Related papers: The 30-Year Cycle In The AI Debate
Companies report on their financial performance for decades. More recently they have also started to report on their environmental impact and their social responsibility. The latest trend is now to deliver one single integrated report where…
Artificial intelligence (AI) like deep learning, cloud AI computation has been advancing at a rapid pace since 2014. There is no doubt that the prosperity of AI is inseparable with the development of the Internet. However, there has been…
Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing…
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution.…
This introductory chapter provides an overview of the evolution and impact of Artificial Intelligence technologies in today society. Beginning with a historical context while exploring a few general definitions of AI, the author provides a…
Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contemporary learning algorithms, we argue that the bottleneck in…
Artificial General Intelligence (AGI) is promoted by technology leaders and investors as a system capable of performing all human intellectual tasks, and potentially surpassing them. Despite its vague definition and uncertain feasibility,…
AI that can accelerate research could drive a century of technological progress over just a few years. During such a period, new technological or political developments will raise consequential and hard-to-reverse decisions, in rapid…
As Artificial Intelligence (AI) becomes increasingly integrated into our lives, the need for new norms is urgent. However, AI evolves at a much faster pace than the characteristic time of norm formation, posing an unprecedented challenge to…
Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and…
Artificial intelligence (AI) has made remarkable progress in recent years, yet its rapid expansion brings overlooked environmental and ethical challenges. This review explores four critical areas where AI's impact extends beyond…
The fast paced progress of artificial intelligence (AI) through scaling laws connecting rising computational power with improving performance has created tremendous technological breakthroughs. These breakthroughs do not translate to…
With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they…
Artificial intelligence (AI) tools such as large language models (LLMs) are already altering student learning. Unlike previous technologies, LLMs can independently solve problems regardless of student understanding, yet are not always…
The use of artificial intelligence (AI) in research across all disciplines is becoming ubiquitous. However, this ubiquity is largely driven by hyperspecific AI models developed during scientific studies for accomplishing a well-defined,…
The trajectory of artificial intelligence (AI) has been one of relentless acceleration, evolving from rudimentary rule-based systems to sophisticated, autonomous agents capable of complex reasoning and interaction. This whitepaper…
Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business…
Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (January 2020 - March 2025), we compare research outputs of leading AI companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities…
The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically,…
Generative artificial intelligence (AI) is reshaping higher education, yet many universities remain in early stages of adoption where AI innovation occurs informally and without institutional recognition. This paper presents a framework…