Search Results for author: Michael O'Neill

Found 9 papers, 1 papers with code

Amplifying Limitations, Harms and Risks of Large Language Models

no code implementations6 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.

Large Language Models in Sport Science & Medicine: Opportunities, Risks and Considerations

no code implementations5 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.

An exploration of asocial and social learning in the evolution of variable-length structures

no code implementations16 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.

Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison

no code implementations16 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.

Investigating the Evolvability of Web Page Load Time

no code implementations22 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.

A Search for Improved Performance in Regular Expressions

no code implementations13 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.

PonyGE2: Grammatical Evolution in Python

6 code implementations24 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.

Performance Localisation

no code implementations4 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.

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