🌵 The Intersection of Crypto & AI 🌵

Big Brain Breakdown

Market Metrics

Total Crypto Market Cap: up 3.6% to $2.24T
Total AI Sector Market Cap: up 3.8% to $19.96B

Top Movers (24hrs):

📈 SpectreAI (SPECTRE): up 42.2% to $1.96
📈 HyerGPT (HGPT): up 39.2% to $0.07339
📈 dotmoovs (MOOV): up 25.9% to $0.007801

Daily News

🟠 NIM Network is live. Nim Network is an AI Gaming chain that aims to provide the “ultimate ecosystem for exploration and development of games at the intersection of Web3 and AI.”

🟠 Arkham has partnered with Telegram, bring Arkham data to millions of TON & Telegram users.

🟠 dotmoovs is migrated the MOOV token to Ethereum mainnet. The migration period will end on April 26, 2025.

🟠 Nosana showcased its network explorer, which showcases the number of inferences made on the network, the number of computing resources committed, and various other metrics.

🟠 Nous Research just juiced up Meta’s Llama-3 LLM.

🟠 Taoshi’s Request Network is live on testnet. The Request Network aims to create a marketplace where clients access services and data from subnets

🧠 Big Brain Breakdown

Welcome back to another Big Brain Breakdown, where we help you understand the fundamentals of blockchain AI projects so you can stay ahead of the herd and invest in projects poised for outperformance.

As AI usage continues to sky rocket among the population, a critical issue has emerged that threatens the foundation of these systems: data poisoning.

Data poisoning occurs when the information fed into AI training datasets is intentionally or unintentionally altered, leading to skewed outputs and potentially harmful consequences. This manipulation can happen for various reasons, including financial gain, political influence, ideological bias, web-scraper honey-potting or even as a result of a company's internal culture. The incentives for data poisoning are numerous, and the methods employed can be difficult to detect.

AI models rely heavily on vast amounts of data to learn and make decisions, so the integrity of the data used to train them is extremely important. If ChatGPT was trained on completely incorrect information and gave obviously incorrect answers, it wouldn’t be very useful.

However, what if it was trained on correct data that had a little sprinkle of bias? Or just a small percentage of the data was manipulated slightly? It would be very hard to tell, but every response would be slightly tainted.

This is the current reality of training data, and if left unchecked, the consequences could be far-reaching and devastating.

One recent example of data poisoning's impact is Google's Gemini LLM, which produced historically inaccurate images due to biases in the model's dataset stemming from the company's ideological leanings. This incident caused a significant uproar in the media and highlighted the dangers of opaque data sets curated by centralized providers.

How Blockchain Technology Mitigates Data Poisoning

Blockchain technology stands as a cornerstone in combating data poisoning, primarily through its superior capabilities in data provenance. Data provenance refers to the ability to trace the origin and history of data, which is crucial for maintaining data integrity and identifying any potential manipulations.

By leveraging cryptographic validation and consensus mechanisms, blockchains can establish a robust framework for data integrity. Cryptographic validation ensures that each data entry is accurately verified, while consensus mechanisms could be used to require multiple nodes to agree on the validity of the data before it is added to the dataset. This combination creates an immutable record of data origins and subsequent modifications, making it significantly more difficult for data to be tampered with without detection.

Furthermore, the decentralized nature of blockchain means that data is sourced from a diverse array of end points and sources spread across the globe. This decentralization not only reduces the risk of localized data tampering but also dilutes the impact of any single point of data poisoning, thereby enhancing the overall reliability of the data used in AI systems. Such a setup is vital for maintaining a clean data pipeline, which is the backbone of trustworthy AI model training.

The transparency inherent in blockchain technology also allows for the continuous, real-time auditing of data-sets and also AI models themselves. This feature is instrumental in ensuring that AI operations remain transparent and under scrutiny, enabling stakeholders to quickly identify and rectify any deviations or anomalies that could indicate data poisoning.

Projects like Grass Protocol and Synesis One are examples of two blockchain-based projects tackling this issue. Grass Protocol, for instance, utilizes a layer 2 solution with a Zero-Knowledge processor to validate and embed metadata that verifies the origin of data, ensuring its authenticity and protecting against tampering. Synesis One, on the other hand, records ontologies on the Solana blockchain, capturing critical details like authorship and IPFS addresses to maintain data traceability and integrity, while also processing metadata off-chain for enhanced operational efficiency.

Our Take

The increasing prevalence of data poisoning in AI training datasets presents a significant challenge for the future of AI development. As companies become more protective of their data and resort to rate limiting and honey potting data scrapers, the integrity of the data used to train AI models is at risk. This not only undermines the accuracy and reliability of AI systems but also raises serious concerns about the potential for AI to be manipulated for nefarious purposes, such as swinging elections, influencing public opinion or even just persuading you to buy one product over another.

However, this challenge also presents a unique opportunity for blockchain-based solutions to emerge as the go-to standard for secure, transparent, and decentralized AI training data. Projects like Grass Protocol and Synesis One are two notable projects in this niche, leveraging blockchain technology to ensure data provenance, protect against tampering, and reward users for their participation in data collection.

AI Art of the Day

Take part in @generaitiv’s Daily AI art contest for the chance to win $150 of ETH and GAI. Find out more info in the thread below 👇