AI Reading List: Technologies: Deep Learning
Below is a portion of my informal list of readings related to Artificial Intelligence (AI). This started out as a very short list created for use in conjunction with an academic presentation and has now grown much larger. Please let me know if you have any corrections, additions, suggestions, etc. It is very idiosyncratic and not meant to be comprehensive. Please feel free to share with others.
Artificial Intelligence (AI) Reading List, by Philip Rubin
Technologies — Deep Learning:
Ryan O’Connor. Emergent Abilities of Large Language Models. AssemblyAI, Mar. 7, 2023.
Ben Brubaker. In Neural Networks, Unbreakable Locks Can Hide Invisible Doors. Quanta magazine, Mar. 2, 2023.
Charles Q. Choi. Predicting “Black Swan” Disasters With AI. IEEE Spectrum, Jan. 3, 2023.
Kevin D. Foote. Using Graph Technology in the Evolution of AI. Dataversity, Dec. 14, 2022.
Rebel Science. Deep Learning Is Not Just Inadequate for Solving AGI, It Is Useless. Medium.com, Dec. 7, 2022.
Matthew S. Smith. New AI Speeds Computer Graphics by Up to 5x. IEEE Spectrum, Nov. 25, 2022.
Rishabh Agarwal and Max Schwarzer. Beyond Tabula Rasa: Reincarnating Reinforcement Learning. Google Research, Nov. 3, 2022.
Adam Zewe. In machine learning, synthetic data can offer real performance improvements. MIT News, Nov. 3, 2022.
Vineeth N. Balasubramanian. Toward Explainable Deep Learning. Communications of the ACM, Vol. 65 No. 11, 68-69, 10.1145/3550491, November 2022.
Alexander Lavin, et al. Technology readiness levels for machine learning systems. Nature Communications, 13, Oct. 20, 2022.
Sharon Goldman. 10 years later, deep learning ‘revolution’ rages on, say AI pioneers Hinton, LeCun and Li. VentureBeat, Sep. 14, 2022.
William Fedus, Jeff Dean, and Barret Zoph. A Review of Sparse Expert Models in Deep Learning. arXiv:2209.01667, Sep. 4, 2022.
Dustin Tran and Balaji Lakshminarayanan. Towards Reliability in Deep Learning Systems. Google AI Blog, July 14, 2022.
Samuel K. Moore, David Schneider, and Eliza Strickland. How Deep Learning Works. IEEE Spectrum, Sep. 28, 2021.
Eddie Guy. Deep Learning’s Diminishing Returns. IEEE Spectrum, Sep. 24, 2021.
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. Geometric Deep Learning Course. African Institute for Mathematical Sciences (AIMS). 2021.
Terrence J. Sejnowski. The unreasonable effectiveness of deep learning in artificial intelligence. PNAS, Dec. 1, 2020, 117, no. 48, 30033–30038.
Richard Baraniuk, David Donoho, and Matan Gavish. The science of deep learning. PNAS, 117 (48), 30029-30032, Nov. 23, 2020.
Andrew M. Saxe, James L. McClelland, and Surya Ganguli. A mathematical theory of semantic development in deep neural networks. Proceedings of the National Academy of Sciences, USA, 116(23), 11537-11546. pnas.182022611, 2019. [Supplementary Information.]
Adam H. Marblestone, Greg Wayne, and Konrad P. Kording. Toward an Integration of Deep Learning and Neuroscience. Frontiers in Computational Neuroscience, 10:94, 2016.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep Learning. Nature, 521, 436-444, May 28, 2015.
Hannes Schulz and Sven Behnke. Deep Learning. KI - Künstliche Intelligenz, 26 (4): 357–363, Nov. 1, 2012.
Wikipedia. Deep Learning.