7 Machine Learning Trends to Watch in 2025

Machine learning is now the cornerstone of recent technological progress, which is especially true for the current generative AI stampede. Many use tools such as ChatGPT, Perplexity and Midjourney to help in their day-to-day work, strong evidence that machine learning will continue to shape how we approach work for a long time to come. Closing out 2024, so many things are happening in the machine learning field that it’s defficult to keep up with them all. Yet, we can look forward to many more amazing things in the new year.

This article will explore the emerging machine learning trends you should keep an eye on in 2025. Let’s get into it!

1. Autonomous Agents

If you have been paying attention to the latest machine learning buzz terminology, you know that the autonomous agent, and discussion of them, is everywhere. And this is for good reason: they have the potential to quickly improve our work life.

For the uninitiated, autonomous agents are AI systems that can perform tasks independently without direct human involvement. These systems have actually existed for some time, but with the development of LLMs, especially those which possess strong reasoning, autonomous agent research has grown exponentially in the very recent past.

Using LLM models, agents can process information from the environment and execute in the described direction as best they can on their own. Depending on the environment, the agent can access a variety of available tools, such as web search, web scraping, retrieval augmented generation (RAG) systems, APIs, etc. Agents can iterate and refine their processes to achieve the designed objective, similar to how a human would approach a task.

With the potential to increase workforce productivity and business investment, autonomous agents will undoubtedly continue to be a prominent machine learning trend in 2025.

2. Multimodal Generative AI

2024 was all about generative AI and, of course, this general trend will continue in 2025. Generative AI has already revolutionized various sectors, is in the midst of revolutionizing others, and has both the apprehensive and the curious looking closer and closer at it as time marches on. Contemporary autonomous agents, mentioned above, make use of generative AI, often in a central role, but there will be many more generative AI iterations and applications to come, including multimodal generative AI.

Multimodal AI model processes and generates various data types instead of focusing exclusively on just one — multimodal examples include text-to-image, image-to-audio, etc. This capability to translate, if you will, between modes, will be useful and become important in many industries, and many businesses have started to use multimodal AI in their processes.

Advances in multimodal AI technology will help systems interpret and generate content across different modalities, leading to many interesting applications in various industries, such as: healthcare for diagnosis enhancement; automotive sector for autonomous vehicles; much more robust content generation; and many more exciting applications. The rise of multimodal generative AI will be a prime catalyst of the continued AI industrial revolution in 2025. However, many risks accompany it, leading us to the next trend to watch.

3. Explainable AI

With machine learning, especially in AI applications, taking over so many tasks that humans have traditionally performed, discussions about our confidence in the decisions and decision-making processes coming from these various AI models are bound to accelerate in the new year. As model decisions are not human, but based on historical data, there is much room for doubt regarding the applicability of these generated outputs.

To push for machine learning transparency and raise people’s confidence in decisions made by model, explainable AI will become more of a must-have as opposed to a nice-to-have. Businesses and individuals alike will be more and more interested in the why and how of decisions made, and will want to be able to interrogate those decisions. Explainable AI (xAI) is, thus, technology that is already becoming standard in many companies and will become something that will gain a foothold in numerous industries in 2025.

Explainable AI works by clearly explaining why a model has come up with the results that it has. For example, when the model assesses someone as fraudulent, the xAI will explain why it came to that decision. The higher the risk of the model decision, the more critical xAI becomes, as it allows stakeholders to question the model reasoning and take the model accountability. This may not be terribly important for why the next word was selected in a content generation system that summarizes emails, but certainly would be for a loan-approval AI. Or consider a self-driving vehicle: why did the vehicle ultimately decide to come to a stop… or not come to a stop?

An xAI could also help identify biases in a model, biases which should not be present in the system for ethical and/or legal reasons. xAI allows the business to spot these biases and develop mitigation methods to remove them. In almost any scenario, the fairer the system, the more trustworthy it will be for making decision.

Explainable AI will become a crucial technology for transparency in 2025 because many businesses will continue to rely more and more on AI results. That’s why it’s beneficial for us to keep track of the trend further.

4. Ethical AI

With xAI continuing to trend in 2025, let’s consider its close cousin: ethical AI.

Ethical AI (eAI) refers to developing and deploying AI systems that align with moral principles, societal values, and (ultimately) legal policies. eAI is a standard that will ensure the technology operates responsibly without violating individual rights and preventing harms to arise from a model and its output.

Ethical AI will address the main points related to AI and ethics that businesses increasingly must: bias mitigation, privacy safeguarding, accountability, security, and transparency. As machine learning and AI models become further integrated in business, eAI principles must be upheld.

As we approach 2025, demand for eAI is expected to intensify. With the speed at which AI is integrated into various critical sectors — from healthcare to finance to law and beyond — eAI will also become a concern of governments and regulatory agencies as well. For example, the European Union’s proposed AI Act tries to establish regulations to govern AI applications, focusing greatly on ethics. With time, these legislative efforts will multiply, and eventually become policies we can’t ignore. This is good reason to keep a close eye on eAI in to the new year.

5. Edge AI

Edge AI is the practice of placing AI and ML deployment directly onto consumer devices instead of relying on a centralized server. For example, if a model is deployed on your smartphone, IoT, sensor, etc., you are dealing with edge AI. This deployment “at the edge” allows the AI processes and workflow to occur locally, helping to facilitate real-time outputs and decision-making. It also allows for more secure AI output generation and interaction, without these interactions having to leave your personal devices.

As you could imagine, edge AI is forecast to become increasingly important in applications that require immediate responses, such as the healthcare industry, or somewhere that requires enhanced data security, such as the financial industry.

With many new and emerging AI-related technologies, 2025 will be the year in which edge AI becomes more prominent in industry, and helps facilitate a shift in how we think about developing new applications.

6. Federated Learning

As we finish discussing edge AI, it only makes sense that we follow up with federated learning. Federated learning (FL) is a technique for collaboratively training shared models on multiple devices without exchanging local individual data. This technique allows each device to process its own data locally while only sending the learned updates, such as the model parameters, to a centralized server. This method will enable increased data privacy and reduce exposure while improving a model’s capability.

Federated learning brings significant advantages to industries that require higher privacy compliance, such as healthcare or finance. Given how AI systems have improved in recent years, how they are able to take advantage of sensitive data to help make more important decisions, and that many sectors are aiming for increased security, federated learning is a genuine no-brainer as far as trends to watch.

Another advantage of federated learning is that it reduces extensive data movement, which is beneficial in models that generate large amounts of data, such as IoT applications. By processing the data locally and sending the critical information to the system’s centralized backend, each device can contribute more (more data, more securely) to the model training.

7. AI for Humanitarianism

The last trend we must watch out for in 2025 is how AI will address complex humanitarian challenges. With technological advancements, AI models will inevitably be employed to solve issues and improve humanity.

Many such problems could potentially be addressed with AI. For example, projects such as the Signpost Project use AI to provide real-time information to help individuals in crisis and a chatbot to give critical guidance regarding safety. Another example is how AI models help predict floods in various countries in the Flood Hub Project.

In the years to come, there is little doubt that improved AI will help humanity even more than it currently does, and in more important and impactful ways. With the development of technology, 2025 will provide significant humanitarian promise, and humankind will be getting help from all these new things that come around.