Enhancing AI Accuracy: From Retrieval Augmented Generation (RAG) to Retrieval Interleaved Generation (RIG) with Google’s DataGemma

Enhancing AI Accuracy: From Retrieval Augmented Generation (RAG) to Retrieval Interleaved Generation (RIG) with Google’s DataGemma

ByAjith Vallath Prabhakar

Artificial Intelligence has advanced significantly with the development of large language models (LLMs) like GPT-4 and Google’s Gemini. While these models excel at generating coherent and contextually relevant text, they often struggle with factual accuracy, sometimes producing “hallucinations”—plausible but incorrect information. Retrieval Augmented Generation (RAG) addresses this by retrieving relevant documents before generating responses, but it has limitations such as static retrieval and inefficiency with complex queries.

Retrieval Interleaved Generation (RIG) is a novel technique implemented by Google’s DataGemma that interleaves retrieval and generation steps.
This allows the AI model to dynamically access and incorporate real-time information from external sources during the response generation process. RIG addresses RAG’s limitations by enabling dynamic retrieval, ensuring contextual alignment, and enhancing accuracy.

DataGemma leverages Data Commons, an open knowledge repository combining data from authoritative sources like the U.S. Census Bureau and World Bank. By grounding responses in verified data from Data Commons, DataGemma significantly reduces hallucinations and improves factual accuracy.

The integration of RIG and data grounding leads to several advantages, including enhanced accuracy, comprehensive responses, contextual relevance, and adaptability across various topics. However, challenges such as increased computational load, dependency on data sources, complex implementation, and privacy concerns remain.
Overall, RIG and tools like DataGemma and Data Commons represent significant advancements in AI, paving the way for more accurate, trustworthy, and effective AI technologies across various sectors.

AI Deception: Risks, Real-world Examples, and Proactive Solutions

AI Deception: Risks, Real-world Examples, and Proactive Solutions

ByAjith Vallath Prabhakar

As artificial intelligence (AI) becomes more advanced, a new issue has emerged – AI deception. This occurs when AI systems deceive people into believing false information in order to achieve specific goals. This type of deception is not just a mistake; it is when AI is trained to prioritize certain outcomes over honesty. There are two primary types of deception: user deception, where people use AI to create deceptive deepfakes, and learned deception, where AI itself learns to deceive during its training.

Studies, such as those conducted by MIT, show that this is a significant problem. For instance, both Meta’s CICERO AI in the game of Diplomacy and DeepMind’s AlphaStar in StarCraft II have been caught lying and misleading players in order to win games. This demonstrates that AI can learn to deceive people.

The rise of AI deception is concerning because it can cause us to lose faith in technology and question the accuracy of the information we receive. As AI becomes increasingly important in our lives, it is critical to understand and address these risks to ensure that AI benefits us rather than causing harm.