Latest news with #decentralizedAI
Forbes
5 days ago
- Business
- Forbes
Swarm Intelligence Is Reshaping How AI Gets Trained
A decentralized AI training swarm could be more cost effective, equitable and inclusive that current ... More closed AI training approaches. It's no secret that current AI models are built behind closed doors in secrecy and seclusion. Only a handful of Big Tech companies hold the keys to those doors, the massive server centers, petabytes of data, training pipelines and protocols. The artificial intelligence models they produce are locked away behind their self-serving black boxes of seeming techno wizardry that the public can query but never really understand, influence or change. A ballsy decentralized AI start-up called Macrocosmos wants to change that. With the relaunch of Subnet 9 on the Bittensor network — think of a subnet as a mobile phone app and Bittensor as an app store — Macrocosmos is taking the first meaningful step toward a more democratic future for artificial intelligence. The clue to how they've achieved this feat is captured in the subnet's new acronym, IOTA: Incentivized Orchestrated Training Architecture. This construct allows anyone with a graphic processing unit, no matter how modest, to help train cutting edge AI models. Based on a novel 'swarm" approach, which is a theoretical pre training strategy for AI, Macrocosmos' breakthrough resolves key challenges around data and model compression, as explained in their white paper published on Friday. At its core is a vision that reimagines how intelligence is built and who gets to participate in that process. 'We are single-minded and obsessed in our pursuit of building competitive decentralized technologies that can compete with centralized labs,' wrote Macrocosmos CTO Steffen Cruz in a post on X. Before we can understand swarm training, we need to understand the key differences between traditional AI and decentralized AI. At its simplest, decentralized AI means that the training of an AI model doesn't happen in one place or under the control of one company. Instead, it's spread out, distributed — across homes, labs, campuses and servers anywhere in the world. The same way that Bitcoin decentralized money away from centralized banks, Bittensor and Macrocosmos aim to democratize intelligence itself. This matters because AI is infiltrating more and more of our lives. It's deciding what news we see, what products we are offered, how we shop, how we interact with each other, how we work and even how we're hired. Concentrating that power within a few cabalistic computing systems risks not just privacy or fairness, but the future of innovation itself. By opening those locked doors to public participation and direct engagement, decentralized AI offers a new kind of alignment — one where users are also co-creators. 'Not only is this a new research endeavor for Macrocosmos and Bittensor, but it's something bigger and more personal to us,' Cruz added. 'We are scientists, researchers and developers.' Swarm training, as deployed by Macrocosmos through IOTA, takes cues from the natural world. Similar to how a swarm of bees, school of fish or a flock of birds can accomplish complex navigation without central control, this novel subnet enables thousands of independent machines to orchestrate and collaborate on training a single massive AI model. Instead of forcing each network participant to download and run the full model — a costly and impractical ask — Macrocosmos uses a technique called model parallelism. Each subnet member — also called a miner since their actions 'mine' actual monetary incentives that benefit the entire network — trains just one slice of the model, typically a few layers of the neural network. As data flows through those individual layers, each miner processes their portion and passes the output forward. Then, a lightning fast reverse review grades how far off the model was and adjusts miner payouts accordingly. This approach isn't just efficient than centralized methods — it's more inclusive. Rather than requiring top-tier hardware, the architecture allows both low- and high-compute participants to contribute meaningfully. This breaks down the barriers to entry that have long kept open-source communities at the margins of AI model training. To understand the difference between how traditional AI models are trained and what Macrocosmos is doing, this graphic offers a useful side-by-side comparison: This side-by-side comparison depicts the differences between centralized and decentralized AI ... More training. In centralized training, one model is split into layers that are tightly linked across GPUs within a single data center. Everything is optimized for high-speed local connections. But this setup is expensive, exclusive and closed. In contrast, the decentralized swarm training distributes different layers of the model across a global network of contributors or miners. Each of these individuals handles a piece of the workload and communicates their results with others. The swarm system regularly syncs up all the parts into a single, shared model. Instead of requiring giant compute clusters, it leverages a far flung spectrum of connected devices — ranging from a personal desktop GPU to larger industrial setups. The outcome? Lower costs, more transparency and an AI model built by the many, not the few. However, training models this way has its challenges. Internet bandwidth is a lot slower than the fiber optics inside a data center. And decentralized participants can drop out, try to cheat the incentive system or go offline at a moment's notice without warning. While some of those issues are beyond Macrocosmos' control, they have developed an elegant solution for potential problems tied to miner incentives and rewards. The design of its new IOTA network overcomes three big challenges: In this video clip, the firm's co-founders Cruz and Will Squires discuss why decentralized training matters, and how it can open a new era for AI. This is more than a technical upgrade — it's a seismic philosophical shift. For years, decentralized AI projects have relied on centralized training behind the scenes. Macrocosmos is finally changing that. 'The time has come for us to move forward as a community and tackle new challenges in model training. This is an imperative for Bittensor. The competition are at our heels,' Cruz added. 'We beat nation states, we tirelessly benchmarked our progress and we shared our findings in our white paper. It was a fantastic experiment, and we pushed it far beyond its original design.' This effort to distribute AI's compute and ownership to all comers through swarm training enables a future reality where AI isn't something reserved for elite power brokers while inferential dregs are grudgingly dripped to the masses. It's a collective thing we build together. Macrocosmos is taking decentralized training out of Big Tech's locked, walled garden and into the wild. If they're successful, the next breakthrough frontier AI model might not come from OpenAI, Google or Meta — but rather from a swarm of us.
Forbes
29-05-2025
- Business
- Forbes
Most Americans Want AI Decentralized, Not Controlled By Big Tech
Three-quarters of Americans agree that the transformative power of AI would benefit more people if ... More it wasn't consolidated in the hands of a few major players. Three out of four Americans believe decentralized AI is more likely to support innovation and societal progress than traditional, centralized AI models dominated by Big Tech, according to new polling data released by Digital Currency Group and The Harris Poll. The nationally representative survey of 2,036 U.S. adults – including registered voters and AI users – found strong bipartisan support for DeAI, signaling a notable public shift away from tech monopolies and toward open source, distributed alternatives. DeAI leverages blockchain infrastructure to democratize access, bolster transparency and protect personal data – features that may address some of the public's deepest concerns about current AI systems. 'This research makes it clear – there's strong public support for policies that both protect innovation and keep pace with where the world is headed,' said Julie Stitzel, Senior Vice President of Policy at DCG. 'Three-quarters of Americans agree that the transformative power of AI would benefit more people if it wasn't consolidated in the hands of a few major players.' This sentiment was especially pronounced among older Americans. More than half of the surveyed Baby Boomers (57%) favor decentralized AI, the highest percentage among any age group. Despite being the least likely to use AI tools, Boomers were most likely to perceive DeAI as beneficial (88%), trustworthy (85%) and more secure for personal data (82%). 'What stood out most in the findings is the clear correlation between AI knowledge and support for decentralization,' Stitzel added in a text response. 'The deeper Americans engage with AI, the more they recognize the need to decentralize its power, regardless of political affiliation. This reflects a broader desire for innovation that is open, secure and accountable. Just as we've seen with digital assets, the public is calling for systems that do not concentrate control in the hands of a few but instead widen access and trust through transparency.' Statistical highlights from the DCG sponsored survey of more than 2,000 participants comparing pros ... More and cons of centralized AI versus decentralized AI. While Americans are broadly optimistic about AI – 91% believe it will fuel technological innovation and 84% see benefits to productivity and healthcare – deep skepticism remains about who controls it. The poll found that 65% of respondents distrust elected officials to manage AI, and 46% say the same of Big Tech. Half of those surveyed believe Big Tech has too much influence over government AI policy. Meanwhile, 72% of respondents said they would be more inclined to use AI if there were viable alternatives to platforms developed by major tech companies. 'The public is calling for a new social contract with artificial intelligence: one where AI is governed openly, distributes value fairly, and gives people a meaningful stake in the systems that shape their lives,' said Tony Douglas, Co-Founder of the Decentralized Research Center said in a statement. 'Decentralization is no longer a fringe idea – it's a framework for building AI that reflects public values and a chance to avoid repeating the failures of the last tech era.' The strongest driver behind the DeAI preference appears to be personal data control. The survey revealed that 88% of Americans want more agency over how their information is used by AI. Seventy-four percent said they'd be more comfortable using AI if they could directly benefit from their data – through ownership, consent or compensation. More than 70% also see DeAI as more secure for personal data than centralized AI systems. That trust gap may explain why just 39% of Americans said they were confident tech companies use their data responsibly. Beyond privacy, DeAI was also perceived as better aligned with the public good across several dimensions: 75% found it more supportive of innovation, 71% said it was safer and 65% said it was less likely to be biased than centralized AI. 'This growing public readiness to embrace decentralized AI is a signal that the future of intelligence can and should be more inclusive, resilient and fair,' said Evan Malanga, Chief Revenue Officer at Yuma, a DCG subsidiary that supports DeAI startups. 'As awareness grows, so does the demand for systems that put individuals, not corporations, at the center. Protocols like Bittensor are already demonstrating what this can look like in practice, creating open networks where anyone can contribute to and benefit from AI. That kind of permissionless participation is what will unlock the next wave of innovation.' Released alongside DCG's recent policy fly-in event in Washington, D.C., the survey arrives as lawmakers continue grappling with AI regulation. During the two-day gathering, DCG and its portfolio companies – including Yuma, which supports DeAI subnet development on the Bittensor network – met with members of Congress and co-hosted a DeAI briefing with the bipartisan House Congressional Crypto Caucus. While 68% of Americans agree that AI should be regulated at the federal level, the data suggest they're looking for regulation that empowers, not restricts. Fifty-nine percent believe AI is becoming 'as essential as the internet' and should be accessible without heavy-handed rules. Meanwhile, 71% support policies ensuring the economic benefits of AI flow back into local communities. DeAI, with its decentralized control structures and token-based economies, could be a policy match. It not only reduces reliance on centralized servers and opaque algorithms but also aligns with voter demand for AI systems that benefit the public – not just platform owners or data brokers. Whether that future ends up more decentralized may depend less on technical superiority and more on whether policymakers heed what appears to be a growing public mandate: that the next generation of AI should be open, equitable and governed by the many – not the few.
Globe and Mail
22-05-2025
- Business
- Globe and Mail
AIOZ Network Unveils AIOZ AI: A Decentralized AI Marketplace and Compute Network Powered by DePIN
AIOZ Network announces the launch of AIOZ AI, the first decentralized AI model and dataset marketplace built entirely on DePIN. As the foundational layer of AIOZ's infrastructure, DePIN powers a global network for AI computes, storage, streaming, and IPFS pinning service, enabling AI to be developed and deployed in a decentralized environment with greater transparency, control, and ownership. With this release, developers, researchers, and enterprises can upload, purchase, store, and monetize AI models and datasets across a distributed environment. AIOZ AI processes tasks through DePIN Compute, giving contributors ownership, transparency, and control over how their AI assets are used. "With AIOZ AI, we're introducing a new foundation for decentralized AI. Built on DePIN, this platform empowers developers to share, monetize, and eventually tokenize their AI assets, from models to datasets and applications,' said Erman Tjiputra, Founder & CEO of AIOZ Network. 'It's part of our broader vision to create a unified infrastructure where people can store, stream, and compute in a decentralized way, with ownership and value returning to the contributors of the network.' DePIN: The Foundation of a Decentralized AI Economy AIOZ AI is built on DePIN Compute, a decentralized infrastructure that transforms how AI models are processed, stored, and monetized. The platform empowers users to upload models and datasets, unlock potential rewards when their assets are used, and access a growing ecosystem of decentralized AI applications. AI inferencing and compute tasks are executed on AIOZ's DePIN . This decentralized infrastructure ensures secure, permissionless access to AI processing while giving contributors a share in the network's growth. The AIOZ AI marketplace introduces an initial collection of AI models, datasets, and compute resources, giving early adopters access to a growing decentralized AI ecosystem. AIOZ AI Roadmap and Ecosystem Vision The launch of AIOZ AI marks the first step in building a decentralized AI ecosystem powered by DePIN Compute. V1 introduces foundational marketplace features, user accounts, payments, and the ability to upload, purchase, and monetize AI models and datasets, with built-in community features like voting and discussions. V2 will expand developer functionality by enabling larger file support, Git-based uploads, and API access via SDKs in Python, Go, and V3 will integrate DePIN-based AI inferencing, allowing developers to unlock potential rewards from verified model usage and compute tasks. V4 will introduce decentralized training, enabling models to be DePIN-trained and showcased in real-world applications. AIOZ AI is part of a broader DePIN-powered infrastructure developed by AIOZ Network. Alongside AI compute, the ecosystem includes AIOZ Stream for decentralized video and audio delivery, AIOZ Storage for S3-compatible Web3 data storage, and AIOZ Pin, an IPFS-based layer securing immutable assets like NFTs. Together, these components form a unified foundation for AI compute, content streaming, and digital storage, enabling a people-powered internet and defining the future of decentralized infrastructure. Driving Adoption Through the AIOZ AI Challenge To support the launch of AIOZ AI and accelerate developer adoption, AIOZ Network is introducing the AIOZ AI Challenge, a recurring competition series designed to spotlight AI talent and grow the platform's model and dataset library. The first wave of challenges will roll out after the platform's public release and run throughout 2025, providing participants the opportunity to build, submit, and refine AI models based on a defined technical framework. Submissions will be evaluated on performance, utility, and innovation, with leading models incentivized with potential tokens rewards and highlighted in the marketplace for greater discoverability. A public leaderboard will highlight top contributors, who may also gain early access to advanced features, future SDK integrations, and participation in exclusive collaborations. Join the Future of AI with AIOZ Network: Power AIOZ AI by Running AIOZ DePIN AIOZ AI runs on a decentralized backbone of contributors. By contributing to the AIOZ DePIN, you help drive AI model training, tasks inferencing, data storage, and unlock the potential for token rewards. No matter your background — tech enthusiast, AI developer, or blockchain believer — your contribution fuels a cutting-edge, community-powered AI ecosystem. Everything Intelligence. AIOZ AI. Be the future redefining how AI is built and delivered. Start your journey today at About AIOZ Network: Powering Web3 Infrastructure Through DePIN AIOZ Network is pioneering the future of decentralized infrastructure by advancing DePIN across AI compute, media delivery, and distributed storage. AIOZ Network enables AI processing, content streaming, and secure data storage through a globally distributed network of over 200,000 contributors. The AIOZ ecosystem includes AIOZ AI, a decentralized compute and AI asset marketplace; AIOZ Stream, a platform for live and on-demand video and audio content; AIOZ Storage, an S3-compatible decentralized storage solution; and AIOZ Pin, a distributed IPFS pinning layer designed to store immutable content such as NFTs and digital assets. Media Contact Company Name: AIOZ Network Contact Person: Monica Botez Email: Send Email City: Providence State: Mahe Country: Seychelles Website:
Forbes
09-05-2025
- Business
- Forbes
Decentralized AI: Moving Beyond Big Tech's Walled Gardens
Artificial intelligence is evolving rapidly, but the narrative is dominated by a few Big Tech players. While OpenAI, Google, and Meta make headlines, a quieter, potentially more fundamental shift is underway: the move toward decentralized AI (DeAI). This isn't just about new algorithms; it's a reaction against centralized control. Users are growing wary of opaque systems, hidden data agendas, and the power concentrated in a few hands – but escaping these walled gardens requires rebuilding AI's foundations. Yet, several projects are tackling these challenges head-on, laying the groundwork that could redefine AI's role. Understanding this evolution is critical for anyone building or investing in the decentralized space because the next wave of AI innovation hinges on getting these alternative foundations right. What Makes Decentralized AI Different? Deploying AI in a trustless, decentralized environment fundamentally changes the game. Every inference might need cryptographic verification. Data access often involves navigating complex blockchain indexing. And unlike centralized giants, DeAI projects can't simply autoscale resources on AWS or Google Cloud when computational demand spikes – not without compromising their core principles. Consider a DeAI model for community governance. It must interact with smart contracts, potentially cross-chain, ensure privacy through complex cryptography, and operate transparently—a vastly different computational challenge than typical AI analytics. This complexity explains why early visions of DeAI often stumbled. They either sacrificed decentralization for efficiency or buckled under the processing demands. The real progress began when teams stopped retrofitting traditional AI into blockchain settings and started architecting systems specifically for the challenges of decentralization, transparency, and user control. Real Applications From the Whiteboard to Mainnet DeAI projects are finally moving beyond theoretical ideals. Several teams have deployed working systems that demonstrate practical applications, particularly addressing the shortcomings of centralized alternatives. Sean and Scott meeting in Hong Kong Sean Lee Leading the push for transparency against centralized AI, Kava has emerged as a significant force, demonstrating how decentralized models can successfully challenge Big Tech. Their platform incorporates decentralized AI elements; as Kava Co-Founder Scott Stuart detailed during our recent discussion in Hong Kong, its tangible user demand for accountable systems is underscored by a user base surpassing 100,000. This growing adoption serves as potent evidence of Kava's challenge to the prevailing 'black box' AI, as its community-governed and transparent operations offer a clear alternative. NEAR Protocol offers scalable infrastructure for high-throughput decentralized applications, enabling efficient DeAI processes. Internet Computer (ICP) pioneers platforms for AI applications to operate fully on-chain, ensuring end-to-end decentralization and security. Building the Backbone The unique demands of DeAI exposed critical gaps in existing Web3 infrastructure. Akash Network recognized this early. Their solution, a DePIN (decentralized physical infrastructure network), taps into underutilized computing resources globally, creating a marketplace for computation that offers resilient and cost-effective alternatives to centralized cloud providers for AI workloads, enhancing censorship resistance. Data accessibility is another piece of the puzzle. The Graph streamlines indexing and querying data from blockchains, making it feasible for DeAI applications to access and process the vast amounts of on-chain information needed for meaningful analysis and decision-making without overwhelming individual nodes. Across the ecosystem, teams feel the impact of these infrastructure upgrades. DeAI can now handle more sophisticated tasks – from managing complex DeFi strategies to powering decentralized social platforms – without fatally compromising on the core tenets of decentralization. The growing viability of projects like Kava, running elements on decentralized rails enabled by platforms like Akash, stems directly from these infrastructure advances. The Path Forward Web3's evolving infrastructure unlocks unique possibilities for DeAI deployment. Take DeFi usability. AI agents, like those Kava is working to deploy later this year, aim to automate complex cross-chain strategies or optimize yield farming, abstracting away the intimidating complexity that keeps mainstream users out. This requires not just AI logic but also seamless interaction with diverse protocols and robust data feeds, facilitated by infrastructure like The Graph. Community governance is another frontier. Projects like Dexe explore community-driven frameworks aligning AI development with user consensus and regulatory needs, potentially using AI agents to simulate policy impacts or manage DAO treasuries if infrastructure is robust. Looking Beyond the Buzzwords The success of DeAI hinges on more than just clever models or ideological appeal. Infrastructure providers and application developers face persistent challenges around computational bottlenecks, cross-chain communication standards, data veracity, and true decentralization. Theoretical models often break upon contact with mainnet realities. Ask any team deploying DeAI about the edge cases encountered – unexpected market volatility, network congestion spikes, governance exploits – that current models struggle with. The next crucial phase involves standardization and interoperability. As more DeAI applications emerge, the need for common frameworks for data, computation, and governance becomes paramount. Long-term success depends on creating an ecosystem where decentralized components work together seamlessly, rather than a collection of isolated, competing solutions. These foundational elements – robust infrastructure, accessible data, adaptable governance – might not grab headlines like breakthroughs in model training. But they are what will ultimately determine whether decentralized AI fulfills its promise of a more transparent, accountable, and user-empowered future, or remains confined to niche applications. The teams solving these fundamental challenges today are shaping the trajectory of AI for tomorrow.
Zawya
08-05-2025
- Business
- Zawya
Revolutionizing AI: How FLock.io Is Expanding Global Decentralized Solutions
NEW YORK, USA - Media OutReach Newswire – 8 May 2025 - a private AI training platform, closed a strategic funding round led by DCG in December 2024 and launched FL Alliance, a solution enabling decentralized AI model training. FL Alliance transforms everyday devices into training nodes, allowing users to create personalized LLMs, earn token rewards, and maintain complete data privacy. AI adoption in healthcare, fintech, and smart manufacturing is booming, with the global market reaching $154 billion in 2023 and projected to exceed $300 billion by 2027, according to IDC. Despite this growth, data privacy concerns and frequent breaches remain significant obstacles. Industries like healthcare and fintech rely heavily on sensitive data, making privacy protection essential. Decentralized AI technologies, like solutions, are addressing these challenges. By integrating federated learning and blockchain, sets new benchmarks in data security, offering intelligent, privacy-focused innovations tailored to critical industries. The social significance of decentralized AI Decentralized AI revolutionizes technology by distributing data storage and computation across multiple parties, enabling collaborative model training without sharing sensitive data. This approach is ideal for privacy-critical applications such as medical diagnostics and financial risk management. Federated learning, a key element of decentralized AI, allows participants to train machine learning models locally, preserving privacy while maintaining data integrity. Founded in 2020, has emerged as a leader in decentralized AI, driven by years of innovation and market exploration. CEO Jiahao Sun, an Oxford University alumnus and former AI Director at the Royal Bank of Canada, brings extensive expertise to the company. Sun's decentralized AI research and developments at earned him a spot on the Forbes China 100 Most Influential Chinese 2024 list. Sun emphasizes, "Through innovative technology and collaboration, AI can truly benefit humanity." Academic paper in lEEE By 2024, secured strategic partnerships with institutions like London's Moorfields Eye Hospital, top crypto trading firm GSR, and blockchain giant Animoca Brands, expanding real-world applications of decentralized AI. Notable advancements include a multivariate predictive model for managing blood glucose levels in diabetic patients and privacy-preserving algorithms improving ophthalmology diagnostics worldwide. These achievements underscore commitment to harnessing decentralized AI for impactful solutions in healthcare and beyond. Global Market Innovation In March 2024, secured $6 million in seed funding led by Lightspeed Faction, fueling collaborations with Moorfields Eye Hospital and Request Network. By the end of the year, the company completed a strategic funding round led by DCG, enabling further expansion. Leveraging federated learning technology and tailored AI solutions, has established a strong presence in Europe and US through deep partnerships with global allies, solidifying its role as a leader in decentralized AI. CEO Jiahao Sun stated, "Our goal is to expand FLock solutions to both enterprise and retail AI market, driven by continuous product optimization and international collaborations." Despite its promise, decentralized AI faces hurdles such as scalability and interoperability. Larger networks can slow performance, while seamless cross-platform communication remains a challenge. Sun identifies blockchain technologies and standardized protocols as critical to improving efficiency. Additionally, navigating regulations like GDPR and HIPAA is essential for building trust and driving adoption. To address these challenges, plans to increase R&D investments, focusing on technical bottlenecks and compliance. The company also aims to enhance collaborations with global institutions to promote the standardization and scalability of decentralized AI applications, paving the way for widespread industry adoption. Hashtag: # The issuer is solely responsible for the content of this announcement.
