Current AI Research Articles

Refugees and Algorithmic Humanitarianism: Applying Artificial Intelligence to RSD Procedures and Immigration Decisions and Making Global Human Rights Obligations Relevant to AI Governance

Artificial intelligence (ai) has created algorithmic-driven humanitarianism without ethics, justice, and morality. Current ai dynamics do not protect humanity and mitigate its sufferings in refugee status determination procedures and immigration decisions, raising a host of data privacy and confidentiality issues. Data from refugees, asylum–seekers and migrants and the stateless might be deployed and manipulated for geostrategic, geopolitical, geo-engineering, medico-research, socio-economic, and demographical purposes by international organisations and governments. ai lacks anthropogenic sensitivity, critical thinking, and human traits of subjectivity and objectivity. The author ruminates on these issues by examining the application of ai and assessing its impact on the global human rights norms. The author adopts a human rights-based approach while espousing the reprogramming of algorithmic humanitarianism within new ai technologies for sustainable artificial intelligence.

The Age of AI: Artificial Intelligence and the Future of Humanity

The Age of AI: Artificial Intelligence and the Future of Humanity

Synthetic Training Data for Artificial Intelligence-Based Li-Ion Diagnosis and Prognosis

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. Both diagnosis and prognosis are complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available.In this work, we will present a proof-of-concept methodology to generate big data training datasets for intercalation batteries. It will use a mechanistic approach that combines both modeling and experimental techniques to provide a universal tool for the creation of synthetic voltage curves practically indistinguishable from real data. This approach offers the benefits of the broad applicability of the model to various cell chemistries, designs, and operating modes, as well as the high fidelity as inhered from the detailed extraction of the experimental data. The methodology could be applied to the generation of training datasets encompassing the entire degradation spectrum as well as different operating conditions such as rate and temperature. Scenarios leading to lithium plating with adjustable reversibility can be added and the addition of random noise on the data could be considered to make the datasets even more realistic and consider the impact of real-life testing machines' precision and accuracyThe proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges Too

In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness. On the one hand, many generative models, such as generative adversarial networks (GAN), build imitators under an adversarial setting that strongly resembles the Turing test (with the judge being a learnt discriminative model). The term “Turing learning” has been used for this kind of setting. On the other hand, AI benchmarks are suffering an adversarial situation too, with a ‘challenge-solve-and-replace’ evaluation dynamics whenever human performance is ‘imitated’. The particular AI community rushes to replace the old benchmark by a more challenging benchmark, one for which human performance would still be beyond AI. These two phenomena related to the Turing test are sufficiently distinctive, important and general for a detailed analysis. This is the main goal of this paper. After recognising the abyss that appears beyond superhuman performance, we build on Turing learning to identify two different evaluation schemas: Turing testing and adversarial testing. We revisit some of the key questions surrounding the Turing test, such as ‘understanding’, commonsense reasoning and extracting meaning from the world, and explore how the new testing paradigms should work to unmask the limitations of current and future AI. Finally, we discuss how behavioural similarity metrics could be used to create taxonomies for artificial and natural intelligence. Both testing schemas should complete a transition in which humans should give way to machines—not only as references to be imitated but also as judges—when pursuing and measuring machine intelligence.

Artificial intelligence: neither Utopian nor apocalyptic impacts soon

ABSTRACT After a number of AI-winters, AI is back with a boom. There are concerns that it will disrupt society. The immediate concern is whether labor can win a ‘race against the robots’ and the longer-term concern is whether an artificial general intelligence (super-intelligence) can be controlled. This paper describes the nature and context of these concerns, reviews the current state of the empirical and theoretical literature in economics on the impact of AI on jobs and inequality, and discusses the challenge of AI arms races. It is concluded that despite the media hype neither massive jobs losses nor a ‘Singularity’ is imminent. In part, this is because current AI, based on deep learning, is expensive and difficult for most businesses to adopt, not only displaces but in fact also create jobs, and may not be the route to a super-intelligence. Thus AI is unlikely to have either Utopian or apocalyptic impacts soon. Considering Amara's Law, one should however be wary not to underestimate the long-run impacts of AI.

Innovative Research on the Teaching Mode of Piano Group Lessons in Higher Vocational Preschool Education Based on AI

The current AI technology has penetrated into many aspects of the education field (as shown in figure 1), and has made innovations in teaching models, courses and systems. Based on the background of AI, the preschool piano group teaching of higher vocational education has gradually become a mode of personalized learning, adaptive learning, deep learning, and human-computer collaborative learning. This article will analyze the current situation of piano group lesson teaching and propose innovative strategies to improve piano group lesson teaching in higher vocational preschool education.

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Accurate lithium battery diagnosis and prognosis are critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

Instrumental Robots

Advances in artificial intelligence research allow us to build fairly sophisticated agents: robots and computer programs capable of acting and deciding on their own (in some sense). These systems raise questions about who is responsible when something goes wrong—when such systems harm or kill humans. In a recent paper, Sven Nyholm has suggested that, because current AI will likely possess what we might call “supervised agency”, the theory of responsibility for individual agency is the wrong place to look for an answer to the question of responsibility. Instead, or so argues Nyholm, because supervised agency is a form of collaborative agency—of acting together—the right place to look is the theory of collaborative responsibility—responsibility in cases of acting together. This paper concedes that current AI will possess supervised agency, but argues that it is nevertheless wrong to think of the relevant human-AI interactions as a form of collaborative agency and, hence, that responsibility in cases of collaborative agency is not the right place to look for the responsibility-grounding relation in human-AI interactions. It also suggests that the right place to look for this responsibility-grounding relation in human-AI interactions is the use of certain sorts of agents as instruments.

Innovation Practices Using AI technology: Exploring Human-Machine Knowledge Conversion

The use of AI in innovation practices creates a conundrum for knowledge creation theory. Current AI technology possesses the ability to engage with data in ways that differ from the original design due to their response to contextual input data. AI may be said to embody tacit knowledge as it evolves and learns about the human collaborator and the organisational and technological context. We explore knowledge conversion involving human and AI to understand the process of digital innovation starting from the mutually constituted innovation agency. We articulate a process model of knowledge creation extending Nonaka’s seminal SECI model starting with a sociomaterial lens on knowledge conversion and a recursive relationship between the outcome of knowledge creation and the material interactions of human and AI. Our analysis links characteristics of digital innovation with the potential of AI to perform skilful work or, in other words, appear to act on tacit knowledge. This insight contributes to the literature on digital innovation a missing link for seeing material agency as intermingled with human agency and to organizational knowledge creation theory the possibility of the machine becoming part of a team. We discuss theoretical and practical implications of our study for the economics of digital innovation and suggest avenues for future research.

Updating the Frame Problem for AI Research

The Frame Problem is the problem of how to design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use. I apply the generative methodology I have developed elsewhere for cognitive science and AI research to show how the Frame Problem can be appropriately updated.

AI ethics should not remain toothless! A call to bring back the teeth of ethics

Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics can achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now.

Geographical variation in the risk of H7N9 human infections in China: implications for risk-based surveillance

The influenza A (H7N9) subtype remains a public health problem in China affecting individuals in contact with live poultry, particularly at live bird markets. Despite enhanced surveillance and biosecurity at LBMs H7N9 viruses are now more widespread in China. This study aims to quantify the temporal relationship between poultry surveillance results and the onset of human H7N9 infections during 2013–2017 and to estimate risk factors associated with geographical risk of H7N9 human infections in counties in Southeast China. Our results suggest that poultry surveillance data can potentially be used as early warning indicators for human H7N9 notifications. Furthermore, we found that human H7N9 incidence at county-level was significantly associated with the presence of wholesale LBMs, the density of retail LBMs, the presence of poultry virological positives, poultry movements from high-risk areas, as well as chicken population density and human population density. The results of this study can influence the current AI H7N9 control program by supporting the integration of poultry surveillance data with human H7N9 notifications as an early warning of the timing and areas at risk for human infection. The findings also highlight areas in China where monitoring of poultry movement and poultry infections could be prioritized.

Blockchain, AI and Smart Grids: The Three Musketeers to a Decentralized EV Charging Infrastructure

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.

There Can Be no Other Reason for this Behavior: Issues in the Ascription of Knowledge to Humans and AI.

While machine learning techniques have been used to model categorization/decision making tasks that are beyond the capabilities of traditional AI, these new models are typically uninterpretable, i.e., the reasons for their decisions are not clear. Some have argued that, in developing machines that can report the reasons for their decisions, developers should take, as a guide, human explanations for behavior, which make reference to mental states (e.g., knowledge/belief). This proposal is correct, but unattainable given certain characteristics of current AI. To explain, this article draws on recent discourse-analytic research showing that ascriptions of knowledge/belief presume behavioral performances to instantiate particular sorts of broader dispositions. This is reflected by the possibility of ascribing knowledge/belief to an agent on the basis that there can be no other explanation for their observed behavior. The behavior of AI trained through machine learning is unpredictable in ways that precludes such certainty. Consequently, while it is certainly possible to program machines to report mental states of knowledge/belief to account for their decisions, the failure of current AI to engage in typically human forms of life means that such ascribed mental states are inevitably meaningless.

Representations and Benchmarking of Modern Visual SLAM Systems.

Simultaneous Localisation And Mapping (SLAM) has long been recognised as a core problem to be solved within countless emerging mobile applications that require intelligent interaction or navigation in an environment. Classical solutions to the problem primarily aim at localisation and reconstruction of a geometric 3D model of the scene. More recently, the community increasingly investigates the development of Spatial Artificial Intelligence (Spatial AI), an evolutionary paradigm pursuing a simultaneous recovery of object-level composition and semantic annotations of the recovered 3D model. Several interesting approaches have already been presented, producing object-level maps with both geometric and semantic properties rather than just accurate and robust localisation performance. As such, they require much broader ground truth information for validation purposes. We discuss the structure of the representations and optimisation problems involved in Spatial AI, and propose new synthetic datasets that, for the first time, include accurate ground truth information about the scene composition as well as individual object shapes and poses. We furthermore propose evaluation metrics for all aspects of such joint geometric-semantic representations and apply them to a new semantic SLAM framework. It is our hope that the introduction of these datasets and proper evaluation metrics will be instrumental in the evaluation of current and future Spatial AI systems and as such contribute substantially to the overall research progress on this important topic.

Global Challenges in the Standardization of Ethics for Trustworthy AI

In this paper, we examine the challenges of developing international standards for Trustworthy AI that aim both to be global applicable and to address the ethical questions key to building trust at a commercial and societal level. We begin by examining the validity of grounding standards that aim for international reach on human right agreements, and the need to accommodate variations in prioritization and tradeoffs in implementing rights in different societal and cultural settings. We then examine the major recent proposals from the OECD, the EU and the IEEE on ethical governance of Trustworthy AI systems in terms of their scope and use of normative language. From this analysis, we propose a preliminary minimal model for the functional roles relevant to Trustworthy AI as a framing for further standards development in this area. We also identify the different types of interoperability reference points that may exist between these functional roles and remark on the potential role they could play in future standardization. Finally we examine a current AI standardization effort under ISO/IEC JTC1 to consider how future Trustworthy AI standards may be able to build on existing standards in developing ethical guidelines and in particular on the ISO standard on Social Responsibility.We conclude by proposing some future directions for research and development of Trustworthy AI standards.

AI-Based Migrant Crisis Management

Northeast Asian Countries of Korea, China, Japan has not experienced massive migrant crisis before and also not been noticed from migrant issue debating. But the informatization and globalization slowly but gradually drives Northeastern Countries into migrant issues. These countries are coping with various types of migrants, e.g. North Korean Defectors and marriage immigrants. And Korea experienced Syria-Yemen migrant crisis at 2018 which is detonated from European refugee crisis, and that induces forecasting of various forms of migrant issues would be unfold at onshore Northeastern Asian Countries. The characteristics of these kinds of migrant crisis are its large size and unpredictable time line. Current AI cognitive computing technology could be applied to analyze the nature of migrant crisis and to support effective policy making that corresponds migrant crisis.

Artificially intelligent device use in service delivery: a systematic review, synthesis, and research agenda

ABSTRACT This study undertakes a systematic review of Artificial Intelligence and its applications to service encounters and the hospitality industry by reviewing publications that (1) mainly discuss AI technology, (2) are in the context of services, and (3) investigate the use or the adoption of AI technology rather than technical issues such as system design, algorithms, voice recognition modules, or psychological knowledge representations. Seven major themes are identified via a review of 63 publications. The themes are (1) current AI technology in service frontline, (2) levels of artificial intelligence, (3) AI agents, (4) human–AI service encounters, (5) theoretical frameworks of the acceptance of AI, (6) reasons for adopting AI, and (7) potential challenges of AI. This study also offers a further research agenda that highlights nine critical research areas to guide human–AI interaction and AI adoption researches.

Privacy Protection Technology in the Age of A.I.

In the era of artificial intelligence, no user has ever disclosed personal privacy data to a certain party. The current AI software can fully integrate the fragment information of a user on different site platforms, and finally complete the portrait of the user. In order to prevent personal privacy leakage under the condition of AI, this paper studies the technology of personal privacy protection from the perspective of social engineering. It is found that by fully splitting the personal account group, i.e. using multiple mobile phone numbers and account binding status, and avoiding the use of the registration free login function, personal information is split into multiple or even more than ten user information by AI. At this time, these information will be discarded by AI due to incomplete information, so as to achieve the effect of protecting personal information.

The missing G

Artificial general intelligence (AGI) is not a new notion, but it has certainly been gaining traction in recent years, and academic as well as industry resources are redirected to research in AGI. The main reason for this is that current AI techniques are limited as they are designed to operate in specific problem-domains, following meticulous preparation. These systems cannot operate in an unknown environment or under conditions of uncertainty, reuse knowledge gained in another problem domain, or autonomously learn and understand the problem-domain. We shall call AI systems capable of such feats artificial general intelligent (AGI) systems. The three tasks of this paper are to provide a working definition of the term AGI, examine the “missing G”, i.e., the set of abilities that current AI systems lack and whose implementation will result in a basic AGI system, and consider different approaches, including a hybrid one, to a comprehensive solution for an AGI.