Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. A key step toward developing trustworthy COD systems is the estimation and effective utilization of uncertainty. In this work, we propose a human-machine collaboration framework for classifying the presence of camouflaged objects, leveraging the complementary strengths of computer vision (CV) models and noninvasive brain-computer interfaces (BCIs). Our approach introduces a multiview backbone to estimate uncertainty in CV model predictions, utilizes this uncertainty during training to improve efficiency, and defers low-confidence cases to human evaluation via RSVP-based BCIs during testing for more reliable decision-making. We evaluated the framework in the CAMO dataset, achieving state-of-the-art results with an average improvement of 4.56\% in balanced accuracy (BA) and 3.66\% in the F1 score compared to existing methods. For the best-performing participants, the improvements reached 7.6\% in BA and 6.66\% in the F1 score. Analysis of the training process revealed a strong correlation between our confidence measures and precision, while an ablation study confirmed the effectiveness of the proposed training policy and the human-machine collaboration strategy. In general, this work reduces human cognitive load, improves system reliability, and provides a strong foundation for advancements in real-world COD applications and human-computer interaction. Our code and data are available at: https://github.com/ziyuey/Uncertainty-aware-human-machine-collaboration-in-camouflaged-object-identification.

The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.

Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. This study presents a comprehensive investigation of 11 approaches for acquiring translation data, including human-only, machineonly, and hybrid approaches. Our findings demonstrate that human-machine collaboration can match or even exceed the quality of human-only translations, while being more cost-efficient. Error analysis reveals the complementary strengths between human and machine contributions, highlighting the effectiveness of collaborative methods. Cost analysis further demonstrates the economic benefits of human-machine collaboration methods, with some approaches achieving top-tier quality at around 60% of the cost of traditional methods. We release a publicly available dataset containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.

Recent advancements in technology have necessitated a paradigm shift in the people use technology necessitating a new research field called Human-Machine collaboration. ChatGPT, an Artificial intelligence (AI) assistive technology, has gained mainstream adoption and implementation in academia and industry; however, a lot is left unknown about how this new technology holds for Human-Machine Collaboration in Africa. Our survey paper highlights to answer some of these questions. To understand the effectiveness of ChatGPT on human-machine collaboration we utilized reflexive thematic analysis to analyze (N= 51) articles between 2019 and 2023 obtained from our literature search. Our findings indicate the prevalence of ChatGPT for human-computer interaction within academic sectors such as education, and research; trends also revealed the relatively high effectiveness of ChatGPT in improving human-machine collaboration.

Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human digital twins, and cybersecurity.

ChatGPT is all the rage. It is what everyone has been talking about in the last several weeks. In just over a week, it garnered over 1 million users, an incredible achievement for OpenAI, the organization that created it. ChatGPT is an Artificial Intelligence (AI) application that falls under the Generative AI category – GPT stands for Generative Pre-Trained Transformer. Generative AI enable computers to create new content using previously created content, such as text, audio, video, images and code.

Dr Mark van Rijmenam is The Digital Speaker. He is a leading strategic futurist who thinks about how technology changes organisations, society and the metaverse. Dr Mark van Rijmenam is an international keynote speaker, 5x author and entrepreneur. He is the founder of Datafloq and the author of the book on the metaverse: Step into the Metaverse: How the Immersive Internet Will Unlock a Trillion-Dollar Social Economy, detailing what the metaverse is and how organizations and consumers can benefit from the immersive internet. He is the publisher of the'f(x) e x' newsletter, read by thousands of executives, on the future of work and the organization of tomorrow.

The last two years have been some of the most exciting and highly anticipated in Automatic Speech Recognition's (ASR's) long and rich history, as we saw multiple enterprise-level fully neural network-based ASR models go to market (e.g. The accelerated success of ASR deployments is due to many factors, including the growing ecosystem of freely available toolkits, more open source datasets, and a growing interest on the part of engineers and researchers in the ASR problem. This confluence of forces has produced an amazing momentum shift in commercial ASR. We truly are at the onset of big changes in the ASR field and of massive adoption of the technology. These developments are not only improving existing uses of the technology, such as Siri's and Alexa's accuracies, but they are also expanding the market ASR technology serves.

Almost 30 years ago, Bob Thomas, then an MIT professor, published a book called "What Machines Can't Do." He was focused on manufacturing technology and argued that it wasn't yet ready to take over the factory from humans. While recent developments with artificial intelligence have raised the bar considerably since then for what machines can do, there are still many things that they can't do yet or at least not do well in highly reliable ways. AI systems may perform well in the research lab or under highly controlled application settings, but they still needed human help in the types of real-world work settings we researched for a new book, Working With AI: Real Stories of Human-Machine Collaboration. Human workers were very much in evidence across our 30 case studies.

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we show that these approaches fail to approximate the classification probability. On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability. On unseen test data, we demonstrate improved calibration, sensitivity (in two out of three cases) and precision when being compared with the standard approaches. We further motivate the usage of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.