Different Natural Language Processing Techniques in 2025
Artificial Intelligence For Software Engineering: Advances, Applications, And Implications
Over the past decade, the fusion of software engineering (SE) and artificial intelligence (AI) has transformed software development. As software complexity grows, AI techniques like machine learning (ML), deep learning (DL), and large language models (LLMs) are streamlining SE phases, from design to maintenance. These innovations automate tasks, improve defect prediction, and optimize testing.
Breakthroughs in foundation models (e.G., GPT-4, Code Llama) have accelerated this trend, enabling tools like GitHub Copilot and Amazon CodeWhisperer to assist developers. AI-powered solutions, such as GitHub's Copilot for code autocompletion and Facebook's SapFix for automated bug fixes, demonstrate real-world impact. As AI becomes mainstream in SE, it enhances software quality and speeds up development. However, challenges like model interpretability and ethical concerns must be addressed to ensure responsible AI integration. The continued evolution of AI in SE promises both innovation and new complexities.
The integration of AI in SE offers immense potential for enhancing productivity and software quality. However, several challenges hinder its widespread adoption. The absence of standardized frameworks limits consistent AI application, while difficulties in interpreting and trusting AI models create barriers to their effective use in SE. Additionally, the lack of clear ethical guidelines raises concerns about responsible AI deployment in software development.
Emerging issues further complicate adoption, particularly the risks associated with AI-generated code. Vulnerabilities, copyright concerns, and accountability questions pose significant challenges, making it essential to establish rigorous evaluation mechanisms. Addressing these issues will be crucial to unlocking AI's full potential in SE while ensuring reliability, security, and ethical compliance.
The key research goals include:1. Developing Standardized Frameworks: establishing universal frameworks to guide the application of AI across SE activities, ensuring consistency and effectiveness in different phases of the software lifecycle.2. Enhancing Model Interpretability: investigating methods to make AI models more transparent and explainable in the SE context, enabling developers to trust and effectively utilize AI-driven insights.3. Establishing Ethical Standards: defining clear ethical guidelines for using AI in SE, focusing on fairness, accountability, and minimizing biases in AI-driven tools and processes.4. Ensuring Security and Compliance: developing strategies to identify and mitigate AI-introduced software vulnerabilities (e.G., insecure code generation or AI hallucinations that produce faulty code) and clarify legal responsibilities, including copyright compliance and accountability for AI-generated artifacts.5. Fostering Interdisciplinary Collaboration: promoting partnerships between AI researchers and software engineers to co-create innovative, practical solutions that balance AI capabilities with the realities of industrial SE practices.
This Research Topic explores the integration of advanced AI techniques into all facets of SE, emphasizing both the opportunities they provide and the challenges that must be overcome to implement them responsibly. We especially encourage works that highlight practical implementations of AI in real-world SE settings (including industrial case studies and applications), as well as studies that investigate emerging trends and underexplored paradigms such as the use of foundation models, autonomous agents, and multi-modal AI in software development. In parallel, contributors should address the critical AI risks and implications in SE—ranging from technical issues like AI-generated security vulnerabilities to broader concerns like legal and regulatory compliance (e.G., copyright and responsibility for AI-generated code).
Themes of interest include, but are not limited to:- AI system integrity and quality in SE- Data quality and bias in AI models for SE- Robustness and resilience of AI-driven software systems- Incident response and recovery for AI-augmented SE systems- System monitoring and maintenance for AI in SE- Secure deployment and integration of AI in DevOps pipelines- Secure code generation and program synthesis using AI techniques- Explainable AI methods in SE (interpreting AI recommendations in development)- Vulnerability detection in AI-generated code- AI-generated offensive/defense security code in SE- Foundation models in SE (e.G., GPT-4, Code Llama for coding tasks and software design)- Autonomous AI-driven software agents for development and maintenance tasks- Multi-modal AI in SE (combining text, code, and other modalities in SE tools)- Legal and regulatory implications of AI in SE (copyright, licensing, and compliance issues)
We welcome original research, case studies, surveys, theoretical frameworks, and perspective articles that connect these AI techniques (ML, DL, NLP, LLMs, etc.) to SE advancements. Manuscripts should demonstrate clear practical relevance—for example, by evaluating AI-based tools in industrial or opensource project settings—and ensure that ethical, security, and legal considerations are discussed alongside technical contributions. Through this Research Topic, we aim to shed light on how cutting-edge AI can be harnessed to address longstanding SE challenges, while also scrutinizing the risks and responsibilities that accompany this new era of intelligent SE.
Article types and feesThis Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: artificial intelligence, deep learning, machine learning, natural language processing, software engineering, explainable AI, software maintenance, software testing, software development, requirements elicitation
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Artificial Intelligence Or Machine Learning: What's Right For Your Business?
Cory McNeley is a Managing Director at UHY Consulting.
gettyArtificial intelligence (AI) has transformed the business landscape and changed how we work. Its capability to automate tasks, analyze extensive datasets efficiently and provide concise business insights facilitates both the speed and quality of business operations.
"Artificial intelligence" is often used to describe other technologies, such as machine learning (ML) and deep learning (DL). However, each of these technologies is distinct, and those differences impact which solution is right for your specific challenges. Understanding the high-level differences between each and the challenges that remain with implementation and adoption can help you have more meaningful and direct conversations about the role of these technologies in your organization.
Defining Artificial Intelligence And Machine LearningAI is centered on programs that replicate common human-like skills. AI can solve problems, perform advanced calculations, and make decisions through the use of statistical models, neural networks and programmed rules. AI is an umbrella term that also includes various subsets of technology like ML and DL.
ML allows programs to identify patterns from data, which is used to enhance the program's performance over time without the need for explicit programming. Common learning models include supervised, unsupervised and reinforcement learning techniques. This subset of AI is especially useful for data-driven decisions with extremely large data sets, such as sales forecasting. DL uses neural networks, a technique to replicate the human brain that is commonly found in image recognition and detection systems, as well as advanced AI applications such as autonomous vehicles.
Business Applications Of AI And MLThe world of communications, marketing and customer service is experiencing major disruption as a result of advancements in AI. Commercially available and custom-developed AI tools are helping companies provide high levels of customer service by employing advanced chatbots with more knowledge and flexibility than traditional chatbots. They can dissect and resolve complex inquiries without the need for human intervention. The natural language processing (NLP) aspect of modern AI allows these tools to provide customized marketing and communications that are reactive and continually evolving.
Common applications of ML technology include hyper-segmented customer profiling, predictive maintenance and fraud detection. Each of these is based on labeled (structured data), unlabeled (unstructured data) and reinforcement learning, where prior outputs are evaluated and used as inputs to adjust and refine ML's results.
Profiling customers based on previous purchasing habits, location, household income, etc., is nothing new, but combining this data with commuting route data, weather forecasts and social media activity could yield more valuable insight and recommendations.
In predictive maintenance, the mean time to failure by specific machine and physical location in the building—even down to floor orientation—along with machine models with common parts, operator assigned and forecasted demand help management address problems proactively and optimize scheduled downtime.
In fraud detection and prevention, customer profiling, institutional data, travel plans and social media help find potential fraud. Previously, major credit card processors used only a few dozen measures to predict fraud. Today, using ML, the number of parameters the card processor considers is far higher, likely reaching into the hundreds.
Challenges And Learning CurvesThere are challenges with implementing any of these technologies. Data quality ranks as the No. 1 issue. Similar to humans, bad information drives poorly informed decisions from AI. Businesses that plan on implementing any advanced AI tools need to review, catalog and cleanse their data to minimize potential issues with the tool.
Another major issue revolves around acquiring the right talent to work with these tools. According to the Bureau of Labor Statistics, data scientist jobs are projected to increase 36% from 2023 to 2033. With the high demand for expertise in this field, the difficulty in finding skilled and qualified talent to build and deploy could be increasingly difficult with the rising trends of adoption.
Several misconceptions about AI are also prevalent in organizations. While some solutions could be deemed plug-and-play, the vast majority require continual refinement and fine-tuning. This results in unrealistic expectations of what AI can and cannot do for your organization. Before you embark on your AI journey, clearly define your goals and objectives. Then, complete a detailed analysis to ensure the tool you are deploying will yield the expected results. Failed implementations could lead to cynical thinking about AI's capabilities.
ConclusionWhether AI or ML is right for your organization depends on context, and today's tools are advancing rapidly. At the core of the matter is data. These solutions need quality data to operate effectively. Is your organization ready?
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How Artificial Intelligence Is Optimizing Wind Turbines For A Greener Future
As the world transitions toward renewable energy, wind power has emerged as a vital source of clean electricity. However, maximizing efficiency and minimizing environmental impact remain key challenges in wind energy systems.
A recent study titled "Application of Artificial Intelligence in Wind Power Systems" by Mladen Bošnjaković, Marko Martinović, and Kristian Ðokić, published in Applied Sciences (2025), explores how artificial intelligence (AI) is transforming wind energy by improving turbine design, predictive maintenance, and operational efficiency. The research highlights AI's growing role in making wind power more reliable, cost-effective, and environmentally sustainable.
AI-driven wind turbine design and optimizationThe study emphasizes that AI plays a crucial role in optimizing the design of wind turbines, particularly in aerodynamic modeling. By utilizing computational fluid dynamics (CFD) and machine learning algorithms, engineers can refine turbine blade designs to maximize energy output while minimizing turbulence and drag. AI-driven generative design processes enable rapid testing of thousands of blade configurations, selecting the most efficient and structurally sound models.
Additionally, AI assists in siting wind farms by analyzing vast datasets, including wind speed, topography, and environmental impact factors. Geographic information systems (GIS) integrated with AI models help identify optimal locations, balancing high energy potential with minimal disruption to ecosystems. This approach reduces risks associated with bird and bat collisions, noise pollution, and land use conflicts, making wind farms more sustainable in the long run.
Enhancing wind turbine operations with AIAI significantly improves the operational efficiency of wind turbines by enabling real-time monitoring and control. Machine learning models analyze sensor data to adjust turbine settings dynamically, optimizing blade pitch and yaw angles based on wind conditions. This real-time adaptation ensures that turbines operate at peak efficiency, increasing energy output while reducing mechanical stress on components.
Another critical application is AI-powered energy forecasting. Wind energy generation is inherently variable, but AI algorithms enhance predictive accuracy by analyzing historical weather patterns and real-time meteorological data. These predictions help grid operators balance supply and demand, improving grid stability and reducing reliance on fossil fuel backup power. The study highlights how hybrid AI models, combining neural networks and statistical methods, improve short-term wind power forecasting, leading to more efficient energy integration.
Predictive maintenance and reduced downtimeOne of the most transformative aspects of AI in wind power systems is predictive maintenance. Traditional maintenance methods rely on scheduled inspections, which can lead to unexpected failures and costly downtime. AI-driven predictive analytics use machine learning to detect early signs of mechanical wear, allowing for proactive repairs before serious damage occurs.
The study outlines how AI models analyze sensor data - such as vibrations, temperature fluctuations, and acoustic signals - to predict component failures with high accuracy. For example, AI-powered condition monitoring systems can detect abnormalities in gearbox performance, alerting operators before catastrophic failures occur. This reduces maintenance costs, extends the lifespan of wind turbines, and improves overall reliability. The researchers estimate that AI-driven maintenance strategies can lower operational costs by up to 20%, making wind power more economically viable.
Challenges and future directions in AI for wind energyDespite AI's immense potential, several challenges must be addressed to ensure its successful integration into wind power systems. The study highlights concerns related to data accuracy, cybersecurity, and ethical considerations. AI models rely on vast amounts of sensor data, and inconsistencies or biases in these datasets can lead to incorrect predictions or inefficiencies. Ensuring high-quality data and implementing robust validation frameworks are essential for AI's long-term reliability.
Cybersecurity is another critical issue, as AI-driven wind farms are increasingly connected to digital infrastructure. Protecting these systems from cyber threats is crucial to maintaining grid stability and preventing potential disruptions. The researchers advocate for advanced encryption techniques and AI-driven anomaly detection to safeguard wind power networks from cyberattacks.
Looking ahead, the study suggests further research into hybrid AI models that combine deep learning with physics-based simulations for improved predictive accuracy. Additionally, regulatory frameworks must evolve to establish guidelines for ethical AI deployment in renewable energy, ensuring transparency and accountability in decision-making.
Ultimately, AI is revolutionizing wind energy by enhancing design, optimizing operations, and improving maintenance strategies. By overcoming existing challenges and leveraging AI-driven innovations, wind power can become a more efficient, cost-effective, and sustainable energy source, accelerating the transition to a greener future.
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