AI training techniques

  • Chameleon: Early-Fusion Multimodal AI Model for Visual and Textual Interaction

    ByAjith Vallath Prabhakar

    In recent years, natural language processing has advanced greatly with the development of large language models (LLMs) trained on extensive text data. For AI systems to fully interact with the world, they need to process and reason over multiple modalities, including images, audio, and video, seamlessly. This is where multimodal LLMs come into play. Multimodal LLMs like Chameleon, developed by Meta researchers, represent a significant advancement in multimodal machine learning, enabling AI to understand and generate content across multiple modalities. This blog explores Chameleon’s early-fusion architecture, its innovative use of codebooks for image quantization, and the transformative impact of multimodal AI on various industries and applications.

  • 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.