Business Intelligence Advancement Workshop 2019 – Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing Fees Awards Testimonials
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Business Intelligence Advancement Workshop 2019
Feature papers represent cutting-edge research with significant potential for greater impact in the field. A feature paper should be a substantial original article that covers several techniques or methods, provides an outlook for future research directions, and describes possible research applications.
Artificial Intelligence And The Futures Of Learning
Feature papers are submitted at the personal invitation or recommendation of the Scientific Editor and must receive positive feedback from reviewers.
Editors’ Choice articles are based on recommendations from scientific editors of journals around the world. The editors select a small number of recently published articles in the journal that they believe will be particularly interesting to readers or important in the respective research field. The aim is to provide a snapshot of some of the most exciting works published in the various research areas of the journal.
By Abdulaziz Aldoseri Saylit Preprints.org Google Scholar , Khalifa N. Al-Khalifa Khalifa n. Al-Khalifa Skillit Preprints.
Received: 3 May 2023 / Revised: 30 May 2023 / Accepted: 7 June 2023 / Published: 13 June 2023
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The use of artificial intelligence (AI) is becoming increasingly prevalent in industries such as healthcare, finance and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively examines and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretation and interpretation, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smart decisions and gain a competitive advantage in the digital age. As this review article presents and discusses various strategies for data challenges for AI over the past decade, it is expected to be very helpful to the scientific research community to generate new and innovative ideas to rethink our approaches to data strategies for AI.
Artificial intelligence (AI) refers to the ability of machines to mimic human intelligence and perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and natural language understanding [1]. Figure 1 depicts AI technologies including machine learning, natural language processing, robotics, and computer vision. Machine learning is a subset of AI that involves training computer algorithms to learn patterns in data and make predictions or decisions based on the data [2]. Deep learning is a type of machine learning that uses neural networks with multiple layers to process complex data such as images or speech [3]. Natural language processing is the ability of computers to understand, interpret, and produce human language, including speech and text. Computer vision is the ability of computers to analyze and interpret visual information such as images and videos [5].
AI is a rapidly expanding field that has the potential to revolutionize the way we live and work. From healthcare to finance and transportation, AI has the potential to transform a wide range of industries, creating new opportunities for businesses and organizations. AI is transforming various fields, including healthcare, finance, and transportation, with significant advances in machine learning and deep learning techniques [6, 7]. The heart of this transformation is data, which is essential for training and testing AI models. AI models rely on large datasets to identify patterns and trends that are difficult to detect using traditional data-analysis methods. This allows them to learn and make predictions based on the data they are trained on.
However, using AI data is challenging. Data quality, quantity, diversity and privacy are critical aspects of data-driven AI applications, and each presents its own challenges. Poor data quality can lead to inaccurate or biased AI models, which can have serious consequences in fields such as healthcare and finance. Insufficient data can lead to models that are too simplistic and unable to accurately predict real-world outcomes. Lack of data heterogeneity can lead to biased samples that do not accurately represent the population they are designed to serve. Lastly, data privacy is a major concern, as AI models require access to sensitive data, which raises concerns about data privacy and security.
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In this article, we address the challenges of using data for AI and offer recommendations for companies looking to address them. To meet these challenges, businesses and organizations need to develop strategies and frameworks that promote data quality, quantity, diversity and privacy. This may include implementing data cleaning and validation processes to ensure data quality, collecting and managing heterogeneous data, and implementing data privacy policies and procedures to protect sensitive data. By focusing on these challenges, businesses and organizations can harness the power of data to create accurate, efficient and fair AI applications that benefit society.
Data are critical to AI because they are the foundation upon which machine learning algorithms learn, make predictions, and improve their performance over time. A large amount of data is required to train an AI model, recognize patterns, make predictions and enable the model to improve its performance over time.
AI algorithms require data to learn patterns and make predictions or decisions based on the data. AI machine learning techniques are algorithms that allow machines to learn patterns and make predictions from data without explicit programming [8]. These techniques are widely used in various applications such as natural language processing, image and speech recognition, and recommendation systems. Generally, the more data an AI algorithm has available to learn from, the more accurate its predictions or decisions will be. There are several data-learning methods for building AI systems [8, 9]; For the sake of completeness of the article, we add the following as shown in Figure 2.
Supervised Learning: In supervised learning, an AI system is trained on a labeled dataset, where each data point is associated with a label or target variable. The goal is to develop a model that can accurately predict a label or target variable for new data points. This approach is commonly used for tasks such as image classification, speech recognition and natural language processing [10].
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Unsupervised Learning: In unsupervised learning, an AI system is trained on an unlabeled dataset where there is no target variable to predict. The goal is to identify patterns, relationships, and structures in the data. This method is commonly used for tasks such as clustering, anomaly detection and dimensionality reduction [11].
Reinforcement Learning: In reinforcement learning, an AI system learns to make decisions based on feedback from the environment. The system receives rewards or penalties based on its actions and adjusts its behavior accordingly. This approach is commonly used for tasks such as gaming, robotics, and autonomous driving [12].
Transfer Learning: In transfer learning, an AI system leverages knowledge gained from one task to improve performance in another related task. The system is pre-trained on a large dataset and then fine-tuned on a smaller dataset for the specific task at hand. This approach helps reduce the amount of data required to train an AI model and improve its accuracy and performance [13].
Deep Learning: Deep learning is a type of neural-network-based machine learning that is particularly effective for tasks involving large amounts of data and complex relationships. Deep learning models consist of multiple layers of interconnected nodes that can learn more complex representations of data. This approach is commonly used for tasks such as image and speech recognition, natural language processing, and computer vision [14].
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Ensemble Learning: Ensemble learning is a technique in which multiple models are trained and combined to make predictions or decisions. Combining the predictions of multiple models can improve the accuracy and reliability of the final output [15].
Overall, the choice of data learning method depends on the specific task, data and available resources. It is important to carefully evaluate the benefits and limitations of each approach and choose the one that best fits the needs of the AI application being developed.
Data-centric and data-driven are two related but different concepts in the world of data analysis and decision making. By leveraging data, organizations can gain a deeper understanding of their operations, customers and markets and make more informed decisions based on data-driven insights. Data-centric approaches are commonly used in industries such as finance, healthcare and retail, where accurate and timely data is critical to decision making. For example, in the healthcare industry,
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