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

The Opt Out Writing Contest: Final Round Results Announcement 🎉

অপ্ট আউট লেখার প্রতিযোগিতা: চূড়ান্ত রাউন্ডের ফলাফল ঘোষণা 🎉

Soutěž v psaní odhlášení: Vyhlášení výsledků posledního kola 🎉

Anuncio de los resultados de la ronda final del concurso de redacción Opt Out 🎉

مسابقه انصراف از نوشتن: اعلام نتایج دور نهایی 🎉

Opt Out Writing Contest: lopullisen kierroksen tulosilmoitus 🎉

オプトアウト ライティング コンテスト: 最終ラウンドの結果発表 🎉

Atteikšanās rakstīšanas konkurss: pēdējās kārtas rezultātu paziņojums 🎉

अप्ट आउट लेखन प्रतियोगिता: अन्तिम चरणको नतिजा घोषणा 🎉

Concurso de Escrita Opt Out: Anúncio dos Resultados da Rodada Final 🎉

Shindano la Opt Out Kuandika: Tangazo la Matokeo ya Raundi ya Mwisho 🎉

Озмуни хаттии радкунӣ: Эълони натиҷаҳои даври ниҳоӣ 🎉

آپٹ آؤٹ تحریری مقابلہ: فائنل راؤنڈ کے نتائج کا اعلان 🎉

Building a Web Server with Python and Flask

Opbygning af en webserver med Python og Flask

Δημιουργία διακομιστή Web με Python και Flask

Construyendo un servidor web con Python y Flask

ساخت وب سرور با پایتون و فلاسک

בניית שרת אינטרנט עם Python ו-Flask

Վեբ սերվերի կառուցում Python-ով և Flask-ով

Membangun Server Web dengan Python dan Flask

Python と Flask で Web サーバーを構築する

Kotonga Serveur Web na Python na Flask

Construirea unui server web cu Python și Flask

Изградња веб сервера са Питхон-ом и Фласк-ом

Pagbuo ng Web Server gamit ang Python at Flask

The chambers of Tonga's Parliament recently witnessed a defining moment in the nation's political narrative, one that speaks volumes about the delicate dance between personal ambition and national unity. At the heart of this story stands Dr. Viliami Latu, whose campaign for Prime Minister became a testament to the complexities of political timing and the true meaning of leadership. The pivotal moment arrived with Lord Nuku's speech, which cast a telling shadow across the parliamentary floor. The atmosphere in the chamber shifted palpably as both nobles and people's representatives revealed their allegiances through subtle nods and knowing glances. It was a moment that demanded political acumen and selfless wisdom - a chance for Dr. Latu to demonstrate the very unity he championed in his campaign speeches. Yet, as the political winds blew decisively against his favor, Dr. Latu and his team, led by the former Prime Minister Sovaleni, chose to press forward. Their persistence, while perhaps born of conviction, transformed what could have been a moment of graceful unity into a protracted political struggle. They marshaled their resources and time, wielding statistics about their administration's economic achievements like shields against the inevitable. Their claim of reducing inflation from 10.9% to 0.9% echoed through the chambers, but these numbers rang hollow when measured against the International Monetary Fund's sobering report. The 2024 Article IV Consultation painted a different picture with its 4.6% inflation rate, undermining not just their economic narrative but the credibility of their broader campaign. In Tongan politics, as in life, timing and grace often matter more than persistence. A withdrawal following Lord Nuku's speech could have written a different story - one of a leader who chose the path of unity over the pursuit of power. Such a decision would have resonated through Tonga's political memory, marking Dr. Latu not as a defeated candidate but as a statesman who understood that sometimes leadership means stepping aside. The true essence of political leadership in Tonga transcends the mere holding of office. It lies in understanding the subtle rhythms of power, the unspoken signals of support and opposition, and most importantly, in knowing when the pursuit of position must yield to the greater cause of national harmony. This episode in Tonga's political history serves not as a tale of victory or defeat, but as a reflection on the nature of leadership itself - a reminder that in the grand tapestry of governance, the threads of unity often weave a stronger pattern than those of ambition. May I also take this opportunity to congratulate the House for the nomination of Dr. 'Aisake Eke as the new Prime Minister. The nation's future is in good hands, and I look forward to watching them shape a brighter future for all Tongans. Merry Christmas and a Prosperous New Year! The chambers of Tonga's Parliament recently witnessed a defining moment in the nation's political narrative, one that speaks volumes about the delicate dance between personal ambition and national unity. At the heart of this story stands Dr. Viliami Latu, whose campaign for Prime Minister became a testament to the complexities of political timing and the true meaning of leadership. The pivotal moment arrived with Lord Nuku's speech, which cast a telling shadow across the parliamentary floor. The atmosphere in the chamber shifted palpably as both nobles and people's representatives revealed their allegiances through subtle nods and knowing glances. It was a moment that demanded political acumen and selfless wisdom - a chance for Dr. Latu to demonstrate the very unity he championed in his campaign speeches. Yet, as the political winds blew decisively against his favor, Dr. Latu and his team, led by the former Prime Minister Sovaleni, chose to press forward. Their persistence, while perhaps born of conviction, transformed what could have been a moment of graceful unity into a protracted political struggle. They marshaled their resources and time, wielding statistics about their administration's economic achievements like shields against the inevitable. Their claim of reducing inflation from 10.9% to 0.9% echoed through the chambers, but these numbers rang hollow when measured against the International Monetary Fund's sobering report. The 2024 Article IV Consultation painted a different picture with its 4.6% inflation rate, undermining not just their economic narrative but the credibility of their broader campaign. In Tongan politics, as in life, timing and grace often matter more than persistence. A withdrawal following Lord Nuku's speech could have written a different story - one of a leader who chose the path of unity over the pursuit of power. Such a decision would have resonated through Tonga's political memory, marking Dr. Latu not as a defeated candidate but as a statesman who understood that sometimes leadership means stepping aside. The true essence of political leadership in Tonga transcends the mere holding of office. It lies in understanding the subtle rhythms of power, the unspoken signals of support and opposition, and most importantly, in knowing when the pursuit of position must yield to the greater cause of national harmony. This episode in Tonga's political history serves not as a tale of victory or defeat, but as a reflection on the nature of leadership itself - a reminder that in the grand tapestry of governance, the threads of unity often weave a stronger pattern than those of ambition. May I also take this opportunity to congratulate the House for the nomination of Dr. 'Aisake Eke as the new Prime Minister. The nation's future is in good hands, and I look forward to watching them shape a brighter future for all Tongans. Merry Christmas and a Prosperous New Year!

In Tongan Politics, Timing and Grace Often Matter More Than Persistence

Live sports is a massive growth opportunity for New Hollywood streaming businesses who dominate connected TV. The global sports online live video streaming market was valued at $27.93 billion in 2024 and is projected to grow to $133.98 billion by 2031, with a compound annual growth rate of 24.64%. in 2024, digital sports viewers were expected to surpass linear TV viewers for the first time, with 105.3 million digital viewers compared to 85.7 million on traditional platforms. The major streaming brands, including Amazon, Apple, Netflix, Peacock, and YouTube, are locked in a knock-down, drag-out battle to capture live sports viewers and attract more advertising revenue. And the NFL is the coveted prize in this fight. The NFL is one of the world’s most powerful brands. Nielsen says the NFL accounted for a 93 of the 100 most-watched TV broadcasts in 2023. The NFL has sensed and responded to the rise of CTV, agreeing to air games with a variety of streaming companies, among them Amazon Prime, Paramount+, Peacock, and YouTube TV. Which brings us to the epic Beyoncé Bowl on Christmas Day 2024, aka Beyoncé’s halftime performance during Netflix NFL Christmas Gameday. For the first time, Netflix hosted not one but two NFL games on Christmas Day, featuring matchups between the Kansas City Chiefs and Pittsburgh Steelers followed by the Houston Texans hosting the Baltimore Ravens. Netflix’s exclusive livestreams of two Christmas Day NFL games each reached an average of more than 24 million U.S. viewers, making them the most streamed NFL games in U.S. history – and comparable to numbers for a on average, in the U.S. for both games, according to Nielsen figures provided by Netflix. This is in line for a typical NFL game on linear TV. Nice numbers. But Beyoncé beat them both. Her halftime performance during the Texans/Ravens game drew 27 million viewers, and no wonder. Both the NFL games were boring, lopsided contests. But Queen Bey lit up connected TV and the digital world in general with the debut performance of her 2024 genre-bending album Cowboy Carter. This matters because the appearance of Beyoncé elevated a sporting event to a level of cultural relevance as the Super Bowl is capable of doing – and raised the stakes for the intersection of sports and entertainment on connected TV. The #BeyoncéBowl Wins Big By any measure, her 13-minute appearance was a success. According to Netflix, #BeyoncéBowl rose to the #1 worldwide trend on X immediately as her performance kicked off, eclipsing #Christmas. Following her performance, Netflix occupied 10 of the top 12 trending topics on X in the United States. But the numbers don’t capture the significance of her performance. She elevated live sports on connected TV to another level of cultural relevance. This is important because when any brand, including Netflix and the NFL, achieves, cultural relevance, they connect with (and even shape) the values, beliefs, and behaviors of their audiences. And when a brand does that, they lock in loyalty. Beyoncé knows how to elevate a sporting event to a higher level of cultural significance. She did so in 2016 with her performance of the riveting “Formation,” a politically-charged statement of identity at the Super Bowl 50 Halftime Show. She did it again on Christmas Day when her Cowboy Carter setlist drew attention to the rich heritage of Black country music as the album itself did on its release in 2024. In the wake of her performance, news media like The Guardian, The New York Times, and The Week published thoughtful analyses of Black country. Social media blew up as she performed a setlist that included “16 Carriages,” “YA YA,” Texas Hold ‘Em” and more. Cowboy Carter is a much talked about and celebrated artistic achievement that blends country and Americana with elements of blues, folk, soul, and hip-hop, reflecting her Texan roots and the rich tapestry of Southern music. Although it’s not a country album, per se, the country influences have made a profound statement on the inclusivity and evolution of American music genres. Those influences were on display during the halftime show. Beyoncé surrounded herself with musical guests including Post Malone, Shaboozey, and her daughter Blue Ivy. But the most important inclusion were the Black country women who made contributions to Cowboy Carter: Brittney Spencer, Reyna Roberts, Tiera Kennedy, and Tanner Adell. They sang along with her interpretation of the Beatles’ “Blackbird” (fashioned as “Blackbiird” on Cowboy Carter) as they did on the album. The choice of “Blackbiird” was significant: Paul McCartney wrote the original “Blackbird” in 1968 as a response to the civil rights movement in the United States. Inspired by the racial tensions and struggles for equality during that time, McCartney has explained that the “blackbird” in the song symbolizes a Black woman or person enduring oppression but holding hope for change. For Beyoncé to include the song at an NFL game and to include the Black country artists who sang on its album version was a cultural statement of purpose and artistry. For Netflix and the NFL, the Beyoncé halftime show ups the ante for what a sporting event and artist can accomplish together especially on Christmas Day. On linear TV, Christmas Day broadcasts have not featured performers of such global stature. While the NBA’s Christmas Day games often included celebrity cameos in games or promotional material, these moments paled in comparison to Beyoncé’s multi-million-dollar production. By incorporating a performer of Beyoncé’s caliber, Netflix and the NFL upped the ante for the streaming industry in particular. Their collaboration underlines how entertainment and sports are converging in ways that redefine what viewers can expect not only from holiday programming but sports programming. Advertising Implications for Netflix The success of Netflix NFL Christmas Gameday will also help boost Netflix’s already successful advertising business. Advertising for the NFL games appeared across both the ad-supported and ad-free subscription tiers. Ad inventory sold out in November. For advertisers, this ensured access to Netflix’s full NFL audience, creating a unified and highly valuable advertising opportunity that maximizes reach. Analysts estimate that the two NFL Christmas Day games could generate $150 million in advertising revenue, effectively covering Netflix’s $150 million investment in the broadcast rights. This not only offsets costs but also demonstrates the financial viability of live sports as a revenue stream for Netflix. Future NFL broadcasts will give Netflix an opportunity to attract premium advertisers who are eager to reach large, engaged audiences. Netflix can use its advanced data analytics capabilities to provide highly targeted advertising options. Oh, and Netflix and Beyoncé are not done yet. They have a three-project, $60 million deal signed in 2019. The Netflix NFL Christmas Gameday was the second initiative (the first was a 2019 concert documentary, “Homecoming: A Film by Beyoncé”). The two are partnering on a documentary of the #BeyoncéBowl, too, which will keep the moment in the public eye. The Amazon Advantage You can be sure Netflix’s competitors are figuring out how to answer the Netflix challenge, especially Amazon, which achieved some history of its own by streaming NFL Black Friday games in 2023 and 2024. Amazon has its work cut out. The 2024 NFL Black Friday game, streamed exclusively on Amazon Prime Video, achieved an average viewership of 13.51 million, far less than Netflix NFL Christmas Gameday although a 41% increase compared to the 2023 inaugural Black Friday game. But Amazon has something Netflix lacks: online commerce during live sports and a more interactive and interesting viewing experience to complement the action on the field. Expect Amazon to lean into its strength while attempting to engineer its own entertainment moment. Bottom line: the stage is set for sports and entertainment to dominate New Hollywood and connected TV in 2025. Live sports is a massive growth opportunity for New Hollywood streaming businesses who dominate connected TV. The global sports online live video streaming market was valued at $27.93 billion in 2024 and is projected to grow to $133.98 billion by 2031, with a compound annual growth rate of 24.64%. in 2024, digital sports viewers were expected to surpass linear TV viewers for the first time, with 105.3 million digital viewers compared to 85.7 million on traditional platforms. The major streaming brands, including Amazon, Apple, Netflix, Peacock, and YouTube, are locked in a knock-down, drag-out battle to capture live sports viewers and attract more advertising revenue. And the NFL is the coveted prize in this fight. The NFL is one of the world’s most powerful brands. Nielsen says the NFL accounted for a 93 of the 100 most-watched TV broadcasts in 2023 . The NFL has sensed and responded to the rise of CTV, agreeing to air games with a variety of streaming companies, among them Amazon Prime, Paramount+, Peacock, and YouTube TV. $27.93 billion in 2024 $27.93 billion in 2024 surpass linear TV viewers surpass linear TV viewers NFL accounted for a 93 of the 100 most-watched TV broadcasts in 2023 NFL accounted for a 93 of the 100 most-watched TV broadcasts in 2023 Which brings us to the epic Beyoncé Bowl on Christmas Day 2024, aka Beyoncé’s halftime performance during Netflix NFL Christmas Gameday. For the first time, Netflix hosted not one but two NFL games on Christmas Day, featuring matchups between the Kansas City Chiefs and Pittsburgh Steelers followed by the Houston Texans hosting the Baltimore Ravens. Netflix’s exclusive livestreams of two Christmas Day NFL games each reached an average of more than 24 million U.S. viewers , making them the most streamed NFL games in U.S. history – and comparable to numbers for a on average, in the U.S. for both games, according to Nielsen figures provided by Netflix. This is in line for a typical NFL game on linear TV. average of more than 24 million U.S. viewers average of more than 24 million U.S. viewers the most streamed NFL games in U.S. history the most streamed NFL games in U.S. history Nice numbers. But Beyoncé beat them both. Her halftime performance during the Texans/Ravens game drew 27 million viewers, and no wonder. Both the NFL games were boring, lopsided contests. But Queen Bey lit up connected TV and the digital world in general with the debut performance of her 2024 genre-bending album Cowboy Carter . This matters because the appearance of Beyoncé elevated a sporting event to a level of cultural relevance as the Super Bowl is capable of doing – and raised the stakes for the intersection of sports and entertainment on connected TV. Cowboy Carter The #BeyoncéBowl Wins Big The #BeyoncéBowl Wins Big By any measure, her 13-minute appearance was a success. According to Netflix, #BeyoncéBowl rose to the #1 worldwide trend on X immediately as her performance kicked off, eclipsing #Christmas. Following her performance, Netflix occupied 10 of the top 12 trending topics on X in the United States. But the numbers don’t capture the significance of her performance. She elevated live sports on connected TV to another level of cultural relevance. This is important because when any brand, including Netflix and the NFL, achieves, cultural relevance, they connect with (and even shape) the values, beliefs, and behaviors of their audiences. And when a brand does that, they lock in loyalty. Beyoncé knows how to elevate a sporting event to a higher level of cultural significance. She did so in 2016 with her performance of the riveting “Formation,” a politically-charged statement of identity at the Super Bowl 50 Halftime Show. She did it again on Christmas Day when her Cowboy Carter setlist drew attention to the rich heritage of Black country music as the album itself did on its release in 2024. In the wake of her performance, news media like The Guardian , The New York Times , and The Week published thoughtful analyses of Black country. Cowboy Carter The Guardian The Guardian The Guardian The New York Times The New York Times The New York Times The Week The Week The Week Social media blew up as she performed a setlist that included “16 Carriages,” “YA YA,” Texas Hold ‘Em” and more. Cowboy Carter is a much talked about and celebrated artistic achievement that blends country and Americana with elements of blues, folk, soul, and hip-hop, reflecting her Texan roots and the rich tapestry of Southern music. Although it’s not a country album, per se, the country influences have made a profound statement on the inclusivity and evolution of American music genres. Cowboy Carter Those influences were on display during the halftime show. Beyoncé surrounded herself with musical guests including Post Malone, Shaboozey, and her daughter Blue Ivy. But the most important inclusion were the Black country women who made contributions to Cowboy Carter : Brittney Spencer, Reyna Roberts, Tiera Kennedy, and Tanner Adell. They sang along with her interpretation of the Beatles’ “Blackbird” (fashioned as “Blackbiird” on Cowboy Carter ) as they did on the album. Cowboy Carter Cowboy Carter The choice of “Blackbiird” was significant: Paul McCartney wrote the original “Blackbird” in 1968 as a response to the civil rights movement in the United States. Inspired by the racial tensions and struggles for equality during that time, McCartney has explained that the “blackbird” in the song symbolizes a Black woman or person enduring oppression but holding hope for change. For Beyoncé to include the song at an NFL game and to include the Black country artists who sang on its album version was a cultural statement of purpose and artistry. For Netflix and the NFL, the Beyoncé halftime show ups the ante for what a sporting event and artist can accomplish together especially on Christmas Day. On linear TV, Christmas Day broadcasts have not featured performers of such global stature. While the NBA’s Christmas Day games often included celebrity cameos in games or promotional material, these moments paled in comparison to Beyoncé’s multi-million-dollar production. By incorporating a performer of Beyoncé’s caliber, Netflix and the NFL upped the ante for the streaming industry in particular. Their collaboration underlines how entertainment and sports are converging in ways that redefine what viewers can expect not only from holiday programming but sports programming. Advertising Implications for Netflix Advertising Implications for Netflix The success of Netflix NFL Christmas Gameday will also help boost Netflix’s already successful advertising business. Advertising for the NFL games appeared across both the ad-supported and ad-free subscription tiers. Ad inventory sold out in November . For advertisers, this ensured access to Netflix’s full NFL audience, creating a unified and highly valuable advertising opportunity that maximizes reach. Ad inventory sold out in November Ad inventory sold out in November Analysts estimate that the two NFL Christmas Day games could generate $150 million in advertising revenue , effectively covering Netflix’s $150 million investment in the broadcast rights. This not only offsets costs but also demonstrates the financial viability of live sports as a revenue stream for Netflix. generate $150 million in advertising revenue generate $150 million in advertising revenue Future NFL broadcasts will give Netflix an opportunity to attract premium advertisers who are eager to reach large, engaged audiences. Netflix can use its advanced data analytics capabilities to provide highly targeted advertising options. Oh, and Netflix and Beyoncé are not done yet. They have a three-project, $60 million deal signed in 2019. The Netflix NFL Christmas Gameday was the second initiative (the first was a 2019 concert documentary, “Homecoming: A Film by Beyoncé”). The two are partnering on a documentary of the #BeyoncéBowl, too, which will keep the moment in the public eye. The Amazon Advantage The Amazon Advantage You can be sure Netflix’s competitors are figuring out how to answer the Netflix challenge, especially Amazon, which achieved some history of its own by streaming NFL Black Friday games in 2023 and 2024. Amazon has its work cut out. The 2024 NFL Black Friday game, streamed exclusively on Amazon Prime Video, achieved an average viewership of 13.51 million , far less than Netflix NFL Christmas Gameday although a 41% increase compared to the 2023 inaugural Black Friday game. But Amazon has something Netflix lacks: online commerce during live sports and a more interactive and interesting viewing experience to complement the action on the field. Expect Amazon to lean into its strength while attempting to engineer its own entertainment moment. 13.51 million 13.51 million Bottom line: the stage is set for sports and entertainment to dominate New Hollywood and connected TV in 2025. Bottom line : the stage is set for sports and entertainment to dominate New Hollywood and connected TV in 2025. Bottom line

Why the Beyoncé Bowl Matters to Connected TV and Live Sports

لماذا يعتبر Beyonce Bowl مهمًا للتلفزيون المتصل والأحداث الرياضية المباشرة

Защо Beyoncé Bowl има значение за свързаната телевизия и спорта на живо

Por qué el Beyoncé Bowl es importante para la televisión conectada y los deportes en vivo

Por que o Beyoncé Bowl é importante para a televisión conectada e os deportes en directo

Zašto je Beyoncé Bowl važna za povezani TV i sport uživo

למה הקערה של ביונסה חשובה לטלוויזיה מחוברת ולספורט חי

ビヨンセ ボウルがコネクテッド TV とライブ スポーツにとって重要な理由

Beyoncé Bowl이 Connected TV와 라이브 스포츠에 중요한 이유

Холбогдсон ТВ болон шууд спортод Бейонсе Боул яагаад чухал вэ?

Dlaczego Beyoncé Bowl jest ważny dla telewizji połączonej i transmisji sportowych na żywo

Почему Beyoncé Bowl важен для подключенного телевидения и прямых трансляций спорта

Ha Yini Beyoncé Bowl Yi Ri Ya Nkoka Eka TV Leyi Hlanganisiweke Na Mintlangu Leyi Hanyaka

Beyond their practical and aesthetic purpose, clothes are also a reflection of the times we live in. They mirror cultures and environments, but also the technological progress and the level of craftsmanship that humans have achieved. The garments that people wear today are significantly different from the ones worn in centuries past, not only in terms of shape and style but also in the materials, techniques, and tools used to create them. The simple linen wraps made from woven flax that the ancient Egyptians wore are worlds apart from the haute couture creations of the biggest fashion houses or the cutting-edge experimental materials used in performance apparel. Much has changed in the way we design and produce clothes over the years, and we owe these transformations to the numerous innovations that have pushed the fashion and textile industry forward. We tend to equate the evolution of fashion with the emergence of new trends and styles and the contribution of key figures like Coco Chanel, Yves Saint Laurent, Charles Voegele, and Alexander McQueen, who revolutionized this field with their groundbreaking ideas and pioneering visions. However, the elements of change go a lot deeper than that and often start with something as small and seemingly inconsequential as a needle. So, if you’re wondering how we arrived at the modern fabrics, cuts, and designs that we see on the runways and streets, we need to take a look back in history and explore some of the most disruptive innovations that have contributed to the development of the textile and clothing industry and have brought the world of fashion to where it is today. The Sewing Needle We rarely give much thought to these tiny utensils that are ubiquitous nowadays, but their advent almost 60,000 years ago enabled the creation of more complex garments with extra insulation, which, according to anthropologists, may have played a role in human migration as it allowed our ancestors to inhabit regions of the world that had colder climates. The first needles were made of animal bones, and beyond their practical function of stitching pieces of fabric together, they were also used to decorate textiles. So, thanks to sewing needles, for the first time in history, people were able to create clothes that weren’t just functional pieces but also served as a form of creative expression. The Spinning Wheel Cotton is one of the oldest fibers used in the making of clothing, dating back to 5000 BC. However, manually creating treads from cotton or other fibers with the help of a spindle was an arduous and time-consuming process. That’s until the spinning wheel was invented in India, somewhere between 500 and 1000 A.D, although some academics believe it originated in Zhou dynasty China. Regardless of its roots, this machine simplified the process of turning fibers into threads or yarns and, over the next centuries, became a widespread instrument found in households all across the world. Textile Mills While spinning wheels represented a major improvement in yarn manufacturing, they still required manual handling. So, mills were invented in 1740, taking textile production one step further. This machinery integrated spinning wheels and other tools like power looms, which were used to weave clothes and tapestry, and created fully automated processes, thus removing the need for human intervention. The introduction of automated equipment for spinning and weaving also saw clothing manufacturing turn from small-scale craft production into an industrialized factory-centric enterprise, as was the case with many other activities during the Industrial Revolution. The Flying Shuttle One of the most notable breakthroughs of the Industrial Revolution, the flying shuttle, was invented by John Kay in 1733. Although by the name of it, one would think it’s something related to aviation, this small device is, in fact, a mechanism that automated and sped up the weaving process while also making it possible for weavers to create a broader range of fabrics and textiles. With it, production and efficiency increased considerably, so manufacturers could meet the growing demand for textiles. The Sewing Machine The invention of this popular device is attributed to English inventor Thomas Saint, who developed the earliest design in the late 18th century, but several other investors brought their contributions and perfected the model over the following decades, adding new features to it. The first machines were large and heavy, made out of wood and metal, so they weren’t destined for home use, being found exclusively in tailor shops and on factory floors. Later improvements made them lighter, more affordable, and more efficient, and their prevalence revolutionized the textile and fashion industry by enhancing speed and productivity and enabling the mass production of clothing. Nylon and Lycra While cotton and other materials made from natural fibers are still commonly used in the textile industry, the introduction of synthetic fabrics like nylon and spandex opened up a world of possibilities in the fashion world. Nylon, the first completely synthetic fiber, was patented by chemists at DuPont Chemicals in 1935 by combining different types of chemicals like amine, hexamethylene diamine, and adipic acid. Mass production began in 1938, and the invention became instantly popular with the public. Then spandex was invented in 1954, launched on the market under the name Lycra, and used to create soft and comfortable form-fitting garments. 3D Printing Fast forward to today, and we have 3D printing making waves in the textile sector. Initially used in the medical industry, this advanced technology has now made its way into the fashion sphere, facilitating the creation of custom-made garments and accessories and the production of innovative designs and textures. With the textile and clothing industry constantly evolving, we’re bound to see many more innovations emerge in the future, transforming practices, products, and even people’s relationships with fashion. Beyond their practical and aesthetic purpose, clothes are also a reflection of the times we live in. They mirror cultures and environments, but also the technological progress and the level of craftsmanship that humans have achieved. The garments that people wear today are significantly different from the ones worn in centuries past, not only in terms of shape and style but also in the materials, techniques, and tools used to create them. Beyond their practical and aesthetic purpose, clothes are also a reflection of the times we live in. They mirror cultures and environments, but also the technological progress and the level of craftsmanship that humans have achieved. The garments that people wear today are significantly different from the ones worn in centuries past, not only in terms of shape and style but also in the materials, techniques, and tools used to create them. The simple linen wraps made from woven flax that the ancient Egyptians wore are worlds apart from the haute couture creations of the biggest fashion houses or the cutting-edge experimental materials used in performance apparel. The simple linen wraps made from woven flax that the ancient Egyptians wore are worlds apart from the haute couture creations of the biggest fashion houses or the cutting-edge experimental materials used in performance apparel. Much has changed in the way we design and produce clothes over the years, and we owe these transformations to the numerous innovations that have pushed the fashion and textile industry forward. We tend to equate the evolution of fashion with the emergence of new trends and styles and the contribution of key figures like Coco Chanel, Yves Saint Laurent, Charles Voegele , and Alexander McQueen, who revolutionized this field with their groundbreaking ideas and pioneering visions. However, the elements of change go a lot deeper than that and often start with something as small and seemingly inconsequential as a needle. Charles Voegele Charles Voegele So, if you’re wondering how we arrived at the modern fabrics, cuts, and designs that we see on the runways and streets, we need to take a look back in history and explore some of the most disruptive innovations that have contributed to the development of the textile and clothing industry and have brought the world of fashion to where it is today. The Sewing Needle We rarely give much thought to these tiny utensils that are ubiquitous nowadays, but their advent almost 60,000 years ago enabled the creation of more complex garments with extra insulation, which, according to anthropologists, may have played a role in human migration as it allowed our ancestors to inhabit regions of the world that had colder climates. The first needles were made of animal bones, and beyond their practical function of stitching pieces of fabric together, they were also used to decorate textiles. So, thanks to sewing needles, for the first time in history, people were able to create clothes that weren’t just functional pieces but also served as a form of creative expression. The Spinning Wheel Cotton is one of the oldest fibers used in the making of clothing, dating back to 5000 BC. However, manually creating treads from cotton or other fibers with the help of a spindle was an arduous and time-consuming process. That’s until the spinning wheel was invented in India, somewhere between 500 and 1000 A.D, although some academics believe it originated in Zhou dynasty China. Regardless of its roots, this machine simplified the process of turning fibers into threads or yarns and, over the next centuries, became a widespread instrument found in households all across the world. the spinning wheel the spinning wheel Textile Mills While spinning wheels represented a major improvement in yarn manufacturing, they still required manual handling. So, mills were invented in 1740, taking textile production one step further. This machinery integrated spinning wheels and other tools like power looms, which were used to weave clothes and tapestry, and created fully automated processes, thus removing the need for human intervention. The introduction of automated equipment for spinning and weaving also saw clothing manufacturing turn from small-scale craft production into an industrialized factory-centric enterprise, as was the case with many other activities during the Industrial Revolution. The Flying Shuttle One of the most notable breakthroughs of the Industrial Revolution , the flying shuttle, was invented by John Kay in 1733. Although by the name of it, one would think it’s something related to aviation, this small device is, in fact, a mechanism that automated and sped up the weaving process while also making it possible for weavers to create a broader range of fabrics and textiles. the Industrial Revolution the Industrial Revolution With it, production and efficiency increased considerably, so manufacturers could meet the growing demand for textiles. The Sewing Machine The invention of this popular device is attributed to English inventor Thomas Saint, who developed the earliest design in the late 18th century, but several other investors brought their contributions and perfected the model over the following decades, adding new features to it. The first machines were large and heavy, made out of wood and metal, so they weren’t destined for home use, being found exclusively in tailor shops and on factory floors. Later improvements made them lighter, more affordable, and more efficient, and their prevalence revolutionized the textile and fashion industry by enhancing speed and productivity and enabling the mass production of clothing. Nylon and Lycra While cotton and other materials made from natural fibers are still commonly used in the textile industry, the introduction of synthetic fabrics like nylon and spandex opened up a world of possibilities in the fashion world. Nylon, the first completely synthetic fiber, was patented by chemists at DuPont Chemicals in 1935 by combining different types of chemicals like amine, hexamethylene diamine, and adipic acid. Mass production began in 1938, and the invention became instantly popular with the public. Then spandex was invented in 1954, launched on the market under the name Lycra, and used to create soft and comfortable form-fitting garments. 3D Printing Fast forward to today, and we have 3D printing making waves in the textile sector. Initially used in the medical industry, this advanced technology has now made its way into the fashion sphere, facilitating the creation of custom-made garments and accessories and the production of innovative designs and textures. 3D printing 3D printing With the textile and clothing industry constantly evolving, we’re bound to see many more innovations emerge in the future, transforming practices, products, and even people’s relationships with fashion.

From Needles to Nylon: A Journey Through the History of Textile and Clothing Innovations

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

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