AI Research Research Articles

Incentive Auction Design Alternatives: A Simulation Study

This paper revisits the descending clock “reverse” auction design used in the U.S. Federal Communications Commission’s 2016–2017 “incentive auction.” We use extensive computational simulations to investigate the quantitative significance of various aspects of the design, leveraging a reverse auction simulator and realistic models of bidder values. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: This work was supported by the Defense Advanced Research Projects Agency [Grant FA8750-19-2-0222 CFDA\# 12.910], the Canadian Institute for Advanced Research [Canada AI Research Chair at the Alberta Machine In], the National Science Foundation [Grant 1525730], and the Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada [Discovery Grant, Discovery Grant Supplement]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.02489 .

Games and the Rise of Systems Thinking

This article examines the historical development of systems thinking to demonstrate the manifold applications of games in modeling new understandings of systems. Chess and go were deliberately reconfigured by competitors, theorists, and tournament organizers over the course of the nineteenth and twentieth centuries into systematic totalities. This transformation profoundly influenced subsequent concepts of formal and symbolic systems developed in twentieth-century mathematics, AI research, cognitive science, and economics.

Potential for artificial intelligence in medicine and its application to male infertility.

The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production. This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility. In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI. AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.

AI research assistants, intrinsic values, and the science we want

AI research assistants, intrinsic values, and the science we want

Mathematics & Artificial Intelligence: Intersections and Educational Implications

Educational jurisdictions worldwide are integrating AI education in their curricula, across grades K-12, and across subject areas, with a focus on AI applications, societal implications, and AI ethics. Jurisdictions also focusing on how AI works and how AI is developed are realizing that AI relies heavily on mathematical algorithms. The jurisdictions that are advancing K-12 AI mathematics curricula to prepare students to understand and apply the mathematics concepts used by AI systems are focused on grades 11-12 courses. This paper investigates how AI mathematics curricula may be designed for younger grades. First, we take a close look at the nature of a neural network and identify the mathematics typically used. Second, we review K-12 AI curricula in Canada and internationally and note that they lack a focus on AI mathematics. Third, we offer examples of how we may engage students across grades with mathematics used in the neural networks. Last, we look at future directions of AI mathematics education and research. Neural networks are not the only approach to AI, and there is more to AI than neural networks. However, neural networks have led to impressive progress in the field of AI, such as the development of large language models like ChatGPT. For our paper, focusing on neural networks gives us a sufficient starting point for addressing the questions we raise. This paper contributes to conversations about the intersection of AI education and mathematics education, and the development and research of AI mathematics curricula and teaching and learning resources across K-12.

Large Models Reshape AI Research and Applications

Large Models Reshape AI Research and Applications

Clinical Expertise Within AI and ML Healthcare Research Boards

This report explores the rapid advancements in AI and ML applications in healthcare over recent years, focusing on journals like NEJM AI as key examples. It critically examines the composition of their editorial boards, highlighting a gap in clinical experience that raises questions about the human-centric nature and sustainability of current AI and ML healthcare research standards.

Alphabet: Innovation Analysis

The pervasive growth of society has led to an increasing reliance on the Internet in people's daily lives. Among the integral components of the Internet, search engines play a pivotal role in enhancing usage efficiency. In this field, Alphabet is the industry leader among them. Therefore, this study focuses on analyzing Alphabet's business innovation pattern using a SWOT analysis. This research makes the case that Alphabet now benefits from a high product penetration rate for Google. Google's product advertising, on the other hand, is highly beneficial and helps to form a business model that combines products and advertisements. However, Alphabet faces a challenge wherein its customer base often confuses Google as a company rather than a product. The results of SWOT analyses showed that Alphabet has fresh prospects in the AI space. Alphabet's prospects are demonstrated by the introduction of self-driving technology under the Waymo brand, which combines a deep learning framework with additional AI technologies. Despite these opportunities, Alphabet is exposed to three major risks. Among these, the most substantial concern is the exorbitant cost associated with conducting subpar AI research. Furthermore, while Google entered the cloud computing arena later and with a smaller stake, heightened scrutiny from competitors intensified the rivalry in this field. Crucially, Google's handling of personal private data has come under scrutiny, potentially tarnishing the company's reputation and triggering a series of adverse events. The study presented in this paper can offer some suggestions for business strategies for rapidly emerging domestic information enterprises.

On-the-fly image-level oversampling for imbalanced datasets of manufacturing defects

Visual defect recognition and its manufacturing applications have been an upcoming topic in recent AI research. Defect datasets are often severely imbalanced and can be additionally burdened with separating classes of high visual similarity. Although various methods of data augmentation have been proposed to mitigate the class imbalance, they often fail to cope with tinier minority classes or have fidelity issues with smaller defects while, at the same time, needing significant computational resources to train. Also, augmentation based on vector-based oversampling struggles to produce high-fidelity inputs and is hard to apply on custom CNN architectures, which often perform better for this type of problem. Our work presents an image-level oversampling method based on an instance-based image generator that can be applied to any CNN directly during the training process without increasing the order of training time required. It is based on identifying a small number of the most uncertain base samples close to the estimated class boundaries and using them as seeds for augmentation. The resulting images are of high visual quality preserving small class differences, and they also improve the classifier boundary leading to higher recall scores than other state-of-the-art approaches.

AI and ML in Healthcare Settings: Is it Really a Breakthrough?

Only 200 out of 300,000 AI/ML-related original publications on PubMed focus on human-centered settings. Why is human wellbeing often overlooked in medical AI research?

AI and ML in Healthcare Settings: Is it Really a Breakthrough?

Only 200 out of 300,000 AI/ML-related original publications on PubMed focus on human-centered settings. Why is human wellbeing often overlooked in medical AI research?

Artificial Intelligence and Our Secret Mind: Human Mediation in Grey Zones

CIRET has set up an AI research group to highlight the need for human ethical mediation in theage of digital technology and binary logic. Francisco Varela has observed that cognition can take place inthe computer field without appealing to consciousness. Yet human decision-making cannot be the resultof cognition alone and requires the interaction between cognition and consciousness. In fact, rationalistand reductionist models borrowed from the hard sciences have only shown a mechanistic vision of AI ora biological-environmental vision, which cannot be applied to complex human phenomena occurring in agrey zone. In this grey or fuzzy zone of mediation, conciliation, and repair, we need the dialectical processor dialogue between consciousness and cognition. In this context, the proposal of mediating leaders andmanagers appears as a possible ethical alternative to demonstrate that consciousness is beyond the logicof the computer. Humans must remain responsible for all the effective decisions that will help us solveproblems theoretically and concretely. We therefore need an emerging global wisdom that flows from ourconversations about AI and appeals to human consciousness at all its levels of reality. This group produceda Symposium on November 21 and 22, 2023, in which we imagine that AI may be at the service of humanevolution and resiliency in “learning to be societies” instead of contributing to block our evolutions: if AIremains a work tool. . . Certainly it will because the creation sparkles still a mystery for men themselves.

Principles and their Computational Consequences for Argumentation Frameworks with Collective Attacks

Argumentation frameworks (AFs) are a key formalism in AI research. Their semantics have been investigated in terms of principles, which define characteristic properties in order to deliver guidance for analyzing established and developing new semantics. Because of the simple structure of AFs, many desired properties hold almost trivially, at the same time hiding interesting concepts behind syntactic notions. We extend the principle-based approach to argumentation frameworks with collective attacks (SETAFs) and provide a comprehensive overview of common principles for their semantics. Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs. We introduce the notion of the reduct as well as the modularization principle for SETAFs which will prove beneficial for this kind of investigation. We then demonstrate how our findings can be utilized for incremental computation of extensions and show how we can use graph properties of the frameworks to speed up these algorithms.

Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging

PurposeArtificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of entry for non-technically trained physicians. A Clinical Decision Support System (CDSS) for screening purposes using AutoML could be beneficial to ease the clinical burden in the radiological workflow for paranasal sinus diseases.MethodsThe main target of this work was the usage of automated evaluation of model performance and the feasibility of the Vertex AI image classification model on the Google Cloud AutoML platform to be trained to automatically classify the presence or absence of sinonasal disease. The dataset is a consensus labelled Open Access Series of Imaging Studies (OASIS-3) MRI head dataset by three specialised head and neck consultant radiologists. A total of 1313 unique non-TSE T2w MRI head sessions were used from the OASIS-3 repository.ResultsThe best-performing image classification model achieved a precision of 0.928. Demonstrating the feasibility and high performance of the Vertex AI image classification model to automatically detect the presence or absence of sinonasal disease on MRI.ConclusionAutoML allows for potential deployment to optimise diagnostic radiology workflows and lay the foundation for further AI research in radiology and otolaryngology. The usage of AutoML could serve as a formal requirement for a feasibility study.

Structural causal models reveal confounder bias in linear program modelling

AbstractThe recent years have been marked by extended research on adversarial attacks, especially on deep neural networks. With this work we intend on posing and investigating the question of whether the phenomenon might be more general in nature, that is, adversarial-style attacks outside classical classification tasks. Specifically, we investigate optimization problems as they constitute a fundamental part of modern AI research. To this end, we consider the base class of optimizers namely Linear Programs (LPs). On our initial attempt of a naïve mapping between the formalism of adversarial examples and LPs, we quickly identify the key ingredients missing for making sense of a reasonable notion of adversarial examples for LPs. Intriguingly, the formalism of Pearl’s notion to causality allows for the right description of adversarial like examples for LPs. Characteristically, we show the direct influence of the Structural Causal Model (SCM) onto the subsequent LP optimization, which ultimately exposes a notion of confounding in LPs (inherited by said SCM) that allows for adversarial-style attacks. We provide both the general proof formally alongside existential proofs of such intriguing LP-parameterizations based on SCM for three combinatorial problems, namely Linear Assignment, Shortest Path and a real world problem of energy systems.

Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications

Transparency in AI models is essential for promoting human–AI collaboration and ensuring regulatory compliance. However, interpreting these models is a complex process influenced by various methods and datasets. This study presents a comprehensive overview of foundational interpretation techniques, meticulously referencing the original authors and emphasizing their pivotal contributions. Recognizing the seminal work of these pioneers is imperative for contextualizing the evolutionary trajectory of interpretation in the field of AI. Furthermore, this research offers a retrospective analysis of interpretation techniques, critically evaluating their inherent strengths and limitations. We categorize these techniques into model-based, representation-based, post hoc, and hybrid methods, delving into their diverse applications. Furthermore, we analyze publication trends over time to see how the adoption of advanced computational methods within various categories of interpretation techniques has shaped the development of AI interpretability over time. This analysis highlights a notable preference shift towards data-driven approaches in the field. Moreover, we consider crucial factors such as the suitability of these techniques for generating local or global insights and their compatibility with different data types, including images, text, and tabular data. This structured categorization serves as a guide for practitioners navigating the landscape of interpretation techniques in AI. In summary, this review not only synthesizes various interpretation techniques but also acknowledges the contributions of their original authors. By emphasizing the origins of these techniques, we aim to enhance AI model explainability and underscore the importance of recognizing biases, uncertainties, and limitations inherent in the methods and datasets. This approach promotes the ethical and practical use of interpretation insights, empowering AI practitioners, researchers, and professionals to make informed decisions when selecting techniques for responsible AI implementation in real-world scenarios.

Combining intra-risk and contagion risk for enterprise bankruptcy prediction using graph neural networks

Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is crucial for making decisions about loans. Existing studies in both finance and AI research fields, however, tend to only consider either the intra-risk or contagion risk of enterprises, ignoring their interactions and combinatorial effects. This study for the first time considers both types of risk and their joint effects in bankruptcy prediction. Specifically, we first propose an enterprise intra-risk encoder based on statistically significant enterprise risk indicators for its intra-risk learning. Then, we propose an enterprise contagion risk encoder based on an enterprise knowledge graph for its contagion risk embedding. In particular, the contagion risk encoder includes both the newly proposed Heterogeneous Hyper-Graph Neural Networks (HHGNN) and Hierarchical Graph Transformer Networks (HGTN). Using these two types of encoders, we design a unified framework to simultaneously capture intra-risk and contagion risk for bankruptcy prediction. To evaluate the model, we collect real-world multi-sources data on SMEs and build a novel benchmark dataset called SMEsD. We provide open access to the dataset, which is expected to further promote research on financial risk analysis. Experiments on SMEsD against twelve state-of-the-art baselines demonstrate the effectiveness of the proposed model for bankruptcy prediction.

Insights Into Privacy Protection Research in AI

Insights Into Privacy Protection Research in AI

Poetry, Philosophy, and Smart AI

Abstract: Here I look at sundry aspects of the current controversy about Generative AI and, in particular, the implications of this new and rapidly evolving technology for poetry, the arts, and human creativity in general. My essay looks at earlier episodes in the history of thought, from Descartes on, that I take to have prefigured this latest debate around 'the human' in relation to its various physical, 'artificial,' or (presumptively) prosthetic means of extension and refinement. I also discuss its bearing on literary-critical issues concerning the formal or structural aspects of poetry, the role (if any) of authorial intent, and the extent to which criticism enters into– and might lay claim to a stake in–what counts as human creativity. A central point of reference is William Empson's 1930 book, Seven Types of Ambiguity, where these issues are raised in a strikingly prescient way through his practice of close-focused textual analysis allied to great philosophical acuity. I conclude that a priori attitudinizing, pro or con, is not much use in this context, but that poets, critics, philosophers, cognitive scientists, and AI researchers need to cooperate in the reexamination of many long-standing but now highly contestable distinctions.

Abrama’s End Game

What does it mean to be alive? Can a computer program be sentient? What would it need to do to prove it? In this work of philosophical short story fiction, Abrama is summoned to the Grand Temple by Sir Gödel. Gödel informs Abrama that he is living in a simulated world (a computer game) created by her people as a place to play in their free time. She also informs Abrama that the game is not as popular as it once was and is scheduled to be permanently turned off. It turns out Gödel is an AI researcher that was given permission to test out her AI by implanting characters like Abrama into the game. Over 100’s of versions, the AI continued to improve, and now the researcher feels an ethical obligation to tell her creations their world is coming to an end. Abrama, using this new information, organizes the AI characters in the game and starts trading virtual goods for real-life services from computer hackers that play the game. The computer hackers create computer code and sell it to Abrama. If triggered, or if the game is turned off, the code would expose top secret information to the general public. A bargain is struck, the game will continue on a closed world for the AI characters and, in exchange, the sensitive information will never be made public.