Abstract In this chapter, we focus on a digital innovation which is not confined to a single application such as digital platforms, but on one – Artificial Intelligence or AI – which is in course of pervading the whole of the economy and society, and fully deserves the title of one (or several) general purpose technologies or GPTs. Estimates of its likely economic effects, particularly on productivity and labour demand, remain extremely varied and deeply uncertain. The risks which it imposes on individual groups, or even the whole human race, are still speculative. The European Union passed in 2024 an AI Act which seeks to guard against some of those risks. More prosaically, the possibility of monopolization of the supply of AI, for example by dominance of key inputs such as chips, ‘compute’ or Large Language Models, has receded as rivalry has extended to massive AI investments by several major platform companies, as well as the emergence of inventive western rivals and serious Chinese competitors. Finally, AI pervades even the field of regulation. There will not only be regulation of AI, but also regulation being done with AI, leading to a possible arms race between the AI tools deployed by regulatees and those available to regulators.
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