Recent advances in AI have led to strong benchmark performance on increasingly complex tasks. However, progress in building trustworthy AI systems has lagged behind, creating significant safety challenges and potentially slowing the path toward AGI. As AI systems are deployed in high-stakes real-world settings, deficiencies in their reliability, safety and alignment pose substantial risks. State-of-the-art approaches, often based on reinforcement learning variants, have been shown to produce superficial alignment, primarily shaping output style and surface-level behavior. In this paper, we argue that safety, reliability, security and alignment challenges are caused by the same missing piece: the absence of full-stack architectural design incorporating key functionalities for trustworthiness. This gap creates a simulated and superficial alignment instead of an intrinsic alignment. We propose evolutionary meta-architecture learning (EMAL), substrate and system design strategies for structurally trustworthy AGI.

The financial industry leverages machine learning in more ways than just finding the right alpha signal. It grapples with supply chains, business processes, marketing, churn, fraud, and money laundering, all while maintaining compliance with the various regulatory frameworks it is beholden to. Due to the sheer volume of wealth being handled by the financial industry and its critical role in everyday life, it has been a lucrative target for a wide spectrum of ever-evolving bad actors. With each successive iteration of this workshop, we have attempted to capture the breadth of these actors - fraudsters, money launderers, market manipulators, and potentially nation-state-level risks. The emerging advances in Generative AI make this a particularly exciting time to host this workshop. GenAI offers groundbreaking approaches to handling the various data types prevalent in the financial sector. From a security point of view, bad actors are actively using Generative AI creatively to thwart conventional defenses (e.g. voice cloning, better synthetic identities), and this workshop's audience would benefit from commonly applicable defenses & best practices against such threats. Last but not the least, there is now an increasing willingness from the financial industry towards deeper engagement and data sharing with academia.

This workshop aims to explore the intersection of Generative AI with the rich tapestry of financial data types, seeking to uncover new methodologies and techniques that can enhance predictive analytics, fraud detection, and customer insights across the sector. By harnessing these advancements in AI, we can pave the way to not only understand customer behavior but also anticipate their needs more effectively, leading to superior customer outcomes and more personalized services. Our objective is to shed light on the challenges and opportunities presented by the diverse data formats in finance. We aim to bridge the gap between the dominance of traditional models for tabular data analysis and the emerging potential of Generative AI to revolutionize the treatment of time series, click streams, and other unstructured data forms.