Machine Learning for Forecasting Global Atmospheric Models | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
20,600
Published on ● Video Link: https://www.youtube.com/watch?v=XYBRhhGRYfk


Duration: 1:01:18
360 views
8

Speaker(s): Troy Acromano
Moderator: Peetak Mitra

Find the recording, slides, and more info at https://ai.science/e/ml4-climate-machine-learning-for-forecasting-global-atmospheric-models--lcNoI18ghNCoeT1xbEX6

Motivation / Abstract
Data driven approaches to prediction chaotic spatiotemporal dynamical systems have been shown to be successfully for a number of high dimensional, complex systems. One of the most important chaotic systems which impacts our lives daily is the atmosphere. This, naturally, leads to the question whether a purely data driven machine learning algorithm can accurately predict the weather. In this talk, we present a prototype machine learning only model that can skillfully predict the three-dimensional atmosphere for 3-5 days. The parallel machine learning technique used is computationally highly efficient and allows training to take place over thousands of computer cores.


What was discussed?
- Details about the methodology of this novel approach
- Robustness and sensitivity analysis for this approach
- Uncertainty Quantification and generalizability


------
#AISC hosts 3-5 live sessions like this on various AI research, engineering, and product topics every week! Visit https://ai.science for more details



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2020-07-30Information Retrieval for Price Consistency Monitoring - Liu Yang (Amazon)
2020-07-29Quantum Technologies: State of Play | AISC
2020-07-29NLP on Noisy User-generated text - NER for StackOverflow | AISC
2020-07-28Overview of Machine Learning in Marketing | AISC
2020-07-24Cognitive Model Priors for Predicting Human Decisions | AISC
2020-07-22Machine Learning to Assess Trends and Alignment of Funded Research Output | AISC
2020-07-22COVID and Racial Inequity, and Implications for AI
2020-07-21TGN: Temporal Graph Networks for Deep Learning on Dynamic Graphs [Paper Explained by the Author]
2020-07-20Founders in Fundraising, and AI Applications
2020-07-16Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents | AISC
2020-07-16Machine Learning for Forecasting Global Atmospheric Models | AISC
2020-07-15Towards Frequency-Based Explanation for Robust CNN | AISC
2020-07-14Lagrangian Neural Networks | AISC
2020-07-14TeslaSenti: Near real-time sentiment analysis of Tesla tweets | Workshop Capstone
2020-07-14See.Know.Bias - Using AI to Develop Media Literacy and Keep News Neutral | workshop capstone
2020-07-10Video Action Transformer Network | AISC
2020-07-09DLIME - Let's dig into the code (model explainability stream)
2020-07-09Overview of Machine Learning in Behavioral Economics | AISC
2020-07-08Compact Neural Representation Using Attentive Network Pruning | AISC
2020-07-08Navigating the Idea Maze: Continuous discovery frameworks for (AI?) products | AISC
2020-07-02Nvidia's RAPIDS.ai: Massively Accelerated Modern Data-Science | AISC