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 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.
