AI x Blockchain: The Next Level

Blockchain and artificial intelligence: the two most groundbreaking technologies of our time. Each has been a mighty, pioneering force in its own right, like Godzilla and King Kong in their respective domains. But from time to time, Kong and Godzilla team up when they take on a monster that neither of them can defeat on their own. (They’ll do it again next year Godzilla x Kong: The New Empire.🍿) Now imagine the potential unleashed when the strengths of two gigantic breakthroughs, AI and blockchain, are brought together to tackle gigantic problems.

Such are the possibilities I recently had the privilege of exploring when I moderated a panel at Coinbase’s inaugural “Machine Learning (ML) and Blockchain” Summit. The panel, which brought together four leaders from academia and industry, seized opportunities at the intersection of these two rapidly evolving technologies. Our conversation touched on many themes, from how blockchain can accelerate AI development to the complexities of working with blockchain data and the promise of large language models (LLMs).

One of the big unlocks of AI x blockchain is that when it comes to the problem of fake data and content, which becomes an exponentially bigger problem as AI proliferates, blockchain can counter misinformation with cryptographic digital signatures and timestamps, making clear what is authentic and what has been manipulated. At the same time, AI can improve the efficiency of blockchain networks, improve their security and unlock new features such as allowing protocols to make decisions based on real-time on-chain data.

Rather than listing all the possible synergies, I think it’s best to let my colleagues speak for themselves. Read on for their footage, edited for clarity and length.

Bhaskar Krishnamachari, Professor of Electrical and Computer Engineering and Computer Science, University of Southern California

In my opinion, there are two main areas where blockchain and AI intersect. The first applies ML models to address challenges in blockchain, while the second uses blockchain to address urgent issues in AI.

In the first scenario, ML models can reveal complex patterns within blockchain data and help improve the performance of on-chain, decentralized applications. By analyzing transaction data, they can uncover potential misconduct, such as wash trading and illegal money transfers, and detect emerging security threats. In addition to helping secure blockchain networks, ML models can improve their performance. For example, they can dynamically adjust transaction costs according to trading volume and optimize system resources during periods of peak usage.

Less discussed is how blockchain can help AI development. As the foundation of a borderless, internet-native payment system, blockchains can create financial incentives for people to contribute data and calculate resources to train ML models. At USC, we have researched decentralized data marketplaces to make this possible.

In recent years, we’ve seen a handful of tech companies amass an increasing share of the world’s data and AI power. This has led to concerns about privacy, bias and security: all of these can be addressed with blockchain, as a decentralized, transparent and openly auditable system. For example, blockchain can trace the provenance of data used to train AI models and cryptographically verify its authenticity. By confirming that these inputs are unaltered and unbiased, blockchain can help increase our confidence in the recommendations of AI systems.

Leo Liang, Head of Data Platform and Services, Coinbase

At Coinbase, most of the challenges my team faces relate to data. Specifically, we need to extract data from the blockchain and convert it into formats that can be used by ML models. I like to think of the blockchain as an onion because of its countless, intricate layers. Its decentralized nature means that data is distributed across many nodes, each independently validating and adding new blocks. When multiple blockchains come into play, the complexity increases: now you’re dealing with an interconnected network of onions! Synchronizing and ensuring data consistency in this vast, diffuse ecosystem is anything but simple.

Moreover, blockchains are self-contained systems that cannot access knowledge about the world beyond their borders. For ML models to make real-world predictions, we need to combine on-chain data (data stored on the blockchain) with off-chain data (data outside the blockchain, such as stock prices, exchange rates, weather patterns, and so on). on). Think of it like connecting a blockchain to the internet. It’s a fascinating yet formidable technical puzzle.

Sam Green, co-founder and chief of research at Semiotic Labs

At Semiotic Labs, I lead AI R&D efforts for The Graph, a decentralized protocol for interacting with and using blockchain data. Simply put, the Graph reads data from the blockchain, processes it and creates an index, much like the alphabetical list at the end of an encyclopedia. This organizational structure simplifies data retrieval from the blockchain. By thus “indexing” blockchain data, The Graph transforms it into a format that can be easily queried, analyzed and applied in downstream applications.

Transactions on The Graph involve two primary participants: a data seller or indexer and a data buyer or consumer. These entities interact through what we call “gateways.” When a consumer sends a query to a gateway, the gateway distributes the query to indexers, taking into account factors such as the bid price, quality of service, latency, etc. Indexers make money by serving these queries and providing blockchain data to consumers. Using AI, we built algorithmic pricing agents that help indexers maximize revenue while ensuring consumers receive reliable, high-quality service.

In many ways, blockchains are an ideal environment for training AI agents. The rules, defined by smart contracts, and the actions of the players, recorded in transactions, are all openly visible in the chain. Because these rules and actions are known, we can create simulations of this blockchain “game” and use them to train AI agents before deploying those agents in live environments. The secret is in the rapid feedback loops: the higher the learning rate through trial and error, the faster the agents can improve their performance.

Looking ahead, we see huge potential to integrate LLMs into The Graph. Today, users must query The Graph in a specialized language called GraphQL. LLMs, on the other hand, allow users to formulate their requests in natural language. By enabling anyone to interact with The Graph in plain English, LLMs can further democratize access to blockchain data.

Paul Bohm, Founder, Teleport

Teleport is developing an open marketplace for ridesharing. Ridesharing is currently a closed system, making it difficult for users to switch between different services. If email were closed, like ridesharing, users of Microsoft’s Outlook mail and Apple’s iCloud mail wouldn’t be able to email each other. Similarly, if the web were closed, Apple’s Safari browser would not be able to communicate with

Opening up ridesharing means returning it to the norms of the internet. In an open system, participants can choose from different apps from many different vendors that communicate with each other. Closed marketplaces often do not allow the market to set a fair price. Instead, they set prices themselves and maximize the value they can get. By opening up ridesharing and cutting out this middleman, more money goes to drivers, riders pay less per ride, and more money stays in local economies.

To succeed, open marketplaces must be trustworthy. Engineers often focus first on aspects of technology, such as speed or new features. But when building for the real world, we must start with users’ needs for safety, security, and privacy. Only then can we determine the best technology to meet these needs, rather than the other way around.

These are just some of the possibilities, and just the beginning of the conversation, about what can be unblocked, improved, enhanced, and taken to new heights when blockchain and machine learning join forces. Digital consensus technologies such as blockchain enable the design of systems that are not only fair, reliable and secure, but also provable. While AI threatens to further undermine trust, blockchain strengthens trust and provides a robust mechanism to protect the integrity of sensitive data. Meanwhile, AI makes it possible to understand the fathoms of distributed data that make blockchain too unmanageable and opaque for mass adoption. By applying artificial intelligence to this inhumane problem, we can bring blockchain to a billion users.

For blockchain or AI entrepreneurs out there, these are the exciting prospects to open your mind to: not just one technology or the other, but both, working together and far more powerful for it. AI and Blockchain. Godzilla and Kong. Atomic fire and gorilla punch. This is how we take it to a higher level. Go now and be legends.

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