Time: 2024-06-05
The AI Impact Tour on June 5th is approaching quickly, providing a chance to explore auditing AI models. Over the past year, the development of generative AI foundation models has seen a shift towards multi-modal models, allowing for a more accurate representation. The world is adapting to leverage these solutions, creating patterns that cater to diverse needs.
Gen AI 1.0 focused on large language models (LLMs) and emergent behavior from next-generation tokens. Understanding how foundation models work is crucial, as they predict the 'best-next-token' based on user interactions. 'Prompt-engineering' techniques have been developed to enhance model responses.
Gen AI 1.5 introduced retrieval augmented generation, embedding models, and vector databases. Cutting-edge models can process vast amounts of information, resulting in improved accuracy. Technologies like vector databases are scaling up to handle extensive data retrieval.
The next phase, Gen 2.0, involves the integration of agent-based systems utilizing multi-modal models. These systems empower complex problem-solving by separating data gathering, reasoning, and action-taking components. Agent systems like devin.ai and Amazon's Q for Developers service showcase the potential of this approach.
In conclusion, as organizations progress in adopting LLMs, the focus will be on maximizing output quality and efficiency while minimizing costs. Continuous optimization through hardware, frameworks, and models will be crucial. Partnering with experts experienced in running genAI solutions will be beneficial in navigating this rapidly evolving landscape.
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