All tickets have now been allocated for this event and the ballot is now closed.
We are looking forward to seeing you on Tuesday November 7th, 6 – 9PM GMT for our next Sandbox Session with Digitas – Retrieval, search and knowledge in the age of LLM and Vector Databases and Causal Modelling Agents: Augmenting Causal Discovery with LLMs.
Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering enters you into the ticket ballot. The ballot will be drawn two weeks ahead of the event. If you have not received a ticket by November 3rd 2023, unfortunately you have been unsuccessful in getting a ticket. Those who are randomly selected will then be e-mailed tickets for the event.
On your ticket there is a QR code. You will need to scan this QR code on entry so please have this ready on arrival. This can be on your phone or printed.
Retrieval, search and knowledge in the age of LLM and Vector Databases
A talk by Louis Dominique Vainqueur, Data Science Partner at Digitas.
Since the release of ChatGPT late last year, the world has finally embraced vector embeddings and many organisations (from hedge funds to giant retailers) have been experimenting with vector databases. This is because vector embeddings, a component at the heart of large language models, open-up the ability to not only compress information but also to drastically transform search and knowledge retrieval.
In this session we will put a spotlight on the embedding revolution that has taken over natural language processing, computer vision, network science and explain how enterprises can build better systems to understand, interact with, and sell to their customers.
Causal Modelling Agents: Augmenting Causal Discovery with LLMs
Our 2nd session by Ayodeji Ijishakin and Ahmed Abdulaal, Computer Science PhD Student’s at University College London.
Scientific discovery hinges on the effective integration of metadata, which refers to a set of ‘cognitive’ operations such as determining what information is relevant for inquiry, and data, which encompasses physical operations such as observation and experimentation. This talk introduces the Causal Modelling Agent (CMA), a novel framework that synergizes the metadata-based reasoning capabilities of Large Language Models (LLMs) with the data-driven modelling of Deep Structural Causal Models (DSCMs) for the task of causal discovery. We evaluate the CMA’s performance on a number of benchmarks, as well as on the real-world task of modelling the clinical and radiological phenotype of Alzheimer’s Disease (AD). Our experimental results indicate that the CMA can outperform previous data-driven or metadata-driven approaches to causal discovery. In our real-world application, we use the CMA to derive new insights into the causal relationships among biomarkers of AD.
Food and drinks included and a chance to network with our community.
6:00 PM – Doors open – food, drinks and networking
6:30 PM – Intro from DSF and Digitas teams
6:35 PM – Talk session: Retrieval, search and knowledge in the age of LLM and Vector Databases, followed by Q&A
7:15 PM – Short comfort break.
7:25 PM – Talk session: Causal Modelling Agents: Augmenting Causal Discovery with LLMs, followed by Q&A
8:05 PM – Drinks and networking
9:00 PM – Close
Louis Dominique Vainqueur – Partner, Data science @ Digitas, Group Publicis
Ayodeji Ijishakin – PhD Candidate at University College London
Ahmed Abdulaal – PhD Candidate at University College London