Artificial intelligence (AI) has been a trending topic in recent years, and as 2023 ends, multiple AI research trends are worth looking at. AI has already become one of the most notable technological advancements in recent years, with the potential to transform different sectors.
As the AI sector keeps growing, new trends and innovations keep emerging. Hence, it is important to keep up with the latest developments. Here are some of the latest AI research trends that may help predict the future of the nascent industry.
Explainable AI (XAI)
One of the main challenges with AI is that it can be challenging to comprehend how it is making its decisions. The lack of transparency can be a considerable barrier to adoption in some sectors, mostly those where human trust is important, such as finance, healthcare, and defense.
Explainable AI (XAI) is a budding sector that focuses on developing artificial intelligence ecosystems that can explain their decisions. XAI wants to make AI systems more understandable and transparent, mostly in fields where accountability and trust are important. XAI is expected to gain lots of traction in 2024, with the development of new techniques and algorithms for explainable AI.
Based on a Markets and Markets report, the global XAI market is projected to grow from $450 million in 2019 to $2.6 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 41.2% during the projected period. This report highlights that the growing demand for AI models that are transparent and explainable is driving the growth of the XAI market.
Federated learning is a machine learning technique that allows models to get trained on data that is distributed across many nodes or devices. This strategy supports the training of models without centralizing the data, which offers better security and privacy. In 2023, federated learning gained more popularity in sectors that handle sensitive data, such as finance and healthcare.
An Allied Market Research report estimates that the global federated learning market size will grow to $2.17 billion by 2026, growing at a CAGR of 41.2% from 2019 to 2026. The report highlights that the increasing demand for security and privacy in AI models is powering the growth of the federated learning market.
Quantum Machine Learning (QML)
Quantum computing is a quickly developing field that promises to transform many areas of computation, which include machine learning. Quantum machine learning (QML) integrates quantum computing and machine learning to develop new algorithms and models. In 2023, QML is expected to become highly accessible as quantum computing technology keeps improving.
Based on a Research and Markets report, the global quantum machine learning market size is set to reach $513.4 million by 2026, growing at a CAGR of 42.7% in the projected period. The report highlights that the growing need for complex AI models and the growing adoption of quantum computing is powering the growth of the QML market.
Artificial Intelligence For Social Good
Artificial intelligence can address some of the world’s most pressing challenges, from climate change to social justice to healthcare. AI for social good is a growing movement that aims to leverage AI technologies to combat these challenges and develop a positive social impact. 2023 has seen more research and development in this area, as organizations and researchers aim to apply AI to social good initiatives.
Based on a Tractica report, the global AI for the social goods market is expected to grow from $1.9 billion in 2018 to $11.5 billion by 2025, at a CAGR of 28.3% during the projected period. The report notes that the growing demand for AI technologies in social good initiatives is powering the growth of the market.
Autonomous systems refer to machines and robots that can execute tasks without any human intervention. The growth of autonomous networks is a considerable field of research in artificial intelligence. In 2024, we expect to see considerable advancements in autonomous systems, specifically in the areas of autonomous vehicles, robots, and drones.
One of the key benefits of autonomous networks is that they can execute tasks that are dangerous or challenging for humans. For instance, autonomous drones can be utilized for search and rescue missions, while autonomous vehicles can be utilized in the transportation of goods and people without risking the occurrence of human error.
According to a MarketsandMarkets report, the autonomous systems market is expected to hit $83.0 billion by 2023, growing at a CAGR of 14.6% during the projected period. The report notes that the growing demand for autonomous networks in different sectors, such as aerospace, automotive, and defense, is powering the growth of the budding market.
Natural Language Processing (NLP)
Natural language processing (NLP) is a field of artificial intelligence that focuses on enabling machines to comprehend and interact with human language. NLP has multiple applications, including chatbots, virtual assistants, and language translation software. In 2024, we can expect to see more developments in NLP, specifically in the areas of conversational AI and sentiment analysis.
One of the notable challenges in NLP is the ability to understand the context of language. That is because human language is complicated and can be ambiguous, making it hard for machines to comprehend. Nonetheless, recent advancements in neural networks and deep learning have enabled machines to better comprehend the context of language.
As highlighted in one report by MarketsandMarkets, the global natural language processing market is expected to thrive and increase from $7.63 billion in 2018 to $26.41 billion by 2024, at a CAGR of 28.5% during that projection time. Increasing demand for NLP applications, mainly in the healthcare and retail sectors, is powering the growth of the market.
As the use of artificial intelligence becomes more widespread, there is a growing fear about the ethical implications of AI. AI ethics is a field of study that aims to address these issues and guarantee the use and development of AI are ethical and responsible. In 2023, we can expect to find more research and development in the area of AI ethics, mainly in the sectors of transparency, bias, and accountability.
One major challenge in AI ethics is guaranteeing that AI ecosystems are free from bias. AI systems can learn biases from their data, which can result in discriminatory outcomes. To resolve these issues, developers and researchers are creating methods for detecting and mitigating bias in AI networks.
Based on a Grand View Research report, the global AI ethics market will reach $3.06 billion by 2027. The report highlights the growing awareness of the ethical implications of AI and the need for ethical infrastructures and guidelines that are driving the growth of the market.
AI’s Role In Addressing Global Challenges
Artificial intelligence plays an integral role in addressing different challenges across many domains. Some of the notable areas where AI contributes include:
AI helps in medical research, drug discovery, and customized medicine. Notably, predictive analytics helps in identifying disease outbreaks and enhances healthcare management. Remarkably, machine learning helps in diagnostic imaging and early disease detection.
Artificial intelligence facilitates customized learning experiences for students. Also, intelligent tutoring systems adapt to personalized learning styles, boosting educational outcomes. On the other hand, automated grading and assessment tools help streamline educational processes.
AI helps to monitor and manage natural resources. Climate modeling and prediction assist in resolving issues related to climate change. Furthermore, AI helps in optimizing resource use in precision agriculture to reduce environmental impact.
AI boosts threat detection and response in cybersecurity. Remarkably, machine learning algorithms analyze patterns to identify threats and prevent cyberattacks. AI-driven tools boost the resilience of critical systems and infrastructure.
AI supports fraud detection and risk management in the financial sector. On the other hand, algorithmic trading and robo-advisors use AI to enhance investment strategies. Furthermore, predictive analytics supports financial decision-making and market forecasting.
AI is integral to disaster management, aiding in prediction, early warning systems, and risk assessment. Autonomous systems and AI-equipped drones enhance search and rescue operations, boosting emergency response efficiency.
Natural language processing expedites real-time data analysis during crises, facilitating prompt decision-making and resource allocation. These AI applications collectively empower disaster response teams, providing advanced tools and insights that markedly enhance their capacity to mitigate the impact of disasters and save lives.
Essentially, AI is used in many sectors of the global economy and it works as a powerful tool to help address global challenges by offering innovative solutions, enhancing decision-making processes, and optimizing different aspects of human activity in multiple industries.