Upcoming Event

Abstract: Existing approaches to evaluating LLM ethical competence place too much emphasis on the verdicts—of permissibility and impermissibility—that they render. But ethical competence doesn’t consist in one’s judgments conforming to those of a cohort of crowdworkers. It consists in being able to identify morally relevant features, prioritise among them, associate them with reasons and weave

Abstract: Can AI systems such as ChatGPT think? This paper presents an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second

Abstract: LLMs have dramatically improved in capabilities in recent years. This raises the question of whether LLMs could become genuine agents with beliefs and desires. This paper demonstrates an in principle limit to LLM agency, based on their architecture. LLMs are next word predictors: given a string of text, they calculate the probability that various

Date: May 3, 2024 (Friday) Time: 14:00 – 16:00 Venue: CPD 2.42, HKU Centennial Campus Speaker: Kathryn Goldstein, HKU Registration: https://hku.au1.qualtrics.com/jfe/form/SV_cwDbZRfzjnQDbAq This workshop will discuss plagiarism and ethical use of ChatGPT, but we’ll also work on building up prompt engineering skills. Examples of specific skills students will learn, using prompt engineering: Use ChatGPT as a

Speaker: Dr Rachel Robertson, Hong Kong Baptist University Abstract:  This talk argues that AI ought to support the acquisition and exercise of integrity: the alignment within and between our thoughts, motivations, identities, and interactions with each other and the world. I will argue that an integrity-centred framework is very useful for assessing cognitive, normative, and

Speaker: Prof Suzuki Takayuki, The University of Tokyo Abstract:  Classical AI, which has been dominant until 1980s in AI research,  regarded thinking as computation, that is, formal manipulation of symbols. Though this approach worked well in simple tasks, it has repeatedly failed in dealing with difficult tasks. One fundamental problem is that it is extremely

Speaker: Prof Jiji Zhang, The Chinese University of Hong Kong Abstract:  It is now common to call biased algorithmic predictions “unfair”, and many criteria of fairness have been proposed to serve as side constraints in the training of predictive models for optimal predictive accuracy or as parts of the objective function to be optimized along

Speaker: Dr André Curtis-Trudel, Lingnan University Abstract:  Recent high-profile scientific achievements by machine learning (ML) and especially deep learning (DL) systems have reinvigorated interest in ML for automated scientific discovery (e.g., Wang et al. 2023). The hope, in rough outline, is that ML or DL might be used to identify promising novel events, phenomena, hypotheses,

Speaker: Jacqueline Harding, Stanford University Abstract:  What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models’ capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and

Speaker: Prof Herman Cappelen, The University of Hong Kong Abstract:  This paper makes three main claims: 1) There is no universal set of core values for AI systems to align with – moral disagreements run too deep. 2) The increased causal potency that creates the problem of AI risk also implies that imperfect safety mechanisms