Generative AI (LLM)

Several blogs on Gen-AI, see the "Blog!" section

“Classic” ML/AI versus GenAI: what really changed

This short video delves into the nuanced distinctions between Generative AI (Gen-AI) systems and traditional machine learning (ML) algorithms, emphasizing that Gen-AI brings its own set of unique challenges and an endless number of opportunities. It highlights the low barrier to entry for Gen-AI systems, allowing for rapid proof of concept development, yet underscores the complexity and effort required for scalable deployment due to the intricacies of system components and their interactions. Reliability emerges as a significant concern, particularly given the non-deterministic nature of LLM-based systems and their propensity for "hallucinating" or producing inaccurate outputs, necessitating innovative approaches to ensure system reliability. Interpretability, or understanding why an AI system makes certain decisions, is identified as a critical challenge, impacting trust and the ability to improve models. The talk also touches on the complexity introduced by the myriad of parameters and hyperparameters in Gen-AI systems, from prompting strategies to agent learning techniques, highlighting the ongoing need for deep engineering work to normalize data and clarify semantics.

AI Horizon: Progress, Pitfalls, Promises and Ponderings … in less than 10’

The aim of this (short) talk is to provide an update on AI Horizon sharing thought on latest progress, pitfalls but also discussing Promises and Ponderings.

Gen-AI: so many trends ... dealing with fast pace innovations

In recent decades, the landscape of technology has undergone unprecedented transformations, with generative AI systems standing at the forefront of this evolution. Crafting such advanced AI necessitates a deep dive into numerous critical facets and dimensions including data sourcing for training, training strategies, embeddings ,RAG, and the intricacies of algorithm design—covering aspects like size, parameter tuning, and alignment. Moreover, considerations such as the need for interpretability, cost-efficiency in inference processes, and the overarching importance of a robust evaluation framework are paramount. This brief video aims to dissect these complex dimensions, offering viewers an overview that enables informed decision-making in system design, underscored by the essentiality of rigorous evaluation standards.