Patrick Beukema has a Ph.D. in neuroscience and has worked on AI models for brain decoding, which analyzes the brain's activity to decipher what people are seeing and thinking.
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MLOps podcast #225 with Patrick Beukema, Head / Technical Lead of the Environmental AI, Applied Science Organization at AI2, Beyond AGI, Can AI Help Save the Planet?
// Abstract
AI will play a central role in solving some of our greatest environmental challenges. The technology that we need to solve these problems is in a nascent stage -- we are just getting started. For example, the combination of remote sensing (satellites) and high-performance AI operating at a global scale in real-time unlocks unprecedented avenues to new intelligence.
MLOPs is often overlooked on AI teams, and typically there is a lot of friction in integrating software engineering best practices into the ML/AI workflow. However, performance ML/AI depends on extremely tight feedback loops from the user back to the model that enables high iteration velocity and ultimately continual improvement.
We are making progress but environmental causes need your help. Join us fight for sustainability and conservation.
// Bio
Patrick is a machine learning engineer and scientist with a deep passion for leveraging artificial intelligence for social good. He currently leads the environmental AI team at the Allen Institute for Artificial Intelligence (AI2). His professional interests extend to enhancing scientific rigor in academia, where he is a strong advocate for the integration of professional software engineering practices to ensure reliability and reproducibility in academic research. Patrick holds a Ph.D. from the Center for Neuroscience at the University of Pittsburgh and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where his research focused on neural plasticity and accelerated learning. He applied this expertise to develop state-of-the-art deep learning models for brain decoding of patient populations at a startup, later acquired by BlackRock. His earlier academic work spanned research on recurrent neural networks, causal inference, and ecology and biodiversity.
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// Related Links
Variety of relevant papers/talks/links on Patrick's website: https://pbeukema.github.io/
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Timestamps:
[00:00] AI Quality Conference
[01:29] Patrick's preferred coffee
[02:00] Takeaways
[04:14] Learning how to learn journey
[07:04] Patrick's day to day
[08:39] Environmental AI
[11:07] Environmental AI models
[14:35] Nature Inspires Scientific Advances
[18:11] R&D
[24:58] Iterative Feedback-Driven Development
[26:37 - 28:07] LatticeFlow Ad
[33:58] Balancing Metrics for Success
[38:16] Model Retraining Pipeline
[44:11] Series Models: Versatility
[45:57] Edge Models Enhance Output
[50:22] Custom Models for Specific Data
[53:53] Wrap up
