Web3’s generative AI platform on Google Cloud

What is Generative AI for Web3?

The public’s interest has been drawn to generative AI (gen AI). Large language models (LLMs) have appeared in the news seemingly every day this year. The excitement may be shifting from cryptocurrencies to next-generation artificial intelligence, but the academic community is closely examining the convergence of blockchain and AI, and the Web3 community hasn’t been afraid to explore. For instance:

• The Machine Learning and Blockchain Research Summit was held at Coinbase.
• In order to overcome adversarial issues, the decentralized AI platform SAKSHI contains a proofs layer.
• FalconX made Satoshi available to help cryptocurrency traders.
• Code Reader, a tool from Etherscan that teaches about smart contract code.
• A plugin providing real-time data and actual transactions, like as token transfers, was released by Solana Labs.

In this blog post, Google want to talk about what the community is creating in order to move beyond experiments. They will go over some of the digital assets and Web3 use cases that they are assisting our customers with implementing in more detail.

Document search and synthesis use case

Searching for and synthesizing documents is the first use case. This use case focuses on explanations, summaries, and identification. It makes it possible for non-technical (i.e., commercial) users to learn from big document collections. For instance, gen AI can assist in summarizing a blockchain protocol’s whitepaper or assist in identifying flaws in a description of a smart contract. Numerous customers choose these applications as their initial use cases for assessing LLMs, allowing internal users access to both private and public data. This use case is facilitated by Vertex AI Search, which makes it simple to construct generative AI search engines quickly.

Customer service support and enhanced virtual assistants use case

The following use case involves improved virtual assistants and customer service support. This is a client-side application. Conversational AI is the next step in the development of virtual agents, helpers, and bots. They go one step further and enable more natural customer interaction with LLM-powered virtual agents, which includes giving the agent the ability to carry out transactions. You can create conversational AI with cutting-edge virtual agents using Vertex AI Conversation and DialogFlow CX.

Content discovery use case

Another crucial use case involves content discovery. The example for Web3 is what Google call research on cryptocurrency trading. Analysts might begin the day by reviewing the news stories that affected their portfolio of cryptocurrencies. They may hunker down and concentrate intensely on what counts. Consider how this Vertex AI Search demo might be used in cryptocurrency trading, for instance. That’s not all, though. Analysts can trade and create an algorithmic crypto trading strategy based on what they discover.

The developer efficiency use case

Google then enter the developer efficiency use case at this point. The most popular use case for Web3 is probably AI-assisted development in this context. For instance, AI-assisted development can aid programmers in navigating the challenges of writing smart contracts in Solidity. Similar to how Google Cloud’s Codey or other foundation models, such as Duet AI for Google Cloud, which is accessible in the Integrated Development Environment (IDE) and code editor, may be used to generate documentation for APIs, a Web3 infrastructure provider can do the same. Top of mind are four things:

1- Code completion inline

2- The creation of boilerplate code

3 – Code explanation where chatbots can respond to questions about codes

4- Code security checkpoints, including suggested solutions for risky dependencies.

LLM orchestration and routing use case

The more sophisticated use case of LLM orchestration and routing is one that Google have seen their customers develop. The majority of businesses will utilize various LLMs for various use cases. Consider each of the use cases this article mentioned. They won’t all be implemented using the same huge language model. How can you choose the best model to activate based on a certain user inquiry (i.e., prompt)? To do this, we demonstrated using LangChain’s RouterChain and Google’s text-bison model.

The creative use cases

The creative use cases for Web3, notably generative AI for images and videos, are last but certainly not least. Google  now enter the world of NFTs. Google’s Imagen foundation model is made available through Vertex AI. For instance, let’s say you have an NFT and would like to produce fresh photos in its design or new images or films that feature your NFT as their subject. Digital watermarking and verification are now available in Imagen (experimental), enabling you to:

• Create fresh images from a text prompt.
• Image editing using a text prompt
• Create a description for a picture
• Adapt a model to a particular topic

That was a quick overview of some AI use cases for the Web 3. The use cases mentioned above can be built by Web3 and other developers using generative AI on Google Cloud.

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