AI, Machine Learning, and Data Science Trends for 2024: What to Expect and How to Prepare

Artificial intelligence (AI), machine learning (ML), and data science (DS) are transforming the world in unprecedented ways. From healthcare and education to business and entertainment, these fields are enabling new possibilities and solutions for various challenges and opportunities. But what are the key trends that will shape the future of AI, ML, and DS in 2024? Here are some of the most important ones to watch out for:

• Cloud Data Ecosystems: Data ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. This means that data collection, storage, analysis, and sharing will be more efficient, scalable, and integrated in the cloud. By 2024, Gartner expects 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.
• Edge AI: Edge AI is the processing of data at the point of creation at the edge, such as on devices, sensors, or machines. This enables real-time insights, pattern detection, and data privacy. Edge AI also improves the development, orchestration, integration, and deployment of AI models. Gartner predicts that more than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.
• Responsible AI: Responsible AI is the ethical and social aspect of making and using AI. It covers issues such as business and societal value, risk, trust, transparency, and accountability. Responsible AI aims to make AI a positive force rather than a threat to society and to itself. Gartner predicts the concentration of pre-trained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern. Hopefully, this number will go up soon!
• Democratization of AI: Democratization of AI is the availability and accessibility of AI tools and solutions for everyone, regardless of their technical skills or background. This is enabled by various apps, platforms, and frameworks that simplify the creation, testing, and deployment of AI applications. Forbes reports that an ever-growing number of apps put AI functionality at the fingers of anyone and that it’s increasingly simple to create your own AI solutions using no-code or low-code platforms.
• Generative AI: Generative AI is the branch of AI that can create new content or data that is realistic and novel. Examples include text-to-image generation, music composition, video synthesis, and style transfer. Generative AI is driven by advances in deep learning models such as generative adversarial networks (GANs) and transformers. Forbes states that generative AI is revolutionizing various domains such as music, art, gaming, and healthcare.

These trends indicate that AI, ML, and DS will become more pervasive, powerful, and accessible in 2024. They also pose new challenges and opportunities for businesses and organizations that want to leverage them for their goals and missions. To stay ahead of these trends, it is important to:

• Evaluate data ecosystems based on their ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of their immediate environment.
• Identify the applications, AI training, and inferencing required to move to edge environments near IoT endpoints.
• Adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models. Seek assurances from vendors to ensure they are managing their risk and compliance obligations.
• Develop data literacy skills and tools to critically analyze and interpret data, and to communicate data insights effectively.
• Address data gaps and biases that may affect the validity, reliability, and fairness of data analysis and outcomes.
• Share data openly and responsibly with relevant audiences, while respecting data ownership and sovereignty.

AI, ML, and DS are not only technical issues but also social and moral ones. They require us to be mindful of the impacts of our work on ourselves and others and to strive for innovation and inclusion in the age of big data.