Artificial Intelligence Competition Research Articles

Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges.

The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.

Research On the Development of Intelligent Face Recognition System for Business Competition in Chatgpt Industry

Artificial intelligence has become a key area of today's international competition. Analysis of the development trend of artificial intelligence and the strategies of major powers is crucial to winning this competition. This paper refers to (GCN) and Laplace's (LBP) feature equation in ChatGPT. Firstly, an image processing method based on LBP is proposed. GCN analyzes the temporal and spatial correlation of character attributes in the time domain. This method can capture both short-term and long-term temporal and situational correlations simultaneously, effectively improving the extraction of temporal and situational correlations. Then, text analysis determines the implementation intentions, measures and prospects of the EU's "human-centered" artificial intelligence competition strategy. It also evaluates its strategic effects from the relevant reactions of its main competitor, the United States, and infers the weaknesses and risks existing in the strategy.

Impact of Artificial Intelligence in Arms Race, Diplomacy, and Economy: A Case Study of Great Power Competition between the US and China

In the contemporary era, the demand for Artificial Intelligence (AI) is paramount, with existing applications in weak AI seen in products like smartphones, employing features such as Siri, voice recognition, and image recognition. The proliferation of AI extends beyond consumer goods to impact the arms race, diplomacy, and the economy. The emergence of advanced AI models like ChatGPT has intensified the global AI competition among states and tech companies. While nations integrate AI into military technologies, concerns arise about the implications of Artificial General Intelligence (AGI). Scholars debate its potential role in global dominance, job displacement, and economic impact. Despite predictions of job creation, the shift towards AGI requires careful consideration to ensure positive outcomes rather than detrimental consequences for humanity's future in the realm of Artificial Intelligence.

Utilizing integrated artificial intelligence for characterizing mineralogy and facies in a pre-salt carbonate reservoir, Santos Basin, Brazil, using cores, wireline logs, and multi-mineral petrophysical evaluation

In complex carbonate reservoirs, it is crucial to understand the connections between reservoir compositions (minerals, facies, and properties). Conventionally, core samples have been used to measure reservoir parameters and identify minerals and facies. However, core samples are limited to certain wells. Therefore, additional techniques are necessary to overcome this limitation comprehensively. This study aims to identify key mineralogical and facies components of the Barra Velha Formation (BVF) and their relation to reservoir parameters. Dolomite, calcite, quartz, and clay minerals were commonly found using X-ray Diffraction (XRD). By employing multi-mineral (MM) petrophysical evaluations, we accurately recreated mineral quantities from XRD and petrophysical properties from core analysis to ensure reliability. Replications of inputs well logs and the mineralogical volume from spectroscopic (ECS) were used as reliability techniques for validating the MM. A total of 47 wells were analyzed using those methods. In this study, the classification of facies was accomplished through the selection of three prominent supervised artificial intelligence techniques, among which SOM, a widely employed method for facies estimation, was included. Additionally, the ensemble methods of Random Forest and XGBoost were adopted due to their recognized efficacy in handling tabular data and their track record of success in machine learning and artificial intelligence competitions. Remarkably, the performance evaluation revealed that Random Forest and XGBoost algorithms outperformed SOM, yielding the most favorable outcomes in this context. An integrated analysis of mineralogical and facies results was conducted, incorporating production data and special profiles such as nuclear magnetic resonance (NMR) and Wellbore Image (WBI) to identify vug-containing areas. The dolomitic facies exhibited favorable reservoir qualities, influenced by diagenetic processes represented by vuggy porosity, which enhanced permeability. Shrubstones, spherulites, and reworked facies showed superior petrophysical qualities and were connected with productive regions, leading to elevated dolomite concentrations, and vuggy abundance. The study highlights two major innovations: the use of mineralogical volume from multi-mineral assessments as inputs for AI-based property estimation to improve facies estimates, and the discovery of relationships between facies, minerals, and reservoir properties, compared to production data. This understanding allows for more accurate static model creation, optimal production interval selection, improved hydrocarbon recovery, and better specification of stimulation processes.

AI enlightens wireless communication: Analyses and solutions for DMRS channel estimation

In this paper, a systematic description of the artificial intelligence (AI)-based channel estimation track of the 2nd Wireless Communication AI Competition (WAIC) is provided, which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the system model of demodulation reference signal (DMRS) based channel estimation problem and its corresponding dataset are introduced. Then the potential approaches for enhancing the performance of AI based channel estimation are discussed from the viewpoints of data analysis, pre-processing, key components and backbone network structures. At last, the final competition results composed of different solutions are concluded. It is expected that the AI-based channel estimation track of the 2nd WAIC could provide insightful guidance for both the academia and industry.

Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set.

The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a nondisclosure agreement signed by the representative of each team. The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable.

Exploring reinforcement learning approaches for drafting in collectible card games

Collectible card games (CCGs), such as Magic: the Gathering and Hearthstone, are played by tens of millions worldwide and are known for their usually large and intricate rules. From an artificial intelligence (AI) standpoint, playing CCGs consists of two interdependent tasks: deck-building and battling. This paper presents three deep reinforcement learning approaches for deck-building in the arena mode of CCGs. Our approaches are trained in self-play and differ in the handling of past information when drafting new cards for a deck. We formulate the problem in a game-agnostic manner and perform experiments on Legends of Code and Magic, a CCG designed for AI research. Considering the win rate of the decks when used by fixed battling agents, the results show that our trained drafting agents outperform the best ones available for the game and do so by building very different decks. We also reenact the Strategy Card Game AI competition and show that our best drafting strategy improves the win rate of a baseline competitor by 14.9 and 12.7 percentage points in the 2019 and 2020 editions.

Fighting-Game Gameplay Generation Using Highlight Cues

In this article, we propose a fighting-game artificial intelligence (AI) that selects its actions from the perspective of highlight generation using Monte Carlo tree search with three highlight cues in the evaluation function. The intended use of this proposed AI is to generate gameplay in live streaming platforms such as Twitch and YouTube where a large number of spectators watch gameplay to entertain themselves. A gameplay analysis and user study are conducted using FightingICE, a fighting-game platform in an international fighting-game AI competition. Results show that gameplay generated by two AI players of the proposed method has more promising characteristics than not only gameplay generated according to an existing method but also gameplay by the top two AI players in the 2019 competition and that it is more entertaining than the latter gameplay.

China's Ontological Security Perception in the Context of Artificial Intelligence Competition

Tarihsel olarak yaşadığı elim olaylar, Batı’nın tahayyülü ve geç modernleşme sürecinin de etkileri birlikte değerlendirildiğinde Çin’in kimlik algısında önemli izler bırakmıştır. Bu makale, ontolojik güvenlik arayışında olan Çin’in devlet rolüne odaklanmaktadır. Araştırmada ilk olarak, Çin’in, yapay zekâ teknolojisinde lider olma hedefine ulaşmak için gerçekleştirdiği eylemlerle, teknolojik üstünlük faktörüne dayalı olarak ABD’ye (Batı’ya) karşı ‘meydan okuyucu’ ve ‘büyük bir ticaret ulusu’ kimliğini güçlendirdiği ve başta ABD olmak üzere diğer devletlerle ilişki kurma rutinlerini geliştirerek ontolojik güvenlik arayışı davranışı sergilediği savunulmaktadır. Araştırmada ayrıca, Komünist Devrimle birlikte içeride başlatılan kalkınma hamlesi ve dışarıda yaşanan değişimleri takiben, Çin’in teknolojik gelişmeye dayanan büyük bir ticaret ulusu olarak nasıl ontolojik güvenlik arama çabasına giriştiği ortaya koyulmaktadır. Ontolojik güvenlik arayışı sürecini göstermek için yalnızca Çin’in yapay zekâ stratejisine odaklanılırken, Çin’in başka alanlardaki ontolojik güvenlik arayışlarını farklı şekillerde yönlendirdiği savlanmaktadır. Bu makalenin konusunu oluşturan küresel yapay zekâ rekabetinde Çin’in yerine ilişkin olarak, bu sürecin Çin’in başta ABD olmak üzere uluslararası ortamdaki diğer devletlere göre sahip olduğu konumu algılamasını güvence altına almak için hareket ettiği görülebilir.

AI World Cup: Robot-Soccer-Based Competitions

Games have been used as excellent testbeds for research on artificial intelligence (AI) and computational intelligence for their diversity and complexity. In this article, we present AI World Cup, a set of AI competitions based on the game of soccer. We provide an introduction to the three challenges that concern a robot soccer match using both value-based and image-based state representations. AI Soccer runs the robot soccer match by participants managing each team of five two-wheeled robots. AI Commentator and AI Reporter observe the AI Soccer match and output real-time commentary and a summary article, respectively. Also, we introduce the AI World Cup platform along with rationale behind notable design choices. The official international AI World Cups held in 2018 and 2019 and the AI Masters competition held in 2019 as a part of the World Cyber Games are briefly discussed. Technical aspects of the strategies developed by participants are also discussed.

Evolutions and trends of artificial intelligence (AI): research, output, influence and competition

PurposeThis paper throws light on some of the nature of artificial intelligence (AI) development, which will serve as a starter for helping to advance its development.Design/methodology/approachThis work reveals the evolutions and trends of AI from four dimensions: research, output, influence and competition through leveraging academic knowledge graph with 130,750 AI scholars and 43,746 scholarly articles.FindingsThe authors unearth that the “research convergence” phenomenon becomes more evident in current AI research for scholars' highly similar research interests in different regions. The authors notice that Pareto's principle applies to AI scholars' outputs, and the outputs have been increasing at an explosive rate in the past two decades. The authors discover that top works dominate the AI academia, for they attracted considerable attention. Finally, the authors delve into AI competition, which accelerates technology development, talent flow, and collaboration.Originality/valueThe work aims to throw light on the nature of AI development, which will serve as a starter for helping to advance its development. The work will help us to have a more comprehensive and profound understanding of the evolutions and trends, which bridge the gap between literature research and AI development as well as enlighten the way the authors promote AI development and its strategy formulation.

A Benchmark for StarCraft Intelligent Agents

The problem of comparing the performance of different Real-Time Strategy (RTS) Intelligent Agents (IA) is non-trivial. And often different research groups employ different testing methodologies designed to test specific aspects of the agents. However, the lack of a standard process to evaluate and compare different methods in the same context makes progress assessment difficult. In order to address this problem, this paper presents a set of benchmark scenarios and metrics aimed at evaluating the performance of different techniques or agents for the RTS game StarCraft. We used these scenarios to compare the performance of a collection of bots participating in recent StarCraft AI (Artificial Intelligence) competitions to illustrate the usefulness of our proposed benchmarks.

AI enabled wireless communications with real channel measurements: Channel feedback

Artificial intelligence (AI) has shown great potential in wireless communications. AI-empowered communication algorithms have beaten many traditional algorithms through simulations. However, the existing works just use the simulated datasets to train and test the algorithms, which can not represent the power of AI in practical communication systems. Therefore, Peng Cheng Laboratory holds an AI competition, National Artificial Intelligence Competition (NAIC): AI+wireless communications, in which one of the topics is AI-empowered channel feedback system design using practical measurements. In this paper, we give a baseline neural network design, QuanCsiNet, for this competition, and the details of the channel measurements. QuanCsiNet shows excellent performance on channel feedback and the complexity of the neural networks is also given.

Controlled Memory and Threshold Switching Behaviors in a Heterogeneous Memristor for Neuromorphic Computing

AbstractThe fully memristive neural network is emerging as a game‐changer in the artificial intelligence competition. Artificial synapses and neurons, as two fundamental elements for hardware neural networks, have been substantially implemented by different devices with memory and threshold switching (TS) behaviors, respectively. However, obtaining controllable memory and TS behaviors in the same memristive material system is still a considerable challenge that holds great potential for realizing compatible artificial neurons and synapses. Here, a heterogeneous bilayer conductive filamentary memristor comprising two different electrolytes with distinct copper ion mobility is reported: Cu/GeTe/Al2O3/Pt, which can demonstrate the governance of switching types. Experimentally, when the thickness of the Al2O3 layer is 3 nm, stable nonvolatile multilevel memory switching (MS) is observed and employed to mimic the synaptic plasticity. With increasing oxide thickness, the switching behavior under the same compliance current alters from MS to volatile TS and is used to emulate the integrate‐and‐fire neuron function. The controllable switching stems from the change in the metal filament morphology within the Al2O3 layer, which is supported by ab initio calculation results. This method enables a new pathway for constructing functionally reconfigurable neuromorphic devices for intelligence neuromorphic systems.

Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition

In 2018, a landmark challenge in artificial intelligence (AI) took place, namely, the Explainable Machine Learning Challenge. The goal of the competition was to create a complicated black box model for the dataset and explain how it worked. One team did not follow the rules. Instead of sending in a black box, they created a model that was fully interpretable. This leads to the question of whether the real world of machine learning is similar to the Explainable Machine Learning Challenge, where black box models are used even when they are not needed. We discuss this team’s thought processes during the competition and their implications, which reach far beyond the competition itself.Keywords: interpretability, explainability, machine learning, finance

StarCraft AI Competitions, Bots, and Tournament Manager Software

Real-time strategy games have become an increasingly popular test bed for modern artificial intelligence (AI) techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament, the Computational Intelligence in Games competition, and the Artificial Intelligence and Interactive Digital Entertainment competition. In this paper, we will give an overview of the current state of these competitions, describe the bots that compete in them, and describe the underlying open-source Tournament Manager software that runs them.

The frame problem: The AI “arms race” isn’t one

ABSTRACTThere needs to be a change in thinking about AI. Those dealing with AI must insist on greater clarity about its definition. If policy makers and other leaders are not clear about what the term means and entails, they cannot possibly formulate best practices and governance mechanisms. It would help matters if artificial intelligence discussions were framed in an “AI +” framework, because in many cases, AI is merely a tool included in a system involving other functions or capabilities. The news media should stop framing the global artificial intelligence competition as an “arms race.” This misrepresents the competition going on among countries. The policy community needs a clear-eyed appraisal of AI’s capabilities and limitations. Without that orientation, those who hope to steer research and development in positive directions will create more problems than they solve.

Toward a Transformed and Unequal World

With the increasing use of machine learning algorithms in strategic decision-making and military affairs, the artificial intelligence (AI) revolution is bringing about significant changes to the current international system. AI applications will further tilt the global balance of power in favor of actors who can make the best use of the emerging technology. AI-assisted automation is also changing prevailing socioeconomic production models on the global scale; and in the not too distant future, it is expected to exert systemic impacts on the current global order. Having recognized the full potential of AI technology in propelling the next industrial revolution, China has adopted an AI development strategy for guiding a nation-wide campaign to harness AI power at an early stage, so as to seize the strategic initiative in an emerging global AI competition. China’s initial efforts have produced remarkable achievements with theoretical and practical implications.

AN ASSESSMENT ON ARTIFICIAL INTELLIGENCE PRACTISES IN THE EUROPEAN UNION

Artificial Intelligence (AI) has a game-changing role in countries’ economic growth and will transform both societies and the way in which people live and work in near future. AI will let machines to present human-like cognition, it will seize jobs, drive the vehicles, decide on behalf of people and many more. However, impact of AI technology in the future cannot be exactly predicted. This uncertainty brings both potential and risks together for governments and states. States are supposed to have several roles to play in global AI competition. Defining a national AI strategy, setting an agenda with stakeholders, strengthening research and development infrastructure, allocating budget and funds and designing curriculum for education and trainings of the citizens are some of the policies and projects of the governments. The European Union (EU), with the intention of becoming world-leading region for AI, has designed numerous policy sets and strategies and allocated certain amount of funds for the cultivation of AI projects.