Plaid shirts always make an appearance in fall fashion! I’ve rounded up some of my favorite plaid shirts that can be styled in a variety of ways. These colors

Plaid Shirts Are a Style That's Here to Stay! Shop Them Now on Amazon

Loretta’s Boutique is back with the Pink Carpet Style Show. The event will take place on Saturday, Nov. 4 at the Marshall Convention Center. Doors will open at 10 a.m., with free lunch and mimosas at noon, and the show at 1 p.m. There will be vendors, entertainment and of course everything the boutique has to offer.

Marshall boutique putting on style show

Discover Türkiye's diverse fashion narrative that blends rich traditions with contemporary creativity, a reflection of its unique geographic and cultural position

Threads of Tradition and Modernity: Türkiye's Fashion Journey

which was co-hosted by the International Apparel Federation and Sewn Products Equipment & Suppliers of the Americas (SPESA) in Philadelphia, US observed the world of fashion sourcing from both an international and US standpoint. Ex-Tapestry’s Tom Glaser ...

Week in review: Countering fashion sourcing geopolitics with death of US free trade deals

Joanne Crevoiserat described a combination of “magic and logic” in the way the group is putting a wealth of consumer data at the disposal of its creative and marketing teams.

Tapestry’s CEO Touts Its ‘Consumer Engagement Platform’

The warp and weft of Syrian history is being displayed at New York’s Katonah Museum of Art, in an exhibition that spans 2,000 years and highlights the area’s underappreciated but pivotal position in the global textile trade.

Syrian life's rich tapestry: 2,000 years of history told in textiles at New York show

"Amidst the gleaming skyscrapers and lush greenery of this remarkable city, fashion is not merely an endeavour but a national hobby." Marie Greindl examines Singaporean style in the 21st century.

A dive into Singaporean style

Marrakech in autumn is a tapestry of mild weather, vibrant markets, and the echoes of ancient traditions. Here is our guide to the weather in Marrakech in November and travel tips to create a memorable Moroccan experience.

Marrakech in November: weather and travel tips

As Breast Cancer Awareness Month comes to a close, we’re highlighting a local business that makes intimate apparel to support women who have had breast cancer. Before the store’s birth, Alala was just a dream two breast cancer survivors had.

Charleston clothing business has a mission to support women with breast cancer

Children apparel and novelty items for sale in the Lane Stadium Hokie Shop prior to the start of the Pittsburgh Virginia Tech football game in Blacksburg Va. Saturday Sept. 30 2023. This $35 University of Virginia shirt was made in Peru by '47 Brand.

Photos: College clothing made in factories rife with labor violations, poverty wages

Tapestry

tapestry

In a seismic shift in the tech landscape, the has unearthed a disquieting reality: the confidence of U.S. workers in their employers has hit an all-time low, echoing through the corridors of power like a server crash. Glassdoor survey Registering at a paltry 45.6%, this is not just a dip; it's a full-scale system failure, marking the most precipitous decline since Glassdoor started parsing the tech workforce matrix in 2016. Strap in, folks, as we dissect the intricacies of economic entropy, tightening budgets, and the impending algorithmic apocalypse. Layoffs and the Job Market Crunch As the startup unicorns shed their glitter, the Glassdoor survey is the red alert: the majority of tech talents foresee stormy skies in the next six months. like weeds in a neglected codebase, and job options are dwindling faster than the lifespan of a trending app. Layoffs are sprouting It's a survival-of-the-fittest moment, pushing tech professionals into a 'grin-and-bear-it' scenario as they reconcile with the harsh realities of a turbulent job market. Byte-Sized Budgets In this era of innovation, budgetary constraints are threatening to stifle the tech ecosystem. Smaller budgets are akin to a memory leak, sapping the efficiency and potential of the workforce. The equation is simple: less capital equals more burdens for the tech workforce – increased workloads, slashed benefits, and a perilous drop in growth opportunities. The ever-shrinking staffing levels are turning the once vibrant tech hubs into desolate coding wastelands. The Ripple Effect on Digital Morale Beyond the lines of code and the intricacies of algorithms, the nosedive in confidence is causing a digital discontent epidemic. Stress levels are escalating, job satisfaction is crashing harder than outdated software, and the morale of the tech troops is at an unprecedented low. It's a digital meltdown in slow motion, threatening to disrupt the harmonious symphony of innovation. Tech Titans and the Path Forward In the labyrinth of economic uncertainties, transparency emerges as the secret sauce. Tech leaders, take note – open communication regarding the glitches in the system, proactive strategies to navigate the digital turbulence, and fostering a sense of belonging among the workforce can serve as the ultimate debug session. Tech companies need to reboot their support initiatives, provide mental health resources, invest in skill development programs, and establish channels for constructive feedback. In the tech crunch era, cultivating a culture of transparency and prioritizing the digital well-being of the workforce is not just a strategy; it's a survival imperative. As U.S. tech workers grapple with their lowest confidence levels since the dot-com bubble burst, it's a call to action for tech titans to lead with resilience. Rewriting the code, shoring up the digital defenses, and navigating this tech tempest together will be the defining challenge. By embracing transparency, providing unwavering support, and fortifying the digital backbone, tech companies can steer through the current crisis and emerge stronger in the ever-evolving tech landscape.

Unraveling the Tech Tapestry: US Workers' Confidence Plummets in the Face of Economic Dystopia

Authors:
(1) Timothy R. McIntosh;
(2) Teo Susnjak;
(3) Tong Liu;
(4) Paul Watters;
(5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Surveying the Evolution and Future Trajectory of Generative AI - Analysis on Research Taxonomy

The other day, I found myself locked in an online debate with a seasoned data scientist who argued fervently that AI could never replace data analysts. His rationale? Data analytics requires creative problem-solving that is unique to human cognition, such as feature engineering and interpretation of results. “AI,” he insisted, “will never be able to replicate that kind of creativity.” It’s a comforting thought, I suppose. But as the debate progressed, I couldn’t help but notice the shaky foundation on which this argument stood. After all, AI is already disrupting art, writing, and music—fields that are undoubtedly creative. And unlike the deterministic world of data analysis, these creative disciplines are defined by their fluidity and subjectivity. When I pointed this out, our data scientist quickly retorted that art and music are inherently subjective. “Of course, AI can create art,” he said dismissively. “Art’s value lies in personal interpretation. It is easier for them to accept AI outputs. Data analytics, however, is more about logic and objective solutions. It’s not the same.” I remained unconvinced. If art is subjective, does that mean it’s somehow less creative? Moreover, how does one reconcile this argument when considering the mind-boggling achievements of AI in fields that are far from subjective? Take self-driving cars, for example—vehicles that navigate the chaotic tapestry of human road behavior without a constant human hand at the wheel. Or AlphaGo, which conquered the ancient game of Go through creative strategies that baffle even the best human players. And let’s not forget AI’s contributions to protein folding, a previously intractable scientific problem that relied on leaps of creative thinking. All these accomplishments stem from AI’s ability to master highly complex, creative problem-solving tasks that require navigating ambiguity. Aren’t these activities just as—if not more—challenging than deciding which variable to drop in a regression model or which time series model to apply? The data scientist doubled down: “Those are closed-ended problems,” he argued. “Data analytics is more like open-ended exploration. It’s about uncovering hidden insights in a sea of information. You can’t automate that.” This was when I realized the crux of the issue. His argument was rooted not in the reality of AI’s capabilities but in a one-sided, narrow interpretation of what creativity means and where AI fits in the scheme of things. He, like many other professionals in this space, seemed to hold a certain disdainful reverence for the human mind’s perceived uniqueness—a view that AI will never match the human touch in some vaguely defined creative domain. This kind of thinking, however, is symptomatic of denialism—denialism that mirrors how artists reacted to photography when it first emerged. The first photographers were mocked by painters who considered photography a ‘technical trick’ that could never replace the soul of a hand-painted portrait. Over time, as photography evolved, many of these painters found themselves without commissions, grappling with a world that no longer valued their traditional skill set in the same way. What we’re seeing in data analytics today is no different. Those who maintain that AI will never be able to handle creative problem-solving in data analytics are falling into the same trap as those early painters. They’re dismissing AI’s creative potential simply because it challenges their view of what creativity looks like in their specific domain. The truth is, AI’s ability to model complex relationships, surface patterns, and even simulate multiple solutions to a problem means it’s already doing much of what data analysts claim as their domain. The fine-grained feature engineering, the subtle interpretations—AI is not just nibbling around the edges; it’s slowly encroaching into the core of what we’ve traditionally defined as ‘analytical creativity.’ I’m not saying that data scientists or analysts will be replaced overnight. But to assume that AI will never touch their domain simply because it doesn’t fit into an outdated view of what creativity means is shortsighted. This is a transformative era, one that calls for a redefinition of roles, responsibilities, and skill sets. Data analysts and scientists who refuse to keep an open mind risk finding themselves irrelevant in a world that is rapidly shifting beneath their feet. So, let’s not make the same mistake as those painters of the past. Denialism is a luxury we cannot afford. Instead, we must embrace the evolving landscape and find ways to complement AI’s strengths with our own. After all, the scariest part of this AI revolution is not what the machines can do—it’s what we might fail to do if we refuse to adapt. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com The other day, I found myself locked in an online debate with a seasoned data scientist who argued fervently that AI could never replace data analysts. His rationale? Data analytics requires creative problem-solving that is unique to human cognition, such as feature engineering and interpretation of results. “AI,” he insisted, “will never be able to replicate that kind of creativity.” It’s a comforting thought, I suppose. But as the debate progressed, I couldn’t help but notice the shaky foundation on which this argument stood. After all, AI is already disrupting art, writing, and music—fields that are undoubtedly creative. And unlike the deterministic world of data analysis, these creative disciplines are defined by their fluidity and subjectivity. When I pointed this out, our data scientist quickly retorted that art and music are inherently subjective. “Of course, AI can create art,” he said dismissively. “Art’s value lies in personal interpretation. It is easier for them to accept AI outputs. Data analytics, however, is more about logic and objective solutions. It’s not the same.” I remained unconvinced. If art is subjective, does that mean it’s somehow less creative? Moreover, how does one reconcile this argument when considering the mind-boggling achievements of AI in fields that are far from subjective? Take self-driving cars, for example—vehicles that navigate the chaotic tapestry of human road behavior without a constant human hand at the wheel. Or AlphaGo , which conquered the ancient game of Go through creative strategies that baffle even the best human players. And let’s not forget AI’s contributions to protein folding, a previously intractable scientific problem that relied on leaps of creative thinking. AlphaGo All these accomplishments stem from AI’s ability to master highly complex, creative problem-solving tasks that require navigating ambiguity. Aren’t these activities just as—if not more—challenging than deciding which variable to drop in a regression model or which time series model to apply? The data scientist doubled down: “Those are closed-ended problems,” he argued. “Data analytics is more like open-ended exploration. It’s about uncovering hidden insights in a sea of information. You can’t automate that.” This was when I realized the crux of the issue. His argument was rooted not in the reality of AI’s capabilities but in a one-sided, narrow interpretation of what creativity means and where AI fits in the scheme of things. He, like many other professionals in this space, seemed to hold a certain disdainful reverence for the human mind’s perceived uniqueness—a view that AI will never match the human touch in some vaguely defined creative domain. This kind of thinking, however, is symptomatic of denialism—denialism that mirrors how artists reacted to photography when it first emerged. The first photographers were mocked by painters who considered photography a ‘technical trick’ that could never replace the soul of a hand-painted portrait. Over time, as photography evolved, many of these painters found themselves without commissions, grappling with a world that no longer valued their traditional skill set in the same way. considered photography a ‘technical trick’ What we’re seeing in data analytics today is no different. Those who maintain that AI will never be able to handle creative problem-solving in data analytics are falling into the same trap as those early painters. They’re dismissing AI’s creative potential simply because it challenges their view of what creativity looks like in their specific domain. The truth is, AI’s ability to model complex relationships, surface patterns, and even simulate multiple solutions to a problem means it’s already doing much of what data analysts claim as their domain. The fine-grained feature engineering, the subtle interpretations—AI is not just nibbling around the edges; it’s slowly encroaching into the core of what we’ve traditionally defined as ‘analytical creativity.’ I’m not saying that data scientists or analysts will be replaced overnight. But to assume that AI will never touch their domain simply because it doesn’t fit into an outdated view of what creativity means is shortsighted. This is a transformative era, one that calls for a redefinition of roles, responsibilities, and skill sets. Data analysts and scientists who refuse to keep an open mind risk finding themselves irrelevant in a world that is rapidly shifting beneath their feet. So, let’s not make the same mistake as those painters of the past. Denialism is a luxury we cannot afford. Instead, we must embrace the evolving landscape and find ways to complement AI’s strengths with our own. After all, the scariest part of this AI revolution is not what the machines can do—it’s what we might fail to do if we refuse to adapt. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com https://docligot.com

AI Denialism

Reputation, in its most traditional definition, is a fragile tapestry of interaction amongst humans: an elaborate interplay between social proof, intuition, and subjectivity. It's an unspoken calculus by which we decide whom to trust, whom to listen to, and whom best to avoid when operating in the physical world. But in the decentralized world, this fluid-and-frail version of reputation must become hardened into something concretely mathematical, something machine-readable, and yet still quintessentially human. Its realization might be the most ambitious challenge for any designer of decentralized infrastructures. It is not only a question of how to make trust secure but also how to render reputation measurable, reliable, useful, and actionable outside the realm of theory. We are standing in front of a new frontier in trust, an age that requires us to make the notion of reputation programmable and enforceable. While blockchain technology promises a "trustless" world in which centralized intermediaries have no relevance, this vision leaves one critical part of human interaction. Guess what? It is how we quantify such subjective factors as trust and reputation without sacrificing the nuances that make it meaningful and untouched. This is where I want to dig deeper. From Abstract to Measurable: How Reputation Evolved Let me map the key paradox before going into the specifics of the solutions: On decentralized systems, reputation can't exist as an abstract conceptual philosophical issue. The trustworthiness of a user can't be left to subjective judgment on a peer-to-peer network. It has to be quantified, audited, and then enforced. And to actually design this effectively, we really have to resort not just to technology automating trust but encoding quite literally what reputation means. Let me outline three pressing challenges that reputation faces in decentralized systems: Reputation portability. While in a centralized system, reputation might be confined to one particular platform or network; in decentralized ecosystems, reputation has to be portable. This portability is necessary because users span multiple dApps, platforms, and ecosystems, from DeFi protocols to social marketplaces. Reputation data should not be left in silos. In itself, however, portability creates risks: reputation needs to be adapted to context. How can one protocol make assessments about an established reputation elsewhere? How does the user guarantee that standing in a decentralized social network applies in, for instance, a decentralized lending platform? Verifiable trust without full disclosure. Decentralized infrastructures prize privacy and anonymity. Still, trust (at least in its traditional sense) demands transparency. A balance needs to be reached whereby one's reputation can be verifiable without sensitive information about one's identity or past interactions. How can you prove you are reputable without giving away private transactions or other private information? Dynamic reputation systems. Trust is never set in concrete. A brilliant reputation today may be blemished tomorrow, but in the decentralized world, reputation cannot afford to remain static. Yet, blockchain's immutable nature creates an issue. How can we ensure that reputation reflects the real-time evolution of a user's trustworthiness? Autonomy Matrix and Aut: A Unified Approach to Trust Āutonomy Matrix and Āut's unified system is active in the space of proposing reputation, not as a concept but rather, as a real and integral part of the solution within a formalized environment. The system is working toward the implementation of a global reputation economy by using reputation scores pegged to DIDs. The gaps remain, though, even with forward momentum in these systems. Of these many intriguing proposals in this matrix, one of the more exciting involves adding zero-knowledge proofs to reputation management. ZKPs allow a person to prove some statement (for instance, that she has behaved in a trustworthy manner) without actually needing to reveal the underlying actions that came to that conclusion. For example, being able to prove that you have a 90% trust score across a DeFi lending platform without revealing just who lent or paid back what. This technique provides an extra layer of privacy, while a person's reputation is portable and provable in this decentralized and trustless environment. Now, a creative question: what if reputation could be tokenized itself? Just picture tokens of reputation flowing in value as any given user behaves well or badly. These would be transferable across platforms and would represent a liquid form of one's digital reputation. This might create "Reputation Markets" - a place where individuals and organizations can trade, lend, or borrow reputation. Undoubtedly, this would embed a layer of gamification, and reputation could be a currency while opening opportunities for abuse and innovation. Nevertheless, the idea that reputation can be digitized into something that one owns and can invest in, trade, or loan stretches the limits of how we think about trust. Filling the Gaps in Decentralized Reputation Making reputation measurable and, above all, safe, transforms it from an abstraction into something tangible, to which several structural elements need to be developed and deployed. Trust Provenance Protocols Just as decentralized systems record the origin of assets, so too does reputation require provenance. Smart contract histories could be attached to profiles, and decentralized systems might then track the origins of trust: who vouched for whom, on what terms, and under what circumstances. This all needs to be a lot more sophisticated than simplistic upvote or downvote mechanisms, as now exist on centralized platforms such as Reddit or Yelp. The origin of trust needs to weigh algorithmically according to context, not just frequency. For instance, the Reciprocity Index can be a decentralized algorithm that monitors the degree to which a user contributes to others' trust in the system versus the degree to which they benefit from it. Scores would rise for those giving trust freely and authentically, while those who merely benefit from others would see diminishing returns on reputation. Context-Aware Reputation Engines Reputation is inherently context-sensitive: the great coding reputation a user has on GitHub doesn't necessarily hold as much standing in some decentralized financial exchange. The silos can only be bridged by having context-aware reputation engines within decentralized protocols that would assess not raw reputation scores but the relevance of the latter for particular domains, dynamically adapting the user trust profiles in accordance with the environment. Reputation systems should also embed models of time decay in which the trust disappears unless it is kept alive. Applying temporal decay ensures that the reputational value will always be current. For example, a person who frequently participated in decentralized governance would see an increased reputation, whereas non-participation would equate to a decrease in their reputation score. This dynamic ensures that the trust indicators reflect the most recent behavior and, therefore, a real-time evolving snapshot of the reputation. Concluding Thoughts By aligning decentralized infrastructure with dynamic, cross-contextual reputation systems, we can push reputation from the realm of philosophy into a space where it's concrete and programmable. The integration of zero-knowledge proofs, time-weighted trust mechanisms, and cross-platform identity verification systems is not just the next step in decentralization but an entirely different approach to trust. But here's the rub: this is infrastructure that is not just technical; it is cultural. The tools we design to handle decentralized reputation must be ones that perform and reflect the rich nuances in human behavior. Trust is nuanced, multilayered, and evolves over time. If decentralized systems can handle that both in code and in practice, we might just build a future where reputation becomes as liquid an asset as currency itself. Perhaps someday, trust will no longer be a question but an asset to be made clear: securely, privately, and with confidence. Reputation, in its most traditional definition, is a fragile tapestry of interaction amongst humans: an elaborate interplay between social proof, intuition, and subjectivity. It's an unspoken calculus by which we decide whom to trust, whom to listen to, and whom best to avoid when operating in the physical world. But in the decentralized world, this fluid-and-frail version of reputation must become hardened into something concretely mathematical, something machine-readable, and yet still quintessentially human. Its realization might be the most ambitious challenge for any designer of decentralized infrastructures. It is not only a question of how to make trust secure but also how to render reputation measurable, reliable, useful, and actionable outside the realm of theory. We are standing in front of a new frontier in trust, an age that requires us to make the notion of reputation programmable and enforceable. While blockchain technology promises a "trustless" world in which centralized intermediaries have no relevance, this vision leaves one critical part of human interaction. Guess what? It is how we quantify such subjective factors as trust and reputation without sacrificing the nuances that make it meaningful and untouched. This is where I want to dig deeper. From Abstract to Measurable: How Reputation Evolved Let me map the key paradox before going into the specifics of the solutions: On decentralized systems, reputation can't exist as an abstract conceptual philosophical issue. The trustworthiness of a user can't be left to subjective judgment on a peer-to-peer network. It has to be quantified, audited, and then enforced. And to actually design this effectively, we really have to resort not just to technology automating trust but encoding quite literally what reputation means. Let me outline three pressing challenges that reputation faces in decentralized systems: Reputation portability. While in a centralized system, reputation might be confined to one particular platform or network; in decentralized ecosystems, reputation has to be portable. This portability is necessary because users span multiple dApps, platforms, and ecosystems, from DeFi protocols to social marketplaces. Reputation data should not be left in silos. In itself, however, portability creates risks: reputation needs to be adapted to context. How can one protocol make assessments about an established reputation elsewhere? How does the user guarantee that standing in a decentralized social network applies in, for instance, a decentralized lending platform? Reputation portability. While in a centralized system, reputation might be confined to one particular platform or network; in decentralized ecosystems, reputation has to be portable. This portability is necessary because users span multiple dApps, platforms, and ecosystems, from DeFi protocols to social marketplaces. Reputation data should not be left in silos. In itself, however, portability creates risks: reputation needs to be adapted to context. How can one protocol make assessments about an established reputation elsewhere? How does the user guarantee that standing in a decentralized social network applies in, for instance, a decentralized lending platform? Verifiable trust without full disclosure. Decentralized infrastructures prize privacy and anonymity. Still, trust (at least in its traditional sense) demands transparency. A balance needs to be reached whereby one's reputation can be verifiable without sensitive information about one's identity or past interactions. How can you prove you are reputable without giving away private transactions or other private information? Verifiable trust without full disclosure. Decentralized infrastructures prize privacy and anonymity. Still, trust (at least in its traditional sense) demands transparency. A balance needs to be reached whereby one's reputation can be verifiable without sensitive information about one's identity or past interactions. How can you prove you are reputable without giving away private transactions or other private information? Dynamic reputation systems. Trust is never set in concrete. A brilliant reputation today may be blemished tomorrow, but in the decentralized world, reputation cannot afford to remain static. Yet, blockchain's immutable nature creates an issue. How can we ensure that reputation reflects the real-time evolution of a user's trustworthiness? Dynamic reputation systems. Trust is never set in concrete. A brilliant reputation today may be blemished tomorrow, but in the decentralized world, reputation cannot afford to remain static. Yet, blockchain's immutable nature creates an issue. How can we ensure that reputation reflects the real-time evolution of a user's trustworthiness? Autonomy Matrix and Aut: A Unified Approach to Trust Āutonomy Matrix and Āut's unified system is active in the space of proposing reputation, not as a concept but rather, as a real and integral part of the solution within a formalized environment. The system is working toward the implementation of a global reputation economy by using reputation scores pegged to DIDs. The gaps remain, though, even with forward momentum in these systems. Of these many intriguing proposals in this matrix, one of the more exciting involves adding zero-knowledge proofs to reputation management. ZKPs allow a person to prove some statement (for instance, that she has behaved in a trustworthy manner) without actually needing to reveal the underlying actions that came to that conclusion. For example, being able to prove that you have a 90% trust score across a DeFi lending platform without revealing just who lent or paid back what. This technique provides an extra layer of privacy, while a person's reputation is portable and provable in this decentralized and trustless environment. Now, a creative question: what if reputation could be tokenized itself? Just picture tokens of reputation flowing in value as any given user behaves well or badly. These would be transferable across platforms and would represent a liquid form of one's digital reputation. This might create "Reputation Markets" - a place where individuals and organizations can trade, lend, or borrow reputation. Undoubtedly, this would embed a layer of gamification, and reputation could be a currency while opening opportunities for abuse and innovation. Now, a creative question: what if reputation could be tokenized itself? Just picture tokens of reputation flowing in value as any given user behaves well or badly. These would be transferable across platforms and would represent a liquid form of one's digital reputation. This might create "Reputation Markets" - a place where individuals and organizations can trade, lend, or borrow reputation. Undoubtedly, this would embed a layer of gamification, and reputation could be a currency while opening opportunities for abuse and innovation. Nevertheless, the idea that reputation can be digitized into something that one owns and can invest in, trade, or loan stretches the limits of how we think about trust. Nevertheless, the idea that reputation can be digitized into something that one owns and can invest in, trade, or loan stretches the limits of how we think about trust. Filling the Gaps in Decentralized Reputation Making reputation measurable and, above all, safe, transforms it from an abstraction into something tangible, to which several structural elements need to be developed and deployed. Trust Provenance Protocols Just as decentralized systems record the origin of assets, so too does reputation require provenance. Smart contract histories could be attached to profiles, and decentralized systems might then track the origins of trust: who vouched for whom, on what terms, and under what circumstances. This all needs to be a lot more sophisticated than simplistic upvote or downvote mechanisms, as now exist on centralized platforms such as Reddit or Yelp. The origin of trust needs to weigh algorithmically according to context, not just frequency. For instance, the Reciprocity Index can be a decentralized algorithm that monitors the degree to which a user contributes to others' trust in the system versus the degree to which they benefit from it. Scores would rise for those giving trust freely and authentically, while those who merely benefit from others would see diminishing returns on reputation. Reciprocity Index Context-Aware Reputation Engines Reputation is inherently context-sensitive: the great coding reputation a user has on GitHub doesn't necessarily hold as much standing in some decentralized financial exchange. The silos can only be bridged by having context-aware reputation engines within decentralized protocols that would assess not raw reputation scores but the relevance of the latter for particular domains, dynamically adapting the user trust profiles in accordance with the environment. Reputation systems should also embed models of time decay in which the trust disappears unless it is kept alive. Applying temporal decay ensures that the reputational value will always be current. For example, a person who frequently participated in decentralized governance would see an increased reputation, whereas non-participation would equate to a decrease in their reputation score. This dynamic ensures that the trust indicators reflect the most recent behavior and, therefore, a real-time evolving snapshot of the reputation. Concluding Thoughts By aligning decentralized infrastructure with dynamic, cross-contextual reputation systems, we can push reputation from the realm of philosophy into a space where it's concrete and programmable. The integration of zero-knowledge proofs, time-weighted trust mechanisms, and cross-platform identity verification systems is not just the next step in decentralization but an entirely different approach to trust. But here's the rub: this is infrastructure that is not just technical; it is cultural. The tools we design to handle decentralized reputation must be ones that perform and reflect the rich nuances in human behavior. Trust is nuanced, multilayered, and evolves over time. If decentralized systems can handle that both in code and in practice, we might just build a future where reputation becomes as liquid an asset as currency itself. Perhaps someday, trust will no longer be a question but an asset to be made clear: securely, privately, and with confidence.

The Tangible Reputation: Engineering Trust in a Decentralized World

Hmatatelná pověst: Inženýrská důvěra v decentralizovaném světě

La reputación tangible: generando confianza en un mundo descentralizado

La réputation tangible : l'ingénierie de la confiance dans un monde décentralisé

A kézzelfogható hírnév: Mérnöki bizalom egy decentralizált világban

具体的な評判: 分散化された世界における信頼の構築

ხელშესახები რეპუტაცია: საინჟინრო ნდობა დეცენტრალიზებულ სამყაროში

Материалдық бедел: орталықтандырылмаған әлемдегі инженерлік сенім

실체적 평판: 분산화된 세계에서의 신뢰 엔지니어링

Taustāmā reputācija: inženieru uzticība decentralizētā pasaulē

Опипљива репутација: инжењерско поверење у децентрализованом свету

እቲ ዝጭበጥ ዝና፡ ምህንድስናዊ ምትእምማን ኣብ ዘይተማእከለ ዓለም

Ang Tangible na Reputasyon: Engineering Trust sa isang Desentralisadong Mundo

India’s New AVGC-XR Center Wants to Do for Gaming What IITs Did for Engineering

Golden Celestial Ratio (GCR): A Comprehensive Guide to the Divine Ecosystem

গোল্ডেন সেলেস্টিয়াল রেশিও (GCR): ডিভাইন ইকোসিস্টেমের একটি ব্যাপক নির্দেশিকা

Golden Celestial Ratio (GCR): Ein umfassender Leitfaden zum göttlichen Ökosystem

Proporción áurea celestial (GCR): una guía completa del ecosistema divino

Ratio Céleste Doré (GCR) : Un guide complet de l'écosystème divin

स्वर्णिम आकाशीय अनुपात (जीसीआर): दिव्य पारिस्थितिकी तंत्र के लिए एक व्यापक मार्गदर्शिका

黄金比（GCR）：神の生態系への総合ガイド

황금 천체 비율(GCR): 신성한 생태계에 대한 종합 가이드

Proporção Celestial Áurea (GCR): Um Guia Abrangente para o Ecossistema Divino

Золотое небесное сечение (GCR): подробное руководство по божественной экосистеме

Altın Gök Oranı (GCR): İlahi Ekosistem İçin Kapsamlı Bir Kılavuz

Tỷ lệ thiên thể vàng (GCR): Hướng dẫn toàn diện về hệ sinh thái thần thánh

黄金天体比例（GCR）：神圣生态系统的综合指南

For some, blockchain technology is many things rolled into one, while for others, it is simply a complex, over-hyped technology that needs to be abstracted away into the background. Neither of these theses is totally wrong. Meanwhile, readers will be well advised to remember that when it comes to blockchain technology, there is only a thin line between reality and hype. This article focuses on the reality of blockchain as an emerging technology, a silent disruptor, and a potential catalyst for transformation in human society. Blockchain technology, often the subject of headlines due to its association with cryptocurrencies like Bitcoin, is quietly weaving its magic into a tapestry far more intricate than just digital coins. At its core, blockchain is a distributed ledger technology that offers a secure, transparent, and decentralized way of recording and tracking data. How is this unique set of features fundamentally changing the way we interact with information and conduct transactions across a diverse range of industries? Let’s find out! Financial Transformation: Cutting Out the Middleman The traditional financial system is built on a third-party custodial model, which allows financial institutions like banks to act as custodians of your money and other valuable assets. As third-party intermediaries, they manage your accounts, facilitate transactions, and provide financial services. While this system offers some advantages such as security and convenience, its setbacks include high transaction costs, limited control, and limited access. The emergence of blockchain technology has disrupted this model by facilitating peer-to-peer (P2P) transactions without the need for a central authority. Do you remember the last time you received money from a friend or relative who lives in another country? As you might be aware, this process typically involves an international wire transfer, a high transaction fee, and usually takes days to complete. Blockchain cuts out the middlemen by enabling peer-to-peer transactions on a blockchain network. The transaction is recorded on a public ledger, accessible to all participants, ensuring transparency and immutability. This eliminates the need for intermediaries, reduces costs significantly, and speeds up the entire process. In a 2022 report, Accenture found that RippleNet can potentially save financial institutions an average of 70% on cross-border payments. This not only benefits banks but also opens up financial services to unbanked populations who previously lacked access to traditional banking systems. Supply Chain: Tracking The Dots The supply chain industry continues to grapple with certain setbacks emanating from the movement of goods through complex and demanding logistics. In most cases, this process is fraught with errors, fraud, and inefficiency. Blockchain addresses this problem through a tamper-proof record of every step a product takes, from its origin to its final destination. Picture this: a coffee business seeking to ensure the sourcing of its coffee beans. Traditionally, authenticating the source could pose a significant challenge, leaving room for doubt and exploitation. Leveraging blockchain can help track the journey of this produce from the point of harvesting to the time of its processing, packaging, and transporting, with each process stored on a shared public ledger. Example: Tracking Diamonds The diamond industry, known to be historically opaque, has benefited from blockchain technology. Everledger, a diamond company, estimated that its platform has tracked more than 4 million diamonds throughout the supply chain leveraging blockchain technology. It does so by assigning each diamond a unique digital identity linked to its origin, characteristics, and ownership history, allowing consumers to verify the authenticity and ethical sourcing of their diamonds. Decentralized Video Streaming Decentralized Video Streaming (DVS) is a major disruptor in the traditional video streaming landscape. Let's explore this disruption using Replay as a case study. Traditional video streaming relies on centralized servers owned by companies like Netflix or Hulu. This model has its advantages, like content curation and ease of use. However, it also comes with drawbacks such as centralized control, censorship and geoblocking, and profit sharing. Traditionally, it is extremely difficult to ensure that the music is not pirated and the musician receives royalties whenever it is played. Leveraging blockchain, the musician can tokenize their album and create a unique digital token for each copy. These tokens can be tracked and monitored, ensuring the musician receives a fair compensation. Replay As a Case Study Replay is a decentralized video streaming platform that uses blockchain technology to address the challenges of video licensing and tracking in the current streaming landscape. It proposes a solution to ensure transparency and efficiency for all stakeholders, including creators, viewers, and partners. Creators can upload content and receive compensation for views.
Viewers can earn rewards by watching content and engaging with the platform.
Partners can leverage Replay's technology to distribute their content. Rewarded.TV is the first streaming service built on Replay's technology. Identity Management: From Passwords to Portable Identities Blockchain has the capacity to revolutionize identity management by creating a secure, seamless, and portable digital identity. This digital identity would be stored on a blockchain network, accessible only by the user with a private key. It usually contains verifiable information about the individual, such as name, date of birth, and educational qualifications. Picture this: a world where you don't need to remember countless passwords or fill out lengthy forms every time you sign up for a new service. With blockchain-based digital identity, you could grant access to specific pieces of your information to different entities. This eliminates the need for centralized databases of personal information and reduces the risk of identity theft. Example: Estonia's Blockchain Identity System Estonia, a small nation in Europe, has been experimenting with a blockchain-based digital identity system since 2016, making it a pioneer in this field. Estonian citizens can use their digital ID to access various government services, sign documents electronically, and even vote online. This system has been hailed for its security, convenience, and efficiency. The success of Estonia's program demonstrates the potential of blockchain technology to revolutionize identity management on a global scale. By providing individuals with control over their data and streamlining verification processes, blockchain can usher in a new era of secure and user-centric identity management systems. Blockchain Voting: Enhancing Trust and Security The integrity of elections is paramount in a thriving democracy. However, traditional voting systems can be vulnerable to fraud, rigging, and technical difficulties. Blockchain's secure and transparent nature makes it a potential game-changer for voting systems around the world. Imagine a secure online voting platform built on blockchain technology. Voters could cast their ballots electronically, with the entire process recorded on a public ledger. This would ensure the immutability of votes and provide a verifiable audit system. Moreover, blockchain can eliminate the need for physical polling stations, potentially increasing voter turnout and making the voting process more accessible. Example: West Virginia's Pioneering Blockchain Voting Program West Virginia conducted the first-ever public blockchain voting pilot program in the United States in 2020. Although limited in scope, the program allowed overseas military personnel to cast their votes securely through blockchain technology. The program highlighted the potential of blockchain to enhance the security and accessibility of elections. Conclusion Blockchain technology is still in its early stages of development, but its disruptive potential is vast. As the technology advances with various use cases, we can expect to see even more far-reaching changes across various industries. The key to harnessing the power of blockchain lies in its ability to foster trust and transparency. By eliminating the need for intermediaries, streamlining processes, and empowering individuals with control over their data, blockchain promises to usher in a more efficient, secure, and inclusive future. Finally, it is crucial to develop robust regulations and ensure equitable access to this transformative technology. For some, blockchain technology is many things rolled into one, while for others, it is simply a complex, over-hyped technology that needs to be abstracted away into the background. Neither of these theses is totally wrong. Meanwhile, readers will be well advised to remember that when it comes to blockchain technology, there is only a thin line between reality and hype. This article focuses on the reality of blockchain as an emerging technology, a silent disruptor, and a potential catalyst for transformation in human society. Blockchain technology, often the subject of headlines due to its association with cryptocurrencies like Bitcoin, is quietly weaving its magic into a tapestry far more intricate than just digital coins. At its core, blockchain is a distributed ledger technology that offers a secure, transparent, and decentralized way of recording and tracking data. How is this unique set of features fundamentally changing the way we interact with information and conduct transactions across a diverse range of industries? Let’s find out! Financial Transformation: Cutting Out the Middleman The traditional financial system is built on a third-party custodial model, which allows financial institutions like banks to act as custodians of your money and other valuable assets. As third-party intermediaries, they manage your accounts, facilitate transactions, and provide financial services. While this system offers some advantages such as security and convenience, its setbacks include high transaction costs, limited control, and limited access. The emergence of blockchain technology has disrupted this model by facilitating peer-to-peer (P2P) transactions without the need for a central authority. Do you remember the last time you received money from a friend or relative who lives in another country? As you might be aware, this process typically involves an international wire transfer, a high transaction fee, and usually takes days to complete. Blockchain cuts out the middlemen by enabling peer-to-peer transactions on a blockchain network. The transaction is recorded on a public ledger, accessible to all participants, ensuring transparency and immutability. This eliminates the need for intermediaries, reduces costs significantly, and speeds up the entire process. In a 2022 report, Accenture found that RippleNet can potentially save financial institutions an average of 70% on cross-border payments. This not only benefits banks but also opens up financial services to unbanked populations who previously lacked access to traditional banking systems. Supply Chain: Tracking The Dots The supply chain industry continues to grapple with certain setbacks emanating from the movement of goods through complex and demanding logistics. In most cases, this process is fraught with errors, fraud, and inefficiency. Blockchain addresses this problem through a tamper-proof record of every step a product takes, from its origin to its final destination. Picture this: a coffee business seeking to ensure the sourcing of its coffee beans. Traditionally, authenticating the source could pose a significant challenge, leaving room for doubt and exploitation. Leveraging blockchain can help track the journey of this produce from the point of harvesting to the time of its processing, packaging, and transporting, with each process stored on a shared public ledger. Example: Tracking Diamonds Example: Tracking Diamonds The diamond industry, known to be historically opaque, has benefited from blockchain technology. Everledger, a diamond company, estimated that its platform has tracked more than 4 million diamonds throughout the supply chain leveraging blockchain technology. It does so by assigning each diamond a unique digital identity linked to its origin, characteristics, and ownership history, allowing consumers to verify the authenticity and ethical sourcing of their diamonds. Decentralized Video Streaming Decentralized Video Streaming (DVS) is a major disruptor in the traditional video streaming landscape. Let's explore this disruption using Replay as a case study. Traditional video streaming relies on centralized servers owned by companies like Netflix or Hulu. This model has its advantages, like content curation and ease of use. However, it also comes with drawbacks such as centralized control, censorship and geoblocking, and profit sharing. Traditionally, it is extremely difficult to ensure that the music is not pirated and the musician receives royalties whenever it is played. Leveraging blockchain, the musician can tokenize their album and create a unique digital token for each copy. These tokens can be tracked and monitored, ensuring the musician receives a fair compensation. Replay As a Case Study Replay is a decentralized video streaming platform that uses blockchain technology to address the challenges of video licensing and tracking in the current streaming landscape. It proposes a solution to ensure transparency and efficiency for all stakeholders, including creators, viewers, and partners. Creators can upload content and receive compensation for views. Viewers can earn rewards by watching content and engaging with the platform. Partners can leverage Replay's technology to distribute their content. Creators can upload content and receive compensation for views. Viewers can earn rewards by watching content and engaging with the platform. Partners can leverage Replay's technology to distribute their content. Rewarded.TV is the first streaming service built on Replay's technology. Identity Management: From Passwords to Portable Identities Blockchain has the capacity to revolutionize identity management by creating a secure, seamless, and portable digital identity. This digital identity would be stored on a blockchain network, accessible only by the user with a private key. It usually contains verifiable information about the individual, such as name, date of birth, and educational qualifications. Picture this: a world where you don't need to remember countless passwords or fill out lengthy forms every time you sign up for a new service. With blockchain-based digital identity, you could grant access to specific pieces of your information to different entities. This eliminates the need for centralized databases of personal information and reduces the risk of identity theft. Example: Estonia's Blockchain Identity System Example: Estonia's Blockchain Identity System Estonia, a small nation in Europe, has been experimenting with a blockchain-based digital identity system since 2016, making it a pioneer in this field. Estonian citizens can use their digital ID to access various government services, sign documents electronically, and even vote online. This system has been hailed for its security, convenience, and efficiency. The success of Estonia's program demonstrates the potential of blockchain technology to revolutionize identity management on a global scale. By providing individuals with control over their data and streamlining verification processes, blockchain can usher in a new era of secure and user-centric identity management systems. Blockchain Voting: Enhancing Trust and Security The integrity of elections is paramount in a thriving democracy. However, traditional voting systems can be vulnerable to fraud, rigging, and technical difficulties. Blockchain's secure and transparent nature makes it a potential game-changer for voting systems around the world. Imagine a secure online voting platform built on blockchain technology. Voters could cast their ballots electronically, with the entire process recorded on a public ledger. This would ensure the immutability of votes and provide a verifiable audit system. Moreover, blockchain can eliminate the need for physical polling stations, potentially increasing voter turnout and making the voting process more accessible. Example: West Virginia's Pioneering Blockchain Voting Program Example: West Virginia's Pioneering Blockchain Voting Program West Virginia conducted the first-ever public blockchain voting pilot program in the United States in 2020. Although limited in scope, the program allowed overseas military personnel to cast their votes securely through blockchain technology. The program highlighted the potential of blockchain to enhance the security and accessibility of elections. Conclusion Blockchain technology is still in its early stages of development, but its disruptive potential is vast. As the technology advances with various use cases, we can expect to see even more far-reaching changes across various industries. The key to harnessing the power of blockchain lies in its ability to foster trust and transparency. By eliminating the need for intermediaries, streamlining processes, and empowering individuals with control over their data, blockchain promises to usher in a more efficient, secure, and inclusive future. Finally, it is crucial to develop robust regulations and ensure equitable access to this transformative technology.

Five Notable Disruptions Blockchain Is Bringing To The World

Life in 2050 According to Jail-Broken GPT-4o 

Autonomys Network: ex-Jumio Executive Appointed CEO For New deAI Vision Ahead Of Mainnet Launch

অটোনমিস নেটওয়ার্ক: মেইননেট লঞ্চের আগে নতুন ডিএআই ভিশনের জন্য প্রাক্তন জুমিও এক্সিকিউটিভ সিইও নিযুক্ত হয়েছেন

Autonomys Network: ehemaliger Jumio-Manager vor Mainnet-Start zum CEO für neue deAI-Vision ernannt

Autonomys Network: ex ejecutivo de Jumio nombrado director ejecutivo para la nueva visión deAI antes del lanzamiento de Mainnet

Autonomys Network : un ancien cadre de Jumio est nommé PDG de la nouvelle vision deAI avant le lancement du réseau principal

ऑटोनोमिस नेटवर्क: पूर्व-जुमियो कार्यकारी को मेननेट लॉन्च से पहले नए डीएआई विजन के लिए सीईओ नियुक्त किया गया

Autonomys Network: メインネットの立ち上げに先立ち、元Jumio幹部が新たなdeAIビジョンのCEOに任命される

Autonomys Network: 전 Jumio 임원이 메인넷 출시를 앞두고 새로운 deAI 비전을 위해 CEO로 임명됨

Rede Autonomys: ex-executivo da Jumio nomeado CEO para nova visão deAI antes do lançamento da Mainnet

Сеть Autonomys: бывший исполнительный директор Jumio назначен генеральным директором новой концепции deAI в преддверии запуска основной сети

Autonomys Network: Eski Jumio Yöneticisi, Mainnet Lansmanı Öncesinde Yeni deAI Vizyonu İçin CEO Olarak Atandı

Autonomys Network: Cựu Giám đốc điều hành Jumio được bổ nhiệm làm Giám đốc điều hành cho Tầm nhìn deAI mới trước khi ra mắt Mainnet

Autonomys Network：前 Jumio 高管被任命为新 deAI 愿景的首席执行官，主网发布前夕

The urge to delete Social media and messaging apps have created new avenues for people to enter our lives. You might feel the urge to delete someone for various reasons – annoyance, personal growth, or just a shift in content preferences. In a world where change is constant, the desire to curate our online circles reflects this innate need for control and authenticity. Personally, I’ll admit this urge is like an automatic response. Most of the time, it feels cathartic, a satisfying release, and a digital refresh. Other times, it’s how my fear expresses itself, and it helps me feel protected from a perceived threat. Our patterns of behaviour Research into deleting phenomena unveils a tapestry of human behaviour. Deleting certain individuals may lead to the emergence of hidden aspects of ourselves while fostering deeper connections with others. Though deleting generally refers to removing someone from your friends list or connections, it varies depending on the platform. Subtle differences between notifications and accessibility reflect how we engage with each other on these platforms. For example, Facebook carries more emotional weight due to stronger personal connections, while LinkedIn is a professional disconnect. Instagram is more towards social exclusion, while Twitter ranges from negligible to significant, sometimes leading to being silenced within an online community. Over time we’ve learned to sculpt a more intricate inner world, rich with nuance and layers. This creates a maze around our identity where fragments are selectively revealed — a new freedom that also questions our accountability. The power of perception Attention is currency, so the act of deleting carries weight beyond the digital realm. The energy behind our gaze and thoughts can shape the way we perceive and interact with others online. Deleting someone from our virtual sphere doesn’t diminish the impact of our attention, it redirects it. As connections add up over time they weave a complex web of relationships. Being perceived by this network is a novel phenomenon, expanding beyond our immediate physical surroundings to include individuals from anywhere. Engaging with this vast array of minds, each with their own belief systems presents a challenge that transcends our ability to fully comprehend. Tip: Try to visualise your network, what kind of groups are there? What shapes emerge? Express this in a way that feels natural to you, draw it out or write about it. Navigating conflicting emotions Deleting can stir a mix of conflicting emotions – jealousy, loneliness, and misunderstanding. Leaning into these feelings can help you learn about yourself. Perhaps jealousy points you to something you want to do in your life. Feeling misunderstood by your current group of friends might motivate you to take the leap into new groups. Then there’s the possibility of the deleted individual finding out, would they care? Would you care? Try to simulate meeting them in real life, how would you feel and what would you say to them? I’ve found it quite freeing to admit to people about deleting. It takes courage and humor, but it also opens up an opportunity to talk about digital spaces and our personal needs. Remember: digital etiquette is different for everyone, and by acknowledging the complexities of human interaction we can make it more enjoyable. Exploring your digital maze The act of deleting people is both a reflection of our evolving selves and a tool for self-preservation. Growth and change are normal for us, yet our need to hold on accumulates an unnatural web of connections. By embracing the nuance of digital relationships and remaining mindful of our intentions, we can navigate the intricate maze of human connection with a bit more clarity and compassion. Stay tuned for more posts and future webinars, shedding light on the intimate quirks of our digital existence. Much love ₊˚⊹♡ The urge to delete The urge to delete Social media and messaging apps have created new avenues for people to enter our lives. You might feel the urge to delete someone for various reasons – annoyance, personal growth, or just a shift in content preferences. In a world where change is constant, the desire to curate our online circles reflects this innate need for control and authenticity. Personally, I’ll admit this urge is like an automatic response. Most of the time, it feels cathartic, a satisfying release, and a digital refresh. Other times, it’s how my fear expresses itself, and it helps me feel protected from a perceived threat. Our patterns of behaviour Our patterns of behaviour Research into deleting phenomena unveils a tapestry of human behaviour. Deleting certain individuals may lead to the emergence of hidden aspects of ourselves while fostering deeper connections with others. Though deleting generally refers to removing someone from your friends list or connections, it varies depending on the platform. Subtle differences between notifications and accessibility reflect how we engage with each other on these platforms. For example, Facebook carries more emotional weight due to stronger personal connections, while LinkedIn is a professional disconnect. Instagram is more towards social exclusion, while Twitter ranges from negligible to significant, sometimes leading to being silenced within an online community. Over time we’ve learned to sculpt a more intricate inner world, rich with nuance and layers. This creates a maze around our identity where fragments are selectively revealed — a new freedom that also questions our accountability. The power of perception The power of perception Attention is currency, so the act of deleting carries weight beyond the digital realm. The energy behind our gaze and thoughts can shape the way we perceive and interact with others online. Deleting someone from our virtual sphere doesn’t diminish the impact of our attention, it redirects it. As connections add up over time they weave a complex web of relationships. Being perceived by this network is a novel phenomenon, expanding beyond our immediate physical surroundings to include individuals from anywhere. Engaging with this vast array of minds, each with their own belief systems presents a challenge that transcends our ability to fully comprehend. Tip: Try to visualise your network, what kind of groups are there? What shapes emerge? Express this in a way that feels natural to you, draw it out or write about it. Navigating conflicting emotions Navigating conflicting emotions Deleting can stir a mix of conflicting emotions – jealousy, loneliness, and misunderstanding. Leaning into these feelings can help you learn about yourself. Perhaps jealousy points you to something you want to do in your life. Feeling misunderstood by your current group of friends might motivate you to take the leap into new groups. Then there’s the possibility of the deleted individual finding out, would they care? Would you care? Try to simulate meeting them in real life, how would you feel and what would you say to them? I’ve found it quite freeing to admit to people about deleting. It takes courage and humor, but it also opens up an opportunity to talk about digital spaces and our personal needs. Remember: digital etiquette is different for everyone, and by acknowledging the complexities of human interaction we can make it more enjoyable. Exploring your digital maze Exploring your digital maze The act of deleting people is both a reflection of our evolving selves and a tool for self-preservation. Growth and change are normal for us, yet our need to hold on accumulates an unnatural web of connections. By embracing the nuance of digital relationships and remaining mindful of our intentions, we can navigate the intricate maze of human connection with a bit more clarity and compassion. Stay tuned for more posts and future webinars, shedding light on the intimate quirks of our digital existence. Much love ₊˚⊹♡

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