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![[shutterstock: 567338095, Sarah Holmlund]. Credit: e3zine
What can AI/ML do, as a General Purpose Tech?
● Predict, aka slot filling
● Optimize, aka finding better places
● Uncover hidden factors & clusters
● Detect complex relationships
● Mimic the world
● Suggest actions with long-term rewards
● Reason about the world
● Be aware of its own limitations](https://image.slidesharecdn.com/aiml-climate-short-191212151704/75/AI-for-tackling-climate-change-26-2048.jpg)
![Technology alone is never enough
“Technologies [to help fight
climate change] have largely not
been adopted at scale by society.
While we hope that ML will be
useful in reducing the costs
associated with climate action,
humanity also must decide to
act.”
Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).](https://image.slidesharecdn.com/aiml-climate-short-191212151704/75/AI-for-tackling-climate-change-51-2048.jpg)
Uploaded byDeakin University
AI for tackling climate change
The document discusses the challenges posed by climate change and explores how artificial intelligence (AI) and machine learning (ML) can contribute to addressing these issues by improving climate models, enhancing efficiency in various sectors, and facilitating better decision-making. It highlights the potential of AI/ML in predicting climate impacts, optimizing resource use, and supporting conservation efforts, while acknowledging that technology alone is insufficient without societal action. The overall message emphasizes the need for collaboration and integration of AI tools in climate initiatives to mitigate environmental impacts effectively.
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AI for tackling climate change
- 1. Truyen Tran A/Professor Photo credit:Tienphong.vn, 09/07/2019 “Climate change is a driver of global wildfire trends” (WWF) CLIMATE CHANGE: CHALLENGES & -DRIVEN SOLUTIONS AI
- 2. Sir David MacKay (1967-2016) Physicist* AI scientist * Sustainable energy expert * Cancer fighter
- 3. 11/12/2019 3 Introduction to AI/MLAI as an approach The challenges Agenda Joining the global effort
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- 5. Human greenhouse gasfootprints Source: Sceptical Science
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- 10. What are theeffect? • large-scale singular events (such as further sea level rise as major ice sheets melt over Greenland and Antarctica) • threatening the survival of certain ecosystems • exacerbating extreme weather events (e.g. heat waves, drought, extreme rainfall, and coastal flooding) • altering sea ice concentrations, river flow and coastal erosion • pushing plant and animal species towards the poles and to higher elevations • slowing productivity gains for some crops such as wheat and maize • severe impacts on the world’s poorest and most vulnerable populations Source: The Committee on Climate Change, UK
- 12. Hà Tĩnh, 09/07/2019– Tiền Phong
- 13. Reduction • Energy • Industry •Buildings & cities • Farms & forests • Transportation Adaptation • Disaster prevention • Societal impact • Ecological impact Alteration • Sequestration • Geoengineering Adapted from Ng & Zhou 2019 What can we/AI do? Advancing climate sciences • Data-driven climate models • Process-based climate models • Hybrid-models Photo credit: bedford Education • Raise awareness | Enable individual actions
- 14. 11/12/2019 14 Introduction to AI/MLAI as an approach The challenges Joining the global effort Agenda
- 15. What is AI? 11/12/2019 15 Amongthe most challenging scientific questions of our time are the corresponding analytic and synthetic problems: • How does the brain function? • Can we design a machine which will simulate a brain? -- Automata Studies, 1956.
- 16. What makes AI? Perceiving Learning Reasoning Planning 11/12/2019 16 Acting Robotics Communicating Consciousness Automateddiscovery Modern AI is mostly data-driven, as opposed to classic AI, which is mostly expert-driven.
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- 18. Machine learning (system thatimproves its performance with more experience) Supervised learning (mostly machine) A B Unsupervised learning (mostly human) Will be quickly solved for “easy” problems (Andrew Ng) 11/12/2019 18 Anywhere in between: semi-supervised learning, reinforcement learning, lifelong learning, meta-learning, few- shot learning, knowledge-based ML
- 19. ML starts withfeature engineering learning • In typical machine learning projects, 80-90% effort is on feature engineering • E.g., flood prediction: history, current weather, deforestation rate, change in landscape, construction density, etc. • A range of powerful classifiers: Random forests, GBM, SVM, deep neural nets, etc. • Try yourself on Kaggle.com! 11/12/2019 19
- 20. Current AI (deeplearning): Mimic the brain 11/12/2019 20 andreykurenkov.com
- 21. DL basic 1:Repeat the trick, horizontally and vertically Integrate-and-fire neuron andreykurenkov.com Feature detector Block representation 11/12/2019 21
- 22. DL basic 2:Keep looking ahead Classification Image captioning Sentence classification Neural machine translation Sequence labelling Source: karpathy 11/12/2019 22
- 23. DL basic 3:Repeat and vote Source: adeshpande3 11/12/2019 23
- 24. DL basic 4:Dual - guess and judge 11/12/2019 24Adapted from Goodfellow’s, NIPS 2014
- 25. Can you tellwhich one is real? 11/12/2019 25 Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196.
- 26. [shutterstock: 567338095, SarahHolmlund]. Credit: e3zine What can AI/ML do, as a General Purpose Tech? ● Predict, aka slot filling ● Optimize, aka finding better places ● Uncover hidden factors & clusters ● Detect complex relationships ● Mimic the world ● Suggest actions with long-term rewards ● Reason about the world ● Be aware of its own limitations
- 27. 11/12/2019 27 Introduction to AI/ML Thechallenges Joining the global effort Agenda AI as an approach
- 28. One upon atime … in movies Source: opgalImage: © EPA PHOTO/EFE/Columbia TriStar/Robert Zucker Hate Love
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- 30. What can AI/MLdo to tackle climate change? ● Make systems more efficient ● Enable remote sensing and automatic monitoring ● Provide fast approximations to time intensive simulations ● Support interpretable or causal models (e.g. for understanding weather patterns, informing policy makers, and planning for disasters). AI/ML is only one part of the solution! ● It is a tool that enables other tools across fields ● Its performance improves with more data!
- 31. Rolnick, David, etal. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019). Experimentation Control systems Predictive maintenance Hybrid physical models Forecasting Human interaction Remote sensing System optimization ML-enabled methodologies Electricity system Transportation Building & cities Industry Farms & forests CO2 removal Climate prediction Societal impacts Solar geoengineering Individual action Collective decisions Education Finance Actionable areas
- 32. Rolnick, David, etal. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019). Computer vision NLP Causal inference Interpretable ML RL & control Time-series analysis Transfer learning Uncertainty quant. Unsupervised learning Machine learning areas Electricity system Transportation Building & cities Industry Farms & forests CO2 removal Climate prediction Societal impacts Solar geoengineering Individual action Collective decisions Education Finance Actionable areas
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- 34. Transportation Problems ● Increased CO2footprint ● Lost of time ● Health issues (physical and mental) ● Lost of productivity ● Increase transportation cost AI/ML-driven solutions ● Predict traffic congestion, suggest alternative route ● Optimize fuel consumption ● Detect route/traffic management maintenance Vietnam News
- 35. “Forecasting travel timeshelps improve road safety and efficiency. Accurate predictions help commuters make informed decisions about when to travel and on what routes. This helps to lower intensity on problem arterials by encouraging motorists to use underutilised parts of the grid, and where possible, by having them select alternative times and modes of travel. ”
- 36. Smart homes andcities Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).
- 37. Farms and forests ●Sensor network, automated sensing and optimization Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).
- 38. 20tree.ai surveys andmaps forests. From: Financial News Now
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- 40. Source: mila Climate prediction ●Predict effects of climate change ● Extremely fast approximation alternative to complex simulation
- 41. Societal impacts Rolnick, David,et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).
- 42. Conservation effort Norouzzadeh, MohammadSadegh, et al. "Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning." Proceedings of the National Academy of Sciences 115.25 (2018): E5716-E5725.
- 43. AI for social measurements analysison climate
- 44. Source: financial-news-now Education andcollective decisions Source: David MacKay, 2007 Individual actions
- 45. Towards green AI Strubell,Emma, Ananya Ganesh, and Andrew McCallum. "Energy and Policy Considerations for Deep Learning in NLP." arXiv preprint arXiv:1906.02243 (2019).
- 46. South Vietnam, 2050 Credit:New York Times Kulp, Scott A., and Benjamin H. Strauss. "New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding." Nature communications 10.1 (2019): 1-12. Prediction model: Neural network • 23 input features • trained on US LIDAR-derived elevation data • Extrapolated over time and space. However, it has been criticized for using inaccurate data for Vietnam.
- 47. Source: Pacific Standard& Iconfinder (1835-1882) Source: Wiki Jevon paradox in action
- 48. AI as anapproach 11/12/2019 48 Introduction to AI/ML The challenges Agenda Joining the global effort
- 49. UN sustainable developmentgoals - 2030
- 50. 0 5 10 15 20 25 30 35 40 45 AI & CLIMATECHANGE BY GOOGLE TREND
- 51. Technology alone isnever enough “Technologies [to help fight climate change] have largely not been adopted at scale by society. While we hope that ML will be useful in reducing the costs associated with climate action, humanity also must decide to act.” Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).
- 52. First thing first:Speak collaborators’ languages Information system Maths Health informatics Symbolic AI Database Probabilistic AI Old machine learning Statistical machine learning Hard core data mining Current data mining Clinical statistics Theoretical statistics Bioinformatics Statistical epidemiology Biomedical engineering Biostatistics Deep learning Molecular biology Biochemistry Quantum chemistry Chemoinformatics Genetic statistics Domain CS/ML Population genetics (The case of biomedicine)
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- 55. Source: opgal To sumup AI is a General-Purpose Technology (GPT) ● Just like electricity Why AI for climate change? ● Automation, scalability, knowledge and data integration ● Assisting in decision making ● Rational in an irrational world of politics. ● AI should be a green exemplar Can AI fail? ● Yes. We are still learning. ● It is subject to misuse. ● It can be wrongly aligned with human values.
- 56. Sir David MacKay (1967-2016) Sustainableenergy scientist Cancer fighter
- 57. 4.1 mil tonesof CO2 has been emitted since I started talking
- 58. This talk mayhave been written by AI with non-zero probability It have been delivered by human with probability 1 .
- 59. 11/12/2019 59 Thank you TruyenTran @truyenoz truyentran.github.io truyen.tran@deakin.edu.au letdataspeak.blogspot.com goo.gl/3jJ1O0