their contributions they have doubts about whether their contributions are being put to good use. Studies have shown that if there is no transparent and clear information on the operational and performance models of NGOs, the public's confidence in them will reduce which directly impacts their funding and sustainability. The rise of these challenges further calls for solutions, like  -- trustless donation platforms. These are platforms that use blockchain technology to create transparent and immutable records of donations, ensuring that funds are tracked accurately and transparently between donors and NGOs. One such innovative solution is Rootstock, a platform that enhances Bitcoin's capabilities by enabling smart contracts and decentralized applications. By eliminating the need for middle-men and allowing direct oversight through smart contracts, trustless donation platforms built on Rootstock can rebuild confidence among donors. What is Rootstock? Rootstock is the first and longest-lasting sidechain for Bitcoin that represents a significant evolution of blockchain technology. Rootstock or RSK integrates Bitcoin's proof-of-work consensus with the dynamic capabilities of smart contracts that were originally popularized by Ethereum. This enhances Bitcoin's functionality and also opens a new world of possibilities for developers. By introducing smart contract functionality to the Bitcoin network, Rootstock allows developers to create decentralized applications (dApps) that can autonomously execute complex agreements. They were able to achieve this by using a forked version of the Ethereum Virtual Machine (EVM), known as the Rootstock Virtual Machine (RVM). This is great because now developers can leverage existing Ethereum tools and resources, making it easier to build and deploy dApps on Rootstock while also simultaneously leveraging Bitcoin security. In addition, Rootstock adds a programmable layer to Bitcoin by allowing faster creation times -- approximately every 30 seconds compared to Bitcoin's 10 minutes. Now that block generation is faster the execution of smart contracts has become more efficient enabling a great ecosystem of applications that can now easily interact with Bitcoin's infrastructure. The integration of features like the Powpeg — a trustless bridge that allows for the transfer of Bitcoin to and from Rootstock—further solidifies its role as a critical layer in the broader blockchain landscape. Designing a Trustless Donation System Architecture To build or design a system for a trustless donation system we have to bear in mind that the key feature of the system is to foster transparency and accountability in donations. As previously mentioned, as philanthropy evolves, so do the need for solutions that addresses the inherent trust issues between donors and organizations becomes evident. Multi-signature Wallets The foundation of this architecture is the use of multi-signature (multi-sig) wallets that require private keys to authorize transactions. This approach ensures that security is enhanced by distributing control among several parties, thereby reducing the risk of fraud or mismanagement of funds. For example, in a 2-of-3 multisig setup, at least two out of three designated signatories must approve any transaction, ensuring that no single individual has unilateral control over the funds. This unique approach not only safeguards assets but also ensures trust among donors who can be assured that their contributions are being managed responsibly. Smart Contracts In addition to multisig wallets, smart contracts play a very important role in managing the funds donated. Using solidity, you can design contracts that automate certain aspects of fund distribution, including setting donation limits and setting up voting systems for the distribution of funds. This level of flexibility allows responsiveness to the preferences of the donors, allowing a more democratic approach to how funds are managed. Smart contracts can ensure that certain conditions are met before funds are released to enhance accountability. Real-time tracking Another key aspect of this architecture is real-time tracking of funds. Integrating with the RSK block explorer, donors can monitor the flow of their contribution transparently. This is to further enhance transparency and allow donors to hold the NGOs accountable. Two-way Peg Finally, the technical security provided by RSK's two-way peg mechanism ensures trustlessness when converting Bitcoin (BTC) to Rootstock Bitcoin (RBTC) and vice versa. This system allows users to transfer their Bitcoin assets to the RSK platform while maintaining the integrity and security of their original holdings. The two-way peg ensures that each BTC on RSK is equivalent to an RBTC, directly reinforcing trust in the conversion process and enabling seamless interactions within the donation ecosystem. Conclusion: Future Applications and Expansion The benefits of a trustless platform could extend beyond just technological advancements; they could represent a fundamental shift in how donations are perceived and managed in the charitable sector. One of the main features or advantages of a trustless platform is its ability to provide security and transparency. By leveraging a range of Bitcoin's security features and Rootstock's smart contract features, donors can be assured that their contributions are safeguarded against fraud and mismanagement. Looking ahead, there are many ways to expand the platform’s functionality. For example, adding multi-currency support could attract a broader range of donors from different regions, enabling them to contribute using their preferred cryptocurrencies. More so, RSK-based DeFi features could be integrated into the platform to allow for financial products that can allow ending or yield generation from donations. In summary, the future applications and expansion of a trustless donation platform hold immense promise. By leveraging the power of Rootstock and Bitcoin, we can create a more secure, transparent, and trustworthy environment for charitable giving. Resources Rootstock Built for Bitcoin
Blockchain for Good
Sidechain and RSK
Rootstock Fundamental
Does Social Trust Influence Charitable Giving for NGOs?
Why is Trust in NGOs failing – World Economic Forum
What is Multisig and How does it work
Smart Bitcoin Hello developers, let's talk about donations for a minute. Imagine a " hypothetical " world where the top 50% earners around the world donated only but just $1 monthly out of their annual income. Surprisingly, this small gesture could rake in a staggering $42 BILLION - money that could empower small NGOs to bring that "small" change that could bring about transformative change to people's lives across the globe. Yes, in the “ hypothetical” world where this happens, there would be enough to meaningfully improve lives, reduce inequality, and address urgent needs. But, how did I come up with that number? Let's do the math together now, shall we? hypothetical hypothetical” Who are the top 50% of earners? You may be thinking, “I’m not exactly wealthy, just an average person.” Well, the statistics would rather say otherwise. You might be surprised to learn that a lot of people do qualify as top earners by global standards. If you earn approximately $23,380 annually , you are already above the bottom half of the global income distribution -- alongside approximately 3.5 Billion others. If you earn approximately $23,380 annually 3.5 Billion This also means that almost half of the world's population is positioned to make a tangible impact on the other bottom half, even with very small contributions. How did the $42 Billion come about? Since we have now established some of the facts: There are approximately 3.5 BILLION people who are above the bottom half of the global income distribution which makes them 50% of the top earners around the world. The top 50% earners on planet earth earn approximately $23,380 annually which can be said to be $1,948 per month. There are approximately 3.5 BILLION people who are above the bottom half of the global income distribution which makes them 50% of the top earners around the world. The top 50% earners on planet earth earn approximately $23,380 annually which can be said to be $1,948 per month. If each of these 3.5 billion people contributed just $1 per month which by the way is 0.0513% ($1/$1948 = x %100) of their monthly income -- the collective impact would be massive: 3.5 billion people × $1 × 12 months = $42 billion annually 3.5 billion people $1 12 months $42 billion annually The need for Trustless Donation Platforms Currently, the charitable landscape is marred by significant trust issues between donors and non-governmental organizations (NGOs). High-profile scandals, like the cases of Oxfam and Kids Company , where funds raised through donations were misused and there were serious ethical breaches. Events like these have led to increased skepticism amongst potential donors, who fear their contributions would not be utilized as intended. According to the report on a study done by the World Economic Forum , trust in NGOs has rapidly declined due to bad perceptions that these organizations have become overly focused on fundraising at the expense of their core missions, further alienating the public they aim to serve. Oxfam Kids Company a study done by the World Economic Forum This rapid decline in trust is also further compounded by a lack of accountability and most importantly transparency in traditional donation methods. Since donors usually feel disconnected from the impact of their contributions they have doubts about whether their contributions are being put to good use. Studies have shown that if there is no transparent and clear information on the operational and performance models of NGOs, the public's confidence in them will reduce which directly impacts their funding and sustainability. The rise of these challenges further calls for solutions, like  -- trustless donation platforms . These are platforms that use blockchain technology to create transparent and immutable records of donations, ensuring that funds are tracked accurately and transparently between donors and NGOs. One such innovative solution is Rootstock , a platform that enhances Bitcoin's capabilities by enabling smart contracts and decentralized applications. By eliminating the need for middle-men and allowing direct oversight through smart contracts, trustless donation platforms built on Rootstock can rebuild confidence among donors. trustless donation platforms Rootstock What is Rootstock? Rootstock is the first and longest-lasting sidechain for Bitcoin that represents a significant evolution of blockchain technology. Rootstock or RSK integrates Bitcoin's proof-of-work consensus with the dynamic capabilities of smart contracts that were originally popularized by Ethereum. This enhances Bitcoin's functionality and also opens a new world of possibilities for developers. Rootstock is the first and longest-lasting sidechain for Bitcoin By introducing smart contract functionality to the Bitcoin network, Rootstock allows developers to create decentralized applications (dApps) that can autonomously execute complex agreements. They were able to achieve this by using a forked version of the Ethereum Virtual Machine (EVM), known as the Rootstock Virtual Machine (RVM) . This is great because now developers can leverage existing Ethereum tools and resources, making it easier to build and deploy dApps on Rootstock while also simultaneously leveraging Bitcoin security. Rootstock Virtual Machine (RVM) In addition, Rootstock adds a programmable layer to Bitcoin by allowing faster creation times -- approximately every 30 seconds compared to Bitcoin's 10 minutes. Now that block generation is faster the execution of smart contracts has become more efficient enabling a great ecosystem of applications that can now easily interact with Bitcoin's infrastructure. The integration of features like the Powpeg — a trustless bridge that allows for the transfer of Bitcoin to and from Rootstock—further solidifies its role as a critical layer in the broader blockchain landscape. Rootstock adds a programmable layer to Bitcoin by allowing faster creation times -- approximately every 30 seconds compared to Bitcoin's 10 minutes. Designing a Trustless Donation System Architecture To build or design a system for a trustless donation system we have to bear in mind that the key feature of the system is to foster transparency and accountability in donations. As previously mentioned, as philanthropy evolves, so do the need for solutions that addresses the inherent trust issues between donors and organizations becomes evident. trustless Multi-signature Wallets The foundation of this architecture is the use of multi-signature (multi-sig) wallets that require private keys to authorize transactions. This approach ensures that security is enhanced by distributing control among several parties, thereby reducing the risk of fraud or mismanagement of funds. For example, in a 2-of-3 multisig setup, at least two out of three designated signatories must approve any transaction, ensuring that no single individual has unilateral control over the funds. This unique approach not only safeguards assets but also ensures trust among donors who can be assured that their contributions are being managed responsibly. multi-signature (multi-sig) wallets Smart Contracts In addition to multisig wallets, smart contracts play a very important role in managing the funds donated. Using solidity, you can design contracts that automate certain aspects of fund distribution, including setting donation limits and setting up voting systems for the distribution of funds. This level of flexibility allows responsiveness to the preferences of the donors, allowing a more democratic approach to how funds are managed. Smart contracts can ensure that certain conditions are met before funds are released to enhance accountability. Real-time tracking Another key aspect of this architecture is real-time tracking of funds . Integrating with the RSK block explorer, donors can monitor the flow of their contribution transparently. This is to further enhance transparency and allow donors to hold the NGOs accountable. real-time tracking of funds Two-way Peg Finally, the technical security provided by RSK's two-way peg mechanism ensures trustlessness when converting Bitcoin (BTC) to Rootstock Bitcoin (RBTC) and vice versa. This system allows users to transfer their Bitcoin assets to the RSK platform while maintaining the integrity and security of their original holdings. The two-way peg ensures that each BTC on RSK is equivalent to an RBTC, directly reinforcing trust in the conversion process and enabling seamless interactions within the donation ecosystem. Conclusion: Future Applications and Expansion The benefits of a trustless platform could extend beyond just technological advancements; they could represent a fundamental shift in how donations are perceived and managed in the charitable sector. One of the main features or advantages of a trustless platform is its ability to provide security and transparency. By leveraging a range of Bitcoin's security features and Rootstock's smart contract features, donors can be assured that their contributions are safeguarded against fraud and mismanagement. Looking ahead, there are many ways to expand the platform’s functionality. For example, adding multi-currency support could attract a broader range of donors from different regions, enabling them to contribute using their preferred cryptocurrencies. More so, RSK-based DeFi features could be integrated into the platform to allow for financial products that can allow ending or yield generation from donations. In summary, the future applications and expansion of a trustless donation platform hold immense promise. By leveraging the power of Rootstock and Bitcoin, we can create a more secure, transparent, and trustworthy environment for charitable giving. Resources Rootstock Built for Bitcoin Blockchain for Good Sidechain and RSK Rootstock Fundamental Does Social Trust Influence Charitable Giving for NGOs? Why is Trust in NGOs failing – World Economic Forum What is Multisig and How does it work Smart Bitcoin Rootstock Built for Bitcoin Rootstock Built for Bitcoin Blockchain for Good Blockchain for Good Sidechain and RSK Sidechain and RSK Rootstock Fundamental Rootstock Fundamental Does Social Trust Influence Charitable Giving for NGOs? Does Social Trust Influence Charitable Giving for NGOs? Why is Trust in NGOs failing – World Economic Forum Why is Trust in NGOs failing – World Economic Forum What is Multisig and How does it work What is Multisig and How does it work Smart Bitcoin Smart Bitcoin

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