AI Strategy Research Articles

South Korea’s Strategic Culture and China’s National AI STRATEGY: A Neoclassical Realist View

Purpose: South Korea s foreign policy executives need to make security policy choices about China s AI as a security threat with normative nature. The purpose of this article is to identify the ideas of the South Korean security policy executives and to analyze the role of the policy elites’ perception in foreign policymaking about China s artificial intelligence as a newly emerging security challenge. Together with this, it is approached to evaluate the strategic perception of Moon Jae-in s foreign policy executive in connection with China’s artificial intelligence. Method: The article examines the document and speech of the foreign policymakers of Korea and China as well as the media coverage of the two countries by utilizing Kitchen’s neoclassical realism with a focus on the elites’ perception as an intervening variable, international systemic pressure as an independent variable, and foreign policy outcome as a dependent variable. Chapter 2 reviews the strategic culture of a middle power. Chapter 3 finds China’s AI strategy as a systemic threat. Chapter 4 explores the former Korean governments’ strategic ideas for China. Chapter 5 finds Moon Jae-in government’s idea of the China AI strategy. Results: The article reveals the nature of China’s AI, the role of Korea’s strategic culture, and the capacity of policy elites to emerging security. First, China s AI has the nature of systemic pressure. Sec-ond, the foreign policy executives of Korea differently respond to China’s systemic pressure. Third, the Moon Jae-in government’s policy elites have autonomous power against the international systemic pressure and consider the economic interest as important. Conclusion: This article concluded that the foreign policy elites’ perception intervenes between the international systemic pressure and state foreign policy-making. The elites induce the discussion of AI from the view of middle power multilateralism rather than that of AI from the view of ethical and geopolitical context. The article also says that Korea s foreign policy elites can operationalize the ideas in considering the balance of interest with China.

Assessment in Software Development for Competitive Environments: An AI Strategy Development Case Study

Competitions are being widely used to motivate students in diverse learning processes, including those in computer programming. This paper presents a methodology for designing and assessing competitive learning scenarios that allow students to develop three different coding skills: the ability to compete against unknown competitors, the ability to compete against known competitors and the ability to compete against refined versions of known competitors. The proposal is based on peer code review, implemented as an improvement cycle after the dissemination of the code among participants. A case study evaluating the methodology was conducted with two cohorts of students in an undergraduate course. The analysis of the obtained grades suggests that while performance after our assistance was improved, students could still fail or succeed independently of the assistance. Complementary data from student questionnaires and supervisor observations are aligned with this finding. As a conclusion, the evidence supports the validity of the methodology. Additionally, several guidelines based on the experience are provided to transfer the proposal to other environments.

BCS impact report Summer 2021

Abstract The expertise and analysis of BCS members and staff has received media coverage on issues as varied as vaccination passports, the government's AI strategy and our ongoing campaign to make the sector more diverse. Claire Penketh reports.

Data and rights in the digital welfare state: the case of Denmark

ABSTRACT This paper examines how the logic of surveillance capitalism manifests itself within the public sector with a specific interest in how the government’s use of data about its citizens may reconfigure rights and power. In Denmark, for example, the public administration relies heavily on the processing of vast quantities of data about the individual and increasingly uses predictive analytics to identify specific areas of intervention, such as fraud or vulnerability, as part of its decision-making processes. Methodologically, the paper uses Denmark as an example of the digital welfare state, including two public sector cases of automated decision support, namely Gladsaxe municipality and the central processing of welfare benefits (Udbetaling Danmark). It further investigates Danish digitalisation strategies, particularly the governments AI strategy from 2019. The case is examined with a view to understand how technology (and automated decision support in particular) is deployed by state actors, which interests it serves, and how it may benefit or disadvantage the individual. Theoretically, the paper leans towards theories of surveillance capitalism, governance in the digital era, and data politics and rights. The paper argues that unless a more critical and human-centric approach to ‘smart governance’ is taken, the digital welfare state will advance a digital technocracy that treats its citizens as data points suited for calculation and prediction rather than as individuals with agency and rights.

Study on Evaluation of Machine Learning Approaches in Brain Tumour MR Images

The principal intention of this work is to compare the performance of the supervised brain tumour segmentation methods. These segmentation methods are based on machine learning. First, the input MR brain image is denoised by employing the adaptive bilateral filter, and the image contrast is enhanced employing the histogram equalization. Then we retrieve the features from the pre-processed image. Among several feature extraction methods, this work uses the shape, intensity, and texture feature extractors. Subsequent to removing these three types of features, fragment the tumor dependent on these recovered segments. The supervised segmentation approach is used for this. Among several supervised segmentation methods, this work uses three machine learning methods, namely Probabilistic Neural Network (PNN), Artificial Neural Network (ANN), and Convolution Neural Network (CNN). Finally, the retrieved features are feed into these machine learning methods to segment the brain tumour regions. To find out the best machine learning approach, the performance of these three supervised machines learning methods is evaluated by four performance metrics. Based on these evaluations, the best segmentation approach is discovered. Four execution boundaries are utilized, in particular, Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), Jaccard list (JI), and Sensitivity (SEN) to analyze the presentation of the AI strategy. The experimental outputs exposed that the CNN makes greater than other methods.

A Brief Analysis of the 5G+4K/8K+AI Strategic Layout of Central Radio and Television Station —— Taking the 2019 National Day Campaign Publicity Report as an Example

On August 20th, 2019, at the Beijing International Radio, TV & Film Exhibition, the Central Radio and Television General Station announced the 5G+4K/8K+AI strategy, including four main parts: integrated broadcasting, ultra-high-definition television, mobile new media and artificial intelligence. CCTV is now comprehensively abandoning standard production system to achieve comprehensive high-definition, comprehensively promoting the development of 4K/8K, building the first national 5G new media platform, combing artificial intelligence with media, and building a new strategic layout. On the occasion of the 70th anniversary of the Chinese National Day, the Central Radio and Television Station integrated various television channels, broadcast frequency, and website platforms, carried out panoramic and three-dimensional live and broadcast, realized the whole process of 5G+4K ultra-high-definition and 5.1 stereo live broadcast, and provided a new audio-visual experience for end users. This article will take 2019 National Day 70th Anniversary publicity report as an example, excavate the characteristics and advantages of 5G+4K/8K+AI strategic layout, and discuss the development trends and predicament of the initial stage of strategic layout.

A multi-feature hybrid classification data mining technique for human-emotion

Background and objectivesThe ideal treatment of illnesses is the interest of every era. Data innovation in medical care has become extremely quick to analyze diverse diseases from the most recent twenty years. In such a finding, past and current information assume an essential job is utilizing and information mining strategies. We are inadequate in diagnosing the enthusiastic mental unsettling influence precisely in the beginning phases. In this manner, the underlying conclusion of misery expressively positions an extraordinary clinical and Scientific research issue. This work is dedicated to tackling the same issue utilizing the AI strategy. Individuals’ dependence on passionate stages has been successfully characterized into various gatherings in the data innovation climate.MethodsA notable AI multi-include cross breed classifier is utilized to execute half and half order by having the passionate incitement as pessimistic or positive individuals. A troupe learning calculation helps to pick the more appropriate highlights from the accessible classes feeling information on online media to improve order. We split the Dataset into preparing and testing sets for the best proactive model.ResultsThe execution assessment is applied to check the proposed framework through measurements of execution assessment. This exploration is done on the Class Labels MovieLens dataset. The exploratory outcomes show that the used group technique gives ideal order execution by picking the highlights’ greatest separation. The supposed results demonstrated the projected framework’s distinction, which originates from the picking-related highlights chosen by the incorporated learning calculation.ConclusionThe proposed approach is utilized to precisely and successfully analyze the downturn in its beginning phase. It will assist in the recovery and action of discouraged individuals. We presume that the future strategy’s utilization is exceptionally appropriate in all data innovation-based E-medical services for discouraging incitement.

A Machine learning approach to monitor Brain Health and epilepsy detection

Brain health is a major concern worldwide and in India. There are a ton of neurological issues that may influence the mind and one such issue is epilepsy. Epilepsy is one of the preeminent generally happening neurological issues. Electroencephalography (EEG) signals help in the examination and conclusion of epilepsy by recording the exercises inside the cortical districts of the epileptic patient. Conventional techniques for breaking down an EEG signal for epileptic seizure discovery are tedious. A proposed work of robotized seizure recognition systems utilizing an AI strategy is proposed to supplant these conventional techniques. The proposed work is completed with a real-time dataset of epileptic patients taken at a different period. The two basic steps involved during this project are pre-processing and classification. Information pre-preparing is an information mining procedure that includes changing crude information into a reasonable arrangement. Classification is the process of predicting the class of a given dataset. The classifiers used are SVM, Naive Bayes, Random Forest classifier. The random forest is most preferred for its decision trees where the model has low variance and also for its high predictive performance. The proposed system helps in daily monitoring the brain health of epileptic patients using a mobile application. This system mainly focuses on rehabilitated patients. Once when the patients use a wireless wearable headband the signal is monitored and their health condition is detected using a mobile application.

An Interview with Dr. Michael Zeller, Winner of ACM SIGKDD 2020 Service Award

Michael Zeller, Ph.D. is the recipient of the 2020 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Zeller was honored for his years of service and many accomplishments as the secretary and treasurer for ACM SIGKDD, the organizing body of the annual KDD conference. Zeller is also head of AI strategy and solutions at Temasek, a global investment company seeking to make a difference always with tomorrow in mind. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 1999, what he learned from the first-ever virtual conference, his work at Temasek, and what excites him about the future of machine learning, data science and artificial intelligence.

Innovation and Opportunity: Review of the UK’s National AI Strategy

Innovation and Opportunity: Review of the UK’s National AI Strategy

Policy Approaches to Artificial Intelligence Based Technologies in China, European Union and the United States

This paper provides a summary survey of the policy approaches to artificial intelligence-based technologies in China, the European Union, and the United States. China has the most aggressive approach, launching major initiatives since 2015 such as ‘Made in China 2025’, the Internet Plus Plan, the New Generation Artificial Intelligence Development Plan, and the Artificial Intelligence Standardization White Paper. In 2018, the EU finalized both the European AI Strategy and ‘Made in Europe’ or the Coordinated Plan on the Development and Use of Artificial Intelligence. Despite a traditional reticence to adopt national strategies and perhaps pushed by growing concerns about China, the US Government announced the American AI Initiative and a National AI R&D Strategic Plan in 2019. The AI approaches in these three economies reflect their relative strengths—state control in China, citizen voice in Europe, and business practices in America. Unencumbered by privacy concerns, China’s strategy is geared to exploit the abundance of domestic data and to develop AI talent through central schemes and massive injections of money. The European Union’s regulations and spending priorities are guided by the objective of building citizen trust in AI-based technologies by safeguarding privacy and ameliorating disruptions in national labor markets. The mainstay of the US approach is to strengthen the linkages between business and AI-related research, and find ways to fund basic R&D. Despite efforts to indigenize AI innovations, all three economies face challenges. China’s AI strategy continues to rely disproportionately on just three tech giants: Baidu, Tencent and Alibaba, which have investments in more than a 100 AI-involved companies. Europe’s AI resources are unbalanced geographically—a quarter of Europe’s AI talent is in the UK and another quarter in Germany and France—and Brexit poses a serious risk. More than half of the AI talent in the US is foreign born, so immigration policies will inevitably be a central component of a national AI strategy.

AI Strategy 2019: Including Follow-up in 2020

AI Strategy 2019: Including Follow-up in 2020

Transfer Learning for Classification of 2D Brain MRI Images and Tumor Segmentation

The focus of the paper is to classify the images into tumorous and non-tumorous and then locate the tumor. Amongst many medical imaging applications segmentation of Brain Tumors is an important and arduous task as the data acquired is disrupted due to artifacts being produced and acquisition time being very less, so classifying and finding the exact location of tumor is one of the most important jobs. In the paper, deep learning specifically the convolutional neural network is used to demonstrate its potential for image classification task. As the learning from available dataset will be low, so we use transfer learning [4] approach, as it is a developing AI strategy that overwhelms with the best outcomes on several image classification assignments because the pre-trained models have gained good knowledge about the features by training on a large number of images. Since, medical image datasets are hard to collect so transfer learning (Alexnet) [1] is used. Later on, after successful classification the aim is to find the exact location of the tumor and this is achieved using basics of image processing inspired by well-known technique of Mask R-CNN [9].

Prediction of Best Traffic Route using Supervised Classification Machine Learning

As of now, street transport foundation neglecting to adapt up to the exponential increment in vehicular populace. To registering the quickest driving courses and mishaps within the sight of fluctuating traffic conditions is a basic issue in current route frameworks. To forestall this issue is to examine the vehicle office dataset with AI strategy for finding the best street choice without mishap estimating by forecast consequences of best exactness counts. The examination of dataset by administered AI technique(SMLT) to catch a few data resembles, variable distinguishing proof, uni-variate investigation, bi-variate and multi-variate investigation, missing worth medicines and dissect the information approval, information cleaning/planning and information perception will be done on the whole given dataset. Moreover, to think about and examine the presentation of different AI calculations from the given vehicle office dataset with assessment of GUI based UI air quality forecast by given properties.

Strategically managing the artificially intelligent firm

Purpose Artificial Intelligence is a general-purpose technology that is bound to affect every industry. To develop successful corporate and business strategies with AI, leaders have to capture the technological opportunity, understand the economics of AI and lead organizational change. Design/methodology/approach What to do with AI? How to set up for AI? Where is the value? Initial answers to these three fundamental questions are derived based on an interdisciplinary and integrated literature review. Findings AI leaders pursue competitive advantage by taking up the technological offer to predict-prescribe-automate, they enact machine learning by entering into a portfolio of experiments within a modular strategic framework, they organize the business to form of man-machine teams following the concept of human-agent collectives and they avoid falling victim to exponential growth bias. Practical implications In a business environment where the cognification of everything will lead to exponential growth of both, volume of data and intelligence of machines, it will be paramount for strategic leaders to have a robust AI strategy in place and to develop the competencies required for managing the Artificially Intelligent Firm. Originality/value This overview enables strategic leaders to prepare themselves and their organizations for AI. It spearheads making required connections between technology, economics and management of AI.

The backroads of AI: The uneven geographies of artificial intelligence and development

Artificial intelligence is being reconfigured as a set of technologies that can address poverty with precision. The impacts of AI will both exacerbate and ameliorate the conditions of uneven development. Recent debates focus on the disruptive effects of AI, particularly to replication of development trajectories that have had success in reducing poverty. In this paper we further these debates by analysing the backroads of AI. The backroads serve as a metaphor for understanding the ways AI will travel from the sites of incubation to the frontlines of uneven development. We explore dialogues between AI and development in two arenas: the World Bank's Development Impact Evaluation initiative (DIME) and the Government of India's national AI strategy, #AIforAll. We argue that both these arenas serve as hubs from which AI will travel out along the backroads to remote, poor, and fragmented polities. While the World Bank utilises AI as technology to empower experts and mobilise a techno‐political authority, what we refer to as precision AI, India seeks to function as a second‐tier AI hub, making AI cheaper and more accessible domestically and for other developing countries, what we refer to as populist AI. We conclude by discussing the interrelations of precision and populist AI along the backroads, and the potential of backroads research for mapping AI, uneven geographies of development and technology and the impacts of AI's disruptions at different scales.

How to Address the AI Governance Discussion? What Can We Learn From Singapore's AI Strategy?

How to Address the AI Governance Discussion? What Can We Learn From Singapore's AI Strategy?

Gregory C. Allen: Understanding China’s AI Strategy: Clues to Chinese Strategic Thinking on Artificial Intelligence and National Security. Washington, D.C.: Center for a New American Security February 2019

Gregory C. Allen: Understanding China’s AI Strategy: Clues to Chinese Strategic Thinking on Artificial Intelligence and National Security. Washington, D.C.: Center for a New American Security February 2019

Living with AI: A Personal Perspective

Abstract ‘Who is not talking about artificial intelligence?’ asks Ransith Fernando, chair of BCS Sri Lanka. Respectable CIOs are busy adding an AI strategy and road map to make their companies future ready. Most IT students sprinkle a bit of deep learning and some neural networks to make their CVs glow and improve their chances of getting employed.

A Budding Romance: Finance and AI

Enthusiasm for artificial intelligence and multimedia information in the financial industry is at an all time high. Every leader in finance now feels the pressure to answer the question, “What is your AI strategy?” Start-ups are playing a key role in helping the financial sector determine what AI can do and how humans and machines can work together. In this essay, we describe emerging trends and attempts by FinTech start-ups to apply AI and multimedia information processing techniques across a wide range of business needs.