ChatGPT is a generative language model tool launched by OpenAI on November 30, 2022, enabling the public to converse with machine broad range of topics. In January 2023, reached over 100 million users, making it fastest-growing consumer application date. This interview part 2 larger ChatGPT. It provides snapshot current capabilities and illustrates vast potential for medical education, research, practice but also hints at problems limitations. this conversation Gunther Eysenbach, founder publisher JMIR Publications, generated some ideas how use chatbots in education. illustrated its generate virtual patient simulation quizzes students; critiqued simulated doctor-patient communication attempts summarize research article (which turned out be fabricated); commented methods detect machine-generated text ensure academic integrity; curriculum health professionals learn about artificial intelligence (AI); helped draft call papers new theme issue Medical Education The highlighted importance proper "prompting." Although generator does make occasional mistakes, admits these when challenged. well-known disturbing tendency large models hallucinate became evident fabricated references. glimpse into limitations future AI-supported Due impact technology launching e-collection issue. initial was entirely ChatGPT, will edited human guest editors

At the recent international negotiations on climate change in Dubai 2023, potential for artificial intelligence (AI) to play a critical role addressing challenges was recognized through initiative “AI Climate Action (#AI4ClimateAction).” Launched by Technology Mechanism—established under United Nations Framework Convention Change (UNFCCC)—this is focused accelerating implementation of Paris Agreement with focus ensuring that use responsible, and benefits are shared equitably. While exploring promising applications like integrating AI improve early warning systems enhancing agricultural yields, there also need minimize carbon footprint applications. More information can be found at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://unfccc.int/ttclear/artificial_intelligence</uri>

In recent years, artificial intelligence (AI) has become an emerging trend in different fields: science, business, medicine, automotive and education. AI also reached marketing. The aim of the paper is to research how deeply applied marketing what implications there are for practitioners. Authors stated two questions - which areas used delivers managers. To answer those questions, authors conducted on secondary data with examples purpose. analysis gathered shows that widely introduced into field, though applications at operational level. This may be effect careful implementation new technology, still level experimenting it. uncertainty outcome affect caution putting these innovations practice as well. Gathered proved influences all aspects mix impacting both consumer value delivery well organization management. especially ideas about implementing marketing, designing incorporate skills team required by technology.

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice expected near future. Medical image-based diagnoses, such as pathology, radiology, endoscopy, are be first medical field affected intelligence. A convolutional neural network, a kind deep-learning method with multilayer perceptrons designed use minimal preprocessing, was recently reported being highly beneficial including esophagogastroduodenoscopy, colonoscopy, capsule endoscopy. network-based diagnostic program challenged recognize anatomical locations esophagogastroduodenoscopy images, <i>Helicobacter pylori</i> infection, gastric cancer for esophagogastroduodenoscopy; detect classify colorectal polyps; celiac disease hookworm; small intestine motility characterization endoscopy images. help endoscopists provide more accurate diagnosis automatically detecting classifying lesions; therefore, it essential that focus on this novel technology. In review, we describe effects special automatic diagnosis, based endoscopic findings.

Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist designing innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, these errors damage UX even lead to undesired societal impact. However, routinely grapples with complex technologies mitigates their unintended consequences. What makes different? interaction appear particularly difficult design? This paper investigates questions. We synthesize prior research, our own design research experience, observations when teaching interaction. identify two sources of AI's distinctive challenges: 1) uncertainty surrounding capabilities, 2) output complexity, spanning from simple adaptive complex. four levels systems. On each level, designers encounter a different subset the challenges. demonstrate how findings reveal new insights for designers, researchers, tool makers productively addressing going forward.

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep or recurrent drive this area. Numerous applications property activity predictions like physicochemical and ADMET properties have recently appeared underpin the strength of technology quantitative structure-property relationships (QSPR) structure-activity (QSAR). intelligence de novo design drives generation meaningful new biologically active molecules towards desired properties. Several examples establish field. Combination with synthesis planning ease is feasible more automated discovery by computers expected near future.

Despite the great media attention for artificial intelligence (AI), many health care professionals term and functioning of AI remain a "black box," leading to exaggerated expectations on one hand unfounded fears other. In this review, we provide conceptual classification brief summary technical fundamentals AI. Possible applications are discussed basis typical work flow in medical imaging, grouped by planning, scanning, interpretation, reporting. The main limitations current techniques, such as issues with interpretability or need large amounts annotated data, briefly addressed. Finally, highlight possible impact nuclear medicine profession, associated challenges and, last but not least, opportunities.

Smart manufacturing (SM) is a term generally applied to the improvement in operations through integration of systems, linking physical and cyber capabilities, taking advantage information including leveraging big data evolution. SM adoption has been occurring unevenly across industries, thus there an opportunity look other industries determine solution roadmap paths for such as biochemistry or biology. The evolution affords managing significantly larger amounts acting on it with analytics improved diagnostics prognostics. approaches can be defined terms dimensions understand their requirements technology gaps. semiconductor industry by improving existing capabilities fault detection, supporting new predictive maintenance. For most these capabilities: (1) quality important factor delivering high solutions; (2) incorporating subject matter expertise often required realizing effective on-line solutions. In future, environment smart concepts digital twin will further enable analytics; however, anticipated that need design remain.

Artificial intelligence (AI), which includes machine learning and deep has been introduced to nursing care in recent years. The present study reviews the following topics: concepts of AI, learning, learning; examples AI-based research; necessity education on AI schools; areas where is useful. refers an intelligent system consisting not a human, but machine. Machine computers' ability learn without being explicitly programmed. Deep subset that uses artificial neural networks multiple hidden layers. It suggested educational curriculum should include big data, concept algorithms models model coding practice. standard be organized by society. An example area useful prenatal interventions based pregnant women's records prediction risk delivery according age. Nurses able cope with rapidly developing environment influenced understand how apply their field. time for Korean nurses take steps become familiar research, education,

Abstract The study presents the results regarding attitudes of students from humanities and technical specializations in Timișoara towards emergence development artificial intelligence (AI). emphasis was on most likely consequences future, especially negative that its would entail. method used for data collection sociological survey information gathering tool questionnaire. It applied to a total 929 people, ensuring sample representativity margin ± 3%. analysis reveals participants predict due AI, interpersonal relationships will be negatively affected, there fewer jobs, economic crises emerge, it make intelligent weapons, increase military conflicts, take control humanity and, last but not least, destroy mankind. revealed differences responses depending type specialization (humanities or technical) gender respondents.

Ethics approval is not required because all data for this study have been previously published. The findings of review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the scoping widely to decision-makers, health system administrators, healthcare providers, patients family/caregivers as part an implementation AI-based CDS treatment coronary artery disease.

This article begins by asking: "Why are robots not becoming domestic products?" In addressing this question the author borrows from science of ecology and biological concepts evolution domestication to make an analogy between shift habitats that occurs when organism successfully goes through process artificial selection (from natural domestic). addition, paper explores transition emerging technology makes coming out laboratory a suitable product for use, concluding majority proposed essentially maladapted everyday life.

When cognitive computing enabled smart computers are growing in our daily lives, there not many studies that explain how people interact and utilize these solutions, the impact of machines to people’s performance do things. In this paper, a theoretical framework for boosting using assistants (CAs) was developed explained data analysis from 15 interviews. The results show interaction with CAs enhance their levels cognition intelligence help them capabilities. Enhanced capabilities performance.

Data-driven science and its corollaries in machine learning the wider field of artificial intelligence have potential to drive important changes medicine. However, medicine is not a like any other: It deeply tightly bound with large wide network legal, ethical, regulatory, economical, societal dependencies. As consequence, scientific technological progresses handling information further processing cross-linking for decision support predictive systems must be accompanied by parallel global environment, numerous stakeholders, including citizen society. What can seen at first glance as barrier mechanism slowing down progression data must, however, considered an asset. Only adoption transform big into effective breakthroughs health This requires society, scientists citizens, progress together.

The ability to accurately predict the mechanical properties of metals is essential for their correct use in design structures and components. This even more important presence materials, such as metal cast alloys, whose can vary significantly relation constituent elements, microstructures, process parameters or treatments. study shows how a machine learning approach, based on pattern recognition analysis experimental data, able offer acceptable precision predictions with respect main metals, case ductile iron compact graphite iron. metallographic properties, graphite, ferrite perlite content, extrapolated through macro indicators from micrographs by image analysis, are used inputs algorithms, while yield strength, ultimate strain Young’s modulus, derived output. In particular, 3 different algorithms trained starting dataset 20–30 data each material results high accuracy, often better than other predictive techniques. Concerns regarding applicability these techniques product/process quality control also discussed.

Purpose The purpose of this paper is to raise awareness on the need for a more comprehensive approach interdependence between artificial intelligence (AI) and environmental sustainability. It provides an overview existing sustainable AI policy initiatives at national regional level. More precisely, it discusses whether European Union (EU) policies are suitable era or new regulations needed in field. Finally, assesses cross-fertilisation opportunities EU non-EU countries. Design/methodology/approach This study based qualitative analysis applications sustainability AI. Emphasis laid latter, “sustainable by design” proposed, which essence prerequisite transparent, responsible human-centred systems. primarily focuses Findings majority studies focus how use protect environment with very little attention paid design On other hand, EU’s towards closest promoting Several ways have been identified actions can be translated beyond its borders. Research limitations/implications One largest limitations moderate scope. confined as such limited assessment global measures interplay Consequently, did not provide in-depth worldwide that could help better picture possible cooperation areas common grounds. Another limitation aspects accords economic social pillars Social implications With less than 10 years go before reaching development goal deadline, stakeholders understand what being done terms Moreover, given technology still early phase, inspire technologies. Originality/value All strategies published 1 June 2021 were analysed identify extent they prioritise Furthermore, authors also looked aims address from perspective.

Key words: copyright, work, artificial intelligence, computer program In the article, problemsof legislation of Ukraine regarding issues copyright on works created due to artificialintelligence were analyzed. Particularly, who is owner ofworks intelligence. On one hand, it could be a developer ofa program, from other client or an employer. Because,it happen that there situation when robots something newand original, e.g., how happened with project “New Rembrandt”. this case,computers unique portrait Rembrandt. And here question, where isin original and intellectual developers these computers andprograms. contrast, without people developedspecial machines, programs, computers. The article’s author proposes addinto Ukrainian following norm: createddue intelligence should natural person uses intelligencefor purposes within official relationship basis contract. caseof automatic generation such work by copyrightshould developer.Also, another question arises, particularly, will responsible for damagecaused As example, solution issue Resolution2015/2103 (INL) was given, mentioned human agent responsiblefor caused damage. Because, not always damage.Also, justice practice foreign countries explored. Theways overcoming problems in proposed.Such as changing our giving exact explanation ownerof which cases personcould become copyright. However, probably, shouldbe resolved at international level globalization.

The accurate prediction of the mechanical properties foundry alloys is a rather complex task given substantial variability metallurgical conditions that can be created during casting even in presence minimal variations constituents and process parameters. In this study an application different intelligent methods classification, based on machine learning, to estimation hardness traditional spheroidal cast iron less common compact graphite proposed. Microstructures are used as inputs train neural networks, while obtained outputs. As general result, it possible admit ‘light’ open source self-learning algorithms, combined with databases consisting about 20-30 measures already able predict errors below 15 %.

Issues, arising in the field of intellectual property rights connection with development artificial intelligence systems and their impact on legal relations economy culture modern society, have been considered. Aspects mutual policies for innovation creativity examined. Questions copyright ownership interaction man, collective or raised proposed. Issues related to as an object The position author personality objects created by autonomous has presented, which is expressed answers questions project World Intellectual Property Organization wide discussion interested parties, planned 2020 at headquarters Geneva. main conceptual principle issues grant right a dressed subject – person collective, developer fixation latter sub-subject instrument subject. Traditional categories also considered, such patentability inventive level possible generation these intelligence. data, its generation, fabrications regarding data Harmonization international alleviate technological gap between countries context

Certainty and uncertainty are fundamental to science communication. Hedges have widely been used as proxies for uncertainty. However, certainty is a complex construct, with authors expressing not only the degree but type aspects of in order give reader certain impression what known. Here, we introduce new study that models both level scientific findings. Using dataset 2167 annotated findings, demonstrate hedges alone account partial explanation certainty. We show overall individual can be predicted pre-trained language models, providing more complete picture author's intended Downstream analyses on 431K findings from news abstracts modeling sentencelevel aspect-level meaningful areas like Both model datasets this paper released at https://blablablab.si. umich.edu/projects/certainty/

Objective: Artificial intelligence (AI) is the new buzzword that trendy in multiple branches of dentistry. The aim this study was to assess perceptions and utilization AI among dental professionals Saudi Arabia. Methods: This a cross-sectional investigating 389 students dentists from 22 cities Arabia using validated self-reported questionnaire. Results: A total 49.4% participants reported knowing what is; 44.5% having basic knowledge principles, 42.2% know uses most common information source social media (66.07%). Out 17 attitude items, 16 were scored above midpoint. 75.0% agreed or strongly will lead major advances In contrast, 49.1% could replace future. There no significant differences by gender region, but interns had significantly higher scores than did dentists. widespread desire take professional courses use (69.7%), some used applications (25.4%) taken an course (18.5%–20.3%). barriers non-availability (73%) lack time (68.9%). Conclusion: Dental have moderate awareness levels high rates good attitudes about However, practice limited. Incorporating curricula crucial due worldwide digital transformation.

Various international guideline documents suggest a human-centric approach to the development and use of artificial intelligence (AI) in society, ensure that AI products are developed used with due respect ethical principles human rights. Key contained these are: transparency (explainability), accountability, fairness privacy. Some governments using delivery public services, but there is lack appropriate policy legal frameworks responsible government. This paper reviews recent developments concludes that, an framework must be based on key contextualised world AI. A national alone not sufficient should accompanied by practical instrument, such as algorithm impact assessment, aimed at reducing risk or harm. Recommendations for possible South African government proposed.

Knowing about the future returns attract every investor. Investors can take appropriate decisions once they know what would be based on their investments. To returns, earlier studies have proposed basic models like Efficient Marker Hypothesis and Random Walk Model, whereas these theories own limitations in predicting direction of stock next day value stock. Later with evolution Machine learning Deep Learning Techniques, there were many experiments which made to study markets. The present research paper aims at applying technique Artificial Neural Network predict index. data consist daily open price, close high low price volume NIFTY 50, S&P 500, New York Stock Index, Korean Dow Jones Index Shanghai from Jan 2015 May 2020. index is fed as input Network. model evaluated different performance metrics Accuracy, Precision, Recall F1-Score.