ChatGPT's update brings us one step closer to living in the movie Her
When it came out in 2013, the movie Her was set in the 'slight future.'
That 'slight future' is, astonishingly, here. After all, AI-powered chatbots actually are a real thing now, and people are falling in love with them. It's remarkable that the 2013 Spike Jonze sci-fi romance about a lonely mustachioed man, played by Joaquin Phoenix, talking to a robot in an earbud proved to be so prescient.
Still, much of our AI-dominated future is still taking shape.
Vox Culture
Culture reflects society. Get our best explainers on everything from money to entertainment to what everyone is talking about online. Email (required)
Sign Up
By submitting your email, you agree to our Terms and Privacy Notice . This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
AI will certainly shape the culture of the next 25 years, but its biggest transformations to our world still remain to be seen.
Even in its most rudimentary iterations, the technology has caused us to question our grip on reality. One of the first moments that AI broke through as a cultural force was when a fake image of Pope Francis wearing a white puffy Balenciaga coat fooled the entire internet a couple of years ago (if only because it avoided the common AI mistake of six-fingered hands). On the darker end of the spectrum, nonconsensual deepfake porn created using AI has become nearly impossible to keep offline, prompting new legislation to be enacted banning the practice. As people turn to AI for companionship, just like Phoenix's character did in Her, some of them are losing touch with reality, becoming delusional, and in one case, an AI chatbot has been linked to a teen's suicide. These developments inevitably seem to signal that a more seismic shift is on the way.
AI will certainly shape the culture of the next 25 years, but its biggest transformations to our world still remain to be seen.
How will all of that play out? Well, that's the thing about the future: We won't know until it's barrelling down on us. In cultural terms, that could mean we're watching movies that are all at least partially AI-generated, hanging out with AI friends online, and listening to AI-generated music more than human bands.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Business Insider
3 minutes ago
- Business Insider
Tech guru Erik Gordon warns investors will 'suffer' far more from the AI craze than the dot-com crash
A company's stock plunge shows how the financial fallout of the AI boom stalling will be much greater than the dot-com bust, tech guru Erik Gordon told Business Insider. Gordon, an entrepreneurship professor who researches financial markets and technology at the University of Michigan's Ross School of Business, has previously called the AI boom an " order-of-magnitude overvaluation bubble." Other investors have said tech stocks surging on AI bullishness will crash like dot-com companies in the early 2000s, but some, such as Kevin O'Leary, have dismissed the comparison. Gordon contrasted the market values of the online pet-food retailer that became what he called the "poster bozo" of dot-com mania, and CoreWeave, an AI infrastructure startup that went public in March. Nvidia-backed CoreWeave's shares have fallen 30% over the past two days, wiping about $23 billion from its market cap, showing how "more investors will suffer than suffered in the dot-com crash, and their suffering will be more painful" in a bursting AI bubble, Gordon said. The fall came after its latest earnings showed widening losses and infrastructure constraints. backed by Amazon and several renowned VC firms, secured a market value of $410 million at its peak in February 2000. But within the next 12 months, the company declared bankruptcy and said it would liquidate its assets, and its stock was delisted. "If you assume all $410 million was lost, the loss was tiny compared to what we might see in AI," Gordon said. CoreWeave shows how sudden and significant losses can be for shareholders, Gordon said. The loss to its market cap is about 56 times peak market cap. CoreWeave stock still closed at $105 a share on Wednesday, more than double its listing price of $40. CoreWeave didn't immediately respond to a request for comment from Business Insider. CEO Michael Intrator said in a statement accompanying the earnings that they show "continued momentum across every dimension of our business." The collapse of the dot-com bubble saw the S&P, including dividends, drop by around 9% in 2000, 12% in 2001, then 22% in 2002. Scores of startups filed for bankruptcy, and thousands of tech workers lost their jobs. Tech titans make up a large chunk of the US stock market's value, and their profits and market dominance have made them mainstays of retirement portfolios and pension funds. In 2022, Gordon told Business Insider that more people were invested in AI than in dot-com companies 25 years ago. He predicted there would be " more bowls of spaghetti" after the AI boom burst, as people who got burned by the slump would cook at home to save money and cut costs wherever possible. Speaking to Business Insider last week, O'Leary said the AI boom wasn't the "same hype that the internet bubble was, because today, you actually can see the productivity and measure it on a dollar-by-dollar basis."
Fast Company
32 minutes ago
- Fast Company
Why AI is vulnerable to data poisoning—and how to stop it
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 data—either the initial data used to build the system or data the system collects as it's operating to improve—trouble 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 University's 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 doesn't 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 solution— blockchain —comes 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 interoperable—in 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 it's 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 they're being deceived and alert system administrators to intervene.
Yahoo
34 minutes ago
- Yahoo
Enterprise Demand for Hyper-Personalization Fuels Industry Expansion
The digital human avatar market will grow by USD 27.43 billion from 2024 to 2029 at a 43.9% CAGR. Revolutionized AI, demand for hyper-personalization, and 3D graphics boost growth. The report features market size, key vendors like AWS, Google, Meta, upcoming trends, and growth challenges, aiding strategy. Dublin, Aug. 15, 2025 (GLOBE NEWSWIRE) -- The "Digital Human Avatar Market 2025-2029" has been added to offering. The digital human avatar market is projected to expand by USD 27.43 billion between 2024 and 2029, exhibiting a robust CAGR of 43.9% throughout this period. This comprehensive report delivers a holistic view of the market, encompassing market size, forecasts, emerging trends, growth catalysts, and potential challenges. It also features an in-depth vendor analysis covering approximately 25 key market players. Offering the latest insights into the prevailing market dynamics, this analysis highlights transformative advancements in AI and conversational interfaces, rising enterprise demands for enhanced customer experiences, and the democratization of real-time 3D graphics and animation technologies as significant growth drivers. This report identifies the surge of industry-specialized digital human avatars as a pivotal growth driver for the coming years. Key contributing factors include the rise of AI-generated content, the creator economy for digital humans, and their seamless integration into immersive platforms like AR, VR, and the Metaverse. The vendor analysis aims to bolster client market positioning, with detailed insights into leading players such as Amazon Web Services Inc., DeepBrain AI, Didimo, Epic Games Inc., Google LLC, Meta Platforms Inc., Microsoft Corp., and many others. The report also explores upcoming trends and challenges, aiming to equip companies with strategic insights to capitalize on emerging opportunities. A meticulous synthesis of data from multiple sources, this report evaluates key parameters like profit, pricing strategies, competitive landscape, and promotional activities. The data is the result of thorough primary and secondary research, ensuring a comprehensive and reliable depiction of the market environment. Key Topics Covered: Executive Summary Market Overview Market Analysis Price Sensitivity, Lifecycle, and Factors Criticality of Inputs and Differentiation Disruption Factors Drivers and Challenges Market Landscape Market Ecosystem Market Characteristics Value Chain Analysis Market Sizing Market Definition Segment Analysis and Forecast Historic Market Size 2019 - 2023 Analysis (Global, Product, Technology, Type, Application, Geography) Five Forces Analysis Five Forces Summary Bargaining Powers, Threats, and Rivalry Market Segmentation by Product Market Segments and Opportunities Market Segmentation by Technology Market Segments and Opportunities Market Segmentation by Type Market Segments and Opportunities Market Segmentation by Application Market Segments and Opportunities Customer Landscape Customer Landscape Overview Geographic Landscape Geographic Segmentation and Comparison Drivers, Challenges, and Opportunity/Restraints Market Drivers and Challenges Impact and Opportunities/Restraints Competitive Landscape Overview, Disruption, and Industry Risks Competitive Analysis Companies Profiled and SWOT Analysis For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. CONTACT: CONTACT: Laura Wood,Senior Press Manager press@ For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900Sign in to access your portfolio
