Bibliography
[1]
D. Rolnick et al., “Tackling Climate Change with Machine Learning,” ACM Computing Surveys, vol. 55, no. 2, pp. 42:1–42:96, Feb. 2022, doi: 10.1145/3485128.
[2]
N. L. Hickmon, C. Varadharajan, F. M. Hoffman, S. Collis, and H. M. Wainwright, “Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop Report,” Argonne National Lab. (ANL), Argonne, IL (United States), ANL-22/54, Sep. 2022. doi: 10.2172/1888810.
[3]
A. McGovern and A. J. Broccoli, “Editorial,” Artificial Intelligence for the Earth Systems, vol. 1, no. 1, Jan. 2022, doi: 10.1175/AIES-D-22-0014.1.
[4]
A. McGovern, “Creating trustworthy AI for weather and climate.” Feb. 2024. Accessed: Mar. 28, 2024. [Online]. Available: https://www.youtube.com/watch?v=n99yWkrvx2s
[5]
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th edition. Hoboken: Pearson, 2020.
[6]
C. M. Bishop, Pattern Recognition and Machine Learning, First. Springer New York, 2006.
[7]
F. Fleuret, The Little Book of Deep Learning. lulu.com, 2023. Accessed: Feb. 01, 2024. [Online]. Available: https://fleuret.org/public/lbdl.pdf
[8]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
[9]
G. Hinton, “Coursera Neural Networks for Machine Learning lecture 6,” 2018. Available: https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
[10]
J. Duchi, E. Hazan, and Y. Singer, “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization,” Journal of Machine Learning Research, vol. 12, no. 61, pp. 2121–2159, 2011, Accessed: Feb. 09, 2024. [Online]. Available: http://jmlr.org/papers/v12/duchi11a.html
[11]
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Jan. 29, 2017. http://arxiv.org/abs/1412.6980 (accessed Feb. 09, 2024).
[12]
D. Maclaurin, “Modeling, Inference and Optimization With Composable Differentiable Procedures,” PhD thesis, Harvard University, Graduate School of Arts & Sciences, Cambridge, MA, USA, 2016. Available: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493599
[13]
K. Fukushima, “Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements,” IEEE Transactions on Systems Science and Cybernetics, vol. 5, no. 4, pp. 322–333, Oct. 1969, doi: 10.1109/TSSC.1969.300225.
[14]
X. Glorot, A. Bordes, and Y. Bengio, “Deep Sparse Rectifier Neural Networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Jun. 2011, pp. 315–323. Accessed: Feb. 10, 2024. [Online]. Available: https://proceedings.mlr.press/v15/glorot11a.html
[15]
Latent Space podcast, “Ep 18: Petaflops to the People — with George Hotz of tinycorp.” Jun. 20, 2023. Available: https://www.youtube.com/watch?v=K5iDUZPx60E
[16]
K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, vol. 36, no. 4, pp. 193–202, Apr. 1980, doi: 10.1007/BF00344251.
[17]
Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The handbook of brain theory and neural networks, Cambridge, MA, USA: MIT Press, 1998, pp. 255–258.
[18]
A. Vaswani et al., “Attention Is All You Need,” arXiv, Dec. 05, 2017. doi: 10.48550/arXiv.1706.03762.
[19]
A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” Jun. 03, 2021. http://arxiv.org/abs/2010.11929 (accessed Feb. 20, 2024).
[20]
L. Ouyang et al., “Training language models to follow instructions with human feedback,” Mar. 04, 2022. http://arxiv.org/abs/2203.02155 (accessed Feb. 20, 2024).
[21]
J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” Dec. 16, 2020. http://arxiv.org/abs/2006.11239 (accessed Feb. 20, 2024).
[22]
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” Apr. 13, 2022. http://arxiv.org/abs/2112.10752 (accessed Jan. 30, 2024).
[23]
J. Runge et al., “Inferring causation from time series in Earth system sciences,” Nature Communications, vol. 10, no. 1, p. 2553, Jun. 2019, doi: 10.1038/s41467-019-10105-3.
[24]
J. Pearl and D. Mackenzie, The Book of Why: The New Science of Cause and Effect, 1st ed. New York, NY: Basic Books, Inc., 2018.
[25]
J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference. Cambridge, MA, USA: MIT Press, 2017.
[26]
M. Sonnewald, C. Wunsch, and P. Heimbach, “Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions,” Earth and Space Science, vol. 6, no. 5, pp. 784–794, Mar. 2019, doi: 10.1029/2018EA000519.
[27]
K. Tsipis, “Machine learning identifies links between world’s oceans,” MIT News | Massachusetts Institute of Technology. https://news.mit.edu/2019/machine-learning-identifies-links-between-world-oceans-0320, Mar. 2019.
[28]
L. van der Maaten and G. Hinton, “Visualizing Data using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579–2605, 2008, Accessed: Feb. 01, 2024. [Online]. Available: http://jmlr.org/papers/v9/vandermaaten08a.html
[29]
J. Duncombe, “How Machine Learning Redraws the Map of Ocean Ecosystems,” Eos. http://eos.org/articles/how-machine-learning-redraws-the-map-of-ocean-ecosystems, Jun. 2020.
[30]
M. Sonnewald, S. Dutkiewicz, C. Hill, and G. Forget, “Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces,” Science Advances, vol. 6, no. 22, p. eaay4740, doi: 10.1126/sciadv.aay4740.
[31]
T. R. Dieter and H. Zisgen, “Evaluation of the Explanatory Power Of Layer-wise Relevance Propagation using Adversarial Examples,” Neural Process Lett, vol. 55, no. 7, pp. 8531–8550, Dec. 2023, doi: 10.1007/s11063-023-11166-8.
[32]
F. V. Davenport and N. S. Diffenbaugh, “Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation,” Geophysical Research Letters, vol. 48, no. 15, p. e2021GL093787, Jul. 2021, doi: 10.1029/2021GL093787.
[33]
M. Rahnemoonfar, M. Yari, J. Paden, L. Koenig, and O. Ibikunle, “Deep multi-scale learning for automatic tracking of internal layers of ice in radar data,” Journal of Glaciology, vol. 67, no. 261, pp. 39–48, Oct. 2020, doi: 10.1017/jog.2020.80.
[34]
M. Fielding, J. Barrott, and A. Flensburg, “Using artificial intelligence to detect permafrost thawing.” Apr. 08, 2021. Available: https://www.sei.org/featured/artificial-intelligence-permafrost-thawing/
[35]
J. Atkinson, “Severe storms show off their "plume-age".” Aug. 15, 2018. Available: https://climate.nasa.gov/news/2782/severe-storms-show-off-their-plume-age
[36]
M. Brandt et al., “An unexpectedly large count of trees in the West African Sahara and Sahel,” Nature, vol. 587, no. 7832, pp. 78–82, Nov. 2020, doi: 10.1038/s41586-020-2824-5.
[37]
M. Maskey, “A collective agenda for AI on the Earth sciences (AI for Good).” Feb. 2022. Available: https://www.youtube.com/watch?v=ts5XSYgcsiE
[38]
J. Reinders, “Topology can help us find patterns in weather.” Dec. 06, 2018. Available: https://www.hpcwire.com/2018/12/06/topology-can-help-us-find-patterns-in-weather/
[39]
S. Salcedo-Sanz et al., “Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources,” Information Fusion, vol. 63, pp. 256–272, Nov. 2020, doi: 10.1016/j.inffus.2020.07.004.
[40]
M. Reichstein et al., “Deep learning and process understanding for data-driven Earth system science,” Nature, vol. 566, no. 7743, 7743, pp. 195–204, Feb. 2019, doi: 10.1038/s41586-019-0912-1.
[41]
T. Schneider et al., “Climate goals and computing the future of clouds,” Nature Climate Change, vol. 7, no. 1, pp. 3–5, Jan. 2017, doi: 10.1038/nclimate3190.
[42]
P. Bauer, A. Thorpe, and G. Brunet, “The quiet revolution of numerical weather prediction,” Nature, vol. 525, no. 7567, 7567, pp. 47–55, Sep. 2015, doi: 10.1038/nature14956.
[43]
I. Lopez-Gomez, C. Christopoulos, H. L. Langeland Ervik, O. R. A. Dunbar, Y. Cohen, and T. Schneider, “Training Physics-Based Machine-Learning Parameterizations With Gradient-Free Ensemble Kalman Methods,” Journal of Advances in Modeling Earth Systems, vol. 14, no. 8, p. e2022MS003105, 2022, doi: 10.1029/2022MS003105.
[44]
G. Behrens, T. Beucler, P. Gentine, F. Iglesias-Suarez, M. Pritchard, and V. Eyring, “Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models,” Journal of Advances in Modeling Earth Systems, vol. 14, no. 8, p. e2022MS003130, 2022, doi: 10.1029/2022MS003130.
[45]
R. Lagerquist, A. McGovern, C. R. Homeyer, D. J. G. Ii, and T. Smith, “Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction,” Monthly Weather Review, vol. 148, no. 7, pp. 2837–2861, Jun. 2020, doi: 10.1175/MWR-D-19-0372.1.
[46]
T. Kurth et al., “FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators.” arXiv, Aug. 2022. doi: 10.48550/arXiv.2208.05419.
[47]
R. Lam et al., “Learning skillful medium-range global weather forecasting,” Science, vol. 382, no. 6677, pp. 1416–1421, Dec. 2023, doi: 10.1126/science.adi2336.
[48]
K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, “Accurate medium-range global weather forecasting with 3D neural networks,” Nature, vol. 619, no. 7970, 7970, pp. 533–538, Jul. 2023, doi: 10.1038/s41586-023-06185-3.
[49]
P. Laloyaux, T. Kurth, P. D. Dueben, and D. Hall, “Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System,” Journal of Advances in Modeling Earth Systems, vol. 14, no. 6, p. e2022MS003016, 2022, doi: 10.1029/2022MS003016.
[50]
K. Kaheman, S. L. Brunton, and J. N. Kutz, “Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data,” Machine Learning: Science and Technology, vol. 3, no. 1, p. 015031, Mar. 2022, doi: 10.1088/2632-2153/ac567a.
[51]
M. R. Ebers, K. M. Steele, and J. N. Kutz, “Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects,” SIAM J. Appl. Dyn. Syst., pp. 440–469, Mar. 2024, doi: 10.1137/22M148375X.
[52]
M. Sparkes, “Huge protein breakthrough,” New Scientist, vol. 255, no. 3398, pp. 10–11, Aug. 2022, doi: 10.1016/S0262-4079(22)01372-0.
[53]
R. D. Ball et al., “Evidence for intrinsic charm quarks in the proton,” Nature, vol. 608, no. 7923, pp. 483–487, Aug. 2022, doi: 10.1038/s41586-022-04998-2.
[54]
G. Camps-Valls, D. Tuia, X. X. Zhu, and M. Reichstein, Deep learning for the earth sciences: A comprehensive approach to remote sensing, climate science and geosciences. Hoboken, New Jersey: Wiley, 2021.
[55]
S. Mahajan, L. S. Passarella, F. M. Hoffman, M. G. Meena, and M. Xu, “Assessing Teleconnections-Induced Predictability of Regional Water Cycle on Seasonal to Decadal Timescales Using Machine Learning Approaches,” Artificial Intelligence for Earth System Predictability (AI4ESP) Collaboration (United States), AI4ESP-1086, Apr. 2021. doi: 10.2172/1769676.
[56]
Y. Liu, K. Duffy, J. G. Dy, and A. R. Ganguly, “Explainable deep learning for insights in El Niño and river flows,” Nat Commun, vol. 14, no. 1, 1, p. 339, Jan. 2023, doi: 10.1038/s41467-023-35968-5.
[57]
A. Mercer, “Predictability of Common Atmospheric Teleconnection Indices Using Machine Learning,” Procedia Computer Science, vol. 168, pp. 11–18, Jan. 2020, doi: 10.1016/j.procs.2020.02.245.
[58]
K. Dijkstra, J. van de Loosdrecht, L. R. B. Schomaker, and M. A. Wiering, “Hyperspectral demosaicking and crosstalk correction using deep learning,” Machine Vision and Applications, vol. 30, no. 1, pp. 1–21, Feb. 2019, doi: 10.1007/s00138-018-0965-4.
[59]
Y. Xiong, Y. Ye, H. Zhang, J. He, B. Wang, and K. Yang, “Deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance strategy in space division multiplexing elastic optical networks,” Optics Express, vol. 28, no. 3, pp. 2758–2777, Feb. 2020, doi: 10.1364/OE.381551.
[60]
N. Erfanian et al., “Deep learning applications in single-cell genomics and transcriptomics data analysis,” Biomedicine & Pharmacotherapy, vol. 165, p. 115077, Sep. 2023, doi: 10.1016/j.biopha.2023.115077.
[61]
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical Black-Box Attacks against Machine Learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, Apr. 2017, pp. 506–519. doi: 10.1145/3052973.3053009.
[62]
D. George and E. A. Huerta, “Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data,” Physics Letters B, vol. 778, pp. 64–70, Mar. 2018, doi: 10.1016/j.physletb.2017.12.053.
[63]
A. Sinha and R. Abernathey, “Estimating Ocean Surface Currents With Machine Learning,” Frontiers in Marine Science, vol. 8, 2021.
[64]
M. Raissi and G. E. Karniadakis, “Hidden physics models: Machine learning of nonlinear partial differential equations,” Journal of Computational Physics, vol. 357, pp. 125–141, Mar. 2018, doi: 10.1016/j.jcp.2017.11.039.
[65]
M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics, vol. 378, pp. 686–707, Feb. 2019, doi: 10.1016/j.jcp.2018.10.045.
[66]
E. P. L. van Nieuwenburg, Y.-H. Liu, and S. D. Huber, “Learning phase transitions by confusion,” Nature Physics, vol. 13, no. 5, pp. 435–439, Feb. 2017, doi: 10.1038/nphys4037.
[67]
S. Srinivasan et al., “Machine learning the metastable phase diagram of covalently bonded carbon,” Nat Commun, vol. 13, no. 1, 1, p. 3251, Jun. 2022, doi: 10.1038/s41467-022-30820-8.
[68]
A. S. von der Heydt et al., “Lessons on Climate Sensitivity From Past Climate Changes,” Curr Clim Change Rep, vol. 2, no. 4, pp. 148–158, Dec. 2016, doi: 10.1007/s40641-016-0049-3.
[69]
N. Wolchover, “Machine Learning’s ‘Amazing’ Ability to Predict Chaos,” Quanta Magazine. https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/, Apr. 2018.
[70]
J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott, “Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach,” Physical Review Letters, vol. 120, no. 2, p. 024102, Jan. 2018, doi: 10.1103/PhysRevLett.120.024102.
[71]
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
[72]
Y. LeCun et al., “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, Dec. 1989, doi: 10.1162/neco.1989.1.4.541.
[73]
J. Schmidhuber, “Who Invented Backpropagation?” Nov. 2020. Available: https://people.idsia.ch/~juergen/who-invented-backpropagation.html
[74]
A. Halevy, P. Norvig, and F. Pereira, “The unreasonable effectiveness of data,” IEEE Intelligent Systems, vol. 24, no. 2, pp. 8–12, Mar. 2009, doi: 10.1109/MIS.2009.36.
[75]
R. Sutton, “The Bitter Lesson,” Mar. 19, 2019. http://www.incompleteideas.net/IncIdeas/BitterLesson.html (accessed Feb. 09, 2024).
[76]
J. Kaplan et al., “Scaling Laws for Neural Language Models,” Jan. 22, 2020. http://arxiv.org/abs/2001.08361 (accessed Feb. 09, 2024).
[77]
Epoch, “Parameter, compute and data trends in machine learning.” https://epochai.org/data/epochdb, 2024.
[78]
S. Chetlur et al., “cuDNN: Efficient primitives for deep learning,” doi: 10.48550/arXiv.1410.0759.
[79]
T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste, “Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241, pp. 1–124, 2021.
[80]
M. Speiser, “On Sparsity in AI/ML and Earth Science Applications, and its Architectural Implications.” Geneva, Switzerland/Virtual, Jul. 2021.
[81]
M. Radosavljevic and J. Kavalieros, “3D-Stacked CMOS Takes Moore’s Law to New Heights,” IEEE Spectrum. https://spectrum.ieee.org/3d-cmos, Aug. 2022.
[82]
Lex Fridman, “Scott Aaronson: Quantum Computing.” Feb. 17, 2020. Available: https://www.youtube.com/watch?v=uX5t8EivCaM
[83]
F. Tennie and T. N. Palmer, “Quantum Computers for Weather and Climate Prediction: The Good, the Bad, and the Noisy,” Bulletin of the American Meteorological Society, vol. 104, no. 2, pp. E488–E500, Feb. 2023, doi: 10.1175/BAMS-D-22-0031.1.
[84]
A. Jolly, “Researchers Simulate Ice Formation by Combining AI and Quantum Mechanics,” HPCwire. https://www.hpcwire.com/off-the-wire/researchers-simulate-ice-formation-by-combining-ai-and-quantum-mechanics/, Aug. 2022.
[85]
L. Zhang, J. Han, H. Wang, R. Car, and W. E, “Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics,” Physical Review Letters, vol. 120, no. 14, p. 143001, Apr. 2018, doi: 10.1103/PhysRevLett.120.143001.
[86]
Sabine Hossenfelder, It looks like AI will kill Quantum Computing. 2024. Accessed: Feb. 21, 2024. [Online]. Available: https://www.youtube.com/watch?v=Q8A4wEohqT0
[87]
N. Oreskes, “Why Believe a Computer? Models, Measures, and Meaning in the Natural World,” in The Earth Around Us, Routledge, 2000.
[88]
N. Oreskes, “The Role of Quantitative Models in Science,” in Models in ecosystem science, C. D. Canham, J. J. Cole, and W. K. Lauenroth, Eds. Princeton University Press, 2003.
[89]
D. Reed et al., “Computational Science: Ensuring America’s Competitiveness,” p. 117, Jun. 2005.
[90]
T. Hey, S. Tansley, and K. Tolle, The Fourth Paradigm: Data-Intensive Scientific Discovery, 1st edition. Redmond, Washington: Microsoft Research, 2009.
[91]
A. Calhoun, “How Do Deep Networks of AI Learn?” Simons Foundation. https://www.simonsfoundation.org/2020/02/26/how-do-deep-networks-of-ai-learn/, Feb. 2020.
[92]
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv:1207.0580 [cs], Jul. 2012, Available: https://arxiv.org/abs/1207.0580
[93]
C. Metz, “In Two Moves, AlphaGo and Lee Sedol Redefined the Future,” Wired, Mar. 2016.
[94]
K. Hartnett, “Machine Learning Confronts the Elephant in the Room,” Quanta Magazine. https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/, Sep. 2018.
[95]
F. Boutier, “New Copernicus data access service,” European Commission. https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7374, Dec. 2022.
[96]
“ESDS Program - Continuous Evolution,” NASA Earthdata. https://www.earthdata.nasa.gov/esds/continuous-evolution; Earth Science Data Systems, NASA, Jan. 2024.
[97]
A. Ng, Y. B. Mourri, and K. Katanforoosh, “Structuring Machine Learning Projects [MOOC],” Coursera. https://www.coursera.org/learn/machine-learning-projects, 2021.
[98]
M. G. Schultz et al., “Can deep learning beat numerical weather prediction?” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 379, no. 2194, p. 20200097, Apr. 2021, doi: 10.1098/rsta.2020.0097.
[99]
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems, vol. 2, no. 4, pp. 303–314, Dec. 1989, doi: 10.1007/BF02551274.
[100]
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, Jan. 1989, doi: 10.1016/0893-6080(89)90020-8.
[101]
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2009, pp. 248–255. doi: 10.1109/CVPR.2009.5206848.
[102]
J. Markoff, “Seeking a Better Way to Find Web Images,” The New York Times, Nov. 2012.
[103]
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving Language Understanding by Generative Pre-Training,” OpenAI Blog. 2018.
[104]
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language Models are Unsupervised Multitask Learners,” OpenAI Blog. 2019.
[105]
H. Touvron et al., “LLaMA: Open and Efficient Foundation Language Models.” arXiv, Feb. 2023. doi: 10.48550/arXiv.2302.13971.
[106]
H. Touvron et al., “Llama 2: Open Foundation and Fine-Tuned Chat Models.” arXiv, Jul. 2023. doi: 10.48550/arXiv.2307.09288.
[107]
J. Banks and T. Warkentin, “Gemma: Introducing new state-of-the-art open models,” Google. Feb. 2024.
[108]
BigScience Workshop et al., “BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.” arXiv, Jun. 2023. doi: 10.48550/arXiv.2211.05100.
[109]
A. Ramesh et al., “Zero-Shot Text-to-Image Generation.” arXiv, Feb. 2021. doi: 10.48550/arXiv.2102.12092.
[110]
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical Text-Conditional Image Generation with CLIP Latents.” arXiv, Apr. 2022. doi: 10.48550/arXiv.2204.06125.
[111]
J. Betker et al., “Improving Image Generation with Better Captions,” OpenAI Blog. Oct. 2023.
[112]
O. Bar-Tal et al., “Lumiere: A Space-Time Diffusion Model for Video Generation,” Feb. 05, 2024. http://arxiv.org/abs/2401.12945 (accessed Feb. 06, 2024).
[113]
“Video generation models as world simulators,” OpenAI Blog. Feb. 2024.
[114]
J. Porter, “ChatGPT continues to be one of the fastest-growing services ever,” The Verge. https://www.theverge.com/2023/11/6/23948386/chatgpt-active-user-count-openai-developer-conference, Nov. 2023.
[115]
J. Nicas and L. C. Herrera, “Is Argentina the First A.I. Election?” The New York Times, Nov. 2023.
[116]
Z. Wolf, “Analysis: The deepfake era of US politics is upon us CNN Politics,” CNN. https://www.cnn.com/2024/01/24/politics/deepfake-politician-biden-what-matters/index.html, Jan. 2024.
[117]
J. G. Cavazos, P. J. Phillips, C. D. Castillo, and A. J. O’Toole, “Accuracy Comparison Across Face Recognition Algorithms: Where Are We on Measuring Race Bias?” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 1, pp. 101–111, Jan. 2021, doi: 10.1109/TBIOM.2020.3027269.
[118]
“Article 5: Prohibited Artificial Intelligence Practices EU Artificial Intelligence Act (Final Draft January 2024),” EU Artificial Intelligence Act. https://artificialintelligenceact.eu/article/5/.
[119]
“Article 52: Transparency Obligations for Providers and Users of Certain AI Systems and GPAI Models EU Artificial Intelligence Act (Final Draft January 2024),” EU Artificial Intelligence Act. https://artificialintelligenceact.eu/article/52/.
[120]
N. Carlini et al., “Extracting Training Data from Diffusion Models.” arXiv, Jan. 2023. doi: 10.48550/arXiv.2301.13188.
[121]
M. Nasr et al., “Scalable Extraction of Training Data from (Production) Language Models.” arXiv, Nov. 2023. doi: 10.48550/arXiv.2311.17035.
[122]
I. Shumailov, Z. Shumaylov, Y. Zhao, Y. Gal, N. Papernot, and R. Anderson, “The Curse of Recursion: Training on Generated Data Makes Models Forget,” May 31, 2023. http://arxiv.org/abs/2305.17493 (accessed Feb. 01, 2024).
[123]
Y. Guo, G. Shang, M. Vazirgiannis, and C. Clavel, “The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text.” arXiv, Nov. 2023. doi: 10.48550/arXiv.2311.09807.
[124]
R. Hataya, H. Bao, and H. Arai, “Will Large-scale Generative Models Corrupt Future Datasets?” arXiv, Aug. 2023. doi: 10.48550/arXiv.2211.08095.
[125]
J. Pathak et al., “FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators,” Feb. 22, 2022. http://arxiv.org/abs/2202.11214 (accessed Jan. 30, 2024).
[126]
A. J. Charlton-Perez et al., “Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán,” npj Climate and Atmospheric Science, vol. 7, no. 1, pp. 1–11, Apr. 2024, doi: 10.1038/s41612-024-00638-w.
[127]
M. J. Smith, L. Fleming, and J. E. Geach, “EarthPT: A time series foundation model for Earth Observation.” arXiv, Jan. 2024. doi: 10.48550/arXiv.2309.07207.
[128]
G. Mateo-García, V. Laparra, C. Requena-Mesa, and L. Gómez-Chova, “Generative Adversarial Networks in the Geosciences,” in Deep Learning for the Earth Sciences, John Wiley & Sons, Ltd, 2021, pp. 24–36. doi: 10.1002/9781119646181.ch3.
[129]
Lex Fridman, “Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI Lex Fridman Podcast #416.” Mar. 07, 2024. Available: https://www.youtube.com/watch?v=5t1vTLU7s40
[130]
M. Assran et al., “Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.” arXiv, Apr. 2023. doi: 10.48550/arXiv.2301.08243.
[131]
T. H. Trinh, Y. Wu, Q. V. Le, H. He, and T. Luong, “Solving olympiad geometry without human demonstrations,” Nature, vol. 625, no. 7995, 7995, pp. 476–482, Jan. 2024, doi: 10.1038/s41586-023-06747-5.
[132]
M. Zečević, M. Willig, D. S. Dhami, and K. Kersting, “Causal Parrots: Large Language Models May Talk Causality But Are Not Causal,” Transactions on Machine Learning Research, May 2023.
[133]
E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Mar. 2021, pp. 610–623. doi: 10.1145/3442188.3445922.
[134]
O. J. Dunn, “Multiple Comparisons among Means,” Journal of the American Statistical Association, vol. 56, no. 293, pp. 52–64, Mar. 1961, doi: 10.1080/01621459.1961.10482090.
[135]
J. Neyman and E. S. Pearson, “On the Use and Interpretation of Certain Test Criteria for Purposes of Statistical Inference: Part I,” Biometrika, vol. 20A, no. 1/2, pp. 175–240, 1928, doi: 10.2307/2331945.
[136]
J. Pearl, Causality, 2nd ed. Cambridge: Cambridge University Press, 2009. doi: 10.1017/CBO9780511803161.
[137]
D. B. Rubin, “Causal Inference Using Potential Outcomes: Design, Modeling, Decisions,” Journal of the American Statistical Association, vol. 100, no. 469, pp. 322–331, 2005, Accessed: Mar. 28, 2024. [Online]. Available: https://www.jstor.org/stable/27590541
[138]
P. Hoyer, D. Janzing, J. M. Mooij, J. Peters, and B. Schölkopf, “Nonlinear causal discovery with additive noise models,” in Advances in Neural Information Processing Systems, 2008, vol. 21.
[139]
S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen, “A Linear Non-Gaussian Acyclic Model for Causal Discovery,” Journal of Machine Learning Research, vol. 7, no. 72, pp. 2003–2030, 2006.
[140]
J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic, “Detecting and quantifying causal associations in large nonlinear time series datasets,” Science Advances, Nov. 2019, doi: 10.1126/sciadv.aau4996.
[141]
A. Molak, Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more. Birmingham: Packt Publishing, 2023.
[142]
D. Kahneman, Thinking, fast and slow. New York: Farrar, Straus and Giroux, 2011.
[143]
V. Chernozhukov et al., “Double/debiased machine learning for treatment and structural parameters,” The Econometrics Journal, vol. 21, no. 1, pp. C1–C68, Feb. 2018, doi: 10.1111/ectj.12097.
[144]
B. K. Petersen, M. L. Larma, T. N. Mundhenk, C. P. Santiago, S. K. Kim, and J. T. Kim, “Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients,” Feb. 2022.
[145]
W. Tenachi, R. Ibata, and F. I. Diakogiannis, “Deep symbolic regression for physics guided by units constraints: Toward the automated discovery of physical laws,” The Astrophysical Journal, vol. 959, no. 2, p. 99, Dec. 2023, doi: 10.3847/1538-4357/ad014c.
[146]
G. Camps-Valls et al., “Discovering causal relations and equations from data,” Physics Reports, vol. 1044, pp. 1–68, Dec. 2023, doi: 10.1016/j.physrep.2023.10.005.
[147]
P. D. Dueben, M. G. Schultz, M. Chantry, D. J. Gagne, D. M. Hall, and A. McGovern, “Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook,” Artificial Intelligence for the Earth Systems, vol. 1, no. 3, Jul. 2022, doi: 10.1175/AIES-D-21-0002.1.
[148]
J. Kaltenborn et al., “ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning,” Advances in Neural Information Processing Systems, vol. 36, pp. 21757–21792, Dec. 2023.
[149]
D. Carlson and T. Oda, “Editorial: Data publication ESSD goals, practices and recommendations,” Earth System Science Data, vol. 10, no. 4, pp. 2275–2278, Dec. 2018, doi: 10.5194/essd-10-2275-2018.
[150]
Z. Xiong, F. Zhang, Y. Wang, Y. Shi, and X. X. Zhu, “EarthNets: Empowering AI in Earth Observation.” arXiv, Dec. 2022. doi: 10.48550/arXiv.2210.04936.
[151]
S. Rasp et al., “WeatherBench 2: A benchmark for the next generation of data-driven global weather models,” Jan. 26, 2024. http://arxiv.org/abs/2308.15560 (accessed Feb. 20, 2024).