ML Ensemble #4:
Climate Crisis
Coming Soon, Toronto

Share machine learning insights, techniques, methods, and observations with your technical peers

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ML Ensemble Dinner Concept

We’re excited to try something a bit new with the ML Ensemble community.

We’ve in the past organized conferences for a diverse group of carefully chosen highly technical machine learning leaders from industry and academia, with a focus on building community and sharing knowledge.

To double down on this exchange of ideas, and encourage the participation of the entire amazing ML Ensemble community, we are prototyping a new model, and organizing an even smaller group dinner of 15-20 people focused on a facilitated discussion of issues relevant to the group.

For our first dinner we will be talking about climate crisis.

Climate crisis, and ecological collapse, is upon us. Significant changes in our worldviews and ways of life are upcoming, and all communities are called to participate in strategies of mitigation and adaptation. We can either change or die.

The machine learning community is currently extremely powerful and influential. However many of us have trouble knowing how we can bring our particular skill set to bear on this critical issue. A first step is for us to be having these conversations with each other. Themes that could come up:

  • How might we participate in this global movement?
  • Can ML and technology help move things in the right direction?
  • Where might it move things in the wrong direction?
  • How can we work with other groups tackling these questions?
  • Is there a danger of techno-solutionism diverting resources from better places?


So that we have some shared experience to level-set the group, we would like every participant to at least have read the recent Tackling Climate Change with Machine Learning paper, with many prominent AI researchers as co-authors. For the keen, we further suggest reading:

  • Bret Victor’s excellent What can a technologist do about climate change?.
  • The recently viral, significantly more pessimistic Deep Adaptation paper, arguing that the case for significant social collapse is under-represented as there are major structural incentives to discourage such thought in the public arena. The paper has since led to global Deep Adaptation communities across disciplines working to understand how we might build society in the remnants of the one we are losing
  • Project Drawdown is less CS and ML specific, but provides many concrete suggestions of what we might do to mitigate, and is globally very influential, with global communities including one in Toronto


The ML Ensemble dinner will take at the beautiful TinEye HQ.

TinEye HQ,
223 Queen Street East
Toronto, Ontario


Interested? Reach out to and tell us about yourself

Check Back Soon

Past speakers

Yevgeniy Valis

Leveraging privacy preserving machine learning methods to overcome the barriers to data sharing

Borealis AI

ML Ensemble #3, 2018

Martin Snelgrove

Tensor Stasis: Inference without kilowatts

Untether AI

ML Ensemble #3, 2018

Julieta Martinez

Scalable algorithms for large-scale retrieval via multi-codebook quantization

Uber ATG

ML Ensemble #3, 2018

Oren Kraus

Classifying and segmenting microscopy images with deep multiple instance learning

ML Ensemble #3, 2018

Jennifer Listgarten

From genetics to gene editing with machine learning

UC Berkeley

ML Ensemble #3, 2018

Ozge Yeloglu

Customer Experience Analysis by using NLP and Deep Learning Techniques

Microsoft Canada

ML Ensemble #3, 2018

Sageev Oore

Deep Learning for Musical Language Generation

Dalhousie / Vector

ML Ensemble #2, 2018

Jake Cheng

Hubba: Recommendations and Beyond


ML Ensemble #2, 2018

Renjie Liao

Deep Learning on Graphs: Models and Applications

Uber ATG

ML Ensemble #2, 2018

Angela Schoellig

Machine Learning for Safe, High-Performance Control of Mobile Robots

University of Toronto

ML Ensemble #2, 2018

Kalu Kalu

Insights from Building and Applying Behavioural Image Prediction Models


ML Ensemble #2, 2018

Kathryn Hume

Ethical Algorithms: Bias and Explainability in Machine Learning

Integrate AI

ML Ensemble #2, 2018

Renat Gataullin

Learning to Grasp


ML Ensemble #1, 2017

Solmaz Shahalizadeh

Detecting Order Fraud on More Than 400k Stores: Scaling Machine Learning at Shopify


ML Ensemble #1, 2017

Afsaneh Fazly

Consumer Opinion Analysis: Lessons Learned

Thomson Reuters

ML Ensemble #1, 2017

Anna Goldenberg

Building ML Models When the Data is Scarce: The Case of Complex Human Diseases

Sick Kids

ML Ensemble #1, 2017

Xavier Snelgrove

Subjective Objective Functions: High-Resolution Neural Image Creation


ML Ensemble #1, 2017

Jimmy Ba

Progress and Challenges in Optimizing Neural Networks

University of Toronto / Vector

ML Ensemble #1, 2017