We'll be hosting an IndabaX event this Friday (17 September) that includes talks and tutorials with a focus on artificial intelligence and machine learning. Please see the information below and visit https://indabax.co.za/ for more info on this and other upcoming events.
Reinforcement learning, computer vision and AGI!
RAIL Lab and PRIME are co-hosting an IndabaX South Africa Roadshow event and you are cordially invited to join us! RAIL Lab and PRIME are two machine learning research groups from the Computer Science and Applied Math (CSAM / "kazam!") department at the University of the Witwatersrand.
RAIL Lab focuses primarily on learning in autonomous systems. In particular, we are interested in the acquisition of behaviours, as well as knowledge about the environment around a learning system. Our work draws on tools from multiple fields including decision theory, machine learning, and computer vision, using techniques including reinforcement learning, Bayesian models, deep neural networks, and Monte Carlo tree search.
The PRIME Lab is focused on problems in deep learning for computer vision, representation learning, and learning in situations where data and labels are scarce. We have projects based on computer vision, natural language processing, self-supervised and weakly-supervised learning, generative modelling, and latent space analysis; as well as in energy, education, and healthcare.
When: 17 Sept 2021 from 14h00 - 17h00 SAST (convert to your timezone)
Where: We'll be kicking off on Zoom HERE
Event Page: HERE (for full details and tutorial links)
- RAIL and PRIME Intro talks (14h00 to 14h20)
- Prof Benjamin Rosman
- Dr Richard Klein
- Panel: "What might we still require to achieve AGI?" (14h20 to 15h00)
- Prof Christopher Cleghorn
- Geraud Nangue Tasse
- Dr Helen Robertson
- Dr Chris Fourie
- Parallel Tutorials (pick one to attend) (15h00 to 16h00)
- An Introduction to Reinforcement Learning (Geraud Nangue Tasse)
- An Introduction to Physical Reasoning Benchmarks (Divanisha Pillay)
- Google Earth Engine and its Opportunities for Geospatial Machine Learning (Geethen Singh)
- A How-To Guide on Getting Started with RoboCup's 3D Soccer Simulation (Branden Ingram)
- Learning without Labels: An Introduction to Visual Self-Supervised Representation Learning (David Torpey)
- Keynote Talk: "Signal to Symbol (via Skills)" (16h00 - 17h00)
- Prof George Konidaris (Brown University)
The John E. Savage Assistant Professor of Computer Science and director of the Intelligent Robot Lab at Brown University, which forms part of bigAI (Brown Integrative, General AI). I am also the Chief Roboticist of Realtime Robotics, a startup based on our research on robot motion planning. My research aims to build intelligent, autonomous, general-purpose robots that are generally capable in a wide variety of tasks and environments. I focus on understanding how to design agents that learn abstraction hierarchies that enable fast, goal-oriented planning. I develop and apply techniques from machine learning, reinforcement learning, optimal control and planning to construct well-grounded hierarchies that result in fast planning for common cases, and are robust to uncertainty at every level of control. I believe that it will take advances in all of these areas, and additionally advances in how to integrate these areas, to solve the AI problem.