From challenges to implementation and acceptance: Addressing key barriers in artificial intelligence, machine learning, and deep learning

Abstract

Machine learning (ML) and deep learning (DL) have transformed different industries by facilitating sophisticated data analysis, predictive modeling, and autonomous decision-making. Despite the ability to greatly change things, there are many obstacles preventing their widespread use and impact. A major obstacle is the challenge of data quality and quantity; ML and DL models need large amounts of high-quality, labeled data, which can be hard and expensive to acquire. Moreover, the innate intricacy of these models frequently results in a dearth of clarity and visibility, posing difficulties in comprehending and having faith in their decision-making procedures. This has caused worries about ethical ramifications and favoritism, since models may unknowingly continue current biases found in the data used for training. Moreover, the fast rate of technological progress leads to a constantly changing environment, requiring practitioners and organizations to continuously learn and adapt. Security and privacy concerns are significant challenges due to the susceptibility of ML and DL models to attacks and breaches, jeopardizing the security of private data. Additionally, incorporating ML and DL into current systems and processes presents challenges such as requiring unique knowledge and ensuring that technological solutions align with business goals.

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