Sep 2026· Cambridge University Press eBooks· pp. 30-39· 38 references
Abstract
Most definitions of empathy stress its interpersonal nature: Empathy requires a human sender and a human receiver, where all parties involved have the theoretical capacity to accurately understand each other’s minds. Because empathy is inherently interpersonal, some scholars have questioned whether empathy between humans and artificial intelligence as it currently exists (e.g., large language models) is “real.” However, the requirement for two minds, each with the theoretical capacity to fully understand the thoughts and emotions of the other, is also not clearly met in other domains where empathy is discussed, such as empathy between humans and non-human animals or empathy with nature. This chapter critically examines these assumptions about what is needed for empathy to occur, offering several possible sets of empathy requirements that researchers might engage with, and outlining potential challenges researchers might face if they adopt any particular set of requirements in their work. Ultimately, we pose the question of whether two minds and/or the ability for minds to understand each other are truly necessary for empathy, or whether all that is required for empathy to occur is an entity’s perception of giving or receiving it.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17