Transcending Enterprise AI: From LLMs to Next-Gen Autonomous AI-Augmentation for Self-Adaptive Decision Intelligence
The current landscape of AI faces several limitations, including data dependence, intricate feedback loops during training, a lack of common sense, ethical dilemmas, and interpretability challenges. Conventional training methods, coupled with human intervention, can impede system agility. Our journey takes us into a realm of interconnected AI systems designed to critique the responses of other LLMs, updating these models in real time. This optimization process involves Reinforcement Learning with Human Feedback (RLHF) and Statistical Rejection Sampling Optimization, propelling us closer to achieving a state of transcendent intelligence.
AI models become self-aware and self-taught in this state, demonstrating a profound understanding of ethics and morals. This heightened intelligence contributes to decision intelligence for businesses where multiple enterprises operate within specific rules, policies, and process frameworks. At the core of every business lie contracts. Once AI becomes process and law-aware, it can vigilantly monitor operations, strategies, and compliance.
As Autonomous AI becomes intimately acquainted with organizational processes, it gains the capability to swiftly identify process breaches and propose optimizations in workflows, compliance procedures, and more. It acts as an impartial agent, akin to an auditor. Picture an aircraft ready to take off without critical mechanical checks or essential personnel missing, or imagine someone attempting to add a clause that violates one of millions of contracts in a database. Autonomous AI should promptly understand business processes and raise a flag when needed.
In our session, we will explore the essential steps required to establish this setup and the techniques involved in fine-tuning AI for domain-specific businesses. Our aim is to enhance the generative AI capabilities to perfection within business domains.