The AI Lab Partners with Theta EdgeCloud to Enhance AI Education

Somnia, described as "the dream computer for a fully on-chain world," has announced a strategic partnership with Ankr, a leading RPC node provider in the blockchain industry. This collaboration aims to enhance Somnia's ecosystem by providing developers with top-tier infrastructure, cost efficiencies, and innovative opportunities. The partnership is set to facilitate the on-chain integration of data, empowering developers with essential tools necessary for their success in the blockchain space. One of the standout features of this partnership is the provision of industry-leading RPC and subgraph services by Ankr, which will ensure that developers experience high performance and reliability while building on Somnia's blockchain. Additionally, Ankr is offering a generous free tier that allows developers to make up to 1 million free RPC calls per day. This initiative significantly reduces costs for developers, making Somnia an appealing platform for creating scalable decentralized applications. Furthermore, the two companies will collaborate on a hackathon aimed at fostering innovation within the Somnia ecosystem, inviting developers to explore the capabilities of Somnia's high-throughput blockchain. The partnership reflects a shared commitment between Somnia and Ankr to empower developers and expand the horizons of blockchain technology. By providing transparent and affordable pricing for developers, Ankr aligns with Somnia's philosophy of supporting builders in the decentralized application space. Together, they aspire to create a vibrant ecosystem where innovative dApps can thrive and reach a global audience, marking a significant step forward in the evolution of Web3 technology.

The demand for decentralized energy data storage is on the rise, with several tokens leading the charge in this innovative sector. Notably, Filecoin (FIL), Theta Network (THETA), BitTorrent (BTT), MultiversX (EGLD), and AIOZ Network are at the forefront of decentralized physical infrastructure networks (DePIN) within the energy niche. These tokens are not only pivotal in decentralized data storage and content delivery but also play a significant role in blockchain-powered energy solutions. Their price movements and trading volumes present potential opportunities for investors and developers engaged in the intersection of blockchain technology and energy. Filecoin has established itself as a leader in decentralized storage solutions, particularly in renewable energy data management. As of the latest data, Filecoin's price is $5.70 with a trading volume of $721 million, reflecting a modest increase of 1.43% over the past day. Similarly, Theta Network, which specializes in decentralized video delivery systems, is priced at $1.96 with a daily trading volume of $83 million, showing a 1.27% rise. Both tokens underscore the critical need for efficient data and media management in sustainable energy applications, demonstrating their growing importance in the market. Other notable players include BitTorrent and MultiversX, which focus on scalability and smart city integration. BitTorrent, priced at $0.000001, has a trading volume of $52 million but has seen a decline of 2.05% recently. In contrast, MultiversX aims to provide scalable blockchain solutions for smart cities, currently priced at $37.78 with a trading volume of $80 million, despite a slight drop of 1.11%. AIOZ Network, with a price of $0.763 and a trading volume of $16 million, enhances real-time energy monitoring through decentralized content delivery. Together, these tokens are shaping the future of energy management and infrastructure.

Orchid (OXT) has recently witnessed a remarkable surge in trading activity, with its trading volume skyrocketing by an astonishing 2500% within just 24 hours. This surge in trading volume, which reached a cumulative total of $547.74 million, indicates a significant increase in demand for the cryptocurrency. Alongside this, OXT's price has also experienced a notable uptick, rising by 30% during the same period. The data from AMBCrypto and CoinMarketCap highlights that this surge is not just a fleeting moment but rather a reflection of growing investor interest and adoption of the Orchid platform. The increase in trading volume has been accompanied by a corresponding rise in Open Interest, which has surged to a new all-time high of $19 million, marking an increase of 253.88%. This combination of rising trading volume and Open Interest suggests that investors are actively opening new positions, predominantly leaning towards buying. The price of OXT has seen a significant upswing, climbing from a low of $0.0768 to a high of $0.1598, with current trading around $0.1297. The altcoin has also demonstrated impressive gains on both weekly and monthly charts, with increases of 43.15% and 75.15%, respectively. Moreover, the bullish sentiment surrounding OXT is further evidenced by a spike in outflow volume, which has reached a high of $7.1 million. This indicates that many investors are opting to move their assets into cold storage or private wallets, rather than keeping them on exchanges. Additionally, large holders have shown increased interest, with inflows hitting a year-high of 255.79 million OXT tokens. This suggests that significant accumulation is taking place among large investors. If the current trends continue, OXT could potentially face resistance around $0.17, while profit-taking by short-term holders could lead to a drop to $0.09.

Large Language Models (LLMs) have become essential tools in natural language processing, capable of handling a variety of tasks. However, due to their broad training, they may not excel in specific applications without further adaptation. Fine-tuning techniques, such as Q-LoRA, allow researchers to tailor pre-trained models like Llama 3.2 11B for particular tasks, such as extractive question answering. This article outlines the process of fine-tuning Llama 3.2 11B using Q-LoRA on the SQuAD v2 dataset, showcasing the performance enhancements achieved through this method. LoRA, or Low-Rank Adaptation, is a technique that introduces new weights to an existing model without altering the original parameters. By adding adapter weights that adjust the outputs of certain layers, LoRA enables models to retain their pre-trained knowledge while acquiring new capabilities tailored to specific tasks. In this experiment, the focus is on fine-tuning Llama 3.2 11B for extractive question answering, aiming to extract precise text segments that answer user queries directly, rather than summarizing or rephrasing the content. The experiment was conducted on a Google Colab platform utilizing an A100 GPU, with the Hugging Face Transformers library facilitating the implementation. The results of the fine-tuning process were promising, demonstrating a significant boost in the model's performance on the validation set. The BERT score improved from 0.6469 to 0.7505, while the exact match score rose from 0.116 to 0.418. These enhancements indicate that the Q-LoRA technique effectively adapts the Llama 3.2 11B model for extractive question answering tasks. This article serves as a guide for researchers looking to apply similar methods to other models and tasks, highlighting the potential of fine-tuning in the realm of natural language processing.

This week, decentralized distributed GPU resource platform io.net announced a strategic partnership with OpenLedger, a data blockchain specifically designed for artificial intelligence (AI). This collaboration will enable OpenLedger to utilize io.net's global GPU compute resources, enhancing its ability to refine and train AI models. Known as the Internet of GPUs, io.net provides a powerful network of distributed GPU resources, allowing OpenLedger to accelerate the development of its AI models and empowering developers to create more efficient AI-based decentralized applications (DApps). According to Tausif Ahmad, Vice President of Business Development at io.net, this partnership will provide OpenLedger with a reliable infrastructure to scale its AI models and unlock new use cases, reinforcing its position as an innovative provider in the decentralized AI space. In addition to providing GPU resources, io.net's infrastructure will support the inference and hosting of AI models, ensuring optimal performance and scalability. This partnership is expected to enhance OpenLedger's reputation as a leading provider of reliable datasets, fueling innovation at the intersection of blockchain and AI. The collaboration aims to create high-quality data securely and efficiently while driving innovation and performance. A team member from OpenLedger emphasized that leveraging io.net's GPU infrastructure will allow users to fine-tune AI models more efficiently, ultimately leading to the development of trustworthy and explainable AI models. A significant factor in OpenLedger's choice of io.net as its GPU resource provider is the cost-effective and scalable compute solutions offered. This partnership will enable OpenLedger to expand its services without the constraints of high costs associated with centralized cloud providers. By processing larger datasets and developing AI models with unprecedented efficiency, OpenLedger aims to push the boundaries of decentralized AI innovation. Ultimately, this partnership aligns with OpenLedger's mission to foster an open, collaborative data environment while promoting the adoption of blockchain-powered AI solutions.

Hivemapper Network has announced a proposal to increase the price of map credits from $0.005 to $0.0075, effective January 1, 2025. This adjustment is part of a strategy to stabilize the price of map data for end customers while ensuring that the value accrued from customer spending benefits the Hivemapper Network. Map Credits, which are generated by burning HONEY, play a crucial role in this ecosystem, as developers must redeem them to access network data. The proposed increase aims to enhance the deflationary trajectory of HONEY by allowing a greater share of revenue to flow back into the network, thereby supporting its long-term sustainability. The Hivemapper Network operates on a Burn and Mint model, where map contributors earn HONEY for submitting data, while developers burn HONEY to access this data. This system is designed to balance supply and demand effectively. By raising the price of map credits, the network anticipates that developers will still find it viable to build value-added products, as the increased costs will be offset by the potential for higher profit margins. The leadership at Bee Maps, the first developer on the network, has expressed confidence that this price adjustment will not adversely affect network usage, indicating a manageable margin compression. Community engagement is encouraged as the proposal moves forward. Members of the Hivemapper community are invited to share their thoughts and feedback during a comment period from November 26 to December 2, 2024. The Hivemapper Foundation emphasizes the importance of collaboration in refining the network's design to achieve the goal of creating the world’s freshest map. This proposal reflects a commitment to both developer incentives and the overall health of the Hivemapper ecosystem.