Privacy in the age of medical big data.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The rapid development of big data, cloud computing, and artificial intelligence has brought new opportunities to the development of the medical field. The emergence of electronic medical records and medical information systems has provided great convenience for people's medical treatment. However, the arrival of the big data era also poses new challenges to the protection of human medical privacy. The development and application of medical big data have led to explosive growth of data in the medical field, putting pressure on the management of medical institutions. The sharing of medical data between medical institutions and between medical institutions and third parties also poses significant security risks. Moreover, medical data leakage incidents have been continuously exposed in recent years, seriously infringing on the identity and privacy rights of patients. This article aims to explore the research on personal medical information protection based on big data. Firstly, the importance and necessity of protecting personal medical information were analyzed, as well as the current application status and existing problems of big data in medical information management. Secondly, a comparative analysis of domestic and international research on medical privacy protection was elaborated, including the analysis of medical information privacy protection standards in the United States, Europe, and Asia, and the differences and commonalities of international medical information privacy protection standards were compared and analyzed. Then, the theoretical basis of medical information privacy protection was summarized. Then, the application methods and technologies of big data in personal medical information protection were discussed. Finally, relevant countermeasures and suggestions for the protection of personal medical information in big data were discussed, including privacy protection technologies and policy recommendations, as well as ethical issues and norms for the protection of medical information privacy. Through this study, it is hoped that it can provide reference and inspiration for the protection of personal medical information in big data, and provide ideas and suggestions for future development directions.

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

The term “big data” is suddenly pervasive. The New York Times deemed this the “Age of Big Data” in a 2012 article, and a Google search for the term yields over 50 million hits. “Big data” is difficult to define precisely, but it is characterized by three attributes known as “the three V's”: its large volume, its variety, and its velocity, that is, the frequency with which it is generated. Medical big data is a particularly rich but sensitive type of big data, and it holds great promise as a resource for researchers and other analysts. Numerous medical big data initiatives are being launched by public and private enterprises. The transition from paper medical files to EHR systems has facilitated the creation of large health information databases. Computer processing of digitized records permits fast and relatively inexpensive data analysis and synthesis. These databases, therefore, can serve as invaluable research resources. Medical big data can be derived from other, nontraditional sources as well. Google retains all users’ web searches, including those involving medical queries. It can use the data for its own purposes, and at times, the data are requested by government and law enforcement authorities as well. Customer purchasing records, tweets, and Facebook entries can also reveal a great deal of health information. Companies such as Acxiom offer to sell “demographic, behavioral, lifestyle, financial and home data” that they obtain from such sources. This book, however, focuses on medical big data that is derived from electronic health records (EHRs) or from reports submitted by healthcare providers. It does not, therefore, extensively address nontraditional sources of medical big data. Analysts can access large-scale collections of EHR data in two primary ways. First, health information can be collected into large databases and deidentified to protect patient privacy. Such databases could be limited to particular hospital systems, be expanded to cover entire regions, or even be national in scope. In the alternative, researchers may use a federated system. A “federated network” can be defined as one that “links geographically and organizationally separate databases to allow a single query to pull information from multiple databases while maintaining the privacy and confidentiality of each database.”

The article is devoted to the history of the development of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), their current and probable future achievements, and the problems (which have already arisen, but will become even more acute in the future) associated with the development of these technologies and their active introduction in society. The close connection between the development of AI and cognitive science, the penetration of ICT and AI into various fields, in particular the field of health care, is shown. A significant part of the article is devoted to the analysis of the concept of ‘artificial intelligence’, including the definition of generative AI. We analyze recent achievements in the field of Artificial Intelligence, describe the basic models, in particular the Large Linguistic Models (LLM), and forecast the development of AI and the dangers that await us in the coming decades. We identify the forces behind the aspiration to create artificial intelligence, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. The authors emphasize that the threats and dangers of the development of ICT and AI are particularly aggravated by the monopolization of their development by the state, intelligence services, large corporations and those often referred to as globalists. The article forecasts the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, presented below, provides a brief historical overview and characterizes the current situation in the field of ICT and AI, it also analyzes the concepts of artificial intelligence, including generative AI, changes in the understanding of AI related to the emergence of the so-called large language models and related new types of AI programs (ChatGPT). The article discusses the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. The second article, to be published in the next issue of the journal, describes and comments on current assessments of breakthroughs in the field of AI, analyzes various forecasts, and the authors give their own assessments and forecasts of future developments. Particular attention is given to the problems and dangers associated with the rapid and uncontrolled development of AI, the fact that achievements in the field of AI are becoming a powerful means of controlling the population, imposing ideology and choice, influencing the results of elections, and a weapon for undermining security and geopolitical struggle.

In recent years, the continuous development of big data, cloud services, Internet+, artificial intelligence, and other technologies has accelerated the improvement of data communication services in the traditional pharmaceutical industry. It plays a leading role in the development of my country's pharmaceutical industry, deepening the reform of the health system, improving the efficiency and quality of medical services, and developing new technologies. In this context, we make the following research and draw the following conclusions: (1) the scale of my country's medical big data market is constantly increasing, and the global medical big data market is also increasing. Compared with the global medical big data market, China's medical big data has grown at a faster rate. From the initial 10.33% in 2015, the proportion has reached 38.7% after 7 years, and the proportion has increased by 28.37%. (2) Generally speaking, urine is mainly slightly acidic, that is, the pH is around 6.0, the normal range is 5.0 to 7.0, and there are also neutral or slightly alkaline. 8 and 7.5 are generally people with some physical problems. In recent years, the pharmaceutical industry has continuously developed technologies such as big data, cloud computing, Internet+, and artificial intelligence by improving data transmission services. As an important strategic resource of the country, the generation of great medical skills and great information is of great significance to the development of my country's pharmaceutical industry and the deepening of the reform of the national medical system. Improve the efficiency and level of medical services, and establish forms and services. Accelerate economic growth. In this sense, we set out to explore.

With the rapid development and application of artificial intelligence (AI), the computer technology has entered the era of new Information Technology (IT) called Intelligent Technology. AI can accelerate the information construction of science and technology. In the past two years, the AI research has been promoted to the level of the national development strategy in China. This chapter explores the origin and development of AI and the AI development in China. AMiner, a big data analysis and service platform for science and technology, is independently developed by China. It is a successful case in the informatization of science and technology in China. Based on the open dataset of AI in AMiner, we give the classification of the AI research in China. We overview the AI research situation in China based on the experts, chapters, and patents analysis. The AI applications, such as speech recognition, face recognition, automatic driving, and so on, are introduced in the chapter. We also discuss the opportunities and challenges of AI in China. In general, this chapter fills the gaps in the authoritative analysis of the AI research situation in China.

Artificial intelligence is defined as the totality of systems and programs that imitate human intelligence and can eventually surpass this intelligence over time. The rapid development of these technologies has raised various ethical debates such as moral responsibility, privacy, bias, respect for human rights, and social impacts. This study examines the technical infrastructure of artificial intelligence, the differences between weak and strong artificial intelligence, ethical issues, and theological dimensions in detail, providing a comprehensive perspective on the role of artificial intelligence in human life and the problems it brings. The historical development of artificial intelligence has been shaped by the contributions of various disciplines such as mathematical logic, cognitive science, philosophy, and engineering. From the ancient Greek philosophers to the present day, thoughts on artificial intelligence have raised deep philosophical questions such as human nature, consciousness, and responsibility. The algorithms developed by Alan Turing have contributed to the modern shaping of artificial intelligence and have put forward the first models to assess whether machines have human-like intelligence, such as the “Turing Test”. The study first analyzes the technical infrastructure of artificial intelligence in detail and discusses the current limits and potential of the technology through the distinction between weak and strong artificial intelligence. Weak artificial intelligence includes systems designed to perform specific tasks and do not exhibit general intelligence outside of those tasks, while strong artificial intelligence refers to systems with human-like general intelligence and flexible thinking capacity. Most of the widely used artificial intelligence applications today fall into the category of weak artificial intelligence. However, the development of strong artificial intelligence brings various ethical and theological consequences for humanity. The ethical issues of artificial intelligence include fundamental topics such as autonomy, responsibility, transparency, fairness, and privacy. The decision-making processes of autonomous systems raise serious ethical questions at the societal level. Especially autonomous weapons and artificial intelligence-managed justice systems raise concerns in terms of human rights and individual freedoms. In this context, the ethical framework of artificial intelligence has deep impacts on the future of humanity and human-machine interaction, not just limited to technological boundaries. From a theological perspective, the ability of artificial intelligence to imitate the human mind and creative processes raises deep theological issues such as the creativity of God, the place of human beings in the universe, and consciousness. The questions of whether artificial intelligence systems can gain consciousness and whether these conscious systems can have a spiritual status have led to new debates in theology and philosophy. The ethical principles of artificial intelligence are shaped around principles such as transparency, accountability, autonomy, human control, and data management. In conclusion, determining the ethical and theological principles that need to be considered in the development and application of artificial intelligence is critical for the future of humanity. A comprehensive examination of the ethical and theological dimensions of artificial intelligence technologies is necessary to understand and manage the social impacts of this technology. This study emphasizes the necessity of an interdisciplinary approach for the development of artificial intelligence in harmony with social values and for the benefit of humanity. The study provides an important theoretical framework for future research by shedding light on the complex ethical and theological issues arising from the development and widespread use of artificial intelligence.

In recent years, artificial intelligence technologies have begun to be used in many types of human life: education, medicine, business, finance, management, marketing, industry, etc., and have become a key trend of the time. The article provides information and analyzes the current state and trends in the development of artificial intelligence in Ukraine. An analysis of the strategy for the development of artificial intelligence, which is proposed by a number of ministries, considered the concept of development of artificial intelligence, which is proposed by the State Agency for e Government. Data on companies working in the field of artificial intelligence are analyzed and provided. The main activities of Ukrainian companies in the field of artificial intelligence, including a number of Ukrainian startups, have been identified. Provides information on scientific schools and research conducted in the field of artificial intelligence in Ukraine. It is established that the main research focuses on the following areas: neural networks, pattern recognition, mathematical informatics, creation of intelligent information systems and knowledge bases, development of intelligent robotic systems, knowledge-oriented technologies, intelligent search, development of intelligent learning systems, big data analysis, fuzzy systems decision support, multi-agent systems and much more. In general, scientific research conducted in Ukraine in the field of artificial intelligence covers the basis of areas of work that correspond to research in the world. Also in the article the directions of dissertation researches for the last years on intellectual systems and systems of artificial intelligence are analyzed. The main areas in which the dissertation research was performed: intellectual analysis and data processing, knowledge processing and multi-agent technologies, intelligent technologies, including intelligent technologies to support acceptance, image recognition and a number of others.

The aim of the article. The purpose of the article is to analyze the current state and prospects for the development of artificial intelligence in the modern enterprises and marketing campaigns, as well as to develop proposals in the context of their effective use as a priority for ensuring the innovative development of enterprises. Analysis results. The article examines the current problems of artificial intelligence creating and practical application in the modern enterprises. It has been proven that artificial intelligence technologies, the spread of which is based on the mass use of digital information and the rapid computer computing power growth, leave the sphere of purely theoretical research and become one of the world market segments, which can lead to truly revolutionary results. The prospects for artificial intelligence technologies development and the possibility of their integration into various enterprise activity spheres, in particular, into the field of marketing, have been studied. Current areas of artificial intelligence application for the Industry 4.0 implementation are considered. Effective trends and development prospects related to the use of artificial intelligence in various marketing segments and their transformation for marketing sectors are summarized. It is noted that for the effective operation of industrial enterprises, full-fledged information support is necessary at all stages of management decisions design and formation. It was concluded that artificial intelligence is considered today as the most important factor that determines the overall growth of the Ukrainian economy, the future of each country and its position on the world stage in general. The article develops modern approaches to the use of artificial intelligence for the Industry 4.0 implementation, in particular in marketing. A model of artificial intelligence introducing at the enterprise strategy is proposed. The obtained research results can be used in the context of robotics policy and industrial enterprises digital systems future development in order to accelerate innovative, technical and economic development in the long term. Conclusions and directions for further research. The advanced countries of the world are on the revolutionary changes verge in many economy sectors due to the intensive development of intelligent technologies and artificial intelligence. The world’s most influential companies are actively investing in order to increase economic and social benefits from the implementation of technologies in the main artificial intelligence development areas: deep learning technologies, integration technologies, machine learning and cognitive data. In this context, based on the structure and state of the Ukrainian industry, it can be assumed that the indicated industrial robotization trends will persist for a long time. The release of workers as a digital technologies development result will occur, first of all, not in production, but in the sphere of services, management, finance, due to the office worker’s reduction. However, practice confirms that the most important results of artificial intelligence development are not only the increase in productivity and the displacement of live labor from production, but, above all, the transformation of social relations system, including their organization forms, the interaction between people forms, between the state and the population. It is important to determine the place of artificial intelligence in these systems and its potential as a means of regulatory influence on society. At the same time, in order to secure technological development and free society from prejudice and risks, it is necessary to formulate and strengthen the current legislation in this field. This will create an ethical and legal environment for the development of innovations. Artificial intelligence also has significant potential in the field of marketing. This intelligence is changing the way brands and users interact with each other. Since the data growth far outstrips the ability of people to process this data, Marketing 4.0 must align with Industry 4.0 technologies, introducing IT knowledge to their professional competencies. In the direction of further research, proceeding from priorities, there are proposals for the introduction and development of innovations in military affairs in order to protect our country from Russian armed aggression, based on the three key components of military technologies: artificial intelligence, the use of "big data", Internet management technologies production and territorial systems (Internet of things).

The rapid development of artificial intelligence (AI) technology is due to the significant progress in big data, databases, algorithms, and computing power, and medical research is a vital application direction of AI. The integrated development of AI and medicine has improved medical technology, and the efficiency of medical services and equipment has enabled doctors to better serve patients. The tasks and characteristics of the anesthesia discipline also make AI necessary for its development, and AI has also been initially applied in different fields of anesthesia. Our review aims to clarify the current situation and challenges of AI application in anesthesiology to provide clinical references and guide the future development of AI in anesthesiology. This review summarizes progress in the application of AI in perioperative risk assessment and prediction, deep monitoring and regulation of anesthesia, essential anesthesia skills operation, automatic drug administration systems, and teaching and training in anesthesia. Also discussed herein are the accompanying risks and challenges of applying AI in anesthesia: patient privacy and information security, data sources, and ethical issues, lack of capital and talent, and the “black box” phenomenon.

With the advent of the 5G communication era, people's life and work are increasingly dependent on computer network information technology. The development and application of artificial intelligence (AI) technology has laid a solid foundation for the security of computer network. One technology plays an indispensable role in the development of this field, that is, computer communication Technology (CCT). Its core function is to support data, collect, analyze and process a large amount of information, and output scientific and reasonable calculation results. As one of the frontier directions, the application range of multi-agent system is more and more extensive. Compared with traditional software systems, multi-agent systems can provide more effective means to solve complex scientific and engineering problems. This paper focuses on the application of the trusted alliance technology of multi-agent system in the context of the Internet of Things, defines the parameters and conditions of some trusted alliances of the intelligent terminal of the Internet of Things, and preliminarily discusses the negotiation strategy of the process of strengthening the alliance. The organic integration of AI technology and computer network technology is a new direction of the intelligent development exploration and research of network technology. This paper studies and analyzes the application of AI in computer network technology in the age of big data.

The whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

The age of big data is coming. Many data processing and statistical analysis technologies of big data are developing now. They widely impact our live, for examples: society, science, medical industry, military, education, government and business, etc. By using statistical analysis technologies of big data, many valuable information are produced and those results can be used to predict the trend of the future. However, it also brings huge challenges for personal privacy. For protecting the privacy of personal medical data, how to use cryptographic technologies de-identify medical privacy data becomes very important. On the other hand, how to control the access privileges of privacy data for authorized persons are also needed to be solved. This paper bases on Diffie-Hellman protocol to design a privacy protection system for medical big data. It can protect patient privacy information and avoid revealing the medical data. Besides, it can assign access right to authorized doctors, such that the authorized doctors can access and share the patient privacy information. Finally, it can achieve the destination of protecting the privacy and confidentiality of medical big data.