Moshe Sipper
Moshe Sipper (מֹשֶׁה זִיפֶּר) is a professor of artificial intelligence who has published 8 books, 210+ scientific publications, and 90+ essays and stories on Medium. Cited as a top scientist, he has won multiple awards and advised close to 40 graduate students.
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Recent & Highly Cited ♡
(all publications available here)
- On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective, 2024
- Deep Learning-Based Operators for Evolutionary Algorithms, 2024
- Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors, 2024
- XAI-Based Detection of Adversarial Attacks on Deepfake Detectors, 2024
- Task and Explanation Network, 2024
- What’s in an AI’s Mind’s Eye? We Must Know, 2024
- A Melting Pot of Evolution and Learning, 2024
- Open Sesame! Universal Black Box Jailbreaking of Large Language Models, 2023 ♡
- Fitness Approximation through Machine Learning, 2023
- I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models, 2023
- Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors, 2023
- Classy Ensemble: A Novel Ensemble Algorithm for Classification, 2023
- Foiling Explanations in Deep Neural Networks, 2023
- EC-KitY: Evolutionary Computation Tool Kit in Python, 2023
- Combining Deep Learning with Good Old-Fashioned Machine Learning, 2023
- Artificial General Intelligence: Pressure Cooker or Crucible? 2022
- High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms, 2022 ♡
- Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances…, 2022 ♡
- Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 2022
- AddGBoost: A Gradient Boosting-Style Algorithm Based on Strong Learners, 2022 ♡
- Evolution of Activation Functions for Deep Learning-Based Image Classification, 2022
- From Requirements to Source Code: Evolution of Behavioral Programs, 2022
- Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2022
- Neural Networks with À La Carte Selection of Activation Functions, 2021
- Symbolic-Regression Boosting, 2021
- Conservation Machine Learning: A Case Study of Random Forests, 2021 ♡
- Investigating the parameter space of evolutionary algorithms, 2018 ♡
- Flight of the FINCH through the Java wilderness, 2011 ♡
- Machine nature: the coming age of bio-inspired computing, 2002 ♡
- Fuzzy CoCo: A cooperative-coevolutionary approach to fuzzy modeling, 2001 ♡
- On the generation of high-quality random numbers by two-dimensional cellular automata, 2000 ♡
- Toward Robust Integrated Circuits: The Embryonics Approach, 2000 ♡
- The emergence of cellular computing, 1999 ♡
- A fuzzy-genetic approach to breast cancer diagnosis, 1999 ♡
- Design, observation, surprise! A test of emergence, 1999 ♡
- Evolution of parallel cellular machines: the cellular programming approach, 1997 ♡
- A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems, 1997 ♡
- Toward a viable, self-reproducing universal computer, 1996 ♡
- Co-evolving non-uniform cellular automata to perform computations, 1996 ♡
Biography
Moshe Sipper is a professor of artificial intelligence at Ben-Gurion University of the Negev, Israel. He received the BA degree from the Technion — Israel Institute of Technology, and the MSc and PhD degrees from Tel Aviv University, all in computer science. During 1995–2001 he was a senior researcher at the EPFL (Switzerland), and during 2016-2020 he was a visiting professor at the University of Pennsylvania (USA).
Dr. Sipper’s current research focuses on deep learning, machine learning, artificial intelligence, and evolutionary computation. At some point or other he also did research in: artificial life, artificial self-replication, bio-inspired computing, cellular automata, cellular computing, embryonic electronics, evolvable hardware, fuzzy logic, games, robotics, and software engineering.
Dr. Sipper has authored over 210 scientific publications, including the books: Evolved to Win, Machine Nature: The Coming Age of Bio-Inspired Computing, and Evolution of Parallel Cellular Machines: The Cellular Programming Approach. He has supervised close to 40 graduate students, and taught numerous basic and advanced courses in computer science, both undergraduate and graduate.
Dr. Sipper is an associate editor of the journal Genetic Programming and Evolvable Machines, and was an associate editor of the journals: IEEE Transactions on Evolutionary Computation, IEEE Transactions on Computational Intelligence and AI in Games, and Memetic Computing. He organized and chaired several conferences and has served on the program committees of close to 150 conferences. He has also served as a reviewer for nearly 50 journals and funding agencies.
Dr. Sipper won many awards, including the 2015 IEEE CIS Outstanding TCIAIG Paper Award, the 2008 BGU Toronto Prize for Academic Excellence in Research, the 1999 EPFL Latsis Prize, and 6 HUMIE Awards (Human-Competitive Results Produced by Genetic and Evolutionary Computation). He has been cited as a top scientist, appears in the top 2% in the Mendeley list, and was one of “5 Scientists At The Forefront Of Computing And AI”.
Dr. Sipper is the author of four fiction books: Fredric: A Collection of Flash Fiction, Daniel Max and the King in the Tower, Xor: The Shape of Darkness, and The Peaceful Affair. He regularly publishes short pieces of popular science, science fiction, philosophy of AI, and fiction on Medium. He is also a cartoonist and has been known to sing.
A little perspective. That's it. I'd like some fresh, clear, well-seasoned perspective. Can you suggest a good wine to go with that?
— Anton Ego, Ratatouille
All animals are under stringent selection pressure to be as stupid as they can get away with.
— Peter Watts, Echopraxia
