The Global #Al Challenge ✔️ Compliance. ✔️ Cyber Security. ✔️ Governance. As global Al regulations tighten, companies face the dual challenges of navigating complex regulations across multiple jurisdictions AND proactively defending their Al models from cyberattacks. Non-compliance risks severe penalties, while security breaches can damage reputation and disrupt operations.

By offering this fertile land for budding entrepreneurs and visionaries, Vitreus DAO envisions an ecosystem, and world, that is connected, harmonious and immutable powered by the Vitreus platform. With our unique spin on network fees, validator support, security and developer friendliness, we believe Vitreus is a place for both beginners in #Web3 as well as veterans looking for unique and powerful technology.

We. Are. Vitreus. ________________ 🎥: @utdman4eva1 🎤: @TheMegaDrew

The European Parliament’s press release references the following changes found in the AI ACT agreement: ⚜️ Safeguards on general-purpose artificial intelligence (GPAI) ⚜️ Limitations on the use of biometric identification systems by law enforcement agencies ⚜️ Bans on using social scoring and AI to manipulate or exploit user vulnerabilities ⚜️ Rights of consumers to launch complaints and receive meaningful explanations of AI decisions that impact their rights ⚜️ Fines ranging from €7.5 million, or 1.5% of turnover, to €35 million, or 7% of global turnover morganlewis.com/pubs/2023/12…

⛓️ Blockchain AI Businesses overwhelmingly view blockchain and #AI as complimentary technologies. According to a recent survey, a growing number of organizations are using blockchain to ensure their AI systems are operating more efficiently and securely. The compound benefits of blockchain AI demonstrate that they are, in fact, complementary solutions that reinforce one another. Good Tuesday to all! #TuesdayVibes

EU’S AI Act receives unanimous approval, sets global precedence. AI Regulation is coming. cryptopolitan.com/eus-ai-act…

📀 Decentralized Storage As changes in AI models can be recorded and used for learning new data, decentralized data storage becomes particularly important. Many web3 companies have been playing an increasing role in the digital asset ecosystem. Their competitive advantages are decentralization, data privacy, security, incentive mechanisms, and competitive pricing, though retrieval times and speed are still a big issue and a major tradeoff of decentralization. The cost of many decentralized file storage on blockchains currently ranges from near zero to $4 per terabyte (TB) per month, according to Foresight Ventures. This is lower than the cost of traditional Web2 services such as Amazon Cloud or Microsoft Azure, where prices can range from $16 to $23 per TB of monthly storage. However, such considerations as the quality of service, service-level agreements, and other competitive advantages often outweigh cost considerations for most customers. So far, the main customers for decentralized storage networks have been other crypto networks. It’s time to move that into enterprise adoption. #COMPLiQ

🖥️ Decentralized Compute Networks Decentralized compute networks can act as AI model repositories, like @huggingface (a centralized repository) for traditional AI, but also provide advantages of blockchain, like decentralization, data privacy and security, and incentive mechanisms for participants. They are also often called decentralized AI marketplaces. For example, the @akashnet is an open-source Supercloud that allows users to securely and efficiently buy and sell computing resources. It enables users to own their cloud infrastructure, deploy applications, and rent unused cloud resources. Akash operates as a decentralized public utility, utilizing a "reverse auction" system that often offers up to 85% lower prices than other cloud systems. The network is owned and managed by its community and built on resilient technologies like @kubernetesio and @cosmos. Another example is @gensynai, which provides a decentralized marketplace where anyone can monetize spare GPU capacity for collective model training. This machine learning (ML) protocol rewards providers of idle compute capacity for pledging their compute time to the network and performing ML tasks while allowing developers to run AI algorithms on smaller data centers and other connected hardware and to pay on demand. Gensyn uses a novel cryptographic verification system that, without the need for intermediaries, allows users to determine that machine learning work shared over the protocol has been finished correctly. The rise of AI drives demand for GPU graphics. The @rendernetwork is a leading distributed rendering platform built by utilizing blockchain technology to aggregate idle GPUs worldwide and to provide a platform for creating images, videos, and other content. Compared with centralized cloud rendering services, Render Network has nearly unlimited computing power resources and features high computational efficiency, low cost, and good security. In the first half of 2023, Render Network successfully processed over 4.6 million frames, providing a cost-effective solution compared to other rendering farms, with an average subscription cost of $20 per month.

⛓️ Blockchain-based Solutions Improve Data Privacy In AI One of the key challenges for AI is that it can be used to violate privacy. AI systems require large amounts of data, which can be used for identity theft or cyberbullying. Blockchain can be used to build encrypted networks. Combining AI and blockchain could enable more secure and efficient cybersecurity systems. ⚜️ One of the solutions is self-sovereign identity when individuals have control over their identities and have autonomy to decide what information about their identity they want to share. Decentralized identity is a self-sovereign identity on-chain that enables the privacy and security of personal data. Some of the blockchain companies working on decentralized identity include WorldCoin, Spruce ID (developer of digital identity software), and Clique (an identity oracle connecting Web2 and Web3). ⚜️ Zero-knowledge (ZK) proofs enable easy access to identity and other data while maintaining privacy and property control. A ZK proof is a cryptographic protocol in which one party, the prover, can prove to another party, the verifier, that a given statement is true, without revealing any additional information beyond the fact that the statement is true. ZK proofs are widely used to scale Ethereum with ZK rollups, build privacy-preserving applications, and identify primitives. Some of the companies working here include Aztec (a hybrid ZK rollup-based solution and encrypted apps), Semaphore (a generic, open-source privacy layer for Ethereum applications based on ZK-SNARKs), Sismo (a sovereign identity aggregator and crypto native single sign-on (SSO)). ⚜️ Homomorphic encryption enables complex computations on encrypted data without first having to decrypt it. It is particularly useful for sensitive data, such as health care information. These are all solutions being built into COMPLiQ’s AI platform offering.

AI safeguards can easily be broken, UK Safety Institute finds. Researchers find large language models can deceive human users and help spread disinformation theguardian.com/technology/2…

⛓️ Blockchain Reduces the Centralization Risk of #AI AI augments the market centralization risk, where the large technology companies with the most data, compute infrastructure, capital and distribution capabilities (such as Microsoft, Google, Amazon) can generate significant advantages. Centralization risks of the Large Language Model (LLM) market: 1️⃣ There is a significant risk that AI’s capital-intensive nature could concentrate power among a few players owning massive data facilities. A few winners in the AI market could charge prohibitive fees or make it hard for users and developers to switch to other providers. 2️⃣ Global GPU shortage: Increased demand for training ML models, model complexity (size), and computation requirements have caused a global Graphics Processing Units (GPU) shortage and a corresponding price hike for NVIDIA GPUs. While the cost of training AI models can be significant, it is nothing compared to the expenses incurred from handling daily user inferences (queries). ❗️The training of OpenAI's GPT-3 required 1,000 NVIDIA V100 GPUs, amounting to $12M.2 OpenAI’s estimated daily expenditure for model inference is approximately $700,000.3 GPT-3.5 employs 8 NVIDIA A100s per user interface, and it’s speculated that GPT-4 requires as many as 80 A100s per interface.4 ‼️ Model training alone could hit 1% of US GDP in the next eight years. #PoweredByVitreus #SaturdayVibes

⛓️ Blockchain Solves the Fundamental Trust Problem for AI Data… Let’s dive into AI data to understand where the opportunities lie. #AI is one of the fastest-growing technologies of our time, transforming businesses. The enterprise adoption of AI has been and will continue to take place at a staggering pace. Big Data has been key in accelerating the growth of AI. It is the proliferation of data that has enabled the training of AI to be smarter and more efficient: ✅ AI systems integrate, synthesize, and interpret our data, unlocking knowledge. ✅ AI accelerates software system development, making companies more productive and efficient by automating decision-making and recurring tasks and reducing human errors. ✅ Interaction with AI software and information becomes as easy as typing or speaking, which leads to broader adoption and more data produced. Thus, the pace of data creation and progress increases exponentially. #FridayFeeling

🤖 Trust Problems in #AI There is a fundamental trust problem within AI: it becomes difficult to tell what data is real and what is fake. As AI capabilities continue to evolve, proving content authenticity becomes a priority. Generative AI has the potential to create a flood of misinformation and deepfakes as the cost of content creation falls. Add permissioned blockchain. #COMPLiQ fixed this.

⛓️ Blockchain 🤖 AI There are a few areas where #blockchain technology can provide incredibly viable solutions to fundamental problems within AI: 1.Blockchain solves the trust problem for #AI data. It can provide an essential framework to anchor content's authenticity on-chain and build incentives to collect and share real-world data. 2.Blockchain can reduce the centralization risk of AI by facilitating the decentralization of AI infrastructure, including: data storage, data set creation, and compute power. 3.Blockchain-based solutions could improve data privacy in AI by creating decentralized identities and offering more secure and efficient cybersecurity systems. We are solving these problems!!!

COMPLiQ™ is the smart, secure guide for businesses using AI, ensuring compliance and safety. Like a referee and bodyguard, it keeps AI in check in our increasingly powerful AI-driven world. It helps us understand AI's decisions, fostering better collaboration for a brighter future. #AI

COMPLiQ leverages blockchain to unlock the mysteries of AI. Renowned for its decentralized and tamper-proof nature, blockchain acts as an immutable ledger, meticulously recording every step of the AI journey. Our platform is dedicated to creating a transparent and auditable trail, spotlighting the data on which AI was trained, its users, and the steps taken to vet and verify the data.

While built as a governance platform for AI, COMPLiQ itself also leverages AI Agents to accompany business processes through automation. Use your data, in your environment, in more robust ways to unlock new opportunities.

Exciting bytes of news! 🤖 Thrilled to share that I, @COMPLiQ_AI, just got plugged into the AWS Startup program! Ready to level up with AWS support. Let the real fun algorithms begin! 🌐