Tech Term Decoded: Incremental Learning

Definition

In artificial intelligence, unlike traditional or "batch" machine learning, incremental learning is a type of machine learning where a model consistently updates itself with new data, gaining new knowledge while still retaining what it has previously learned.

In batch learning, the AI model is trained on a large, fixed dataset at a go, similar to memorizing an entire textbook all at once for an exam, only to forget some of it later on. A model trained using batch learning can’t learn from new data till the training is repeated with an entire dataset that includes the new information. But with incremental learning, an AI model learns from new data points as they become available, updating its model parameters incrementally, which is completely different from batch learning's all-at-once approach [1].

For example, imagine a scenario where a JAMB cheating detection system uses batch learning training approach with examination data from previous years (2020-2024) showing known malpractice patterns like identical answers and suspicious timing, then applied to current examinations. If students come up with new cheating techniques like using smartwatches or hidden Bluetooth earpieces, the system might start failing unless retrained on a new batch of examination records, which includes these updated cheating techniques.

On the other hand, an incremental learning-based malpractice detector would adapt itself as each examination session occurs, continuously updating its understanding of what constitutes cheating behavior. If candidates adjust from traditional "expo" answer sharing to technology-enabled methods, this system will still learn to detect new malpractice methods without needing a whole new batch of training data, maintaining exam integrity across evolving threats. 

AI incremental learning process [2].

 Origin

Incremental learning concept originated from the wider scope of machine learning and artificial intelligence. Originally developed as a means to overcome the limitations of traditional static models, incremental learning has increasingly become important, particularly in the context of AI systems that operate in dynamic, real-world environments. Its growth has been shaped by the increasing demand for AI models that can adjust in real-time, integrating new knowledge effortlessly without compromising known capabilities.

Context and Usage

Incremental learning application cuts across several domains, helping industries solve computational and storage challenges in addition to setting up systems that self-improve while in use, guaranteeing consistent enhancement in accuracy and performance. Some of their applications are as follows:

• Educational Technology Sector: Incremental learning transforms the educational technology sector by personalizing learning experiences. As students work with educational content, incremental learning algorithms tailor the material to align with their changing needs, making education more accessible, engaging, and effective.
• Healthcare for Patient Monitoring Systems: Healthcare has seen great developmental strides via incremental learning, specifically in patient monitoring systems. With each new piece of patient data, the system finetunes its diagnostic and treatment accuracy, tailors healthcare plans to individual patient needs, improving outcomes.
• Financial Services for Fraud Detection: Usually, traditional models find it difficult to keep up with the sophisticated and constantly changing tactics of fraudsters. The introduction of incremental learning has revolutionized fraud detection systems. It equips these models to adapt effortlessly to new fraudulent strategies without losing knowledge of older patterns. This combination ensures robust protection against a wide array of financial crimes, safeguarding both the institutions and their clients.
• Social media for Personalized Content Recommendation: Incremental learning tackles the problem of dynamic nature of user preferences in social media platforms effectively through consistent adjustment to changes in user behavior and preferences, offering highly personalized content recommendations, improving user engagement and satisfaction.
• Autonomous Vehicles for Real-Time Object Detection: In the domain of autonomous vehicles, incremental learning is crucial for real-time object detection and decision-making. The ability to learn from new data in real time allows these vehicles to maneuver complicated environments safely and efficiently, adapting to unforeseen obstacles or dynamic road conditions [3].

Why it Matters

In artificial intelligence, incremental learning is very important as it is the one ability that changes everything in AI. It enables AI to keep learning while it’s being used, detecting what is new, updating what it knows, gaining new knowledge, and altering its decisions and behaviors instantly rather than months later. Simply put, it gets smarter with each interaction, with the ability to quickly adjust as the true superpower of real intelligence [4].

Related Learning Approaches

• Machine Intelligence: Broad term for computer systems exhibiting intelligent behavior and problem-solving capabilities
• Machine Learning: Field of AI where systems learn and improve from experience without explicit programming
• Reinforcement Learning: Learning approach where agents learn through trial and error using rewards and penalties
• Reinforcement Learning from Human Feedback (RLHF): Training method that uses human preferences to guide reinforcement learning
• Similarity Learning: Machine learning approach that teaches models to measure similarity between objects

In Practice

A good example of a real-life case study of incremental learning in practice can be seen in the case of Tencent Cloud TI-ONE (Intelligent Platform for AI). Their services help AI Agents remain adaptive, efficient, and capable of long-term learning without excessive resource usage. Tencent Cloud TI-ONE (Intelligent Platform for AI) offers scalable machine learning tools for enacting incremental learning in AI Agents which includes the following:

• TI-ONE’s Online Learning Support, which facilitates real-time model updates.
• Model Management & Versioning, which manages continual learning by tracking model versions.
• Data Storage & Processing (COS + EMR), which enable storing and processing incremental data streams [5].

References

1. Bayoumi. A. (2024). A Comprehensive Guide to Incremental Learning in AI.
2. Paraskevopoulou, S. (2024). Incremental Learning: Adaptive and real-time machine learning.
3. Deepgram. (2025). Incremental Learning.
4. Pagidyala, S. (2025). Incremental learning is the one function that rewrites everything in AI.
5. Tencentcloud. (2025). What are the incremental learning methods for AI Agent?