Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that make the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.

Abstract--In this paper, we propose a method for estimating in-hand object poses using proprioception and tactile feedback from a bimanual robotic system. Our method addresses the problem of reducing pose uncertainty through a sequence of frictional contact interactions between the grasped objects. As part of our method, we propose 1) a tool segmentation routine that facilitates contact location and object pose estimation, 2) a loss that allows reasoning over solution consistency between interactions, and 3) a loss to promote converging to object poses and contact locations that explain the external forcetorque experienced by each arm. We demonstrate the efficacy of our method in a task-based demonstration both in simulation and on a real-world bimanual platform and show significant improvement in object pose estimation over single interactions. These failure modes result in unreliable interactions between the robot and its environment when performing tasks such as Our method works by iteratively bringing the two objects into tool use and assembly. These filters visual feedback to identify objects in the scene and estimate exploit mutual information between known object geometries their poses [6, 9, 11, 12, 15, 19, 21, 25, 26, 29].

We propose LLM-Eval, a unified multi-dimensional automatic evaluation method for open-domain conversations with large language models (LLMs). Existing evaluation methods often rely on human annotations, ground-truth responses, or multiple LLM prompts, which can be expensive and time-consuming. To address these issues, we design a single prompt-based evaluation method that leverages a unified evaluation schema to cover multiple dimensions of conversation quality in a single model call. We extensively evaluate the performance of LLM-Eval on various benchmark datasets, demonstrating its effectiveness, efficiency, and adaptability compared to state-of-the-art evaluation methods. Our analysis also highlights the importance of choosing suitable LLMs and decoding strategies for accurate evaluation results. LLM-Eval offers a versatile and robust solution for evaluating open-domain conversation systems, streamlining the evaluation process and providing consistent performance across diverse scenarios.

Selecting an effective training signal for tasks in natural language processing is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. At the same time, recent work in NLP has demonstrated that learning from a distribution over labels acquired from crowd annotations can be effective. However, there are many ways to acquire such a distribution, and the performance allotted by any one method can fluctuate based on the task and the amount of available crowd annotations, making it difficult to know a priori which distribution is best. This paper systematically analyzes this in the out-of-domain setting, adding to the NLP literature which has focused on in-domain evaluation, and proposes new methods for acquiring soft-labels from crowd-annotations by aggregating the distributions produced by existing methods. In particular, we propose to aggregate multiple-views of crowd annotations via temperature scaling and finding their Jensen-Shannon centroid. We demonstrate that these aggregation methods lead to the most consistent performance across four NLP tasks on out-of-domain test sets, mitigating fluctuations in performance from the individual distributions. Additionally, aggregation results in the most consistently well-calibrated uncertainty estimation. We argue that aggregating different views of crowd-annotations is an effective and minimal intervention to acquire soft-labels which induce robust classifiers despite the inconsistency of the individual soft-labeling methods.

Introduction: The COVID-19 pandemic highlighted the importance of making epidemiological data and scientific insights easily accessible and explorable for public health agencies, the general public, and researchers. State-of-the-art approaches for sharing data and insights included regularly updated reports and web dashboards. However, they face a trade-off between the simplicity and flexibility of data exploration. With the capabilities of recent large language models (LLMs) such as GPT-4, this trade-off can be overcome. Results: We developed the chatbot "GenSpectrum Chat" (https://cov-spectrum.org/chat) which uses GPT-4 as the underlying large language model (LLM) to explore SARS-CoV-2 genomic sequencing data. Out of 500 inputs from real-world users, the chatbot provided a correct answer for 453 prompts; an incorrect answer for 13 prompts, and no answer although the question was within scope for 34 prompts. We also tested the chatbot with inputs from 10 different languages, and despite being provided solely with English instructions and examples, it successfully processed prompts in all tested languages. Conclusion: LLMs enable new ways of interacting with information systems. In the field of public health, GenSpectrum Chat can facilitate the analysis of real-time pathogen genomic data. With our chatbot supporting interactive exploration in different languages, we envision quick and direct access to the latest evidence for policymakers around the world.

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ranging from temperature scaling to different Bayesian treatments of neural networks, have been proposed to mitigate overconfidence, most often supported by the numerical observation that they yield better calibrated uncertainty measures. In this work, we provide a sharp comparison between popular uncertainty measures for binary classification in a mathematically tractable model for overparametrized neural networks: the random features model. We discuss a trade-off between classification accuracy and calibration, unveiling a double descent like behavior in the calibration curve of optimally regularized estimators as a function of overparametrization. This is in contrast with the empirical Bayes method, which we show to be well calibrated in our setting despite the higher generalization error and overparametrization.

The famous first image of a black hole just got two times sharper. A research team used artificial intelligence to dramatically improve upon its first image from 2019, which now shows the black hole at the center of the M87 galaxy as darker and bigger than the first image depicted. I'm an astronomer who studies and has written about cosmology, black holes and exoplanets. Astronomers have been using AI for decades. In fact, in 1990, astronomers from the University of Arizona, where I am a professor, were among the first to use a type of AI called a neural network to study the shapes of galaxies.

'It feels like a gold rush," says Timnit Gebru. "In fact, it is a gold rush. And a lot of the people who are making money are not the people actually in the midst of it. But it's humans who decide whether all this should be done or not. We should remember that we have the agency to do that." Gebru is talking about her specialised field: artificial intelligence. On the day we speak via a video call, she is in Kigali, Rwanda, preparing to host a workshop and chair a panel at an international conference on AI. It will address the huge growth in AI's capabilities, as well as something that the frenzied conversation about AI misses out: the fact that many of its systems may well be built on a huge mess of biases, inequalities and imbalances of power. This gathering, the clunkily titled International Conference on Learning Representations, marks the first time people in the field have come together in an African country – which makes a powerful point about big tech's neglect of the global south. When Gebru talks about the way that AI "impacts people all over the world and they don't get to have a say on how they should shape it", the issue is thrown into even sharper relief by her backstory. In her teens, Gebru was a refugee from the war between Ethiopia, where she grew up, and Eritrea, where her parents were born. After a year in Ireland, she made it to the outskirts of Boston, Massachusetts, and from there to Stanford University in northern California, which opened the way to a career at the cutting edge of the computing industry: Apple, then Microsoft, followed by Google. But in late 2020, her work at Google came to a sudden end. As the co-leader of Google's small ethical AI team, Gebru was one of the authors of an academic paper that warned about the kind of AI that is increasingly built into our lives, taking internet searches and user recommendations to apparently new levels of sophistication and threatening to master such human talents as writing, composing music and analysing images. The clear danger, the paper said, is that such supposed "intelligence" is based on huge data sets that "overrepresent hegemonic viewpoints and encode biases potentially damaging to marginalised populations". Put more bluntly, AI threatens to deepen the dominance of a way of thinking that is white, male, comparatively affluent and focused on the US and Europe. In response, senior managers at Google demanded that Gebru either withdraw the paper, or take her name and those of her colleagues off it. This triggered a run of events that led to her departure. Google says she resigned; Gebru insists that she was fired. What all this told her, she says, is that big tech is consumed by a drive to develop AI and "you don't want someone like me who's going to get in your way.

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of natural language processing tasks. Among them, ChatGPT is the most popular one which has attracted wide attention from the software engineering community. However, it still remains unclear how ChatGPT performs in (automatic) code summarization. Therefore, in this paper, we focus on evaluating ChatGPT on a widely-used Python dataset called CSN-Python and comparing it with several state-of-the-art (SOTA) code summarization models. Specifically, we first explore an appropriate prompt to guide ChatGPT to generate in-distribution comments. Then, we use such a prompt to ask ChatGPT to generate comments for all code snippets in the CSN-Python test set. We adopt three widely-used metrics (including BLEU, METEOR, and ROUGE-L) to measure the quality of the comments generated by ChatGPT and SOTA models (including NCS, CodeBERT, and CodeT5). The experimental results show that in terms of BLEU and ROUGE-L, ChatGPT's code summarization performance is significantly worse than all three SOTA models. We also present some cases and discuss the advantages and disadvantages of ChatGPT in code summarization. Based on the findings, we outline several open challenges and opportunities in ChatGPT-based code summarization.