Sep 2026· Cambridge University Press eBooks· pp. 1-12· 17 references
Abstract
In 2023, the arrival of Chat GPT 3o heralded many viral conversations about what it means to interact with artificial intelligence (e.g., Roose, 2023). People read about individuals having extended conversations with chatbots, often leading to complex consequences that raise deeper questions. What do we mean by “empathy” in these spaces? What are the ethical consequences for building or using technology for emotional support? There have been cases of large language models giving better and more empathetic medical advice than trained medical workers (Ayers et al., 2023; see later replication by Ovsyannikova et al., 2025). There have also been popular write-ups about people developing relationships with Artificial Intelligence (AI) platforms explicitly positioned as romantic partners (Patel, 2024). In this introductory chapter, we will highlight core themes that unite the chapters throughout the volume, highlighting connections across social sciences, humanities, and engineering and computers science to juxtapose perspectives and illustrate connections and ongoing controversies.
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
