Hasil untuk "Fossil man. Human paleontology"

M. Foucault

Jun-xuan Fan, S. Shen, D. Erwin et al.

A finer record of biodiversity We have pressing, human-generated reasons to explore the influence of environmental change on biodiversity. Looking into the past can not only inform our understanding of this relationship but also help us to understand current change. Paleontological records depend on fossil availability and predictive modeling, however, and thus tend to give us a picture with large temporal jumps, millions of years wide. Such a scale makes it difficult to truly understand the action of environmental forces on ecological processes. Enabled by a supercomputer, Fan et al. used machine learning to analyze a large marine Paleozoic dataset, creating a record with time intervals of only ∼26,000 years (see the Perspective by Wagner). This fine-scale resolution revealed new events and important details of previously described patterns. Science, this issue p. 272; see also p. 249 A temporally refined record derived from 11,000 marine fossils elucidates patterns of diversification and extinction events. One great challenge in understanding the history of life is resolving the influence of environmental change on biodiversity. Simulated annealing and genetic algorithms were used to synthesize data from 11,000 marine fossil species, collected from more than 3000 stratigraphic sections, to generate a new Cambrian to Triassic biodiversity curve with an imputed temporal resolution of 26 ± 14.9 thousand years. This increased resolution clarifies the timing of known diversification and extinction events. Comparative analysis suggests that partial pressure of carbon dioxide (Pco2) is the only environmental factor that seems to display a secular pattern similar to that of biodiversity, but this similarity was not confirmed when autocorrelation within that time series was analyzed by detrending. These results demonstrate that fossil data can provide the temporal and taxonomic resolutions necessary to test (paleo)biological hypotheses at a level of detail approaching those of long-term ecological analyses.

Haichang Li, Anjun Zhu, Arpit Narechania

In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings.

Kevin L. Wei, Patricia Paskov, Sunishchal Dev et al.

In this position paper, we argue that human baselines in foundation model evaluations must be more rigorous and more transparent to enable meaningful comparisons of human vs. AI performance, and we provide recommendations and a reporting checklist towards this end. Human performance baselines are vital for the machine learning community, downstream users, and policymakers to interpret AI evaluations. Models are often claimed to achieve "super-human" performance, but existing baselining methods are neither sufficiently rigorous nor sufficiently well-documented to robustly measure and assess performance differences. Based on a meta-review of the measurement theory and AI evaluation literatures, we derive a framework with recommendations for designing, executing, and reporting human baselines. We synthesize our recommendations into a checklist that we use to systematically review 115 human baselines (studies) in foundation model evaluations and thus identify shortcomings in existing baselining methods; our checklist can also assist researchers in conducting human baselines and reporting results. We hope our work can advance more rigorous AI evaluation practices that can better serve both the research community and policymakers. Data is available at: https://github.com/kevinlwei/human-baselines

Guilherme Guerino, Luiz Rodrigues, Bruna Capeleti et al.

Heuristic evaluation is a widely used method in Human-Computer Interaction (HCI) to inspect interfaces and identify issues based on heuristics. Recently, Large Language Models (LLMs), such as GPT-4o, have been applied in HCI to assist in persona creation, the ideation process, and the analysis of semi-structured interviews. However, considering the need to understand heuristics and the high degree of abstraction required to evaluate them, LLMs may have difficulty conducting heuristic evaluation. However, prior research has not investigated GPT-4o's performance in heuristic evaluation compared to HCI experts in web-based systems. In this context, this study aims to compare the results of a heuristic evaluation performed by GPT-4o and human experts. To this end, we selected a set of screenshots from a web system and asked GPT-4o to perform a heuristic evaluation based on Nielsen's Heuristics from a literature-grounded prompt. Our results indicate that only 21.2% of the issues identified by human experts were also identified by GPT-4o, despite it found 27 new issues. We also found that GPT-4o performed better for heuristics related to aesthetic and minimalist design and match between system and real world, whereas it has difficulty identifying issues in heuristics related to flexibility, control, and user efficiency. Additionally, we noticed that GPT-4o generated several false positives due to hallucinations and attempts to predict issues. Finally, we highlight five takeaways for the conscious use of GPT-4o in heuristic evaluations.

Martijn IJtsma, Salvatore Hargis

Studies of human-robot interaction in dynamic and unstructured environments show that as more advanced robotic capabilities are deployed, the need for cooperative competencies to support collaboration with human problem-holders increases. Designing human-robot systems to meet these demands requires an explicit understanding of the work functions and constraints that shape the feasibility of alternative joint work strategies. Yet existing human-robot interaction frameworks either emphasize computational support for real-time execution or rely on static representations for design, offering limited support for reasoning about coordination dynamics during early-stage conceptual design. To address this gap, this article presents a novel computational framework for analyzing joint work strategies in human-robot systems by integrating techniques from functional modeling with graph-theoretic representations. The framework characterizes collective work in terms of the relationships among system functions and the physical and informational structure of the work environment, while explicitly capturing how coordination demands evolve over time. Its use during conceptual design is demonstrated through a case study in disaster robotics, which shows how the framework can be used to support early trade-space exploration of human-robot coordination strategies and to identify cooperative competencies that support flexible management of coordination overhead. These results show how the framework makes coordination demands and their temporal evolution explicit, supporting design-time reasoning about cooperative competency requirements and work demands prior to implementation.

Emmanuel Fashae, Michael Burke, Leimin Tian et al.

Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning.

Jessica He, Stephanie Houde, Justin D. Weisz

AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contributions to an artifact-under-creation alongside its human partner(s). One question that arises in these scenarios is the extent to which AI should be credited for its contributions. We examined knowledge workers' views of attribution through a survey study (N=155) and found that they assigned different levels of credit across different contribution types, amounts, and initiative. Compared to a human partner, we observed a consistent pattern in which AI was assigned less credit for equivalent contributions. Participants felt that disclosing AI involvement was important and used a variety of criteria to make attribution judgments, including the quality of contributions, personal values, and technology considerations. Our results motivate and inform new approaches for crediting AI contributions to co-created work.

Laura Edith Cruz, Juan Carlos Fernicola

At the end of the 19th century, Ameghino studied the fossils of the “conglomerado osífero” (Late Miocene, Paraná, Entre Ríos, Argentina), erecting at least 13 new species of cingulates. Some of the type specimens of these species have been considered lost, relying only on the descriptions of Ameghino and some figures of the type or referred materials of his 1889 atlas. The specimens described by Ameghino belonged to private (e.g., Lelong Thévenet, Ameghino) and public collections (e.g., those of Professor Scalabrini). This work aims to record the type specimens and referred materials of Cingulata within the Lelong Thévenet Collection and deposited in the collections housed at the Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”. The Lelong Thévenet Collection was acquired by this institution in 1886, but the specimens were formally included, at different times, in the Colección Nacional de Paleovertebrados. An exhaustive search of the cingulate specimens referred to said collections was carried out, which resulted in the identification of several of them in Ameghino’s 1889 atlas. We found materials collected by Lelong Thévenet and Ameghino in the Colección Nacional de Paleovertebrados and Colección Nacional Ameghino, respectively. Many of these materials have been recognized as type specimens or referred materials of these armored mammals, identifying 10 original materials, of which four correspond to holotypes and six to referred materials; added to these, 17 casts were identified, six of them from holotypes and the other 11 referred by Ameghino in his atlas.

Yang Zhao, Jordan Bestwick, Jan Fischer et al.

Abstract Chondrichthyan egg capsules, fossil and recent, have a taxonomical significance that can provide important insights into the occurrence and reproductive strategy of their producers. However, the rare occurrence of fossil capsules and their sometimes difficult identification hinder our understanding of their systematics and significance. Laffonia from the Late Jurassic of Switzerland and its probable junior synonym, Pseudocaudina, from the Late Jurassic lithographic limestones of southern Germany, have been interpreted in a variety of ways including as a fructification of a plant, a possible egg capsule of a shark or ray, a presumed holothurian, a possible actinarian, or even a ctenophore. Here, we redescribe the holotype of Laffonia, which has a fusiform body that is ornamented with over seven longitudinal ribs and two narrow striated flanges at its lateral edges. These morphological features are incompatible with a diploblast or echinoderm affinity, but highly resemble the characteristics of certain holocephalan egg capsules in several respects. Our phylogenetic analysis places Laffonia within a group containing the Carboniferous fossil capsules Crookallia and Vetacapsula, as well as recent chimaerid capsules. Thus, we suggest that the Mesozoic Laffonia represents an intermediate morphotype between the Carboniferous species and extant chimaerid capsules. Laffonia is the only known fossil chimaerid-like capsule from the Mesozoic so far, which offers novel insights into the morphology and evolution of holocephalan egg capsules.

Silvio Renesto, Cinzia Ragni, Fabio Magnani

Abstract A new virtually complete specimen of the eosauropterygian nothosauroid Lariosaurus valceresii is described. The specimen was collected in the Kalkschieferzone of the Meride Limestone (Ladinian, Middle Triassic) in the UNESCO World Heritage area of Monte San Giorgio (Switzerland/Italy). The new specimen is the first L. valceresii collected in Switzerland and the first known Lariosaurus specimen with remains of the skin. The skin is preserved as a carbon film revealing the shape of the scales. It outlines the body and limbs, showing that the hands and feet were webbed. The skin is present postaxial to both the humeral shafts and the anterior portion of the trunk suggesting the possible presence in life of large and very strong retractor muscles for the forelimbs indicative that Lariosaurus could have performed a paraxial, otariid-like, "flying-rowing" swimming for rapid acceleration.

R. Yoshizawa

The article explores the concept of “place” through Deep Time using fossil discoveries from the Burgess Shale, a 508-million-year-old fossil site which preserves some of the earliest lifeforms on Earth. I apply a performative analysis of semi-structured in-depth interviews and to ethnographic data collected on hikes to the shale; at museum exhibits, paleontological symposia, and conferences; and at two major North American paleontological collections. A performative analysis of place uncovers how places are always made and remade in actions upon and through materializing bodies and nonhuman entities. The present article addresses doings and makings of place through Deep Time, or vast passages of time that are unintelligibly long to humans. Place is not just a spatial and cultural concept, but also a temporal one, and the timeline is nonlinear. I argue that fossils contribute to the relativity, contested nature, and multi-synchronicity of place. With a focus on “place-making,” I argue that the shale is a process which never attains a fixity or finality to its meaning, but is rather negotiated and often, a site of struggle. As time-travellers from Deep Time, fossils are strictly traces of ancient critters and environments, so their meaning is contingent upon social, cultural, historical, and political conditions under which they are interpreted. Fossils are therefore sources of resistance on a changing and maybe-dying planet.

B. Villmoare, W. Kimbel

Smith and Smith and Wood proposed that the human fossil record offers special challenges for causal hypotheses because “unique” adaptations resist the comparative method. We challenge their notions of “uniqueness” and offer a refutation of the idea that there is something epistemologically special about human prehistoric data. Although paleontological data may be sparse, there is nothing inherent about this information that prevents its use in the inductive or deductive process, nor in the generation and testing of scientific hypotheses. The imprecision of the fossil record is well‐understood, and such imprecision is often factored into hypotheses and methods. While we acknowledge some oversteps within the discipline, we also note that the history of paleoanthropology is clearly one of progress, with ideas tested and resolution added as data (fossils) are uncovered and new technologies applied, much like in sciences as diverse as astronomy, molecular genetics, and geology.

F. Giustini, Alessio Iannucci, Giovanni Porcelli et al.

Grotta Polesini is one of the most famous paleontological and archaeological sites of central Italy, which testifies to its human occupation during the Lateglacial. The site comprises a cave system where systematic excavation campaigns have been carried out since the 1950s. In 1974, 656 mammal remains were collected but never studied. This fossil collection is here described for the first time through taxonomic and stable isotope analyses of the enamel of selected mammal teeth. The aim is to reconstruct the paleoenvironmental and climatic conditions of the site and to offer new information on terrestrial ecosystems during the Lateglacial in central Italy. The faunal assemblage studied herein, in addition to other species reported in previous works, suggests cold climate conditions. We also describe a right radius of an adult individual of Homo sapiens, increasing the human fossil record of the site. Carbon isotope data point to a scenario dominated by C3 plants in open and dry habitats, such as grasslands and steppes, in accordance with the pollen data from central Italy. The oxygen isotope data suggest the use of water resources with a local origin, i.e. local precipitation and surface waters with a provenance from the nearby Apennine chain. The ecology of the taxa influenced the oxygen isotope values, especially in the case of semi‐obligate to non‐obligate drinker species.

Romy Müller

Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along several factors related to the human tasks, AI performance, XAI methods, images to be classified, human participants and comparison conditions. In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements. Moreover, benefits were usually restricted to incorrect AI predictions in AI-focused tasks but to correct ones in image-focused tasks. XAI-related factors had surprisingly little impact. The evidence was limited for image- and human-related factors and the effects were highly dependent on the comparison conditions. These findings may support the design of future user studies.

Yi-Shiuan Tung, Matthew B. Luebbers, Alessandro Roncone et al.

Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those models' utility for interactions requiring coordination, including safety-critical or close-proximity tasks. Our key insight is that robot teammates can deliberately configure shared workspaces prior to interaction in order to reduce the variance in human motion, realizing classifier-agnostic improvements in goal prediction. In this work, we present an algorithmic approach for a robot to arrange physical objects and project "virtual obstacles" using augmented reality in shared human-robot workspaces, optimizing for human legibility over a given set of tasks. We compare our approach against other workspace arrangement strategies using two human-subjects studies, one in a virtual 2D navigation domain and the other in a live tabletop manipulation domain involving a robotic manipulator arm. We evaluate the accuracy of human motion prediction models learned from each condition, demonstrating that our workspace optimization technique with virtual obstacles leads to higher robot prediction accuracy using less training data.

Hyo Jin Do, Rachel Ostrand, Justin D. Weisz et al.

While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advancements have been made in algorithms that detect hallucinated content by assessing the factuality of the model's responses and attributing sections of those responses to specific source documents. However, there is limited research on how to effectively communicate this information to users in ways that will help them appropriately calibrate their trust toward LLMs. To address this issue, we conducted a scenario-based study (N=104) to systematically compare the impact of various design strategies for communicating factuality and source attribution on participants' ratings of trust, preferences, and ease in validating response accuracy. Our findings reveal that participants preferred a design in which phrases within a response were color-coded based on the computed factuality scores. Additionally, participants increased their trust ratings when relevant sections of the source material were highlighted or responses were annotated with reference numbers corresponding to those sources, compared to when they received no annotation in the source material. Our study offers practical design guidelines to facilitate human-LLM collaboration and it promotes a new human role to carefully evaluate and take responsibility for their use of LLM outputs.

Ali Ayub, Jainish Mehta, Zachary De Francesco et al.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github.com/aliayub7/cl_hri.

Yannick Metz, David Lindner, Raphaël Baur et al.

To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider human factors involved in providing feedback of different types. However, the systematic study of learning from diverse types of feedback is held back by limited standardized tooling available to researchers. To bridge this gap, we propose RLHF-Blender, a configurable, interactive interface for learning from human feedback. RLHF-Blender provides a modular experimentation framework and implementation that enables researchers to systematically investigate the properties and qualities of human feedback for reward learning. The system facilitates the exploration of various feedback types, including demonstrations, rankings, comparisons, and natural language instructions, as well as studies considering the impact of human factors on their effectiveness. We discuss a set of concrete research opportunities enabled by RLHF-Blender. More information is available at https://rlhfblender.info/.

Younes Lakhnati, Max Pascher, Jens Gerken

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with robot agents through natural language, each powered by individual GPT cores. By means of OpenAI's function calling, we bridge the gap between unstructured natural language input and structure robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their robot collaborators. Still, those users who did explore where able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.