Integrating artificial intelligence with non-destructive experimental methods for effective food analysis: a case study on spices.

Along with flavor, texture is one of the most important attributes of fresh-cut produce, which plays a critical role in consumer acceptance, influencing perceived freshness, quality, and overall eating experience. Salads, specifically green salads, usually contain leafy greens and other fresh-cut fruit vegetables such as cucumbers and tomatoes. Each of these components has its own unique characteristics, together giving the salad a wide range of textures and consumer experiences. Texture can be measured by both instrumental analysis and sensory evaluation techniques. Instrumental analysis consists of destructive and non-destructive methods, whereas sensory evaluation consists of descriptive and consumer studies. However, a comprehensive understanding and correlation of instrumental analysis and sensory evaluation of texture in salad-related fresh-cut fruits and vegetables is lacking in the literature. This review aims to address this gap by providing an in-depth understanding of the techniques used to quantify texture-related parameters and establishing relationships between the two techniques in fresh-cut salad products. The two different modes of analysis, along with the vastly different texture-related characteristics of fresh-cut salad produce, make it challenging to correlate instrumental analysis with sensory evaluation results. Furthermore, the integration of advanced technologies, including hyperspectral imaging and artificial intelligence-driven texture analysis, is emerging as a promising approach to enhance texture characterization and quality control. In addition to providing a comprehensive approach to enhance quality assessment methods, this review highlights the importance of predictability models powered by artificial intelligence and machine learning, enabling correlating instrumental analysis and sensory evaluation.

Assessing perspectives on artificial intelligence applications to gastroenterology

The terms artificial intelligence (AI), machine learning and robotics are no longer deemed futuristic or restricted to the realms of science fiction. Society has embraced and adopted a range of AI technologies. The purpose of this case study is to review established and experimental uses of AI across the hospitality and tourism sector. As a result, students will be able to analyse existing practices and develop innovative ideas for AI implementation. In particular, the case study will evaluate the uses and benefits of AI in providing personalized experiences for tourists through aspects such as service innovation, adding value and empowered AI robots. The case study will introduce and compare narrow and artificial general intelligence and explore applications of big data and machine learning. On completion of this case study, learners will be able to fully understand the relevance of AI in the hospitality and tourism industry and how it can be a significant aspect of business decision making and strategic implementation.

Necessity is the mother of invention. Artificial Intelligence is playing key role in the decision making of business growth. The paper discusses about the impacts of Artificial Intelligence and Machine Learning in the business decision making. Due to rapid change and advancement in the technology, there is impact of Artificial Intelligence and Machine Learning on businesses and global economy. Companies are using Artificial Intelligence and Machine Learning by taking more interest in their respective sector. Artificial Intelligence and Machine Learning are helping them to make their products and services more intelligent for the business growth. Literature review of Artificial Intelligence and Machine Learning related research paper is done that discussed the impact of Artificial Intelligence and Machine Learning for business growth and improvement. Various applications of Artificial Intelligence and Machine Learning in different domains are discussed. The literature review shows that Artificial Intelligence and Machine Learning have impacted human life quality and business growth. If decision makers wish to make benefits of Artificial Intelligence and Machine Learning for their businesses, they have to understand different Artificial Intelligence techniques and Machine Learning tools. They must focus on careful analysis of the risks and initial investment to avoid some common problems. It will help them to create value for their businesses.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in business analytics has fundamentally transformed decision-making, operational efficiency, and competitive advantage across various industries. This study explores the impact of AI-driven business intelligence, process automation, and predictive analytics on enhancing organizational agility, risk management, financial performance, and innovation. Adopting a case study approach, the research examines 12 case studies across multiple sectors, including finance, retail, healthcare, supply chain management, and digital marketing, to provide empirical insights into AI’s role in optimizing business operations. The findings reveal that AI-driven automation significantly improves process agility, enabling companies to respond more effectively to market fluctuations and operational risks. Additionally, AI-powered predictive analytics enhances financial performance by optimizing cost management, fraud detection, and customer engagement strategies. The study also highlights AI’s growing role in fostering innovation, particularly in research and development (R&D), product optimization, and personalized business recommendations. However, the research identifies key challenges in AI adoption, including data integration complexities, algorithmic biases, and the need for effective workforce adaptation, emphasizing the importance of structured AI implementation and governance. By synthesizing insights from 12 real-world case studies, this study underscores AI’s transformative impact in modern business environments and provides practical recommendations for organizations seeking to leverage AI for sustained growth and competitive differentiation.

In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.

SummaryThe recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

Methods. The article is based on a theoretical review of the impact of artificial intelligence and machine learning on changing business models. In the course of study, the methods of scientific abstraction were used – when establishing the relationship between artificial intelligence and machine learning, analysis and synthesis – when determining the advantages of using artificial intelligence in business. Results. The article examines the essence of artificial intelligence and machine learning, shows the relationship between them. The impact of these new digital tools on economic processes and, above all, on the dynamic aspects of the functioning of business structures is characterized. The opinion of experts is presented, who predict that artificial intelligence will do everything that humans can do, but with much higher accuracy. Discussions on the ethical aspects of using artificial intelligence are analyzed. It was determined that Business Operation allows organizations to quickly cope with their business opportunities, reduce the number of errors, increase the transparency of their activities and thus create favorable conditions for significantly improving the results of their economic activities. Along with this, companies get the opportunity to observe their workforce, on the basis of which to create favorable conditions for improving its quality and introducing innovative content. This allows to significantly increase the innovative activity of companies, since the use of artificial intelligence allows forming requirements for teams, as it allows seeing the first obstacles to the development of innovative solutions. At the same time, if businesses maintain a better erudition about artificial intelligence, they will be able to modernize their business early and succeed. Novelty. The study demonstrated important aspects of the interconnection between artificial intelligence and machine learning and their impact on changing business models. Practical value. The study demonstrated important aspects of the relationship between artificial intelligence and machine learning and their impact on changing business models.

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Artificial Intelligence (AI) is an interdisciplinary field of study that focuses on building machines that are able to think and, in particular, act in an intelligent manner. In this context, it is preferable to characterise intelligence as the ability to accomplish complex tasks, as opposed to anchoring this concept on the notion of human intelligence or thought. Machine Learning (ML) is a subfield of AI that encompasses software that improves with experience. A variety of teaching methods can be used to train ML algorithms, including supervised learning, unsupervised learning and reinforcement learning. In turn, Deep Learning (DL) is a subfield of ML that uses deep neural networks (DNN) to identify useful patterns in the input data. On the technical side, this chapter draws a distinction between artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). All current instances of AI fall within the realm of ANI, but the increasing power of learning algorithms is gradually shifting the balance towards the AGI paradigm. We also explored the distinction between weak AI and strong AI based on consciousness, though concerns have been raised about the relevance and falsifiability of these concepts. AI is already playing an important role in the financial industry, including in the banking, investing and insurance sectors. However, the journey towards widespread adoption has been bumpy and halted by several roadblocks. An appreciation of key historical milestones helps to put the technology’s achievements into perspective and understand some of the future directions that AI research may take.

Can an Artificial Intelligence (AI) Be an Author on a Medical Paper?

Focus issue: Artificial intelligence in medical physics.

Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.

Advancements in artificial intelligence and machine learning are transforming how designers work with computers. This thesis explores the developing relationship between artificial intelligence (AI) and design processes, focusing on how it impacts the creation of digital experiences. This study examines the impact of AI on design, highlighting how AI can improve efficiency and creativity alongside ethical challenges and the need to preserve human uniqueness in digital creations. It investigates AI's effects on design workflows, creativity, and the designer's evolving role through interviews, content analysis, case studies, and surveys. The aim is to understand AI's influence on designer productivity, innovation, and ethical issues. The need for this research stems from significant changes in the design field due to AI, focusing on optimizing design workflows within ethical and user-centric frameworks. This thesis contributes to discussions on AI in design, advocating for a thoughtful integration of AI. It is based on assumptions about design's digital transformation and AI's ethical implications, framing the inquiry into this intricate topic. With an emphasis on the collaborative intelligence model, wherein human and AI synergies enhance design outcomes, the study investigates the practical, ethical, and creative facets of AI in design. This research aims to empower designers to navigate the evolving AI landscape effectively, advocating for a future where AI enhances human creativity and problem-solving in the design of compelling digital experiences.