Most blockchain execution environments, like the EVM, have very limited computation capacity. This means decentralized apps and protocols are limited in their complexity of interaction. Now with the AI Oracle, smart contracts can use AI inference within their own execution logic. This means that smart contracts can use AI to evaluate data, and improve their decision making.

Recommendation systems are integral to many modern sector behemoths of the Internet. Evolving from personal email filtering to matrix factorization enhanced by machine learning, today they are increasingly sophisticated. Modern day recommendation systems more closely resemble AI systems due to the benefits LLMs provide in improving adaptability and contextual understanding. Deep learning models also address challenges with data sparsity and noise. Together, these AI-based improvements have made recommender systems more personalized, efficient and interactive, therefore making the applications in which they’re embedded more engaging for users.

“In a world of disinformation, Facticity.AI, an artificially intelligent fact-checking tool developed by Singapore start-up AI Seer, seeks to tip the scales in the truth’s favor. Facticity’s online tool, released in beta in June, checks claims in text and video, and provides references and links to reliable sources. The company claims 92% accuracy, beating competitor tools like Bing CoPilot, Perplexity, and Originality.ai (which claims about 72% accuracy on fact-checking). Facticity was put to the test during the most recent presidential debate, checking almost 250 claims in near real-time. Jacob Kozhipatt, a former Business Insider reporter who worked with Facticity to manually verify its checks, says he was impressed by “how much more and how much faster it can fact check than humans.”

Batch Inference is an innovative feature that enables developers to submit a single request for multiple AI inferences. By providing a prompt (e.g., "a bottle of orange juice") along with a specified batch size (e.g., 100), the AI Oracle node generates a collection of inference results using the same prompt but with different random seeds. This results in a diverse array of outputs, all returned to the requester in one go.

The gaming industry has consistently been a platform for technological innovation, often integrating cutting-edge advancements to enhance player experiences. From early procedural generation techniques in Rogue (1980) to the incorporation of machine learning in Middle-earth: Shadow of Mordor (2014), games have evolved alongside developments in artificial intelligence (AI). Recent progress in generative models, reinforcement learning, and agent-based systems promises a unique evolution in how games are developed and played.

Sincere thanks to David Shi (Operator.io) for working with me on this idea.

Open-source is the backbone of the modern internet. Linux and the World Wide Web protocol are perhaps the greatest examples of how open-source software invokes the power of network effects to improve, refine and spread more rapidly than any proprietary software could.

As we look ahead to 2025, it’s clear that AI decentralized applications (dApps) are poised to transform the blockchain ecosystem. But what makes us so confident? Let’s dive into the compelling data that supports this trend.

f(A.I.)r Launch introduces an innovative way to launch new crypto projects. By leveraging AI to determine how tokens are distributed, it ensures fair opportunities for all participants. Utilizing an Onchain AI Oracle (OAO), this system maintains transparency and is rooted in genuine user engagement.