Artificial intelligence (AI) has transitioned from a future fantasy to a current working capability. It is expanding faster than it inspires and frightens. With the emergence of agentic AI, generative AI, cognitive AI, affective AI, and many other AI forms, some say the world is moving toward building AI smarter than humans. It draws a direct parallel to the invention of the atomic bomb in its potential for irreversible impact. As governments rush to harness AI efficiency, malicious actors build AI systems that may soon exceed human control. 2024 Nobel Prize winner Geoffrey Hinton, the "Godfather of AI," issued a stark warning in an interview with BBC Radio 4's Today program that aired on December 27, 2024. Hinton revisited his prediction that AI would lead to human extinction in the next 30 years, saying the risk was closer than we think. This article critically examines the regulatory approaches taking shape across jurisdictions, including the European Union, the United States, China, India, and Canada. We also explore the specific incentives behind their legislative activities, which include human rights protection, international competitiveness, geopolitical dynamics, and social concerns regarding emerging technologies. Our analysis reveals that despite these efforts, current national laws and rules still create a fragmented patchwork rather than a cohesive and proactive global framework. This particular fragmentation distinctly highlights not only convergent aims, such as prioritizing public safety, but also divergent fundamental assumptions, particularly concerning the ideal balance between comprehensive state control and promoting rapid market innovation. While countries continue to craft their regulations tailored to local needs, in order to make AI safe for everyone we must establish global AI principles and cross-border rules to ensure consistency, foster trust, and manage risks that transcend national boundaries. By pairing strong local governance with harmonized international standards, we can co-create AI systems that adhere to ethical principles like transparency, accountability, and professional oversight to promote societal well-being. The world needs a "Geneva Convention for AI" to guide this collaborative effort

Overview : This essay is a sweeping exploration of the idea that machines might someday display behaviors indistinguishable from human intelligence. It traces the evolution of that idea from early mechanical devices, through electronic computation, to artificial intelligence and large language models. It examines the philosophical, historical, and technological foundations of “machine intelligence”. It argues that, while machines have made extraordinary progress in replicating some aspects of human reasoning and learning, they remain fundamentally unable to reproduce tacit knowledge, which is essential for human intelligence. Tacit knowledge includes common sense, perceptions, embodied performance skill, and contextual understanding. Although machines can enhance and augment human capacities, they cannot yet, and may never, become intelligent in the human sense. The essay comes to a sobering warning that machines can develop their own tacit knowledge and intelligence, which does not understand humans and their concerns. There is a great risk of an approaching AI automation singularity — a point at which automated systems, though not as intelligent as humans, may exert pervasive control over human life, institutions, and decision-making. The essay concludes with a declaration of hope for eluding this future by practices of navigating in turbulent social space, harmonizing humans with AI machines.

Large language models (LLMs) are the first neural network machines capable of carrying on conversations with humans. They are trained on billions of words of text scraped from the internet. They generate text responses to text inputs. They have transformed the public awareness of artificial intelligence, bringing on reactions ranging from astonishment and awe to trepidation and horror. They have spurred massive investments in new tools for drafting texts, summarizing conversations, summarizing literature, generating images, coding simple programs, supporting education, and amusing humans. Experience with them has shown them likely to respond with fabrications (called "hallucinations") that severely undermine their trustworthiness and make them unsafe for critical applications. Here, we will examine the limitations of LLMs imposed by their design and function. These are not bugs but are inherent limitations of the technology. The same limitations make it unlikely that LLM machines will ever be capable of performing all human tasks at the skill levels of humans.

Artificial intelligence is changing our world. The arrival of ChatGPT in 2022 brought AI into the public spotlight with a dramatic new capability. A user can how engage in a fluent conversation in ordinary language with a computer. Suddenly many people want to know what AI is, whether it is safe, and what benefits it might bring. Artificial intelligence is defined to be a collection of machines and algorithms that perform tasks normally considered to require human cognition. Opinions and claims about the possible benefits and risks of AI are all over the map. In response to the confusion, the Ubiquity editors are launching a symposium on AI. It will be a series of about two dozen articles on the many aspects of AI and its applications. The series will be synthesized into the "Ubiquity Report on Artificial Intelligence," which is intended to be an authoritative and trustworthy summary of present and future AI.

We do not agree on what our core abstractions mean. They are useful anyway.

More than the 70 years since its emergence in the early 1950s, artificial intelligence (AI) is performing cognitive tasks traditionally considered the unique province of humans. This progress did not occur in a vacuum. AI emerged against a rich background of technologies from computer science and ideas about intelligence and learning from philosophy, psychology, logic, game theory, and cognitive science. We sketch out the enabling technologies for AI. They include search, reasoning, neural networks, natural language processing, signal processing and computer graphics, programming and conventional software engineering, human-computer interaction, communications, and specialized hardware that provides supercomputing power. Beyond these technologies is the notion of Artificial General Intelligence that has or exceeds the capabilities of the human brain. Currently this is completely aspirational and is not expected to be possible before 2025, if ever. Artificial Intelligence is based on a variety of technologies, none of which seek to emulate human intelligence.

Large language models brought language to machines. Machines are not up to the challenge.

A hierarchy of AI machines organized by their learning power shows their limits and the possibility that humans are at risk of machine subjugation well before AI utopia can come.

Resistance to innovation proposals is a gift. Harness it and your innovation will move faster.

The much-sought holy grail of more and faster innovation will come from integrating pipeline thinking and adoption thinking.

It will be a long road to learning how to use generative AI wisely.

Innovations are adoptions of new practice in communities. They are adopted because people perceive value from them. However, around the edges of the standard community practice, new practices can emerge that are of negative value. They are called dark innovations. Four examples are examined here: privacy, subscriptions, automation, and class abstraction. There is no easy way to eliminate dark innovations.

There is so much more to language and human beings than large language models can possibly master.

Innovation is less elusive with the right navigational map.

The artificial intelligence design challenge of teaming humans and machines is difficult because machines cannot read the context of use.