Superintelligent AI and the Right to Be Forgotten

Psychological essentialism in selecting the 14th Dalai Lama

Can there be a dumb Superintelligence? A critical look at Bostrom's notion of Superintelligence

Artificial intelligence is experiencing unprecedented advancements, leading to the emergence of autonomous superintelligent systems that surpass human intelligence in various fields. These systems present novel legal challenges, particularly concerning criminal liability for crimes they may commit. This research examines the current legal frameworks. These frameworks are designed to determine the criminal liability of autonomous superintelligent system, with a focus on issues of intent, autonomous will, and their implications in the context of superintelligent AI. The study highlights specific potential crimes, including cybercrimes and privacy violations, and underscores the urgent need to develop new legal frameworks that address the unique risks posed by these systems. Additionally, the role of international conventions, such as the Budapest Convention, in shaping global standards for these challenges is evaluated. The research argues that current legislation is inadequate and emphasizes the need for legal reform to keep pace with technological advancements, offering a forward-looking approach to criminal responsibility in the age of Artificial Super intelligent.

Stephen Hawking, Elon Musk, Bill Gates, and artificial intelligence experts are warning the world about the potential dangers of artificial intelligence growing beyond human control as it becomes super intelligence, artificial general intelligence, or strong AI-the ability to autonomously rewrite its own software code based on feedback, implement the new software simultaneously around the world, modify its goals, and outperform human intellect. Nick Bottom’s expert survey in 2012/2013 found a 50-50 chance that “high-level machine intelligence” could be achieved by 2040-2050 and that super intelligence could be archived 30 years thereafter.

Pathways to Superintelligence

In some quarters, there is intense concern about high--level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity; in other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high--level machine intelligence coming up within a particular time--frame, which risks they see with that development and how fast they see these developing. We thus designed a brief questionnaire and distributed it to four groups of experts. Overall, the results show an agreement among experts that AI systems will probably reach overall human ability around 2040--2050 and move on to superintelligence in less than 30 years thereafter. The experts say the probability is about one in three that this development turns out to be 'bad' or 'extremely bad' for humanity.

What is the best overarching ethical principle to give a possible future superintelligent machine, given that we do not know what the best ethics are today or in the future? Eliezer Yudkowsky has suggested that a superintelligent AI should have as its goal to carry out the coherent extrapolated volition of humanity (CEV), the most coherent way of combining human goals. The article discusses some problems with this proposal and some alternatives suggested by Nick Bostrom. A slightly different proposal is then suggested, which I argue solves the problems better than Yudkowsky’s proposal.

Is superintelligent AI nearly here?

Applications of wireless networks beyond 5G are vulnerable to various security and privacy concerns. This research aims to identify the security and privacy flaws beyond 5G network applications and their defense mechanisms. This research study has reviewed 44 research articles and presented the taxonomy of several concerns in security and privacy of artificial intelligence and machine learning empowered 6G applications. Furthermore, this study has identified the defense mechanism technologies for such issues. A few defense mechanism approaches are homographic encryption, physical layer authentication, biometric authentication, explainable, trustworthy, and superintelligent AI. The implications of this study are applicable to prevent attacks, eavesdropping, jamming, and other security and privacy concerns when developing wireless networks.

There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040–2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity.

How does information infrastructure shape long-term cultural evolution? Using over four centuries of professional game records from the game of Go, this study explores how strategic dynamics in opening moves reflect historical shifts in the ‘infostructure’ of skilled Go players. Drawing from recent work on how population size, AI, and information technology affect cultural evolution and innovation dynamics, I analyze over 118,000 games using measures of cultural diversity, divergence, and player network composition. The results show distinct eras of collective innovation and homogenization, including an early 20th-century explosion of novel opening strategies, a Cold-War-era die-off, and a recent increase in evolutionary tempo with the arrival of the internet and superhuman AI programmes like AlphaGo. Player population size shows an inverse-U relationship with opening move diversity, and a recent decline in strategic diversity has accompanied a shift in the player network, from many small subgroups to a few large ones. Surprisingly, the influence of AI has produced only a modest, short-lived disruption in the distribution of opening moves, suggesting convergence between humans and AI and incremental rather than revolutionary cultural change.

There are many defi nitions of artificial intelligence (AI), but I often use this one: the science of mimicking human mental faculties in a computer. One of the criticisms of this definition is that it stops at human intelligence rather than superhuman intelligence, says Adrian Hopgood FBCS CITP, Pro Vice-Chancellor and Dean of the Sheffield Business School at Sheffield Hallam University.

In 1993, Verner Vinge [3] introduced the notion of the Singularity -- a step function to nearly unlimited technological capability -- which would be realized if the acceleration of scientific progress continues to produce such things as strong AI, nanotechnology, and super-human intelligence. Since its introduction, the idea of the Singularity has been met with both evangelism (by Ray Kurzweil [2]) and apocalyptic warnings (by Bill Joy [1]).In this talk, I will introduce a more modest version of the idea, which I call the Internet Singularity. Like the original, the Internet Singularity suggests continued acceleration of progress, but makes greater emphasis on our ability to improve science, analytic methods, and engineering on data as opposed to the physical world. I make the case for the Internet Singularity in four steps.First, there is a general trend of more capabilities being more available to more people. These increasing capabilities span content creation, community, and commerce, yielding more power to today's amateur than yesterday's professional. As a result, the boundary between producers and consumers is becoming increasingly blurred over time.Second, in many parts of the Internet we see power law distributions with a heavy tail. One implication of heavy tail distributions is that the aggregate impact of small participants can be greater than that of the large participants.Third, with the Internet comes entirely new means for authoring new and derivative works: aggregations, mashups, tagging, remixing, etc. The greater emphasis on collaboration and sharing yields direct and indirect network effects. Network effects, can produce entirely new utility, making online activities potentially more efficient or valuable than the offline equivalent.Fourth, on the Internet, advances are effectively decoupled from the physical constraints of the offline world: startups costs are smaller; customer, collaborator, and audience pools are dramatically larger; and improvements happen in more of a continuous rather than discreet manner. As a result the effective clock cycle of progress is potentially much faster online.Putting these four pieces together reveals a compelling pattern: more people contribute to the collective pool; the collective pool contains entirely new value that is derived from its data; and the new value from the data increases individual and aggregate capabilities. In combination, these components mutually reinforce one another, forming something of a virtuous cycle. This is the Internet Singularity.Conceptually, if we consider engineering to be the ability to create artifacts, mathematical analysis to be the ability to analyze numerical properties, and science to be the pursuit of knowledge, then each of these activities -- when focused on digital objects as they exist on the Internet -- can be amplified in a manner consistent with the Internet Singularity.The implications for the Internet Singularity are profound as they suggest nothing less than the evolution of the scientific method itself. Moreover, these trends also imply that now may be the best possible moment in the history of the universe to be a computer scientist.

An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.

Human and artificial cognition