Videos

Financial technologists are being challenged to incorporate Generative AI into their workflows, particularly when workflows involve, say, proprietary data and market data in addition to unstructured data, such as Twitter feeds or analyst reports. Terms like RAG (Retrieval Augment Generation) and “vector database” are littering desks that engage with AI and analytics. In this session, Ryan gives a background into the recent AI explosion and the new lexicon, and in particular highlights the vector database as a repository of stored enterprise knowledge. He will demonstrate several examples that use contextual intelligence sourced from unstructured data using the KDB.AI vector database.

A key part of analysing financial data is communicating results. In this talk, we shall look at various ways of visualising financial data in Python, using a number of visualisation libraries, such as Plotly and Matplotlib. We’ll explore using GPU accelerated plotting too, to create animations based on market data, as well as creating dashboards (eg. to explore US unemployment at a more granular level).

On 6th April 2022, OpenAI publicly announced DALL.E 2, a ground-breaking AI system that can create realistic images and art from a description in natural language. In this talk, we will explore how DALL.E 2 works from first principles, including how it combines several techniques such as CLIP image embeddings and diffusion models to produce a state-of-the-art text-to-image AI system. We will also discuss the potential future opportunities and implications of using DALL.E 2 and the risks of making powerful AI models commercially available through APIs.

We propose and analyze differentially private (DP) mechanisms for call auctions as an alternative to the complex and ad-hoc privacy efforts that are common in modern electronic markets. We prove that the number of shares cleared in the DP mechanisms compares favorably to the non-private optimal and provide a matching lower bound. We analyze the incentive properties of our mechanisms and their behavior under natural no-regret learning dynamics by market participants. We include simulation results and connections to the finance literature on market impact.