Hasil untuk "artificial intelligence"

Matthew Crosby, Ronald P. A. Petrick

J. Adams

I. Bratko

J. Zurada

Sharon C. Salveter

John Haugeland

A. H. Bond, L. Gasser

Meryem Duygun Fethi, Fotios Pasiouras

A. Mellit, S. Kalogirou

Owies M. Wani, H. Zeng, A. Priimagi

The sophistication, complexity and intelligence of biological systems is a continuous source of inspiration for mankind. Mimicking the natural intelligence to devise tiny systems that are capable of self-regulated, autonomous action to, for example, distinguish different targets, remains among the grand challenges in biomimetic micro-robotics. Herein, we demonstrate an autonomous soft device, a light-driven flytrap, that uses optical feedback to trigger photomechanical actuation. The design is based on light-responsive liquid-crystal elastomer, fabricated onto the tip of an optical fibre, which acts as a power source and serves as a contactless probe that senses the environment. Mimicking natural flytraps, this artificial flytrap is capable of autonomous closure and object recognition. It enables self-regulated actuation within the fibre-sized architecture, thus opening up avenues towards soft, autonomous small-scale devices. Stimuli-responsive materials are commonly used in autonomous systems, whilst it is challenging to power them in a programmable manner. Here, Waniet al. use an optical fibre to control the shape of light-responsive liquid-crystal elastomers, which allow the device to detect and trap targeted objects.

Cândida Ferreira

G. Luger

Yu-chen Wu, Jun-wen Feng

J. Yoon, Zhongrui Wang, K. Kim et al.

A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the “no adaptation” and “sensitization” phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with critical dynamics for the first time. Using this artificial nociceptor, we further built an artificial sensory alarm system to experimentally demonstrate the feasibility and simplicity of integrating such novel artificial nociceptor devices in artificial intelligence systems, such as humanoid robots. The development of humanoid robots with artificial intelligence calls for smart solutions for tactile sensing systems that respond to dynamic changes in the environment. Here, Yoon et al. emulate non-adaption and sensitization function of a nociceptor—a sensory neuron—using diffusive oxide-based memristors.

Changjin Wan, Pingqiang Cai, Ming Wang et al.

Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intelligence, which would profoundly advance a broad spectrum of applications, such as prosthetics, robotics, and cyborg systems. Here, the recent developments in the design and fabrication of artificial sensory memory devices are summarized and their applications in recognition, manipulation, and learning are highlighted. The emergence of such devices benefits from recent progress in both bioinspired sensing and neuromorphic engineering technologies and derives from abundant inspiration and benchmarks from an improved understanding of biological sensory processing. Increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes. Pending challenges are also addressed to aspects such as integration level, energy efficiency, and functionality, which would undoubtedly shed light on the future development of translational implementations.

Daniel E. O’Leary

Hyejin Choe, Heesung Yoon, Inhyang Kim et al.

Halide perovskites have attracted significant interest due to their potential in optoelectronic devices. However, challenges related to complex compositional spaces, environmental sensitivity, and stability limitations continue to constrain their systematic development and application. Machine learning (ML) has emerged as an effective tool to address these challenges by enabling the prediction of material properties, the identification of promising compositions, and optimization of processing conditions, while reducing reliance on conventional trial‐and‐error methods. By capturing complex, nonlinear relationships among compositional, structural, and processing parameters, ML enables the exploration of broad design spaces that are essential for advancing perovskite research. Additionally, ML accelerates the discovery and optimization of perovskite materials through data‐driven approaches, including high‐throughput screening and inverse design, enabling rapid identification of optimal compositions and processing conditions for enhanced device performance and stability. This review provides an overview of recent efforts to integrate ML into halide perovskite studies, discussing workflows, implementation strategies, and notable progress in device‐level development. This article highlights how ML enables systematic materials discovery and optimization, supporting the advancement of stable and efficient perovskite optoelectronic devices.

Farzad Rafiei, Saina Sadeghipour, Shahab Sheikhalishahi et al.

Abstract Large language models are increasingly integrated into medical education, yet their performance in non-English clinical examinations, particularly Persian, remains limited. This study evaluated how GPT-4o and GPT-5.1 perform on Iranian Rheumatology Board examination questions. A total of 204 multiple-choice items were administered in Persian using a similar prompt. Accuracy was determined using the official answer key, and six board-certified rheumatologists independently scored each model’s clinical reasoning on a 1–5 scale. GPT-5.1 demonstrated markedly superior performance, achieving 76% accuracy compared with 64.5% for GPT-4o, alongside significantly higher reasoning scores. Unlike GPT-4o, which showed considerable variability across question types, GPT-5.1 performed consistently across basic science, clinical scenarios, diagnosis, and treatment domains. Although inter-rater agreement among rheumatologists was modest, it remained statistically significant. These findings suggest that newer-generation LLMs provide more reliable reasoning and accuracy in Persian medical assessments. Nevertheless, despite their promising role as educational aids, current models are not yet suitable for high-stakes clinical decision-making and require continued evaluation across diverse languages and specialties.

Maria Tsolia, Nikolaos C. Zygouris, Spyros Kamnis et al.

Early identification of reading difficulties enables timely classroom intervention; however, teachers often have limited time and restricted access to specialist assessment. This study explores a brief, teacher-friendly screening approach based on short oral reading recordings to support classroom decision-making. Oral reading samples were collected from 77 Greek primary school pupils (Grades 3–6) during a standardized reading task. Recordings were segmented into 7 s excerpts, converted into spectrogram images, and analyzed using a deep learning model to classify each excerpt as indicative of reading difficulties or not. To reflect realistic school implementation, model development followed an 80/20 participant-level split, with validation conducted on pupils not included in the training set. At the selected operating threshold, the model achieved approximately 84% overall accuracy and a balanced accuracy of 0.85. For practical applicability, a pupil-level indicator—representing the proportion of excerpts flagged as difficult—showed a strong association with expert judgments (r ≈ 0.74). These findings suggest that brief oral reading recordings can provide teachers with an interpretable screening signal to inform monitoring, prioritization, and early classroom support while underscoring the need for further validation under routine school conditions.

Zahra Shakeri, Mohammad Matin Miladi qomi

Chatbots are a modern manifestation of artificial intelligence that are now placed for public interaction and conversation with individuals. They can engage in a two-way interaction with a user, responding to their questions and even offering suggestions. This system is based on the capabilities arising from the development of artificial intelligence and is continuously advancing. Among these, the legal issue concerns whether chatbots are responsible for what they respondand whether what they provide is their intellectual property. Essentially, can personalities be attributed to chatbots? The present article finally concludes, with the analytical-descriptive approach, that chatbots may potentially possess a degree of personality in the future, but in current circumstances, the effects and content provided by chatbots are attributed to their owners and creators. Although their development and the reinforcement of chatbots' autonomy can pose numerous challenges to classical legal theories.