The paper analyzes Turing’s 1948 report "Intelligent Machinery" as a foundational source for later imitation games, highlighting key design concepts such as the possibility of machine errors, the exclusion of irrelevant physical traits, the role of a human judge, and Turing’s view that intellectual activity is largely search. It argues that limiting the human contestant to a weak chess player heightens the importance of intellectual search, making human behavior more comparable to machine behavior. This reframes the 1948 game as a human‑approximates‑machine scenario, suggesting that imitation games can probe when human intelligence becomes machine‑like under specific task constraints.
arXiv:2608. 05558v1 Announce Type: cross Abstract: This paper examines Turing's 1948 report, "Intelligent Machinery", as an important conceptual source for the later imitation games.
arXiv:2501. 17629v2 Announce Type: replace-cross Abstract: Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely.
The article proposes an updated Turing Test that incorporates energy consumption as a key metric, arguing that the original test is insufficient for distinguishing human from machine intelligence in the context of modern AI. It suggests that by adding an energy constraint, the test evaluates intelligence through the lens of efficiency, linking abstract thinking to tangible resource limits. The new test also provides a measurable, practical endpoint, encouraging society to balance AI time savings against total resource costs.
arXiv:2605.10851v2 Announce Type: replace Abstract: We initiate the study of the Generalized Turing Test (GTT), a formal generalization of Turing's imitation game from humans to arbitrary interactive...
The paper presents a systematic mapping of recent chess research involving humans, engines, neural and reinforcement‑learning systems, large language models (LLMs), and hybrid approaches. It identifies 84 core study families and classifies them by agent type, strategic‑reasoning stages, and evaluation dimensions, highlighting a strong focus on situation assessment, evaluation, and action selection while noting gaps in planning, explanation, metacognition, and human–AI collaboration. The study also distinguishes hybrid systems by integration timing and cautions that improved human performance in evaluations does not automatically imply human–AI synergy.
arXiv:2510. 05743v3 Announce Type: replace Abstract: We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models.
The paper investigates whether large language model (LLM) agents can design AI methods that outperform or differ from human-designed approaches. By mapping both human- and agent-designed methods into task‑specific algorithmic design spaces, the authors evaluate performance and algorithmic differences across multiple modalities. Results show that while agents occasionally match or exceed human state‑of‑the‑art performance, 96.8% of their designs fall within human‑derived spaces, often recombining or exactly matching existing human algorithms.
arXiv:2510. 11503v2 Announce Type: replace-cross Abstract: Games have long been a microcosm for studying planning and reasoning in both natural and artificial intelligence (AI), often focusing on expert-level or even super-human play.
arXiv:2510. 02660v2 Announce Type: replace-cross Abstract: When researchers claim AI systems possess ToM or mental models, they are fundamentally discussing behavioral predictions and bias corrections rather than genuine mental states.
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv:2609.23640v1 Announce Type: new Abstract: Human-feedback alignment has made language models useful assistants and is commonly described as aligning them with humans. However, the responses peop...
arXiv:2608. 16213v1 Announce Type: new Abstract: Intelligence is constituted by \textit{process} (iterative activity through which output emerges), not in the output itself.