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Nostalgia is everywhere in marketing today. Legacy fonts, throwback packaging, retro tech revivals, all deployed with the hope that sentimentality alone can stir emotion and move product. But heres the problem: nostalgia is novelty, and novelty, by definition, doesnt last. Its seductive. It gets clicks. Its emotionally charged, delivering a quick dopamine hit. And lets be real, its easy. But slapping an old label on a can isnt strategy. When brands engage nostalgia at a surface level, they often do more harm than good. Take Coca-Cola’s recent Diet Cherry Coke revival. It was cool for a week, but, for me, it lacked any meaningful connection to what the brand has stood for across decades. It missed the opportunity to ask: What did this product mean to people then? What does it mean now? And how could that story be told with modern nuance? Without that narrative bridge, the move felt shallow. Worse, it overlooked the real power of brand heritage: a blend of familiarity and relevance that creates long-term equity, not fleeting impressions. Reimagining legacy This is where modern heritage comes in. It isnt about retro aesthetics or recycling old logos. Its a strategic act of excavation, going deep into a brands history to uncover what made it resonate in the first place. Sometimes that means literal digging: storage units, eBay listings, forgotten ad reels. The goal isnt to replicate the past, its to retool it for now. And thats hard to do well. Plenty of brands stumble when they go too far back or misinterpret their legacy. Like when Kelloggs Canada revived retro packaging and mascots for a limited edition run of Froot Loops, Frosted Flakes, and other classics. As a 90s kid, I felt the instant pang of nostalgia. But was there any meaningful connection between the throwback design and the bigger picture world Kelloggs is building today? Nope. Did it invite consumers on a journey to where its heading next? Not really. Thats the trap: when nostalgia leans on a version of the brand that no longer resonates, it creates confusion, not clarity. The most effective brand work strikes a balance: it leans into familiar cues but evolves them with purpose. It doesnt just bring back old stuffit makes history useful again. When we rebranded Sizzler, we didnt reinvent the wheel. We uncovered what was already there: a family-first spirit; a charming mid-century mascot; buried design cues that still felt unmistakably Sizzler. The goal wasnt to re-create the past, but to make it feel current. And thats the key distinction: nostalgia copies. Modern heritage builds. This approach also brings clarity. It makes creative decisions easier, because theres a right answer. When you anchor a brand in its authentic legacy, you stop chasing trends. You build with intention, using assets no other brand can replicate because no one else lived that story. Guinness hasnt changed its harp in over 250 years; it has just evolved it. Thats what timelessness looks like. The modern heritage playbook So, in a trend-driven world, how can brands strike the right balance between timeless legacy with modern relevance that cuts through? One critical aspect of modern heritage is its ability to tell rich, multidimensional, and ever-evolving stories. Where nostalgia leans on artifactslogos, packaging, ad slogansmodern heritage pulls from narrative. It asks deeper questions. Who were we? Who are we now? What stories still matter? Here are three principles we use when revitalizing a brand through this lens: 1. Lead with the story, not just the aesthetic Design is an output, not a starting point. Start with the brands origin story. Who has it always been? What has it always stood for? What stories and symbols have mattered most over time? Sometimes those stories are buried. Thats why you go beyond Pinterest boards: into archives, libraries, historical societies, even conversations with long-time employees. Once the right story is uncovered, design becomes a powerful tool to tell it: visually, verbally, and emotionally. 2. Dont get stuck in one era. Curate the best of every chapter Some brands try to plant a flag in a single decade, usually the one marketers think will trend. But being an 80s brand or a Y2K brandoften aimed at Gen Z and millennials with a wink and a pixelated smiley facelimits longevity. Modern heritage takes a more layered approach. It draws from across time, curating the best, most iconic moments to create a flexible identity that doesnt feel trapped. That allows brands to celebrate the most iconic, lovable parts of their legacy without getting stuck there. 3. Own what only you can own This is the most important, and most overlooked, point. Your heritage isnt just a design system; its your story and your competitive edge. When used well, your past becomes a brand asset no one else can duplicate. Thats how you stand out: by being undeniably, authentically you. The power of modern heritage More than a methodology, though, modern heritage is a mindseta search for timelessness. One thats increasingly valuable as brands need to cut through digital clutter, avoid design whiplash, and maintain relevance without losing soul. For legacy brands hoping to stay relevant, and new brands aiming to build something lasting, the future wont be found in trendy gradients or meme fonts. Its in the archives. In the origin stories. In the truths they already own.
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E-Commerce
Imagine walking down the street and stumbling upon a soccer match, except the competitors are robots, not humans. Would you be surprised to learn that this isnt a Steven Spielberg futuristic movie set but a real-life athletic competition? The first-ever World Humanoid Robot Sports Games kicked off in Beijing on August 15, hosted by the citys municipal government in hopes of promoting Chinas technological advancements and fostering further dialogue internationally. Lets take a look at the details of this event and muse about what it might mean for our future: Is there any precedent for this event? While this is the first-ever full-scale event of its kind, it is coming on the heels of the 10th World Robot Conference in Beijing, held August 8-12, according to the Asia Times. Thats not the only precursor event held in the Chinese capital. In April, 21 humanoid robots participated in the first-ever half-marathon. Only six completed the race, which seems rather relatable. The Tien Kung Ultra robot, created by China’s National and Local Co-built Embodied AI Robotics Innovation Center, finished the course in 2 hours and 40 minutes. In Hangzhou in May, the China Media Group World Robot Competition-Mecha Fighting Series took place. Four Unitree G1s lived out the rockem-sockem robot dream. Back in Beijing in June, a practice soccer match was held with robots facing off three-on-three. It was the first time AI was utilized instead of human intervention. The robots were even equipped with the ability to recover from falls, but that technology has room for improvement, as some of the robots had to be taken off the field on stretchers. Where is the event taking place? The competition is being held in two very special Olympic areas in Beijing. The first is the National Stadium, known as the Bird’s Nest. The second is the National Speed Skating Oval, aka the Ice Ribbon. More than 500 humanoid robots across 280 teams from 16 countries are throwing down in 26 events. What does this mean for the future? Technology is moving fast, and its hard not to have visions of ominous science fiction movies in your head when thinking about the ramifications of artificial intelligence. This event will allow the robots out of the lab and into a big stress test to look for errors in programming and design. Heres hoping we can create a world where robots support human innovation instead of, well, taking over and murdering us. A happy ending of coexistence and cooperation might not sell at the box office, but it would be a much better reality to live in. You can check out a preview of the event in the embedded video below.
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E-Commerce
Imagine a busy train station. Cameras monitor everything, from how clean the platforms are to whether a docking bay is empty or occupied. These cameras feed into an AI system that helps manage station operations and sends signals to incoming trains, letting them know when they can enter the station. The quality of the information that the AI offers depends on the quality of the data it learns from. If everything is happening as it should, the systems in the station will provide adequate service. But if someone tries to interfere with those systems by tampering with their training dataeither the initial data used to build the system or data the system collects as its operating to improvetrouble could ensue. An attacker could use a red laser to trick the cameras that determine when a train is coming. Each time the laser flashes, the system incorrectly labels the docking bay as occupied, because the laser resembles a brake light on a train. Before long, the AI might interpret this as a valid signal and begin to respond accordingly, delaying other incoming trains on the false rationale that all tracks are occupied. An attack like this related to the status of train tracks could even have fatal consequences. We are computer scientists who study machine learning, and we research how to defend against this type of attack. Data poisoning explained This scenario, where attackers intentionally feed wrong or misleading data into an automated system, is known as data poisoning. Over time, the AI begins to learn the wrong patterns, leading it to take actions based on bad data. This can lead to dangerous outcomes. In the train station example, suppose a sophisticated attacker wants to disrupt public transportation while also gathering intelligence. For 30 days, they use a red laser to trick the cameras. Left undetected, such attacks can slowly corrupt an entire system, opening the way for worse outcomes such as backdoor attacks into secure systems, data leaks, and even espionage. While data poisoning in physical infrastructure is rare, it is already a significant concern in online systems, especially those powered by large language models trained on social media and web content. A famous example of data poisoning in the field of computer science came in 2016, when Microsoft debuted a chatbot known as Tay. Within hours of its public release, malicious users online began feeding the bot reams of inappropriate comments. Tay soon began parroting the same inappropriate terms as users on X (then Twitter), and horrifying millions of onlookers. Within 24 hours, Microsoft had disabled the tool and issued a public apology soon after. The social media data poisoning of the Microsoft Tay model underlines the vast distance that lies between artificial and actual human intelligence. It also highlights the degree to which data poisoning can make or break a technology and its intended use. Data poisoning might not be entirely preventable. But there are commonsense measures that can help guard against it, such as placing limits on data processing volume and vetting data inputs against a strict checklist to keep control of the training process. Mechanisms that can help to detect poisonous attacks before they become too powerful are also critical for reducing their effects. Fighting back with the blockchain At Florida International Universitys Sustainability, Optimization, and Learning for InterDependent networks (SOLID) lab, we are working to defend against data poisoning attacks by focusing on decentralized approaches to building technology. One such approach, known as federated learning, allows AI models to learn from decentralized data sources without collecting raw data in one place. Centralized systems have a single point of failure vulnerability, but decentralized ones cannot be brought down by way of a single target. Federated learning offers a valuable layer of protection, because poisoned data from one device doesnt immediately affect the model as a whole. However, damage can still occur if the process the model uses to aggregate data is compromised. This is where another more popular potential solutionblockchaincomes into play. A blockchain is a shared, unalterable digital ledger for recording transactions and tracking assets. Blockchains provide secure and transparent records of how data and updates to AI models are shared and verified. By using automated consensus mechanisms, AI systems with blockchain-protected training can validate updates more reliably and help identify the kinds of anomalies that sometimes indicate data poisoning before it spreads. Blockchains also have a time-stamped structure that allows practitioners to trace poisoned inputs back to their origins, making it easier to reverse damage and strengthen future defenses. Blockchains are also interoperablein other words, they can talk to each other. This means that if one network detects a poisoned data pattern, it can send a warning to others. At SOLID lab, we have built a new tool that leverages both federated learning and blockchain as a bulwark against data poisoning. Other solutions are coming from researchers who are using prescreening filters to vet data before it reaches the training process, or simply training their machine learning systems to be extra sensitive to potential cyberattacks. Ultimately, AI systems that rely on data from the real world will always be vulnerable to manipulation. Whether its a red laser pointer or misleading social media content, the threat is real. Using defense tools such as federated learning and blockchain can help researchers and developers build more resilient, accountable AI systems that can detect when theyre being deceived and alert system administrators to intervene. M. Hadi Amini is an associate professor of computing and information sciences at Florida International University. Ervin Moore is a Ph.D. student in computer science at Florida International University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
Category:
E-Commerce
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