Moshe Sipper is a professor of AI who has published 7 books, 210+ scientific publications, and 80+ essays and stories on Medium. Cited as a top scientist, he has won multiple awards and advised close to 40 graduate students.
medium ∙ scholar ∙ github ∙ linkedin ∙ youtube ∙ mastodon ∙ orcid ∙ sipper@bgu.ac.il ∙ Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel ∙ Alon Building for Hi-Tech (37), Room 121 ∙ +97286477880 ∙ for prospective grad students
Recent Papers (all available here)
⬫ Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors
⬫ XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
⬫ Task and Explanation Network
⬫ Open Sesame! Universal Black Box Jailbreaking of Large Language Models
⬫ What's in an AI's Mind's Eye? We Must Know
⬫ Fitness Approximation through Machine Learning
⬫ I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models
⬫ A Melting Pot of Evolution and Learning
⬫ Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors
⬫ Classy Ensemble: A Novel Ensemble Algorithm for Classification
⬫ Foiling Explanations in Deep Neural Networks
⬫ EC-KitY: Evolutionary Computation Tool Kit in Python
⬫ Artificial General Intelligence: Pressure Cooker or Crucible?
⬫ High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
⬫ An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks
⬫ Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution
⬫ Combining Deep Learning with Good Old-Fashioned Machine Learning
⬫ AddGBoost: A Gradient Boosting-Style Algorithm Based on Strong Learners
⬫ Evolution of Activation Functions for Deep Learning-Based Image Classification
⬫ From Requirements to Source Code: Evolution of Behavioral Programs
⬫ Binary and Multinomial Classification through Evolutionary Symbolic Regression
⬫ Neural Networks with À La Carte Selection of Activation Functions
⬫ Symbolic-Regression Boosting
⬫ Conservation Machine Learning: A Case Study of Random Forests
⬫ Conservation Machine Learning
⬫ New Pathways in Coevolutionary Computation
⬫ Automated discovery of test statistics using genetic programming
⬫ Investigating the parameter space of evolutionary algorithms
⬫ Artificial intelligence: more human with human
⬫ Evolutionary computation: the next major transition of artificial intelligence?
⬫ Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors
⬫ XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
⬫ Task and Explanation Network
⬫ Open Sesame! Universal Black Box Jailbreaking of Large Language Models
⬫ What's in an AI's Mind's Eye? We Must Know
⬫ Fitness Approximation through Machine Learning
⬫ I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models
⬫ A Melting Pot of Evolution and Learning
⬫ Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors
⬫ Classy Ensemble: A Novel Ensemble Algorithm for Classification
⬫ Foiling Explanations in Deep Neural Networks
⬫ EC-KitY: Evolutionary Computation Tool Kit in Python
⬫ Artificial General Intelligence: Pressure Cooker or Crucible?
⬫ High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
⬫ An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks
⬫ Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution
⬫ Combining Deep Learning with Good Old-Fashioned Machine Learning
⬫ AddGBoost: A Gradient Boosting-Style Algorithm Based on Strong Learners
⬫ Evolution of Activation Functions for Deep Learning-Based Image Classification
⬫ From Requirements to Source Code: Evolution of Behavioral Programs
⬫ Binary and Multinomial Classification through Evolutionary Symbolic Regression
⬫ Neural Networks with À La Carte Selection of Activation Functions
⬫ Symbolic-Regression Boosting
⬫ Conservation Machine Learning: A Case Study of Random Forests
⬫ Conservation Machine Learning
⬫ New Pathways in Coevolutionary Computation
⬫ Automated discovery of test statistics using genetic programming
⬫ Investigating the parameter space of evolutionary algorithms
⬫ Artificial intelligence: more human with human
⬫ Evolutionary computation: the next major transition of artificial intelligence?
Biography
Moshe Sipper is a professor of Computer Science 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 evolutionary computation, machine learning, artificial intelligence, and deep learning. 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 40 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 humor on Medium. He is also a cartoonist and has been known to sing.
Moshe Sipper is a professor of Computer Science 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 evolutionary computation, machine learning, artificial intelligence, and deep learning. 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 40 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 humor 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 🐭
— Anton Ego, Ratatouille 🐭
All animals are under stringent selection pressure to be as stupid as they can get away with.
— Peter Watts, Echopraxia
— Peter Watts, Echopraxia