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Abstract: This review provides a novel examination of the emerging field of collaborative intelligence and demonstrates the value that human-AI teams can deliver. Humans and artificial intelligence (AI) systems have complementary strengths. This complementarity creates the potential to achieve a step-change in performance by combining inputs from human and AI on a common task. We introduce the construct of “collaborative intelligence” and develop a set of criteria, for evaluating whether an AI system enables collaborative intelligence. Applications utilizing collaborative intelligence had to have (1) complementarity (i.e. the collaboration draws upon complementary human and AI capability to improve outcomes), (2) a shared objective and outcome, and (3) sustained, two-way task-related interaction between human and AI. A systematic review of 1,250 AI applications published between 2012 and 2021 was carried out to investigate whether real-world examples of “collaborative intelligence” could be identified. The review yielded 16 AI systems which met the criteria, demonstrating that collaboration between humans and AI systems is possible and that these systems offer a wide range of performance benefits including efficiency, quality, creativity, safety, and human enjoyment.
Abstract: The main advantages of AI in pharmaceutical formulation are its capacity to analyse vast amounts of data and spot patterns and connections that human researchers would miss. Various tools and technolo-gies, such as ANN, fuzzy logic, neuro-fuzzy logic, and genetic algorithm are used for analysing the date, of which ANN is popular and mostly used. AI enables the discovery of novel pharmacological targets and the creation of more potent medications. AI may also be used to improve medication formulations by forecasting the solubility, stability, and bioavailability of drug candidates, increasing the likelihood that clinical trials will be successful.AI is also applied in designing clinical trials, reducing the time and cost of the process by identifying patient popula-tions that are most likely to benefit from the treatment. Additionally, AI can monitor patients during clinical trials, detecting real-time adverse effects and adjusting dosages to improve patient outcomes.
Abstract: Researchers have sought for decades to automate holistic essay scoring. Over the years, these programs have improved significantly. However, accuracy requires significant amounts of training on human-scored texts—reducing the expediency and usefulness of such programs for routine uses by teachers across the nation on non-standardized prompts. This study analyzes the output of multiple versions of ChatGPT scoring of secondary student essays from three extant corpora and compares it to quality human ratings. We find that the current iteration of ChatGPT scoring is not statistically significantly different from human scoring; substantial agreement with humans is achievable and may be sufficient for low-stakes, formative assessment purposes. However, as large language models evolve additional research will be needed to continue to assess their aptitude for this task as well as determine whether their proximity to human scoring can be improved through prompting or training.
Abstract: The technological advancements made in recent times, particularly in artificial intelligence (AI) and quantum comput-ing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the ‘quantum threat’. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, includ-ing the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.
Abstract: According to World Health Organization (WHO) data, cardiovascular diseases (CAD) continue to take the lives of more than 17.9 million people worldwide each year. Heart attacks are considered a fatal disease in this category, especially for older adults, which highlights the need to employ artificial intelligence to anticipate this disease. This research faces many challenges, starting with data quality and availability, where AI models require large and high-quality datasets for training. Elderly populations exhibit various health conditions, lifestyle factors, and genetic diversity. Creating AI models that can accurately generalize across such a diverse group can be challenging. Two datasets for CAD diseases were used for this study. Traditional machine learning (ML) techniques were used on these datasets, as well as a neural network method based on extreme learning machines (ELM), which provided varying percentages of accuracy, time, and average estimated error. The ELM algorithm outperformed all other algorithms by attaining the best accuracy, the shortest execution time, and the lowest percentage of average estimated error. Experimental results showed that the Extreme learning machine performed well with 200 hidden neurons, even with the proposed absence of parts of the dataset, with an accuracy of 97.57–99.06%.
Abstract: Large language models (LLMs) have proven capable of assisting with many aspects of organizational decision making, such as helping to collect information from databases and helping to brainstorm possible courses of action ahead of making a choice. We propose that broad adoption of these technologies introduces new questions in the study of decision support systems, which assist people with complex and open-ended choices in business. Where traditional study of decision support has focused on bespoke tools to solve narrow problems in specific domains, LLMs offer a general-purpose decision support technology which can be applied in many contexts. To organize the wealth of new questions which result from this shift, we turn to a classic framework from Herbert Simon, which proposes that decision making requires collecting evidence, considering alternatives, and finally making a choice. Working from Simon’s framework, we describe how LLMs introduce new questions at each stage of this decision-making process. We then group new questions into three overarching themes for future research, centered on how LLMs will change individual decision making, how LLMs will change organizational decision making, and how to design new decision support technologies which make use of the new capabilities of LLMs.
Abstract: This article aims to study the application of artificial intelligence robots based on machine learning and visual algorithms in music classroom interactive experience assistance. In artificial intelligence robots, mobile adaptive networks can be used to optimize the perception and decision-making abilities of robots. By continuously learning and adapting to environmental changes, robots can better understand and respond to the interactive needs of music classrooms, providing more accurate and targeted auxiliary services. By learning and analyzing rich training data, robots can possess higher-level cognitive and comprehension abilities. In terms of music recommendation, the K-nearest neighbor algorithm is used to recommend music works that are suitable for students. By analyzing students’ music preferences and learning needs, robots provide personalized music recommendations to students based on this information, helping them better participate in and enjoy music classes. By applying machine learning and visual algorithms to music classroom interaction experiments, artificial intelligence robots based on machine learning and visual algorithms have the potential to assist in music classroom interaction experience, and teaching optimization strategies for music classrooms have been proposed.
Abstract: Wind energy is a promising renewable source, necessitating effective monitoring of wind turbine (WT) conditions for reliable and cost-effective energy production, amidst environmental challenges. Condition monitoring of WTs employs traditional methods, signal processing, and emerging artificial intelligence (AI) approaches. AI-driven techniques excel in data-driven decision-making, addressing big data challenges in condition monitoring. This review paper presents a comprehensive overview of all streams of condition monitoring associated with WT, offering detailed insights into the related tasks. It also provides details on AI-based approaches and their application in executing various tasks within condition monitoring for WT. Finally, the study summarises the current trends, advantages, and disadvantages of AI-based techniques for real-world decision making in condition monitoring. This systematic review covers fundamentals to future developments in AI-driven approaches in condition monitoring for WT, serving as a valuable resource for readers and novice researchers in this field.
Abstract: The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.
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