Latest news with #decentralizedAI
Yahoo
21 hours ago
- Business
- Yahoo
Imagen Network Builds Adaptive Content Filters Using Grok to Boost Engagement Quality
Imagen integrates Grok intelligence to deliver more responsive feed moderation in decentralized apps. Singapore, Singapore--(Newsfile Corp. - August 7, 2025) - Imagen Network (IMAGE), the decentralized AI-powered social platform, has introduced a new suite of adaptive content filters designed to improve engagement quality across its Web3 environments. This development leverages Grok's intelligent infrastructure to identify context and tone in real-time, providing more precise feed moderation without compromising transparency. Advanced AI tools designed for smarter decentralized social interactions. To view an enhanced version of this graphic, please visit: The new filters support dynamic adjustments based on behavioral feedback and allow communities to retain autonomy while benefiting from advanced moderation. Grok's capabilities are embedded to optimize how user content is evaluated and organized, adapting to conversational shifts and sentiment trends as they occur. This initiative reinforces Imagen's commitment to empowering user-led spaces with scalable AI systems. This upgrade follows Imagen's broader roadmap to streamline on-chain user experiences and enhance social authenticity. By combining Grok's real-time reasoning with decentralized control, the platform strengthens its position as the standard for intelligent, creator-led social networks. About Imagen Network Imagen Network is a decentralized AI platform focused on building user-led social applications that combine personalization, transparency, and smart interaction systems. It enables peer engagement through intelligent tools and adaptive AI systems for the Web3 generation. Media Contact Dorothy Marley KaJ Labs +1 707-622-6168 media@ Social Media Twitter Instagram To view the source version of this press release, please visit Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
7 days ago
- Business
- Forbes
AI Training Gets 10x Faster, 95% Cheaper With Decentralized Strategy
A quiet shift in the foundations of artificial intelligence (AI) may be underway, and it is not happening in a hyperscale data center. 0G Labs, the first decentralized AI protocol (AIP), in collaboration with China Mobile, recently announced a technical breakthrough that could have sweeping implications for how businesses access and deploy large language models. Their innovation is a new method of training massive AI models with over 100 billion parameters, without needing the ultra-high-speed internet or expensive centralized infrastructure typically required. At first glance, this might sound like a win for the engineering world. But the real story is economic and strategic. What 0G Labs has achieved could lower the cost of building AI, put more control back into the hands of enterprises, and open the door for new players to enter the space. What It Means For AI Training To understand the shift, it helps to revisit how large-scale AI models are currently trained. Models like OpenAI's GPT-4 or Anthropic's Claude require vast computing power and network throughput. Traditionally, this means training them on powerful GPUs connected across high-speed, centralized data centers owned or rented from companies like Amazon Web Services, Google Cloud, or Microsoft Azure. As of early 2025, OpenAI's leadership, including Sam Altman, publicly stated that training GPT‑4 cost over $100 million. This is supported both by official statements and multiple cost models in recent AI analysis reportsIt is a model that demands capital, talent, and infrastructure that few organizations can afford. 0G Labs Is Challenging That Assumption For AI Training Their newly published framework, called DiLoCoX, introduces a low-communication training method that dramatically reduces the need for high-bandwidth connectivity. In practical terms, they successfully trained a 107 billion parameter model on a 1 Gbps network using decentralized clusters. This record is a 10x improvement of the previous record and the 300x speed-up breakthrough that made this possible for the first time. This is roughly the bandwidth of a typical office internet connection. Instead of building everything in one giant compute center, their approach links together smaller, distributed machines and optimizes how information is shared between them. The result is a highly scalable, cost-efficient way to train massive models outside the traditional cloud. In speaking with 0G labs founder and CEO Michael Heinrich, he said 'DiLoCoX marks a pivotal step in democratizing LLM training: bridging the gap between massive foundation models and decentralized clusters connected by slow, unreliable networks. By combining pipeline parallelism, delay‑tolerant communication overlap, and adaptive gradient compression, the framework delivers scale and speed previously thought exclusive to high‑bandwidth data centers. This will usher in a new era where large‑scale AI training is no longer tethered to centralized infrastructure.' Why Does AI Training Matter for Business At a time when every enterprise is under pressure to do more with AI, infrastructure is quickly becoming the bottleneck. Some businesses are starting to look at decentralized AI by design. Building large models remains expensive, exclusive, and largely confined to companies with deep resources or strategic cloud partnerships. 0G's breakthrough opens up a third path. This is not just a story of cost savings. It is a story of optionality and control. 1. Lowering the Barrier to Entry DiLoCoX's approach reduces the infrastructure by up to 95% required to participate in the LLM race. For startups, this means the ability to experiment and scale without burning through venture capital on GPU spend. For mid-sized enterprises, it offers the possibility of training models in-house without making large cloud commitments. For governments and research labs, it means more accessible and sovereign development of AI capabilities. 2. Strategic Independence from Hyperscalers Most AI training today depends on three cloud providers. That concentration carries risk in terms of cost escalation, vendor lock-in, and compliance. If your business depends on AI but also operates in a sensitive sector like healthcare, defense, or finance, the ability to train or fine-tune models independently becomes a powerful strategic lever. Decentralized AI offers a route toward digital autonomy. By breaking the assumption that cutting-edge AI must be trained inside centralized cloud platforms, 0G's model creates new room for competition and for innovation. 3. Aligning with Data Privacy and Compliance Needs Many companies are cautious about uploading proprietary data to cloud-based models or training environments. With decentralized training, it becomes possible to keep data local within jurisdiction, within the firewall, or even on edge devices while still participating in large-scale AI development. This is particularly attractive in regions with strict data sovereignty laws such as the European Union or countries building their own AI ecosystems. The 0G network never sees any of the private data 4. Accelerating Innovation in Underserved Markets The high cost of entry has kept many countries and industries on the sidelines of advanced AI development. DiLoCoX lowers that threshold. A university in Kenya, a telecom provider in Southeast Asia, or a regional bank in Latin America may not have access to the same compute as Silicon Valley, but they may soon have the tools to train and deploy their intelligent systems on existing infrastructure. 5. Geopolitical and Regulatory Risks While the technical achievement is impressive, the involvement of China Mobile raises questions. As tensions between the United States and China continue to escalate over technology leadership and national security, businesses must weigh the potential regulatory scrutiny, data governance concerns, and reputational risks associated with partnerships involving Chinese state-affiliated entities. For companies based in the United States or operating in allied markets, any integration of infrastructure or research tied to China could face export controls, legal restrictions, or public backlash. Organizations exploring decentralized AI solutions will need to consider not just performance and cost, but also political alignment, compliance frameworks, and long-term viability. However, having DiLoCoX on a decentralized infrastructure where the network is trustless, this is not a concern because China Mobile never sees your data, and the system doesn't rely on them for results. Reframing the Business Model of AI If DiLoCoX is widely adopted, it could create ripple effects across the broader AI ecosystem. Cloud revenue models, currently boosted by AI workloads, could face new pricing pressure. AI-as-a-service platforms may need to re-architect to support hybrid or decentralized deployments. Open-source frameworks might grow in influence as decentralization emphasizes interoperability and local control. Enterprise software vendors may need to rethink their AI strategies to reflect a more distributed compute landscape. This shift also aligns with the broader trend of AI for everyone. From low-code agent builders to edge-based inferencing, the movement is toward more accessible, modular, and customizable AI stacks. Decentralized training is the natural extension of that philosophy. An AI Signal for CIOs and CTOs For enterprise leaders, 0G's work serves as a signal not of immediate disruption, but of near-future opportunity. AI is evolving from its critical beginning. Now is the time to reevaluate infrastructure strategy. Should your organization continue investing in cloud-based model hosting, or begin exploring decentralized alternatives? Could your internal data center serve as a node in a distributed training system? Decentralized federated learning is a great way of tapping into private data from different parties on a network, like hospitals training a cancer diagnostic model. Might you partner with others in your sector to co-develop models using decentralized protocols? Even if the answer is not yes today, the emergence of frameworks like DiLoCoX should push AI infrastructure planning higher on the strategic agenda. Businesses that prepare for this shift by building internal capacity, evaluating partners, and understanding the technical stack will be best positioned to move when the economics tip in their favor. A Future Where AI is Built Differently What 0G Labs and China Mobile have demonstrated is more than just a technical proof of concept. It is a new way of thinking about how intelligence is built, trained, and distributed. By showing that it is possible to train 100 billion parameter models without centralized supercomputers, they are not just pushing the boundaries of scale. They are expanding access. For business, that means AI may soon be less about who owns the biggest data center and more about who can build the smartest systems with the most flexibility. That is an AI future worth preparing for.
Associated Press
29-07-2025
- Business
- Associated Press
Imagen Network Integrates Grok Framework to Enhance Feed Logic and Adaptive User Experience
Grok-powered enhancements boost AI social curation and personalize decentralized peer interactions. Singapore, Singapore--(Newsfile Corp. - July 29, 2025) - Imagen Network (IMAGE), the decentralized AI social platform, has integrated the Grok framework to expand its adaptive feed logic and user experience personalization tools. The move empowers real-time adjustments to user feeds based on behavior patterns, interests, and social engagement data. [ This image cannot be displayed. Please visit the source: ] Powering dynamic social feeds with intelligent, decentralized personalization. To view an enhanced version of this graphic, please visit: Grok's intelligent routing and inference capabilities enhance Imagen's curation engine—allowing AI systems to filter, prioritize, and surface content more accurately. This ensures that user feeds evolve dynamically, offering each participant a unique, context—aware social experience while preserving privacy and sovereignty. Combined with Imagen's decentralized content governance and token-based engagement mechanics, this integration represents a leap forward in scalable AI moderation for social applications. Peer-to-peer curation is faster, more intuitive, and better aligned with user intent and values. The Grok-backed enhancements support Imagen's broader mission: to give individuals creative control, better discovery, and real-time relevance in a decentralized digital world. About Imagen Network Imagen Network is a decentralized social platform that blends AI content generation with blockchain infrastructure to give users creative control and data ownership. Through tools like adaptive filters and tokenized engagement, Imagen fosters a new paradigm of secure, expressive, and community-driven networking. Media Contact Dorothy Marley KaJ Labs +1 707-622-6168 [email protected] Social Media Twitter Instagram To view the source version of this press release, please visit
Yahoo
20-07-2025
- Business
- Yahoo
TAO Synergies Becomes Largest Public Holder of Bittensor Token With $10M Purchase
TAO Synergies (TAOX), formerly a biotech firm Synaptogenix, said it purchased $10 million worth of Bittensor's TAO token, becoming the largest publicly traded holder of the cryptocurrency. The New York-based company has acquired 29,899 TAO tokens at an average price of $334 each, according a press release. It plans to stake the tokens within the Bittensor network, which rewards participants for contributing to the development of AI models. The company's bet on TAO, rather than on other cryptocurrencies more popular for corporate treasuries, rests on its 'expected continued growth and expansion of decentralized AI,' according to the company's executive chairman Joshua Silverman. Bitcoin (BTC) and ether (ETH) are the most commonly chosen for corporate treasuries. BTC held by publicly-traded firms has skyrocketed over the last few months to now stand at 860,766 according to BitcoinTreasuries. Similarly, ETH held by corporate treasuries and DAOs is now taking off and has reached 1.8 million ETH. 'Decentralized AI and TAO remind me of the Internet in 1996,' said James Altucher, the company's treasury strategist. 'It's still early, but growth is accelerating.' TAOX shares closed up 7.55% in Friday's trading session and moved up an additional $1.29% in after-hours trading to $10.24. TAO went up 7% over the past week, but is down 2.5% in the last 24-hour period. Sign in to access your portfolio
Gizmodo
19-07-2025
- Business
- Gizmodo
Crypto Founder Ken DiCross on How He Uses AI: ‘I Don't Trust AI,' but ‘I Use It for Everything'
Ken DiCross is building the infrastructure to connect blockchains, and he's doing it with AI. The founder of Wire Network, a blockchain interoperability company, DiCross says he uses AI for everything from pitching investors to stress-testing white papers. In this week's edition of How Do You Use AI?, he explains how it saves him hours every day, why he doesn't trust it blindly, and why decentralized AI could be the next big revolution. No TED Talk nonsense, just real life. Episode 2: Ken DiCross—Crypto Believer. Gizmodo: How do you use AI right now? DiCross: I use AI for everything I possibly can; from helping with my schedule to categorizing and helping with email responses. I definitely use it for search. I can't tell you the last time I went to Google. I think that is just atrocious. I use it for business plans, for pitch decks. I use it to compare other interoperability or blockchain or AI companies. Gizmodo: How does that work? DiCross: I go to their website, grab their white paper, and download it into the LLM. Then I start asking a series of questions until I can find the issue. It's always centralization. Rather than solving it properly in a decentralized way, they just add a fix that creates some kind of risk—security, cost, or time. I create my list and send it off to my team or investors to show we still have a moat. That would take an enormous amount of time if I did it manually. AI helps me get it done in five minutes. Gizmodo: What was the last thing AI helped you do? DiCross: I created a contract. We're starting to bring on advisors, ambassadors, and consultants. I took all the requirements for what we want these people to do and asked the AI to categorize them—what belongs in each role—and generate a base contract. I still send it to legal, but it saves me an hour-long call or a lengthy email. I just feed it in real time and it gets me 80% there. Gizmodo: Are there tasks you've completely handed over to AI? DiCross: Yes, especially presentations. I don't create slides from scratch anymore. I just prompt it with the bullet points, and it fills out the rest. The LLM already knows Wire; we feed it everything. The output is fast and on point. It's the same for my devs. My top-tier engineers use it so efficiently that they're basically two or three people in one. We don't need junior-level devs anymore. Gizmodo: Do you trust AI completely? DiCross: No. It still hallucinates. I gave it a math problem the other day, and it botched it; something a six-year-old could do. I had to walk it through the right answer. So when people do 'vibe coding' with AI, it can be dangerous. If you don't understand the code, you won't know if the AI's output is secure. You still need experts in the loop. Gizmodo: Do you use it in your personal life? DiCross: Definitely. I use it for search all the time. I don't use Yelp or Google anymore. If I'm traveling, I ask for the best restaurants nearby, their hours, anything. I pick up 5 or 10 minutes here and there and end up with a few extra hours in my day. Gizmodo: Has AI ever surprised you? DiCross: It's not the answers that surprise me. It's when it won't answer something. You hit a wall and don't always know why. It's frustrating. That's why we need decentralized AI: so people can get the information they need without worrying that it's being filtered or censored. Gizmodo: What do you say to people who are scared AI knows too much? DiCross: That's exactly why it has to be decentralized. I agree with them. Every time I use ChatGPT, I sigh. I know it's building a profile on me. You want your data encrypted and private. Right now, if you type in something like a medical concern, that data can be sold. Your insurance could go up in minutes. That's dystopian. We need decentralized, encrypted AI that works for us, not for Microsoft. Gizmodo: Do you feel uneasy using it? DiCross: Yes, because I know my data is getting hoovered up. But I also feel optimistic. Decentralized AI is coming. Just like Linux became the backbone of every server, we'll have open-source AI that anyone can use without fear. That's where the future is heading. Andy Cohen on How He Uses AI: 'It's an Incredible Tool. But It's Going to Make Idiots'
