Abu Dhabi Machine Learning Season 4 Episode 1
12.09.2023 - Abu Dhabi Machine Learning - ~4 Minutes
When?
- Tuesday, September 12, 2023 from 3:00 PM to 5:00 PM (Abu Dhabi Time)
Where?
- ADGM Academy, 20F, Al Maqam Tower, Al Maryah Island, Abu Dhabi
The Meetup page of the event:
Abu Dhabi Machine Learning Meetup Season 4 Episode 1
Programme:
Talk 1: Taming The Parrot: A Tutorial on Retrieval Assisted Generation
Abstract: Retrieval Augmented Generation (RAG) is an architecture that integrates a generative text model with external sources not present during training time, providing an effective, nonparametric alternative to model fine tuning. This tutorial will provide an overview of all the components in RAG and how to wire them together. Additionally, we will describe some useful extensions to RAG and recent advances, such as re-ranking with cross encoders and hypothetical document embeddings.
Speaker: Luis L. Perez is a data scientist with experience in the financial and supply chain industries, currently working for a sovereign wealth fund in the Gulf region. Luis received his Ph.D. in Computer Science from Rice University in Houston, TX. Luis is a co-recipient of the IEEE ICDE 2017 Best Paper Award for work on the applicability of distributed relational engines to scale linear algebra computations.
Talk 2: An introduction of self-supervised learning in Image, Video, and Audio
Abstract:
(ChatGPT generated ;) Discover the game-changing world of self-supervised learning! This beginner-friendly talk introduces you to the exciting realm of self-supervised learning techniques for images, videos, and audio. Instead of relying on tons of manual labels, self-supervised learning leverages the structure information within data to generate useful representations. We’ll delve into the basics, methods, and uses of self-supervised learning in these domains. Get ready to see how this approach supercharges deep neural network training, making it efficient and scalable. Join us to explore the world of self-supervised learning and how it’s shaping the future of tackling intricate tasks in image, video, and audio.
Speaker: Haiyan Jiang, Ph.D. in Statistics, currently Postdoc in the Machine Learning Department @ MBZUAI. Former Research Scientist @ Baidu Research’s Big Data Lab. Visiting Researcher @ HKUST.
Talk 3: LLMs and the Groq Language Processing Unit (LPU™)
Abstract: Groq’s newly announced Language Processor Unit, the Groq LPU, has demonstrated that it can run 70-billion-parameter enterprise-scale language models at a record speed of more than 100 tokens per second. Groq chips (with Dataflow inside) are optimized for the sequential nature of natural language and other sequential data like DNA, music and code. Being so specific in the design of the LPU leads to much better performance on language tasks than, for example, GPUs that are optimized for parallel graphics processing.
On the hardware front, Groq utilizes SRAM instead of a GPUs HBM, Dataflow instead of multi-core parallelism, and a kernel-less compiler from Python, instead of hand-tunes kernels. This has allowed Groq to be first at reaching over 100 Tokens / second for large language model inference at model sizes where GPUs are struggling, such as 70B or more parameters, or ultra low latency.
Speaker: Oskar Mencer is a pioneer of bringing Dataflow computers into production in Finance, Oil&Gas, and Government. Today, as CEO of Maxeler Technologies, a Groq company, Oskar is leading the services arm of Groq, and pushing the limits of Groq technology also beyond LLMs into HPC, Security and Quantum-inspired computing. Prior to Maxeler, Oskar was a Member of Technical Staff at the Unix group at Bell Labs, HIVIPS visitor at Hitachi Central Research Center, and the Rockwell computer science laboratory in Palo Alto. Oskar holds a PhD from Stanford University and a B.Sc. from the Technion.
Talk 4: Experimentation for Multi-Modal AI: Insights from DataRobot
Abstract:
The landscape of multi-modal AI offers valuable opportunities to extract insights & signals from diverse sources like text, images, and geospatial data. This can be a particularly appealing approach for asset valuations, for example with real estate.
However, addressing the complexity of these challenges requires streamlined approaches to experimentation and deployment in order to efficiently evaluate them. This session aims to equip attendees with practical insights and tools to navigate this complexity.
Speaker: Conor Spicer is a seasoned data scientist and a dedicated member of the DataRobot Applied AI Experts team.