1. Introduction

Artificial intelligence (AI) has undergone transformative changes over the past few decades, with significant advancements in both the theoretical and practical aspects of algorithm development. Traditional algorithm structures, such as decision trees, support vector machines, and genetic algorithms, have formed the bedrock of AI systems. However, as deep learning techniques have rapidly evolved, they have introduced a new paradigm in algorithm design, enabling AI systems to surpass traditional methods in both performance and scalability [1]. Despite this progress, several gaps remain in understanding the full potential of integrating traditional algorithms with deep learning.

The primary focus of this paper is to explore the evolution of algorithm structures within the realm of AI, with a specific emphasis on the impact of deep learning on these structures. The key questions to be addressed include the strengths and weaknesses of traditional algorithm structures, as well as how deep learning has influenced their performance and applicability. This study uses a comparative analysis methodology, drawing from various case studies and empirical research. By examining these developments, this study aims to contribute to the ongoing discussion on optimizing algorithmic approaches for more efficient and scalable AI solutions.

This research holds significance not only in bridging the gap between traditional algorithms and deep learning but also in offering insights into future AI applications, where the fusion of these technologies will play a critical role in addressing complex problems across industries.

2. Overview of Artificial Intelligence and Deep Learning

2.1. The Development and Key Technologies of Artificial Intelligence

The development of artificial intelligence (AI) has progressed through several key stages since its early conceptualization in the mid-20th century. Initially, AI focused on symbolic reasoning, with expert systems and knowledge-based approaches playing a pivotal role in decision-making by using predefined rules and symbolic representations [2].

The evolution of AI was accelerated with the rise of machine learning (ML) in the 1980s, which introduced data-driven models such as decision trees, support vector machines (SVM), and genetic algorithms. These methods allowed systems to learn from data and automate tasks like classification and prediction. The development of neural networks further expanded the capabilities of AI, leading to practical applications in fields such as education [3].

The advent of deep learning in the 21st century marked another major leap. With its multi-layered neural networks, deep learning revolutionized fields such as image and speech recognition and autonomous systems, reshaping AI’s potential across industries. Today, deep learning remains a driving force behind AI’s rapid growth and ongoing innovation.

2.2. The Principles and Characteristics of Deep Learning

Deep learning (DL) is a branch of machine learning that uses multi-layered neural networks to model complex data patterns. Central to DL is the concept of hierarchical learning, where each layer extracts increasingly abstract features, allowing for significant advances in tasks like image classification and speech recognition [4].

The key principle of DL lies in the use of backpropagation, a process that adjusts neuron weights based on errors between predicted and actual outputs. This enables deep learning models to automatically extract features from large datasets, eliminating the need for manual feature engineering, which is a common limitation in traditional machine learning.

DL is characterized by its reliance on vast amounts of data, high computational demands, and scalability, making it suitable for a wide array of applications, including real-time analytics and autonomous systems [5]. While DL models often require significant resources and labeled data, emerging technologies such as edge computing and federated learning are helping to address these challenges, enabling more efficient deployment in distributed environments.

3. Analysis of Traditional Algorithmic Structures

3.1. Common Traditional Algorithm Structures: Classification and Characteristics

Traditional algorithm structures in artificial intelligence (AI) have laid the groundwork for many machine learning applications. Among the most widely used are decision trees and support vector machines (SVMs). Decision trees, first introduced by Quinlan (1986), are known for their simplicity and effectiveness in classification tasks [6]. By recursively partitioning the data based on the most significant attribute, decision trees can classify complex datasets while maintaining interpretability. They are often applied in areas such as medical diagnosis and game theory due to their transparent decision-making process.

Another key traditional algorithm is the support vector machine (SVM), introduced by Cortes and Vapnik (1995). SVMs are particularly effective in high-dimensional spaces, using hyperplanes to separate data into distinct classes. Their ability to handle both linear and non-linear data through kernel functions has made them a powerful tool in fields like image recognition and bioinformatics [7]. Although these traditional algorithms have limitations in handling large-scale and unstructured data, they remain foundational in AI due to their robustness and versatility.

3.2. Advantages and Limitations of Traditional Algorithm Structures

Traditional algorithm structures, such as decision trees and k-nearest neighbor (KNN), possess several notable advantages that contribute to their popularity in machine learning applications. One of the key strengths of these algorithms is their simplicity and interpretability, which facilitate easy comprehension and debugging of models. For example, decision trees are highly transparent, providing clear decision rules, which is particularly beneficial in applications requiring explainability, such as medical diagnosis [8]. Additionally, traditional algorithms are computationally efficient for smaller datasets, enabling rapid deployment and experimentation.

However, these algorithms also face significant limitations. One major drawback is their inability to effectively handle large, high-dimensional datasets, where performance tends to degrade compared to more advanced algorithms like deep learning models. Traditional methods, such as k-nearest neighbor, may encounter challenges when dealing with noisy or redundant data, which can ultimately lead to a reduction in their accuracy [9]. Furthermore, traditional algorithms often lack the scalability and adaptability of modern machine learning approaches, making them less suitable for complex real-world applications that require handling vast amounts of data and non-linear relationships.

Overall, while traditional algorithms provide a solid foundation and are beneficial in specific contexts, their limitations call for the use of more advanced techniques in modern machine learning tasks.

4. Impact of Deep Learning on Traditional Algorithmic Structures

4.1. The Impact on Performance Enhancement

Deep learning (DL) has revolutionized the field of AI by significantly improving the performance of traditional algorithm structures. One of the key advantages of DL is its ability to automatically extract complex features from a large dataset, enabling models to surpass traditional machine learning algorithms in accuracy and scalability. For example, in computational chemistry, deep learning models consistently outperform non-neural network models across various applications, including molecular prediction and structural analysis [10].

Moreover, DL's use of multi-layered neural networks allows for better generalization in tasks such as image recognition and text analysis. This capacity for enhanced learning and adaptability has led to the widespread adoption of DL techniques in domains where traditional methods struggled, such as handling large-scale unstructured data and non-linear relationships.

4.2. The Impact on Application Expansion

Deep learning (DL) has significantly expanded the application domains of traditional algorithms. By automating feature extraction and handling large-scale unstructured data, DL has enabled AI to tackle complex tasks that were previously beyond the capabilities of traditional models. For example, DL has been instrumental in applications such as autonomous vehicles, healthcare, and predictive analytics, providing robust solutions to dynamic real-world problems [11]. This expansion into diverse industries has been driven by deep learning's ability to improve both accuracy and efficiency, allowing AI systems to adapt and learn from massive datasets without manual intervention.

4.3. Changes in Algorithm Design Philosophy

Deep learning (DL) has fundamentally altered traditional algorithm design by introducing new methodologies that leverage the latest hardware innovations. Unlike traditional algorithms, which rely on deterministic computation, DL embraces non-deterministic hardware designs to improve computational efficiency. For example, DL models can use stochastic circuits, allowing for approximate matrix computations that enhance speed and energy efficiency without compromising performance [12]. This shift towards hardware-software co-design, where the underlying hardware influences algorithm design, represents a significant departure from conventional approaches. It enables the development of scalable systems capable of handling increasingly complex tasks in real-time applications, such as image recognition and autonomous systems.

5. Opportunities and Challenges

The integration of deep learning (DL) with traditional algorithms has led to significant advancements, particularly through the development of hybrid models that combine the strengths of both approaches. One notable case is the improvement of decision tree algorithms through the incorporation of deep learning techniques, leading to enhanced accuracy in classification tasks. For example, DL-based enhancements have allowed traditional algorithms to effectively process more complex and unstructured data, leading to significant improvements in performance across domains such as image classification and natural language processing [13]. This hybrid approach not only increases model accuracy but also offers greater adaptability to diverse datasets and contexts.

Additionally, deep learning's capacity for automatic feature extraction has revolutionized applications in fields such as healthcare and autonomous driving. In these scenarios, DL models have been integrated with traditional algorithms to enhance decision-making processes, allowing systems to perform real-time data analysis and adaptive learning, which is critical for dynamic environments [14]. This fusion has expanded the applicability of AI into new sectors, offering unprecedented flexibility in handling diverse data types, from structured datasets to complex sensory inputs in real-time systems.

Moreover, as AI continues to evolve, deep learning-driven models have introduced new paradigms in algorithmic design, allowing for more efficient energy consumption and faster computation, which is crucial for edge computing and Internet of Things (IoT) applications [15]. These developments demonstrate the ongoing adaptability of AI systems as they meet the growing demands of modern technology.

Despite the significant advancements that deep learning (DL) has brought to traditional algorithmic structures, there are still several challenges that remain. One of the primary technical difficulties is the computational and resource-intensive nature of DL models. These models demand large amounts of data and processing power, often requiring specialized hardware like GPUs, which can be costly and inaccessible for many organizations. Moreover, training deep learning models is time-consuming, and optimizing them for specific tasks can be complex, leading to issues in scalability and real-time applications [16].

Another critical challenge lies in the interpretability of DL models. Unlike traditional algorithms that offer transparency in decision-making processes, DL models often function as "black boxes," making it difficult to understand how specific outcomes are derived. This lack of explainability raises concerns in domains where trust and accountability are paramount, such as healthcare, finance, and law.

In addition to technical challenges, ethical and social issues are becoming increasingly prominent in academic discourse. The widespread use of AI, particularly DL, has sparked debates on data privacy, algorithmic bias, and the societal impact of automation. These concerns emphasize the need for strict regulations and ethical guidelines to ensure that AI technologies are deployed responsibly and equitably [17].

6. Conclusion

This paper has provided a comprehensive review of the evolution of algorithm structures in artificial intelligence (AI), with a focus on the impact of deep learning (DL) on traditional algorithms. The integration of DL into AI has fundamentally transformed the field by significantly enhancing the performance, scalability, and adaptability of traditional algorithms such as decision trees and support vector machines. These improvements have been particularly notable in areas such as image and speech recognition, where DL-based models have surpassed the capabilities of traditional methods. Through case studies and analyses, we have demonstrated how deep learning enhances both the accuracy and application scope of AI models, enabling systems to handle more complex, unstructured data, thereby expanding their capabilities.

Moreover, DL has expanded the applicability of AI into new domains, including healthcare, autonomous systems, and real-time analytics. By automating feature extraction and improving decision-making processes, DL has enabled AI systems to operate in dynamic, data-rich environments with minimal human intervention. This expansion into diverse industries showcases the versatility of AI technologies and their capability to solve complex, real-world problems across multiple sectors.

Looking ahead, the continued fusion of traditional algorithmic structures with deep learning holds immense potential. Innovations such as edge computing and federated learning are expected to play crucial roles in overcoming current limitations, including the computational demands of DL models and the need for large-scale datasets. As AI systems become more efficient and adaptable, they are likely to drive further innovations in fields such as the Internet of Things (IoT) and automated decision-making.

However, with these advancements come challenges. Issues related to the ethical deployment of AI, data privacy, and algorithmic bias must be addressed through responsible AI governance. Future research should focus not only on enhancing the technical aspects of AI but also on ensuring that these technologies are implemented in a way that benefits society as a whole.