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Faking it : artificial intelligence in a human world
In an increasingly AI-driven world, renowned expert Toby Walsh examines what the 'artificial' in artificial intelligence truly means.
BOOK
Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation
There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda.
Journal Article
If anyone builds it, everyone dies : why superhuman AI would kill us all
\"In 2023, hundreds of AI luminaries signed an open letter warning that artificial intelligence poses a serious risk of human extinction. Since then, the AI race has only intensified. Companies and countries are rushing to build machines that will be smarter than any person. And the world is devastatingly unprepared for what would come next. For decades, two signatories of that letter -- Eliezer Yudkowsky and Nate Soares -- have studied how smarter-than-human intelligences will think, behave, and pursue their objectives. Their research says that sufficiently smart AIs will develop goals of their own that put them in conflict with us -- and that if it comes to conflict, an artificial superintelligence would crush us. The contest wouldn't even be close. How could a machine superintelligence wipe out our entire species? Why would it want to? Would it want anything at all? In this urgent book, Yudkowsky and Soares walk through the theory and the evidence, present one possible extinction scenario, and explain what it would take for humanity to survive. The world is racing to build something truly new under the sun. And if anyone builds it, everyone dies.\" -- Provided by publisher.
BOOK
Human Randomness in the Rock-Paper-Scissors Game
In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.
Journal Article
Our final invention : artificial intelligence and the end of the human era
\"The Internet is usually considered a breakthrough in technological--and even social--progress. The promises that it holds for our future are discussed in terms of an utopian vision--intelligent, helpful robots, enhanced brain function, disease-and-famine ridding nanotechnology, and other positive benefits. But there's another, rarely discussed and far darker possibility. As [this book] argues, we may be racing towards our own annihilation, as the military, academia, and corporate advances in artificial intelligence may lead to an uncontrollable new lifeform far smarter and more powerful than we can imagine\"-- Provided by publisher.
BOOK
The Authenticity of Machine-Augmented Human Intelligence: Therapy, Enhancement, and the Extended Mind
Ethical analyses of biomedical human enhancement often consider the issue of authenticity — to what degree can the accomplishments of those utilizing biomedical enhancements (including cognitive or athletic ones) be considered authentic or worthy of praise? As research into Brain-Computer Interface (BCI) technology progresses, it may soon be feasible to create a BCI device that enhances or augments natural human intelligence through some invasive or noninvasive biomedical means. In this article we will (1) review currently existing BCI technologies and to what extent these can be said to enhance or augment the capabilities of the respective users, (2) describe one hypothetical type of BCI device that could augment or enhance a specific aspect of human knowledge — namely, mathematical ability, and (3) relate these concepts to the active externalism view of the extended mind as espoused by Clark and Chalmers in order to (4) argue that knowledge of mathematics derived from the usage of a BCI and the application thereof constitutes authentic knowledge and achievement.
Journal Article
Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training
Objectives
Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-image annotations of the dehiscent visceral pleura for algorithm training boosts algorithm’s performance and suppresses confounders.
Methods
Our single-center evaluation cohort of 3062 supine CXRs includes 760 PTX-positive cases with radiological annotations of PTX size and inserted TTs. Three step-by-step improved algorithms (differing in algorithm architecture, training data from public datasets/clinical sites, and in-image annotations included in algorithm training) were characterized by area under the receiver operating characteristics (AUROC) in detailed subgroup analyses and referenced to the well-established “CheXNet” algorithm.
Results
Performances of established algorithms exclusively trained on publicly available data without in-image annotations are limited to AUROCs of 0.778 and strongly biased towards TTs that can completely eliminate algorithm’s discriminative power in individual subgroups. Contrarily, our final “algorithm 2” which was trained on a lower number of images but additionally with in-image annotations of the dehiscent pleura achieved an overall AUROC of 0.877 for unilateral PTX detection with a significantly reduced TT-related confounding bias.
Conclusions
We demonstrated strong limitations of an established PTX-detecting AI algorithm that can be significantly reduced by designing an AI system capable of learning to both classify and localize PTX. Our results are aimed at drawing attention to the necessity of high-quality in-image localization in training data to reduce the risks of unintentionally biasing the training process of pathology-detecting AI algorithms.
Key Points
• Established pneumothorax-detecting artificial intelligence algorithms trained on public training data are strongly limited and biased by confounding thoracic tubes.
• We used high-quality in-image annotated training data to effectively boost algorithm performance and suppress the impact of confounding thoracic tubes.
• Based on our results, we hypothesize that even hidden confounders might be effectively addressed by in-image annotations of pathology-related image features.
Journal Article
Hybrid collective intelligence in a human–AI society
Within current debates about the future impact of Artificial Intelligence (AI) on human society, roughly three different perspectives can be recognised: (1) the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; (2) the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and (3) the collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: (a) a clear description for each of the three perspectives, along with their history and background; (b) an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and (c) a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society.
Journal Article