In this episode, Neil interviews Professor Max Welling, one of the foremost experts in Machine Learning about AI4Science: the use of machine learning and AI to solve challenges in various scientific disciplines. They discuss and debate between data-driven and physics-driven approaches, the potential for foundational models, the importance of open sourcing models and data, the challenges of data sharing in science, and the ethical considerations of releasing powerful models. The conversation covers the role of academia, industry, and startups in driving innovation, with a focus on the field of AI. Professor Welling discusses the advantages and limitations of each sector and shares his experience in academia, big tech companies, and startups. The conversation then shifts to Professor Wellings new company; CuspAI, which focuses on material discovery for carbon capture using metal organic frameworks and machine learning. Prof. Welling provides insights into the potential applications of this technology and the importance of addressing sustainability challenges. The conversation concludes with a discussion on career advice and the future of AI for science.

Links

CuspAI : https://www.cusp.ai
University website: https://staff.fnwi.uva.nl/m.welling/
Google scholar: https://scholar.google.com/citations?user=8200InoAAAAJ&hl=en
AI4Science NeurIPS 2023 workshop: https://neurips.cc/virtual/2023/workshop/66548
AI4Science NeurIPS 2022 workshop: https://nips.cc/virtual/2022/workshop/50019
Aurora paper: https://arxiv.org/abs/2405.13063

Chapters

00:00 Introduction to the Neil Ashton Podcast
00:39 Guest Introduction: Professor Max Welling
11:12 Data-Driven vs. Physics-Driven Approaches in Machine Learning for Science
17:00 Foundational models for science
23:08 Discussion around Open-Sourcing Models and Data
29:26 Ethical Considerations in Releasing Powerful Models for Public Use
33:14 Collaboration and Shared Resources in Addressing Global Challenges
34:07 The Role of Academia, Industry, and Startups
43:27 Material Discovery for Carbon Capture
52:02 Career Advice for Early-stage Researchers
01:01:07 The Future of AI for Science and Sustainability

Keywords

AI for science, machine learning, data-driven approaches, physics-driven approaches, foundational models, open sourcing, data sharing, ethical considerations, blockchain technology, academia, industry, startups, AI, material discovery, carbon capture, metal organic frameworks, machine learning, sustainability, career advice, future of AI for science

This episode sets the scene for upcoming discussions on AI4Science with world renowned experts on machine learning. The focus is on using machine learning to solve scientific problems, such as computational fluid dynamics, weather modeling, material design, and drug discovery. The episode introduces the concept of machine learning and its potential to accelerate simulations and predictions. The episode also discusses the differences between machine learning for scientific problems and large language models, and the ongoing debate on incorporating physics into machine learning models.

Chapters
00:30 Introduction: AI for Science and Machine Learning
02:29 The Importance of Computational Fluid Dynamics
04:53 The Limitations of Physical Testing and Simulation
05:53 Accelerating Simulations and Predictions with Machine Learning
09:51 Data-Driven vs Physics-Informed Approaches in Machine Learning
13:10 The Future of Machine Learning in Science: Foundational Models

In this episode of the Neil Ashton podcast, Neil interviews Dr. Chris Rumsey, Research Scientist at NASA Langley Research Center. Chris is one of the main CFD experts at NASA Langley is globally reconised as a leader in CFD, particularly for aeronautical applications. The conversation focuses on computational fluid dynamics (CFD) and turbulence modeling. They discuss Chris's career, his role in public dissemination of CFD methods, and his involvement in the Turbulence Modeling website. They also explore the High Lift Prediction Workshop and the role of machine learning in CFD and turbulence modeling. The conversation provides insights into working at NASA and the challenges and advancements in CFD and turbulence modeling. In this conversation, Neil and Chris Rumsey discuss the progress and challenges in solving the problem of high-lift aerodynamics in aircraft design. They explore the concept of certification by analysis and the role of computational fluid dynamics (CFD) in reducing the need for expensive wind tunnel and flight tests. They also delve into the use of machine learning in CFD and the challenges of reproducibility. The conversation then shifts to conferences, with Neil and Chris sharing their experiences and favorite events. They conclude by discussing career advice for aspiring aerospace professionals and the unique aspects of working at NASA.

00:00 Introduction to the Neil Ashton podcast
01:09 Focus on Computational Fluid Dynamics and Turbulence Modeling
06:51 Chris Rumsey's Journey to NASA
09:13 From Art to Aeronautical Engineering
13:08 Transitioning to Turbulence Modeling
15:34 The Origins of the Turbulence Modeling Website
20:40 Verification and Validation in Turbulence Modeling
24:34 The Role of Machine Learning in Turbulence Modeling
26:00 Advancements in High Lift Prediction
27:28 Challenges in High Lift Prediction
28:25 Thoughts on Working at NASA
29:42 Certification by Analysis: Reducing the Cost of Aircraft Certification
31:09 The Role of Machine Learning in CFD and Certification by Analysis
34:03 The Value of Conferences in Networking and Specialized Learning
40:30 Career Advice for Aspiring Aerospace Professionals
48:45 Curating and Documenting Knowledge in the Aerospace Community