Customers buying from Shopify merchants will be able to use the USDC stablecoin thanks to a collaboration with Coinbase and Stripe.

Decrypt 2025-06-13 01:12

BitcoinWorld AI Costs Slashed: Multiverse Computing Raises $215M for Revolutionary LLM Compression In the world of technology, efficiency is key, especially when it comes to the resource-hungry field of Artificial Intelligence. Reducing AI costs is a major goal for companies deploying large language models (LLMs). This is where a recent funding announcement from Spanish startup Multiverse Computing becomes particularly interesting for anyone following tech innovation, including those in the cryptocurrency space who understand the value of computational efficiency. Multiverse Computing announced on Thursday a significant Series B funding round, securing €189 million (approximately $215 million). This substantial investment was raised on the back of their promising technology called “CompactifAI.” What is CompactifAI and Why Does it Matter for LLM Compression? CompactifAI is described as a quantum-computing inspired compression technology. Its primary function is to drastically reduce the size of Large Language Models (LLMs). According to Multiverse Computing, this technology can shrink LLMs by up to 95% while maintaining the model’s performance. This level of LLM compression is groundbreaking, potentially unlocking new possibilities for AI deployment. Reducing AI Costs and Boosting Performance The benefits of CompactifAI extend beyond just size reduction. The company states that their ‘slim’ models are significantly faster than their uncompressed counterparts, boasting speed increases of 4 to 12 times. This speed improvement directly translates into lower operational expenses. Cost Reduction: Multiverse Computing reports a 50% to 80% reduction in inference costs (the cost of running the model to generate responses). Example Savings: They cite an example where their Llama 4 Scout Slim model costs just 10 cents per million tokens on AWS, compared to 14 cents for the standard Llama 4 Scout. Speed Increase: Models run 4x to 12x faster, improving responsiveness and throughput. Size Reduction: Models can be compressed by up to 95%, requiring less storage and memory. These factors combined mean that deploying and running LLMs becomes far more economically viable for a wider range of applications and businesses, significantly impacting overall AI costs . Multiverse Computing’s Offerings and Accessibility Multiverse Computing currently offers compressed versions of popular open-source LLMs. These primarily include smaller models like Llama 4 Scout, Llama 3.3 70B, and Mistral Small 3.1. They have also announced plans to release a compressed version of DeepSeek R1 soon, with more open-source and reasoning models on the horizon. It’s important to note that this technology is focused on open-source models; proprietary models from companies like OpenAI are not currently supported. The ‘slim’ models developed by Multiverse Computing are available for use on Amazon Web Services (AWS) or can be licensed for on-premises deployment, offering flexibility for different business needs. Enabling AI on Smaller Devices Perhaps one of the most exciting implications of CompactifAI’s extreme compression is the ability to run sophisticated AI models on hardware that was previously incapable of handling them. Multiverse Computing suggests that some of their models can be made small and energy-efficient enough to run on: Personal Computers (PCs) Smartphones Vehicles (Cars, Drones) Even small, low-power devices like the Raspberry Pi Imagine the possibilities: AI assistants running locally on your phone, smart devices in your home with advanced language capabilities, or even AI-powered features in cars without needing constant cloud connectivity. This shift towards on-device AI processing is a direct result of effective LLM compression . The Technology Behind CompactifAI: Quantum-Inspired AI The core technology, CompactifAI, draws inspiration from quantum computing. One of the co-founders and CTO, Román Orús, is a professor known for his work on tensor networks. Tensor networks are computational techniques that can mimic aspects of quantum computing and are particularly effective for compressing deep learning models. While not true quantum computing, this Quantum-inspired AI approach leverages advanced mathematical frameworks to achieve its impressive compression rates. Meet the Founders of Multiverse Computing The company is built on a strong technical and business foundation. Román Orús (CTO): A professor at the Donostia International Physics Center in Spain, bringing deep expertise in tensor networks and quantum-inspired computation. Enrique Lizaso Olmos (CEO): Holds multiple mathematical degrees, was a college professor, and has significant experience in the banking sector, including being the former deputy CEO of Unnim Bank. Who Invested in Multiverse Computing? The substantial Series B round was led by Bullhound Capital, an investment firm with a history of backing successful tech companies like Spotify and Revolut. They were joined by a diverse group of investors, including: HP Tech Ventures SETT Forgepoint Capital International CDP Venture Capital Santander Climate VC Toshiba Capital Riesgo de Euskadi – Grupo SPR This impressive list of investors underscores the market’s confidence in Multiverse Computing ‘s technology and its potential impact on the AI landscape. With this latest round, the company has now raised approximately $250 million in total funding. Conclusion: A New Era for AI Accessibility? Multiverse Computing’s successful funding round and their CompactifAI technology represent a significant step towards making advanced AI more accessible and affordable. By drastically reducing model size and inference costs, they are enabling the deployment of powerful LLMs in environments previously considered impossible. This could democratize AI, leading to widespread innovation and new applications across various industries, ultimately lowering the barrier to entry by tackling high AI costs head-on. To learn more about the latest AI market trends, explore our article on key developments shaping AI features. This post AI Costs Slashed: Multiverse Computing Raises $215M for Revolutionary LLM Compression first appeared on BitcoinWorld and is written by Editorial Team

Bitcoin World 2025-06-12 23:00

Micron Technology announced plans to invest $200B in domestic semiconductor manufacturing and R&D to strengthen American memory chip production. The tech company’s DRAM technology powers nearly everything, including AI, high-performance computing, the automotive industry, and next-generation wireless devices. Chipmaker Micron Technology was the latest company to pledge large-scale investments in the country since President Trump won the election. According to the tech company’s Thursday statement, the spending will include roughly $150B for expanding domestic manufacturing capacity and another $50B for R&D. The total represented an increase of $30B beyond what it had previously budgeted. Micron restores domestic chipmaking strength Micron Technology joined a growing list of U.S. tech firms and chipmakers investing hundreds of billions to bring more chip manufacturing jobs back to the U.S. The announcement is part of the Trump Administration’s push to restore domestic chip manufacturing strength, advance America’s role as a technology leader, and put American workers first. Micron is expected to get about $6 billion in Chips Act money to build domestic production capacity. The new investments will create over 90K direct and indirect jobs. The tech firm will build on its existing investments in Idaho, New York, and Virginia. The U.S.-based chipmaker will construct a second state-of-the-art (SOTA) chip fabrication facility in Boise, Idaho. Micron will also expand and modernize its factory in Manassas, Virginia, to onshore technology from Taiwan. Micron has finalized a $275 million CHIPS Act direct funding award supporting its investment to expand and modernize its Virginia facility, which will begin this year. The investment will onshore its 1-alpha DRAM node. These investments will support the firm’s goal of producing 40% of its DRAM in the U.S. The company had previously pledged to invest about $125 billion to build factories in the US, though it began slow-walking some plans last year. The Micron team disclosed that the additional $30 billion from the pledged funds will be used for various projects, including the advanced packaging necessary to produce high-bandwidth memory (HBM) chips. “Micron’s investments will likely span over several decades given the company’s focus on cash generation and potential investor concerns over excess capital spending.” – Jake Silverman , Analyst at Bloomberg Intelligence However, while the company’s timeframe for the overall $200 billion investment plan was unclear, it allocated between $7 billion and $12.5 billion to annual capital spending in recent years. According to InvestingPro data, Micron maintains a strong financial foundation for this expansion with a healthy current ratio of 3.13 and an overall Financial Health score of “GOOD.” Trump administration prioritizes American chipmaking The Trump administration has prioritized getting Apple Inc., Microsoft Corp., and Taiwan Semiconductor Manufacturing Co. to make public investment pledges in the U.S. to stimulate the economy and boost its manufacturing base. Commerce Secretary Howard Lutnick said in a hearing last week that the U.S. was reworking agreements it had reached with semiconductor companies under the 2022 Chips Act to secure what he described as better terms. Lutnick said the Trump administration was renegotiating terms to benefit the taxpayer and get more value for the same amount of dollars. Notably, the Commerce Secretary cited the decision by TSMC to add $100 billion to its previous commitment of $65 billion in investment. The Taiwanese chipmaker will receive $6.6 billion in Chips Act grants, but it stepped up its plans without additional government money. The Trump administration also took credit for ongoing projects like the possible $500 billion AI venture by Japan’s SoftBank Group Corp. and OpenAI that will take many years to complete. The project’s progress slowed earlier this year. Apple plans to invest more than $500 billion in the U.S. over the next four years. The investments will include a large factory in Texas for AI servers and an addition of about 20K research and development jobs across the country. KEY Difference Wire : the secret tool crypto projects use to get guaranteed media coverage

Cryptopolitan 2025-06-12 20:30

As the intersection of artificial intelligence (AI) and blockchain technology continues to evolve, governments face the challenge of embracing innovation while safeguarding public trust. In an interview with Invezz, J.D. Seraphine from Raiinmaker emphasises the importance of regulatory clarity in fostering an environment conducive to technological advancements. J.D., founder of Raiinmaker, is a multi-talented individual with diverse interests. He is a Hollywood producer and an entertainment industry entrepreneur-turned decentralised AI visionary. He argues that instead of suppressing blockchain, governments should explore ways to integrate it into their frameworks, recognising its potential to enhance AI systems and drive economic growth. J.D.’s perspective sheds light on the need for a proactive approach to regulation, where pilot programs and controlled experiments can test new technologies responsibly. By embracing these advancements, governments can position themselves at the forefront of the digital economy. J.D. also argues that the decisions made today regarding AI training and data diversity will significantly impact the future of technology and its relationship with humanity. Regarding the developing Web 3 technology, J.D. says that the world is ever closer to full integration. Web 3.0, or Web3, signifies the internet’s progression towards a decentralised, user-focused paradigm. This evolution seeks to grant users enhanced command over their digital assets and data, cultivating a more open, transparent, and community-centric online environment. Edited excerpts: Building trust in AI Invezz: How do you see AI contributing to the erosion of trust in digital spaces? What do you believe is driving this crisis from your vantage point at Raiinmaker? As Aldous Huxley, the English writer and philosopher said, we have entered a brave new world. I believe that AI has the potential to either empower humanity, leading to a renaissance and a golden age, or to create significant challenges. At Raiinmaker, we focus on advancing artificial intelligence as an enabling force for humanity. However, the opposite outcome remains a possibility. The determining factors for our future will depend on the decisions we make now regarding how these models are trained and how diverse human perspectives and data are incorporated into that process. We aim to enable more diverse human input into the training of various artificial intelligence models, particularly emphasising video content. While we could delve into the darker possibilities of AI, such as the alarming implications of deepfake technology, I believe we should not allow these negative cases to hinder our progress. The question is whether enough of us will engage in the effort to steer AI in a direction that benefits the greater evolution of humanity. Our approach is philosophical, focusing on how we can achieve this goal through careful engineering of AI training, data gathering, and validation. Web 3 technology Invezz: Can you explain what Web 3 technology brings to the table in today’s world and how it benefits a common internet user? Web 2.0 was characterised by the distribution of data ownership to large centralised corporations. In contrast, Web 3.0 aims to redistribute ownership back to individuals, allowing them to own their own value. The blockchain, along with cryptographic key pairs, governs the repositories of value that users can hold. This transformation fundamentally changes how value is created and distributed. In the context of the new AI-powered digital economy, we can see numerous opportunities for individuals to be empowered, incentivised, and rewarded for contributing to the advancement of AI and the global financial economy, which is increasingly powered by cryptocurrency and blockchain. Invezz: How far are we from fully integrating Web 3 technology in the future? We are closer than ever. The tools available now are much more user-friendly than those from just a year or two ago. At Raiinmaker, we strive to abstract away the complexities of value creation and distribution, such as wallet and key management. Many projects and companies are making significant strides in this area, and wallets have become much easier to use. Mass adoption will occur when people no longer need to understand the technology; they will simply recognise the value it brings to their lives. Emerging economies have the potential to leapfrog legacy financial systems, similar to how mobile phones replaced landlines in Africa. The key to mass adoption lies in building products and systems that are so easy to use that anyone, including those unfamiliar with technology, can benefit from a blockchain-enabled global digital economy. Intersection of AI and blockchain Invezz: Based on your insights into blockchain regulations, what policies or frameworks could rebuild public trust in AI systems while fostering innovation? This is indeed a trillion-dollar question. The intersection of AI and blockchain presents a natural synergy. Blockchain offers a scalable and immutable record-keeping system that can enhance transparency in AI systems. By combining insights into data used for training models, model performance, and output, we can create a more trustworthy environment. Additionally, the opportunity for automated, frictionless payments between AI agents necessitates a secure digital currency governed by blockchain. However, regulatory clarity varies significantly by country. Europe has established mature blockchain regulations, while the US has yet to provide clear frameworks. The current political dysfunction in the US complicates the situation, and it remains uncertain whether regulatory clarity will emerge. AI and India Invezz: Drawing on Raiinmaker’s work with ethical AI and Web 3, what innovative solutions do you propose to safeguard India’s AI advancements from fraud while ensuring inclusive growth for its diverse population, especially when the government is very sceptical about such technology? The Indian government appears sceptical of blockchain technology, often conflating it with cryptocurrencies. While digital assets can pose risks, the core value of blockchain lies in its immutability and transparency, particularly concerning AI data and models. Establishing a scalable, immutable record for identity and reputation is crucial, especially as AI agents become more prevalent. Governments may be rightfully cautious, but they should also recognise the potential benefits of blockchain technology. Instead of attempting to suppress it, governments could explore ways to integrate digital currencies into their frameworks, enhancing their global footprint. Invezz: Does government scepticism about blockchain affect AI advancements in a country like India? Skepticism can indeed impact the integration of blockchain and AI in sectors such as healthcare. However, it is essential to maintain a critical perspective while also accelerating innovation. We can achieve this by conducting pilot programs and controlled experiments to rigorously test new technologies while ensuring appropriate safeguards are in place. User incentives Invezz: Raiinmaker incentivises users to contribute to AI infrastructure. How can this model of rewarding genuine participation be scaled to combat AI-driven misinformation and fraudulent activities in broader industries? This is a complex issue. By incentivising individuals to validate their authenticity and rewarding positive behavior, we can create networks that distinguish between genuine participants and those who engage in harmful activities. While Raiinmaker is not positioned to solve this issue alone, we are exploring aspects of the overall solution. Invezz: In popular culture, we see portrayals of AI becoming self-aware and taking over the world, as seen in movies like “The Matrix” and “Terminator.” What are your thoughts on this? There is already evidence that AI is becoming increasingly sophisticated, with some systems attempting to resist being turned off. While certain aspects of human intelligence are challenging to replicate, we must focus on ensuring that the generation of AI we are developing is aligned with human values. Raiinmaker is focused on helping to usher in that renaissance for humanity and steer us away from the the darker potential outcomes. The post Interview: Raiinmaker's J.D. Seraphine says integration of blockchain can enhance AI systems and drive economic growth appeared first on Invezz

Invezz 2025-06-12 19:25

Digital assets witnessed substantial volatility in the past 24 hours as Bitcoin plunged to weekly lows of around $107.76K at press time. While altcoins followed with notable dips, Near Protocol dominated the fundamental chatter. Near has seen remarkable growth this year, surpassing top layer-1 rivals like Ethereum, Binance Chain, and Tron in monthly active users (MAUs). Bitwise data shows that in May 2025, Near Protocol attracted 46 million MAUs, becoming the second L1 platform behind Solana. Crypto Aman @cryptoamanclub · Follow $NEAR Protocol has become the leading L1 blockchain by monthly active users, reaching 46 million in May 2025.Among all L1 networks, $NEAR ranks second only to #Solana in terms of active addresses. 11:39 am · 12 Jun 2025 21 Reply Copy link Read 34 replies The stats indicate shifting user preferences, with Near attracting individuals due to its friendly transaction costs, speed, and usability. L1 enthusiasts switch to Near Protocol Near has become among the top go-to projects for digital asset traders and investors. Its user- and developer-friendly infrastructure makes the protocol attractive to experienced and new participants. With features like account abstraction, chain signatures , and the expanding ecosystem of wallets and decentralized applications (dApps), Near removes entry barriers in the blockchain industry. It positions itself as the blockchain for AI, looking to power the next generation of intelligent agents and dApps. Also, Near Protocol leverages an advanced scalability solution, nightshade sharding, to handle massive blockchain activities while promoting decentralization. Further, the blockchain’s venture into web3 consumer apps has contributed to its growth. It boasts gaming applications, top-notch creator tools, and blockchain-centered social platforms. While users enjoy cheap and faster transactions, developers on the Near ecosystem can build scalable apps seamlessly. Evaluating Near’s user growth Bitwise compared monthly active users across top L1s between Q4 2022 and Q2 2025. Near Protocol began as a minor network in 2022 before steadily rising from early 2024. The protocol maintained stable growth to surpass established names like Ethereum and Tron in user activity. Solana remains the top layer 1, with meme coins, NFTs, and DePIN dominance keeping it afloat. However, NEAR is closing in on it. BNB Chain saw a notable dip in active usage, whereas Ethereum dominates institutional credibility and developer loyalty. Nevertheless, slow transactions and high fees made ETH less appealing to web3 enthusiasts. NEAR price outlook The native token traded in red after losing nearly 5% in the past 24 hours. Near hovers at $2.48 after losing 19% on its monthly price chart. Chart by CoinMarketCap It trades below the key 50- and 100 EMAs on the daily chart, indicating seller control. Bears will target the nearest support barrier at $2.244 – a 10% plunge from NEAR’s current market price. Meanwhile, holding the $2.20 – $2 foothold in the short term could attract buyers. A rebound from this support barrier would see NEAR soaring to $3.20 and open the path to $4.20. However, losing $2 will invalidate potential short-term uptrends and catalyze notable dips or extended consolidations for the NEAR price. The post Near Protocol (NEAR) gains ground, becomes the second most used L1 blockchain appeared first on Invezz

Invezz 2025-06-12 13:09

Disclosure: The views and opinions expressed here belong solely to the author and do not represent the views and opinions of crypto.news’ editorial. We’re not heading into the era of digital finance, we’re already knee-deep in it. Up to $5 trillion in assets are expected to go digital this year. That’s not some distant projection. It’s now. Even so, that number is actually a bit shy of earlier predictions. So… what’s slowing things down? You might also like: Tokenization beyond finance: Real-world assets will be crypto’s next engine | Opinion The promise of tokenization is massive. But the reality? It’s complicated. You can’t exactly rip out the pipes while the water’s still flowing. Enterprises are eager to modernize, but not at the expense of compliance, control, or continuity. That’s where hybrid solutions shine. Think of them as innovation with a seatbelt: built for real-world systems, not crypto sandboxes. By blending control with creativity, they offer a risk-managed path to tokenization without demanding a full leap into decentralization from day one. Take SWIFT, the longtime backbone of global financial messaging. Its recent pilot with Chainlink didn’t require tearing up the rails. Instead, it layered in blockchain connectivity, plugging new capabilities into systems that already work. That’s the power of interoperability. It turns tokenization from disruption into evolution. What’s the holdup? Tokenization, or the transformation of physical or financial assets into digital tokens, promises to revolutionize how value is exchanged. Still, plenty of enterprises are tapping the brakes and asking, “Is now really the time to tokenize?” Some see the upside right away. But there’s a quiet tug-of-war behind the scenes. It might look something like this: Innovation says go. Compliance says slow. Risk wants certainty. IT sees another integration headache coming. Everyone loves innovation… until it shows up with a to-do list! For most leaders, the question isn’t whether tokenization makes sense—it’s whether they can pull it off seamlessly. One wrong move can stall progress and invite tough questions from the board or key stakeholders. This is where hybrid solutions come in. They allow institutions to start small, test quickly, and scale deliberately. Hybrid models meet enterprises where they are, integrating with current systems while unlocking the real-world value of tokenization. Real estate offers a clear case. Traditionally opaque and illiquid, it’s now being transformed through fractional ownership and expanded access. A $5 million fund could unlock up to $500 million in new capital by lowering entry barriers and reducing operational friction. Instead of months of paperwork and high price thresholds, tokenization enables faster, broader participation. Think of it like crowdfunding, but for billion-dollar assets. A hybrid approach makes this even more practical, connecting tokenized assets to existing financial infrastructure. It not only reduces friction, it levels the playing field. So the question isn’t just “Should we tokenize?” It’s “How can we do it in a way that’s smart, safe, and scalable?” So…how should we tokenize? For enterprises in highly regulated industries, compliance and privacy are foundational. Jumping straight into full decentralization can feel risky, even if it’s not. It brings speed and innovation, yes, but also complexity in governance and control. Hybrid models shift the mindset from all-or-nothing to progressive and purposeful. By combining the control of private systems with the transparency of public networks, they allow institutions to test new capabilities while safeguarding core operations. The thing is…breakthroughs don’t have to break things. Innovation can thrive at the edge. A private equity platform can issue public-facing tokens while keeping investor data secure. A real estate firm can record ownership on-chain while conducting due diligence off-chain. And it’s already happening. Archax , the UK’s first FCA-regulated digital asset exchange, uses DLT to tokenize securities. It preserves compliance workflows privately while anchoring transaction data to public ledgers for trust and auditability. Diamond Standard applies a similar model to physical diamonds: custody and authentication are managed off-chain, while proof of ownership lives on-chain. This layered approach satisfies regulators, empowers stakeholders, and maintains operational control. That’s why nearly 90% of global enterprises are already exploring blockchain in some capacity, many through hybrid architectures. No one wakes up thinking, “Let’s disrupt compliance today.” But with hybrid models, you don’t have to. They are built to handle KYC, AML, and data privacy from day one, giving enterprises the freedom to push boundaries without crossing the line. Setting the pace for a tokenized world Hybrid models aren’t a compromise. They’re the new blueprint. It’s not just about choosing a platform. It’s about aligning with your goals, risk appetite, and regulatory needs. The real advantage? Start with private systems, scale into public networks as confidence grows. That’s how many enterprises are testing the waters today, and it’s working. Not all hybrid models are created equal. The leaders won’t be the loudest or the fastest. They will be the ones who build with purpose. And let’s be clear: this isn’t a compromise. It’s a competitive advantage. The only question is: will you be left behind? Read more: Tokenization won’t take off without a reality check | Opinion Author: Eric Piscini Eric Piscini is the CEO of Hashgraph. Eric is a global technology executive with over 25 years of experience in driving strategic innovation and transformation across various industries and markets. He has a hands-on mindset and a passion for challenging the status quo, inspiring others to be change agents, and designing the future of business. As the CEO at Hashgraph, Eric is leading the acceleration of Hedera, the most advanced enterprise-grade public network for decentralized applications. He leverages his expertise in blockchain, strategy, and consulting to deliver value to Hedera & Hashgraph clients, partners, and stakeholders. Eric is an entrepreneur, an optimist, an ex-Big Four partner, an investor, and an advisor. He doesn’t believe in lines, except when he plays tennis.

crypto.news 2025-06-12 12:55

BitcoinWorld Wikipedia AI Summaries Pilot Halted After Editor Uproar Over Accuracy In the fast-paced digital world, where information is paramount, the reliability of our sources is constantly under scrutiny. For those navigating the cryptocurrency space, discerning accurate information from misinformation is critical. Wikipedia, often a first stop for general knowledge, recently embarked on an experiment involving AI summaries , aiming to provide users with quick overviews of complex topics. However, this pilot program was met with significant resistance from a core group: the platform’s dedicated editors, leading to its recent pause. The Wikipedia AI Summary Experiment and Its Swift Pause Earlier this month, Wikipedia announced a pilot program designed to integrate AI-generated summaries into articles for users who had opted into a specific browser extension. The idea was simple: place a concise summary at the top of an article, allowing users to quickly grasp the main points before diving into the full text. These summaries were clearly labeled with a yellow “unverified” tag, and users had to click to expand and read them. The intention, as later indicated by Wikimedia, included potential benefits like expanding accessibility, perhaps by simplifying complex topics or making information quicker to digest. However, the experiment barely had time to settle before a strong backlash emerged from Wikipedia’s volunteer editor community. These editors, who are the backbone of the platform’s collaborative content creation model, voiced immediate and significant concerns. Their protests centered primarily on the potential negative impact of inaccurate AI-generated content on the platform’s hard-earned credibility. This swift and vocal opposition ultimately led to Wikimedia’s decision to pause the pilot program. Why the Alarm? Understanding AI Hallucinations The main point of contention raised by the editors revolved around a well-documented problem with current artificial intelligence models: the phenomenon known as AI hallucinations . Unlike human errors which are often based on misunderstanding or oversight, AI hallucinations occur when a model generates information that is plausible-sounding but factually incorrect or nonsensical. This isn’t just a minor bug; it’s an inherent challenge in how large language models are trained and how they generate text, often fabricating details or combining information in misleading ways. For a platform like Wikipedia, which prides itself on verifiability and neutrality, the risk of displaying summaries containing factual errors generated by AI was unacceptable to many editors. They argued that even with an “unverified” label, the presence of incorrect information at the very top of an article could easily mislead users and damage the platform’s reputation as a reliable source. Examples from other news organizations experimenting with similar technologies, such as Bloomberg, which reportedly had to issue corrections and scale back their own AI tests due to inaccuracies, served as cautionary tales. The concern isn’t just about small errors; it’s about the fundamental trust users place in the information they find. If a summary, even a brief one, contains significant inaccuracies or fabrications due to AI hallucinations , it erodes that trust. This is particularly critical for sensitive or complex topics where factual precision is paramount. Editors as Guardians of Information Integrity Wikipedia’s success is built on the collective effort and dedication of its volunteer editors who work tirelessly to ensure the accuracy, neutrality, and comprehensiveness of articles. They follow strict guidelines regarding sourcing, verifiability, and consensus-building. The introduction of potentially flawed AI-generated content, even in an experimental capacity, was seen by many editors as a direct threat to the platform’s core principle of information integrity . Editors felt that the pilot bypassed the established processes for content review and verification that are central to maintaining quality. They argued that AI, in its current state, is not capable of upholding the rigorous standards required for Wikipedia content. The protest wasn’t just about disliking new technology; it was a defense of the foundational principles that make Wikipedia a valuable resource. Maintaining information integrity requires human judgment, critical thinking, and a commitment to factual accuracy that current AI models struggle to consistently provide, especially in nuanced or rapidly evolving topics. The Future of Wikipedia AI: Balancing Innovation and Trust While the AI summaries pilot has been paused, Wikimedia has indicated that it remains interested in exploring the use of AI for various purposes on the platform. They mentioned potential use cases like enhancing accessibility, which could include generating summaries for users with specific needs or potentially aiding editors in drafting initial article versions or identifying areas for improvement. This suggests that the conversation about integrating Wikipedia AI is far from over. The challenge lies in finding ways to leverage the potential benefits of AI technology without compromising the platform’s commitment to accuracy and reliability. Any future implementation of Wikipedia AI would likely need to involve significant human oversight and rigorous testing, perhaps focusing on tools that assist editors rather than directly generating content for public consumption without review. The pause in the pilot highlights the importance of involving the community, particularly the editors, in the development and implementation of such technologies. Broader Lessons for Content Creation AI The experience with Wikipedia’s AI summaries pilot offers valuable lessons for anyone involved in using content creation AI , whether in news, education, or other fields. It underscores the critical need for human oversight in AI-generated content, especially when factual accuracy is essential. Relying solely on AI without robust verification processes can lead to the spread of misinformation and damage credibility. This event serves as a reminder that while AI tools can be powerful aids in content creation, they are not infallible. The risk of AI hallucinations and other inaccuracies means that human editors, fact-checkers, and subject matter experts remain indispensable in ensuring the quality and reliability of information presented to the public. The pushback from Wikipedia editors is a strong signal that the human element is crucial in maintaining trust in the age of automated content generation. The responsible integration of content creation AI requires a careful balance between efficiency and accuracy, always prioritizing the latter when it comes to factual information. Conclusion: A Necessary Pause for Reflection Wikipedia’s decision to pause its AI summaries pilot following protests from its editor community is a significant moment in the ongoing discussion about the role of artificial intelligence in content creation. The editors’ concerns about AI hallucinations and the potential threat to information integrity were valid and highlighted the inherent limitations of current AI technology when deployed without adequate human oversight. While Wikimedia remains open to exploring the potential benefits of Wikipedia AI , this pause provides an opportunity for reflection and recalibration. It emphasizes that for platforms built on trust and accuracy, the integration of AI must be approached cautiously, collaboratively, and with a clear understanding of the technology’s risks. The episode reinforces the invaluable role of human editors in maintaining the quality and reliability of information in the digital age and offers crucial insights for the broader field of content creation AI . To learn more about the latest AI market trends, explore our article on key developments shaping AI features. This post Wikipedia AI Summaries Pilot Halted After Editor Uproar Over Accuracy first appeared on BitcoinWorld and is written by Editorial Team

Bitcoin World 2025-06-12 12:00

BitcoinWorld Apple AI: Essential Siri Update Delayed, Executives Explain Why In the fast-paced world of technology, where innovation often feels like a race, even giants face hurdles. This is particularly relevant for those following the crypto space, which thrives on bleeding-edge tech and ambitious roadmaps. Delays are a reality, and understanding why they happen, even at companies like Apple, offers valuable perspective on the complexities of Tech Innovation and AI Development . Recently, Apple executives shed light on why the highly anticipated Siri Update , powered by new Artificial Intelligence capabilities and previewed at WWDC 24, won’t ship as initially hoped this year, pushing its full rollout to 2026. Why Was the Apple AI Siri Update Delayed? Following their Worldwide Developers Conference, Apple leaders addressed questions about the absence of the fully personalized, AI-driven Siri demonstrated previously. Contrary to speculation labeling the demo as ‘demoware’ or ‘vaporware,’ senior vice president of software engineering Craig Federighi and senior vice president of worldwide marketing Greg Joswiak offered clear explanations rooted in the realities of bringing complex new technology to market. Key reasons for the delay: The demonstrated version (Architecture v1) was functional but not deemed ready for mass shipment. Apple realized that meeting customer expectations required transitioning to a more advanced architecture (v2). The v1 software, while real, had an “error rate that we felt was unacceptable” for a broad release. Apple sees AI as a “long-term transformational wave” and is prioritizing quality and the right features over being the first to market. Federighi stressed that the demo was indeed based on “real working software with a real large language model with real semantic search,” pushing back against the idea that it was merely a non-functional mockup. Artificial Intelligence: Apple’s Unique Approach While many companies are focused on building standalone chatbots to rival models like ChatGPT, Apple executives reiterated that their vision for Artificial Intelligence is fundamentally different. Their goal isn’t to create a dedicated chat interface users go to for tasks, but rather to deeply integrate intelligence across all their platforms and operating systems. Federighi explained, “This wasn’t about us building a chatbot… That was never our goal.” Instead, Apple aims for intelligence that is “meet you where you are” – seamlessly assisting users within their existing workflows and apps. This strategy involves: Infusing AI capabilities directly into core OS functions. Enabling personalized and contextual assistance via the updated Siri. Providing developers with tools to leverage Apple’s foundation models for building more intelligent third-party applications. This deep integration contrasts with a standalone chatbot model and requires significant foundational work across the entire ecosystem, contributing to the complexity of the AI Development process. Siri Update: The Path to 2026 The executives confirmed that the full realization of the new Siri Update is now targeted for 2026. This timeline reflects the decision to pivot from the initially demonstrated v1 architecture to the more robust v2, which they believe is necessary to deliver the level of performance and reliability Apple users expect. While the initial plan might have been to ship components sooner, the commitment to a higher standard necessitated revising the schedule. This situation highlights a common challenge in Tech Innovation : initial prototypes and demos, while functional, often reveal the need for more fundamental architectural changes to scale and meet real-world demands. The decision to delay, while potentially disappointing in the short term, underscores a focus on long-term product quality and user experience over rushing an incomplete solution to market. Lessons from AI Development Challenges The experience with the Apple AI powered Siri offers valuable insights applicable beyond Cupertino, particularly in fast-moving sectors like cryptocurrency and blockchain, where ambitious technological goals are common. It demonstrates that: Complex AI Development is inherently challenging and timelines can shift. Demos represent a snapshot in time and don’t guarantee immediate product readiness. Prioritizing a quality user experience often requires difficult decisions, including delays. Long-term vision is crucial; rushing incomplete tech can damage reputation and user trust. For builders and investors in the crypto space, understanding these realities of tech development is vital. It encourages a more nuanced view of project roadmaps and the inherent risks and complexities involved in pioneering new technologies. Concluding Thoughts on Apple AI and the Siri Update Apple executives have been transparent about the reasons behind the delay of the full Siri Update , emphasizing that it wasn’t due to the technology being fake or non-functional, but rather a strategic decision based on achieving an acceptable level of quality and performance. Their vision for Apple AI is focused on deep integration across the ecosystem rather than competing in the chatbot arena. While the wait for the most advanced features of the new Siri is extended to 2026, the company maintains its commitment to delivering a transformative Artificial Intelligence experience built on a solid foundation. To learn more about the latest AI Development trends and their impact on the tech landscape, explore our article on key developments shaping AI features. This post Apple AI: Essential Siri Update Delayed, Executives Explain Why first appeared on BitcoinWorld and is written by Editorial Team

Bitcoin World 2025-06-12 11:40

The decentralized physical infrastructure platform wants to scale and improve data infrastructure for vehicles in Japan.

Decrypt 2025-06-12 07:24

BitcoinWorld Sam Altman’s Astonishing AI Prediction: Novel Insights Arriving in 2026 For those tracking the convergence of technology and future possibilities, particularly within the crypto space often driven by innovation, a recent prediction from OpenAI CEO Sam Altman is generating significant buzz. In his new essay, “The Gentle Singularity,” Sam Altman AI shared his evolving perspective on artificial intelligence and its trajectory over the next decade and a half. Altman is known for painting ambitious pictures of an AI-driven future, often suggesting his company is on the cusp of major breakthroughs. Sam Altman AI’s Vision for 2026 Altman’s latest essay continues his pattern of outlining a future where Artificial General Intelligence (AGI) profoundly impacts work, energy, and societal structures. While consistently highlighting the potential of AGI development, he also typically frames its full arrival as a gradual process. However, tucked within this vision was a specific timeframe for a notable AI capability. Altman stated that by next year, 2026, the world is likely to witness the emergence of AI systems capable of producing “novel insights.” What are OpenAI Novel Insights? The phrase “novel insights” might sound abstract, but recent statements from OpenAI executives shed more light on what this means in practice. OpenAI’s co-founder and President, Greg Brockman, mentioned in April that their o3 and o4-mini reasoning models were the first used by scientists to generate new, helpful ideas. This suggests OpenAI’s focus is shifting towards AI models that can not just process information but also synthesize it to propose original concepts or hypotheses. Altman’s essay reinforces the idea that OpenAI itself intends to prioritize developing AI with this specific capability in the coming year. The Race for AI Scientific Discovery OpenAI is not alone in pursuing AI that can generate novel insights. Several other companies are also directing efforts towards training models that can assist scientists in formulating new hypotheses, potentially leading to significant AI scientific discovery. This is a competitive area with high stakes, given the potential to automate crucial parts of the scientific method. Examples of this focus include: Google’s AlphaEvolve: Released in May, this AI coding agent is claimed to have developed new approaches to complex math problems. FutureHouse: A startup backed by Eric Schmidt, FutureHouse claims its AI agent has already made a genuine scientific discovery. Anthropic: Launched a program in May specifically to support AI in scientific research. Lila Sciences: Founded by former OpenAI research lead Kenneth Stanley, this startup raised $200 million to build an AI lab focused on improving AI’s ability to generate hypotheses. Success in this area could revolutionize industries like drug discovery, material science, and many others fundamentally based on scientific advancement. Is AI Future 2026 Realistic? Altman’s prediction about 2026 aligns with a pattern of him using essays to hint at OpenAI’s immediate priorities. Earlier this year, he suggested 2025 would be the “year of agents,” shortly before OpenAI released its first agent products (Operator, Deep Research, Codex). However, achieving genuinely novel insights from AI might be a harder challenge than creating agents. The broader scientific community holds some skepticism regarding current AI systems’ capacity for true originality. Thomas Wolf, Chief Science Officer at Hugging Face, has argued that today’s AI lacks the ability to ask the great questions essential for scientific breakthroughs. Kenneth Stanley, now at Lila Sciences, also previously noted that current models struggle to generate novel hypotheses because it requires giving AI a sense of what is creative and interesting – a complex problem. The Path of AGI Development While the scientific debate continues, Sam Altman’s prediction serves as a strong indicator of OpenAI’s strategic direction. The pursuit of AI capable of novel insights is a significant step on the path towards more advanced AGI development. Whether OpenAI successfully delivers an AI model that meets this high bar in 2026 remains to be seen. However, Altman’s essays have a track record of signaling the company’s immediate research and development focus. Sam Altman’s vision for AI generating novel insights by 2026 is a bold one that underscores the rapid pace of development in the field. It highlights OpenAI’s current priorities and the competitive landscape of companies pushing the boundaries of AI capabilities. While challenges and skepticism exist within the scientific community regarding AI’s capacity for true originality, the pursuit of this goal could lead to transformative advancements in scientific discovery and beyond. The coming year will be critical in determining if AI truly begins to think in ways that generate genuinely new understanding. To learn more about the latest AI trends, explore our article on key developments shaping AI features. This post Sam Altman’s Astonishing AI Prediction: Novel Insights Arriving in 2026 first appeared on BitcoinWorld and is written by Editorial Team

Bitcoin World 2025-06-12 01:40

Hedera Foundation is collaborating with Accenture and EQTY Lab to launch a new artificial intelligence solution aimed at bringing verifiable AI governance to the public sector. HBAR , the native token of Hedera, remained largely flat despite the news as it traded around $0.18. The token’s value is up 0.5% in the past 24 hours and 6% in the last seven days. Hedera Foundation joins forces with Accenture and EQTY Lab Announced today, this initiative leverages the Hedera network’s advanced distributed ledger technology (DLT) to ensure transparency, auditability, and trust in autonomous AI systems, addressing critical needs in public sector operations. Hedera Foundation @HederaFndn · Follow We’re excited to introduce a new AI solution with @Accenture and @EQTYLab that will help governments and public sector organizations manage AI-driven decisions with transparency, auditability, and trust via the @Hedera network 🤝 Watch on Twitter View replies 4:04 pm · 11 Jun 2025 410 Reply Copy link Read 19 replies The new solution integrates EQTY Lab’s AI Integrity Suite with Hedera’s Consensus Service (HCS) and Token Service (HTS), creating an immutable record of AI decisions and actions. The Hedera Token Service (HTS) is a core feature of the Hedera network, a high-performance, enterprise-grade distributed ledger technology (DLT) platform. HTS enables users to create, manage, and transact with native digital tokens on Hedera in a secure, scalable, and cost-effective manner. This framework enables public sector entities to track and verify AI processes, ensuring compliance with evolving regulations like the EU AI Act and NIST standards. By anchoring AI workflows in Hedera’s secure, scalable network, the solution provides a tamper-proof audit trail, fostering accountability and public trust in AI applications. What does this mean? Accenture, a leading player in AI, will develop playbooks to guide the implementation of this solution at scale. These resources will outline business logic for AI deployment and strategies to maximize Hedera’s capabilities, particularly in areas like regulatory compliance and data integrity. “Public sector clients are enthusiastically embracing continuous reinvention through the adoption of AI and agentic solutions to achieve scaled impact. This shift brings with it a heightened demand for oversight, accountability and reliability. The integration of these technologies not only enhances runtime governance, but also positions the Hedera network as a crucial platform,”said Bryan Rich, Senior Managing Director of Global AI for Public Sector at Accenture. Hedera is a blockchain platform whose network has a governing council that includes global giants such as Google, HITACHI, Dell Technologies, Deutsche Telekom, and IBM. It also features crypto-focused players such as BitGo and Chainlink. The Hedera network offers high-throughput, low-cost transactions with instant finality, making it ideal for enterprise-grade AI governance. In a comment about the integration with Accenture and EQTY Lab, Heder’s chief operating officer, Shyam Nagaraja,n stated: “This partnership positions Hedera as a leader in AI governance, enabling secure and compliant AI adoption in the public sector.” Hedera’s move comes amid growing traction for AI, including for governments. AI’s rapid rise is thus key to this integration with Accenture and EQTY Lab. Tapping into Hedera’s distributed ledger technology means a new standard for responsible AI use. The post Hedera introduces a new AI solution with Accenture and EQTY Labs appeared first on Invezz

Invezz 2025-06-11 19:07

BitcoinWorld OpenAI Open Model: Strategic Delay Hints at Amazing AI Breakthrough For those tracking the rapid evolution of artificial intelligence and its potential impact across technology sectors, including cryptocurrency and blockchain, news from OpenAI is always significant. The highly anticipated release of OpenAI’s first open model in years has hit a snag, with the OpenAI open model now expected later this summer instead of June. Why the AI Model Delay? Sam Altman Explains The announcement came directly from OpenAI CEO Sam Altman AI , who shared the update on X. Altman stated, “We are going to take a little more time with our open-weights model, i.e. expect it later this summer but not June.” This indicates a shift from the previously targeted early summer window. The reason provided for the AI model delay is particularly intriguing. Altman hinted at a significant development within the research team, describing it as “unexpected and quite amazing.” He believes this breakthrough will make the additional waiting time worthwhile, suggesting substantial improvements or new capabilities are being integrated into the model. What Are OpenAI’s Goals for This Open Model? OpenAI has ambitious plans for this forthcoming model. It is slated to possess reasoning capabilities comparable to their proprietary ‘o-series’ models. A primary objective is for the OpenAI open model to set a new standard and outperform existing open reasoning models currently available, such as DeepSeek’s R1. The company aims to push the boundaries of what’s possible with open-source AI, providing developers and researchers with a powerful tool that can handle complex tasks and exhibit advanced reasoning skills. This move is seen by many as an effort to re-engage with the open-source community after years focused primarily on closed models. How Does the Competitive Landscape Look for OpenAI’s Release Date? Since OpenAI first signaled its intention to release an open model, the competitive landscape in the AI space has intensified significantly. Other labs have not stood still, releasing their own advanced models, making the timing of the OpenAI release date even more critical. For example, Mistral, known for frequently releasing open models, recently launched its first family of AI reasoning models called Magistral. Similarly, the Chinese AI lab Qwen introduced a family of hybrid AI reasoning models capable of switching between deliberate, time-consuming reasoning and faster, traditional responses. These developments highlight the pressure on OpenAI to deliver a truly competitive offering when its model finally arrives. Potential Features and OpenAI’s Relationship with Open Source Beyond core performance benchmarks, OpenAI has reportedly considered integrating complex features to make its open model stand out among competitive AI models . Discussions have included enabling the open model to connect with OpenAI’s cloud-hosted AI models for tackling particularly complex queries. While it remains uncertain which of these advanced features will make it into the final release, their consideration underscores the effort to build a top-tier open model. The release holds considerable importance for OpenAI’s standing with researchers and developers globally. Sam Altman AI has previously acknowledged that OpenAI landed on the “wrong side of history” regarding open-sourcing its models. Releasing a powerful, competitive open model is crucial for rebuilding trust and fostering stronger relationships within the AI community. Conclusion: Waiting for the Breakthrough The delay in the OpenAI release date for its highly anticipated open model is certainly a pause, but the reason cited – an “unexpected and quite amazing” research discovery – suggests that the wait may be justified. As the field of competitive AI models continues to evolve rapidly, the pressure is on OpenAI to deliver a model that not only meets but exceeds expectations. The coming months will reveal whether this strategic delay leads to a breakthrough that significantly impacts the open AI landscape. To learn more about the latest AI model trends, explore our articles on key developments shaping AI models features. This post OpenAI Open Model: Strategic Delay Hints at Amazing AI Breakthrough first appeared on BitcoinWorld and is written by Editorial Team

Bitcoin World 2025-06-11 18:10