no code implementations • 6 Jul 2023 • Michael O'Neill, Mark Connor
We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence (AI) and its capabilities, and the distraction provided by the associated talk of science-fiction scenarios that might arise if AI should become sentient and super-intelligent.
no code implementations • 5 May 2023 • Mark Connor, Michael O'Neill
However, there are also potential risks associated with the use and development of LLMs, including biases in the dataset used to create the model, the risk of exposing confidential data, the risk of generating harmful output, and the need to align these models with human preferences through feedback.
no code implementations • 16 Apr 2021 • Michael O'Neill, Anthony Brabazon
We wish to explore the contribution that asocial and social learning might play as a mechanism for self-adaptation in the search for variable-length structures by an evolutionary algorithm.
no code implementations • 16 Dec 2020 • Mark Connor, Michael O'Neill
This initial research would suggest that global evolutionary computation based optimization algorithms may present a fast and robust alternative to local algorithms when fitting the parameters of non-linear dose-response models.
no code implementations • 9 Jul 2018 • Dimitrios Korkinof, Tobias Rijken, Michael O'Neill, Joseph Yearsley, Hugh Harvey, Ben Glocker
The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection.
no code implementations • 22 Feb 2018 • Brendan Cody-Kenny, Umberto Manganiello, John Farrelly, Adrian Ronayne, Eoghan Considine, Thomas McGuire, Michael O'Neill
We investigate whether a mutate-and-test approach can be used to optimise web page load time in these environments.
no code implementations • 13 Apr 2017 • Brendan Cody-Kenny, Michael Fenton, Adrian Ronayne, Eoghan Considine, Thomas McGuire, Michael O'Neill
In this paper, Genetic Programming is used to find performance improvements in regular expressions for an array of target programs, representing the first application of automated software improvement for run-time performance in the Regular Expression language.
6 code implementations • 24 Mar 2017 • Michael Fenton, James McDermott, David Fagan, Stefan Forstenlechner, Michael O'Neill, Erik Hemberg
Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process.
no code implementations • 4 Mar 2016 • Brendan Cody-Kenny, Michael O'Neill, Stephen Barrett
Typically a profiler can be used to find program code execution which represents a large portion of the overall execution cost of a program.