15 papers · ranked by Valyu relevance
Mohammadreza Nehzati
Introduction Conventional artificial intelligence (AI) systems are limited by static architectures that require periodic retraining and fail to adapt efficiently to continuously changing data environments. To address this limitation, this research introduces a novel biologically inspired computing paradigm that…
Tsehaye Haidemariam
The rise of agentic artificial intelligence (Agentic AI) marks a transition from systems that optimize externally specified objectives to systems capable of representing, evaluating, and revising their own goals. Whereas earlier AI architectures executed fixed task specifications, agentic systems maintain recursive…
Sheri Markose, Tony Prescott, Georg Northoff, Emily Cross + 1 more
A team of multi-disciplinary editors, whose views are reflected in the themes underscored in this Research Topic, has come together to help take stock of the phenomenal success of narrow Statistical Artificial Intelligence (SAI) and to examine new perspectives on achieving Artificial General Intelligence (AGI). The…
Parisa Kordjamshidi, Dan Roth, Kristian Kersting
Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models'…
Tomas Veloz
We examine and compare autopoietic systems (biological organisms) and machine learning systems (MLSs) highlighting crucial differences in how causal reasoning emerges and operates. Despite superficial functional similarities in behavior and cognitive abilities, we identify profound structural differences in how…
Sheri Markose
The security of code-based digital records is a major concern of the 21st century. AI and artificial general intelligence (AGI) can be hacked to pieces by digital adversaries, and some AI objectives can lead to existential threats. The former arises from sitting duck problems that all software systems are vulnerable…
Pasquale Stano, Ede Bodoki, Iacob Bogdan-Cezar
The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called “synthetic cells” (SCs) (or “artificial cells”, or “protocells”), lead us to consider the role of wetware technologies in the “Sciences of Artificial”, where they constitute the third pillar, alongside the…
Matthew Sims
Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this…
Susan Stepney
The field of artificial life (ALife) studies ‘life as it could be’, in contrast to biology’s study of ‘life as we know it to be’. This includes a wide range of potential physical substrates, from synthetic biology (new genes), through xenobiology (new amino acids and DNA bases), inorganic chemistry (different…
Julian Kiverstein, Michael D. Kirchhoff, Tom Froese
Biological agents can act in ways that express a sensitivity to context-dependent relevance. So far it has proven difficult to engineer this capacity for context-dependent sensitivity to relevance in artificial agents. We give this problem the label the “problem of meaning”. The problem of meaning could be circumvented…
Georg Northoff, Maia Fraser, John Griffiths, Dimitris A. Pinotsis + 3 more
'Prakash Panangaden' 'Rosalyn Moran' 'Karl Friston'] Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI - but…
Gautier Hamon, Mayalen Etcheverry, Bert Wang-Chak Chan, Clément Moulin-Frier + 1 more
The field of artificial life studies how life-like phenomena such as agency and self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open question is whether it is possible to find environment rules that self-organize robust “individuals” from an initial state with no prior…
Giulio Sandini, Alessandra Sciutti, Pietro Morasso
The conception of autonomous, intelligent, collaborative robots has been the subject of science fiction rather than science in the second half of the previous century, with practical applications limited to industrial machines without any level of autonomous, intelligent, and collaborative capacity. The new century is…
Han Zhang, Shiyi Wang, Zijian Li, Alice Mado Proverbio
AI code generators are increasingly used in creative contexts, offering operational efficiencies on the one hand and prompting concerns about psychological and neurophysiological strain on the other. This study employed a multimodal approach to examine the affective, autonomic, and creative consequences of AI-assisted…
Robert J. Sternberg
Technology alters both perceptions of human intelligence and creativity and the actual processes of intelligence and creativity. Skills that were once important for human intelligence, for example, computational ones, no longer hold anywhere near the same importance they did before the age of computers. The advantage…