Hasil untuk "Human evolution"

Thomas Herrmann

This contribution explores how the integration of Artificial Intelligence (AI) into organizational practices can be effectively framed through a socio-technical perspective to comply with the requirements of Human-centered AI (HCAI). Instead of viewing AI merely as a technical tool, the analysis emphasizes the importance of embedding AI into communication, collaboration, and decision-making processes within organizations from a human-centered perspective. Ten case-based patterns illustrate how AI support of predictive maintenance can be organized to address quality assurance and continuous improvement and to provide different types of sup-port for HCAI. The analysis shows that AI adoption often requires and enables new forms of organizational learning, where specialists jointly interpret AI output, adapt workflows, and refine rules for system improve-ment. Different dimensions and levels of socio-technical integration of AI are considered to reflect the effort and benefits of keeping the organization in the loop.

Jia-xin Zhang, Xue Zhao, Ji-hong Tao et al.

The integration of artificial intelligence (AI) has shown strong performance in well-defined clinical tasks, particularly in reader studies and workflow simulations. Translation into routine clinical environments, however, depends on local integration strategies, threshold selection, and governance arrangements. This perspective article adopts a nursing science perspective to argue that human-AI collaboration represents more than a technological addition—it constitutes a fundamental shift toward a synergistically enhanced nursing practice. Central to this paradigm is the effective integration of nursing expertise with algorithmic capabilities throughout all stages of care. When appropriately implemented and supervised, such integration has the potential to enhance both precision and efficiency in nursing practice. Importantly, it should be carried out in a way that preserves core nursing values, including patient-centered care, respect for individual dignity, and the integrity of therapeutic relationships. The proposed framework establishes a conceptual foundation intended to support the design of ethically aligned and clinically relevant human-AI systems. It further aims to guide the evolution of nursing practice within personalized breast cancer care.

Gong Xiaoyan, Dai Xingyuan, Li Ruilin et al.

Urban transportation systems are rapidly evolving into CPSS (cyber-physical-social System), driven by the continuous integration of autonomous vehicles, unmanned aerial vehicles, and diverse intelligent agents. This evolution has dramatically increased system complexity, dynamics, and coupling, rendering traditional human-centric research paradigms insufficient for timely understanding and response to fast-evolving system behaviors. To address these challenges, an autonomous framework called "AI Transportation Scientist" was proposed to revolutionize transportation research through parallel intelligence. The architecture leveraged a synergy between large language model and multi-agent systems across four functional layers (interaction, cognitive, experimental, and support). At its core, a dynamic routing engine adaptively scheduled intelligent agents to tackle mechanism discovery, strategy validation, and system optimization. By implementing a full-chain collaborative closed loop—encompassing problem identification, simulation, and feedback optimization—the framework enabled the autonomous discovery of transportation laws and the continuous evolution of control strategies. This research establishes a scalable technical paradigm for advancing transportation science within CPSS environments, ensuring both efficient problem-solving and innovative strategy iteration.

Manuel Dominguez-Rodrigo, Enrique Baquedano, Abel Moclan et al.

The role of megafaunal exploitation in early human evolution remains debated. Occasional use of large carcasses by early hominins has been considered by some as opportunistic, possibly a fallback dietary strategy, and for others a more important survival strategy. At Olduvai Gorge, evidence for megafaunal butchery is scarce in the Oldowan of Bed I but becomes more frequent and widespread after 1.8 Ma in Bed II, coinciding with the emergence of Acheulean technologies, but not functionally related to the main Acheulian tool types. Here, we present the earliest direct evidence of proboscidean butchery, including a newly documented elephant butchery site (EAK). This shift in behavior is accompanied by larger, more complex occupation sites, signaling a profound ecological and technological transformation. Rather than opportunistic scavenging, these findings suggest a strategic adaptation to megafaunal resources, with implications for early human subsistence and social organization. The ability to systematically exploit large prey represents a unique evolutionary trajectory, with no direct modern analogue, since modern foragers do so only episodically.

D. P. Van Vuuren, D. P. Van Vuuren, B. C. O'Neill et al.

<p>Scenarios serve as a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations in this century. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the Earth system uncertainty space. The proposal also includes plans for long-term extensions (up to 2500 AD) to study long-term impacts, climate change-related processes on long timescales, and (ir)reversibility. This proposal forms the basis for further implementation of the framework in terms of the derivation of emissions and land use pathways for use by Earth system models and additional variants for adaptation and mitigation studies.</p>

Morten Roed Frederiksen, Kasper Støy

In an effort to improve how robots function in social contexts, this paper investigates if a robot that actively shares a reaction to an event with a human alters how the human perceives the robot's affective impact. To verify this, we created two different test setups. One to highlight and isolate the reaction element of affective robot expressions, and one to investigate the effects of applying specific timing delays to a robot reacting to a physical encounter with a human. The first test was conducted with two different groups (n=84) of human observers, a test group and a control group both interacting with the robot. The second test was performed with 110 participants using increasingly longer reaction delays for the robot with every ten participants. The results show a statistically significant change (p$<$.05) in perceived affective impact for the robots when they react to an event shared with a human observer rather than reacting at random. The result also shows for shared physical interaction, the near-human reaction times from the robot are most appropriate for the scenario. The paper concludes that a delay time around 200ms may render the biggest impact on human observers for small-sized non-humanoid robots. It further concludes that a slightly shorter reaction time around 100ms is most effective when the goal is to make the human observers feel they made the biggest impact on the robot.

Alexander Scarlatos, Yusong Wu, Ian Simon et al.

Recent advances in generative artificial intelligence (AI) have created models capable of high-quality musical content generation. However, little consideration is given to how to use these models for real-time or cooperative jamming musical applications because of crucial required features: low latency, the ability to communicate planned actions, and the ability to adapt to user input in real-time. To support these needs, we introduce ReaLJam, an interface and protocol for live musical jamming sessions between a human and a Transformer-based AI agent trained with reinforcement learning. We enable real-time interactions using the concept of anticipation, where the agent continually predicts how the performance will unfold and visually conveys its plan to the user. We conduct a user study where experienced musicians jam in real-time with the agent through ReaLJam. Our results demonstrate that ReaLJam enables enjoyable and musically interesting sessions, and we uncover important takeaways for future work.

Yiru Chen, Sally Fang, Sai Sree Harsha et al.

Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we present Summit Concierge, a domain-specific AI assistant developed for Adobe Summit. The assistant handles a wide range of event-related queries and operates under real-world constraints such as data sparsity, quality assurance, and rapid deployment. To address these challenges, we adopt a human-in-the-loop development workflow that combines prompt engineering, retrieval grounding, and lightweight human validation. We describe the system architecture, development process, and real-world deployment outcomes. Our experience shows that agile, feedback-driven development enables scalable and reliable AI assistants, even in cold-start scenarios.

Mingkang Wu, Devin White, Evelyn Rose et al.

Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we leverage human ratings in reward-free environments to infer a reward function, introducing learnable weights that balance the contributions of both classification and regression models. This design captures the inherent uncertainty in human decision-making and allows the model to adaptively emphasize different strategies. We conduct several experiments using synthetic human ratings to validate the effectiveness of the proposed approach. Results show that our method consistently outperforms existing rating-based RL methods, and in some cases, even surpasses traditional RL approaches.

Rune Møberg Jacobsen, Joel Wester, Helena Bøjer Djernæs et al.

This paper investigates the impact of artificial intelligence integration on remote operations, emphasising its influence on both distributed and team cognition. As remote operations increasingly rely on digital interfaces, sensors, and networked communication, AI-driven systems transform decision-making processes across domains such as air traffic control, industrial automation, and intelligent ports. However, the integration of AI introduces significant challenges, including the reconfiguration of human-AI team cognition, the need for adaptive AI memory that aligns with human distributed cognition, and the design of AI fallback operators to maintain continuity during communication disruptions. Drawing on theories of distributed and team cognition, we analyse how cognitive overload, loss of situational awareness, and impaired team coordination may arise in AI-supported environments. Based on real-world intelligent port scenarios, we propose research directions that aim to safeguard human reasoning and enhance collaborative decision-making in AI-augmented remote operations.

Yannick Becker, Cornelius Eichner, Michael Paquette et al.

Abstract The arcuate fascicle (AF) is the main fibre tract in the brain for human language. It connects frontal and temporal language areas in the superior and middle temporal gyrus (MTG). The AF’s connection to the MTG was considered unique to humans and has influenced theories of the evolution of language. Here, using high-resolution diffusion MRI of post-mortem brains, we demonstrate that both wild and captive chimpanzees have a direct AF connection into the MTG, albeit weaker than in humans. This finding challenges the notion of a strictly human-specific AF morphology and suggests that language-related neural specialisation in humans likely evolved through gradual evolutionary strengthening of a pre-existing connection, rather than arising de novo. It is likely that this neural architecture supporting complex communication was already present in the last common ancestor of hominins and chimpanzees 7 million years ago, enabling the evolution of language processes in the human lineage.

Zonglin He, Haixiao Feng, Jialin Zhang et al.

Abstract Medical Functional Experimental Science (MFES) integrates physiology, pathophysiology, and pharmacology laboratory courses into a cohesive laboratory curriculum in China’s medical education. However, limited research exists on its implementation and evolution over the past two decades. This cross-sectional study surveyed experienced teachers from China’s top 100 medical schools. A total of 89 valid responses were received. A decline in technician numbers was reported by 62.9% of schools, potentially due to equipment automation and resource reallocation. The majority of the schools accommodated fewer than 30 students per laboratory. Over the past 20 years, laboratory sizes increased in 40.5% of the schools. Regarding the ratio of human experiments to animal experiments, of the schools surveyed, 60% reported less than 1 to 6, and 12% showed 1 to 5. The study also highlights the adoption of advanced teaching equipment, such as integrated signal acquisition systems and wireless human experiment systems, which have enhanced laboratory efficiency and student engagement. Furthermore, the integration of innovative and comprehensive experiments has been instrumental in fostering critical thinking and problem-solving skills among students. Despite progress, challenges remain, including technician shortages and uneven regional resource distribution, requiring policy interventions and global benchmarking.

Ben Wang, Jiqun Liu, Jamshed Karimnazarov et al.

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.

Hoang Anh Just, Ming Jin, Anit Sahu et al.

Aligning language models with human preferences through reinforcement learning from human feedback is crucial for their safe and effective deployment. The human preference is typically represented through comparison where one response is chosen over another for a given prompt. However, standard preference datasets often lack explicit information on why a particular choice was made, presenting an ambiguity that can hinder efficient learning and robust alignment, especially given the high cost of acquiring extensive human annotations. While many studies focus on algorithmic improvements, this work adopts a data-centric perspective, exploring how to enhance learning from existing preference data. We propose augmenting standard preference pairs with rationales that explain the reasoning behind the human preference. Specifically, we introduce a simple and principled framework that leverages machine-generated rationales to enrich preference data for preference optimization algorithms. Our comprehensive analysis demonstrates that incorporating rationales improves learning efficiency. Extensive experiments reveal some advantages: rationale-augmented learning accelerates convergence and can achieve higher final model performance. Furthermore, this approach is versatile and compatible with various direct preference optimization algorithms. Our findings showcase the potential of thoughtful data design in preference learning, demonstrating that enriching existing datasets with explanatory rationales can help unlock improvements in model alignment and annotation efficiency.

Xuanxin Wu, Yuki Arase

Recent studies have used both automatic metrics and human evaluations to assess the simplification abilities of LLMs. However, the suitability of existing evaluation methodologies for LLMs remains in question. First, the suitability of current automatic metrics on LLMs' simplification evaluation is still uncertain. Second, current human evaluation approaches in sentence simplification often fall into two extremes: they are either too superficial, failing to offer a clear understanding of the models' performance, or overly detailed, making the annotation process complex and prone to inconsistency, which in turn affects the evaluation's reliability. To address these problems, this study provides in-depth insights into LLMs' performance while ensuring the reliability of the evaluation. We design an error-based human annotation framework to assess the LLMs' simplification capabilities. We select both closed-source and open-source LLMs, including GPT-4, Qwen2.5-72B, and Llama-3.2-3B. We believe that these models offer a representative selection across large, medium, and small sizes of LLMs. Results show that LLMs generally generate fewer erroneous simplification outputs compared to the previous state-of-the-art. However, LLMs have their limitations, as seen in GPT-4's and Qwen2.5-72B's struggle with lexical paraphrasing. Furthermore, we conduct meta-evaluations on widely used automatic metrics using our human annotations. We find that these metrics lack sufficient sensitivity to assess the overall high-quality simplifications, particularly those generated by high-performance LLMs.

Advait Sarkar

Describing our interaction with Artificial Intelligence (AI) systems as 'collaboration' is well-intentioned, but flawed. Not only is it misleading, but it also takes away the credit of AI 'labour' from the humans behind it, and erases and obscures an often exploitative arrangement between AI producers and consumers. The AI 'collaboration' metaphor is merely the latest episode in a long history of labour appropriation and credit reassignment that disenfranchises labourers in the Global South. I propose that viewing AI as a tool or an instrument, rather than a collaborator, is more accurate, and ultimately fairer.

Badour AlBahar, Shunsuke Saito, Hung-Yu Tseng et al.

We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image. NeRF and its variants typically require videos or images from different viewpoints. Most existing approaches taking monocular input either rely on ground-truth 3D scans for supervision or lack 3D consistency. While recent 3D generative models show promise of 3D consistent human digitization, these approaches do not generalize well to diverse clothing appearances, and the results lack photorealism. Unlike existing work, we utilize high-capacity 2D diffusion models pretrained for general image synthesis tasks as an appearance prior of clothed humans. To achieve better 3D consistency while retaining the input identity, we progressively synthesize multiple views of the human in the input image by inpainting missing regions with shape-guided diffusion conditioned on silhouette and surface normal. We then fuse these synthesized multi-view images via inverse rendering to obtain a fully textured high-resolution 3D mesh of the given person. Experiments show that our approach outperforms prior methods and achieves photorealistic 360-degree synthesis of a wide range of clothed humans with complex textures from a single image.

Nur Yildirim, Mahima Pushkarna, Nitesh Goyal et al.

Artificial intelligence (AI) presents new challenges for the user experience (UX) of products and services. Recently, practitioner-facing resources and design guidelines have become available to ease some of these challenges. However, little research has investigated if and how these guidelines are used, and how they impact practice. In this paper, we investigated how industry practitioners use the People + AI Guidebook. We conducted interviews with 31 practitioners (i.e., designers, product managers) to understand how they use human-AI guidelines when designing AI-enabled products. Our findings revealed that practitioners use the guidebook not only for addressing AI's design challenges, but also for education, cross-functional communication, and for developing internal resources. We uncovered that practitioners desire more support for early phase ideation and problem formulation to avoid AI product failures. We discuss the implications for future resources aiming to help practitioners in designing AI products.

Marta Gomes, Rita Domingues, Dann Turner et al.

Bacteriophages are the most diverse genetic entities on Earth. In this study, two novel bacteriophages, nACB1 (<i>Podoviridae</i> morphotype) and nACB2 (<i>Myoviridae</i> morphotype), which infect <i>Acinetobacter beijerinckii</i> and <i>Acinetobacter halotolerans</i>, respectively, were isolated from sewage samples. The genome sequences of nACB1 and nACB2 revealed that their genome sizes were 80,310 bp and 136,560 bp, respectively. Comparative analysis showed that both genomes are novel members of the <i>Schitoviridae</i> and the <i>Ackermannviridae</i> families, sharing ≤ 40% overall nucleotide identities with any other phages. Interestingly, among other genetic features, nACB1 encoded a very large RNA polymerase, while nACB2 displayed three putative depolymerases (two capsular depolymerases and one capsular esterase) encoded in tandem. This is the first report of phages infecting <i>A. halotolerans</i> and <i>beijerinckii</i> human pathogenic species. The findings regarding these two phages will allow us to further explore phage—<i>Acinetobacter</i> interactions and the genetic evolution for this group of phages.