eventSeminar
Andrew Shen | Unlocking LLM Creativity in Science through Analogical Reasoning
LectureOct 05, 20264:00 PM - 5:30 PM
October
05
2026
A Center for Neural Data Science Seminar Series
Event
A Center for Neural Data Science Seminar Series
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A Center for Neural Data Science Seminar Series
A Center for Neural Data Science Seminar Series
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
"In this talk I hope to illustrate how AI ethics can avoid the undesirable extremes of two dimensions:
First dimension: Complacency vs Inflation
On the one hand, I will argue that we should eschew the complacent view that AI presents no novel challenges for ethics, the view that AI is just a technology like any other, so extant ethical principles for non-AI technologies are all we need. On the other hand, I will also argue that we should also avoid the inflationary view that the need for new ethical principles for AI derives from the fact that AI systems are themselves moral agents and/or patients.
Second dimension: Reactive systems vs Robots with Obligations
On the one hand, I will argue that the ethical construction of autonomous AI systems (including, but not limited to, autonomous robots such as driverless cars) will require that such systems do more than merely transform an input signal to an output signal (as is prevalent in much machine learning technology); at least part of that transformation, to have the right counterfactual richness that ethics requires, will have to have deliberative structure. On the other hand, AI systems that reason about their ethical obligations and what is morally permissible for them are not a solution since AI systems will not, for the foreseeable future, be the kinds of things that could have ethical obligations or moral permissions.
For each of these dimensions, I give a specific example (a policy, and a design, respectively) that avoids the horns of the dilemma, and the moral hazards they entail." - Ron Chrisley
