Continual Learning
Continual Learning - Related Videos
09:40
Batch and Continuous Bioreactors
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2023
Bioreactors are used to grow organisms in large volumes, thereby enabling the production of mass quantities of the target product. These reactors can be batch reactors, which contain all of the components needed for cell growth, or continuous reactors, which have inlet and outlet ports allowing for the addition of fresh growth media and the removal of cell waste. This video presents batch and continuous reactors and demonstrates the use of bioreactors to grow bacteria in the laboratory.
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
11:18
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Cited by 18 •
2015
A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.
01:12
Equation of Continuity
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2023
Fluid motion is represented by either velocity vectors or streamlines. The volume of a fluid flowing past a given location through an area during a period of time is called the flow rate Q, or more precisely, the volume flow rate. Flow rate and velocity are related—for instance, a river has a greater flow rate if the velocity of the water in it is greater. However, the flow rate also depends on the size and shape of the river. The relationship between flow rate (Q) and average speed (v)...
01:20
Continuity Equation
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2023
The total amount of current flowing per unit cross-sectional area is called the current density. Hence, the current passing through a cross-sectional area can be written as the surface integral of the current density. According to the conservation of charge, the total current flowing out of a given volume equals the rate of decrease of charge within that volume. Now, the total charge can be written in terms of the volume charge density. The charge density is a space function. Hence, applying...
FAQs
The model uses plasticity to incorporate information from new tasks or changing data streams, while stability preserves capabilities learned earlier. Excessive plasticity can overwrite established knowledge, whereas excessive stability can prevent useful adaptation. This balance matters in engineering systems because robots, vehicles, and monitoring platforms must respond to new conditions without losing performance on situations they already learned to handle.
These strategies protect earlier knowledge through different mechanisms. Rehearsal or replay revisits previously learned information, regularization limits damaging parameter changes, parameter isolation separates some knowledge from later updates, and knowledge distillation transfers earlier capabilities into the updated model. Selecting among them depends on how the system must preserve prior behavior while learning from new tasks or data.
Incremental updates allow a model to incorporate incoming tasks or data without requiring complete retraining after every change. This supports operation in environments where information arrives sequentially. However, each update can affect previously acquired capabilities, so the update process must be paired with a forgetting-control strategy to maintain a useful balance between adaptation and retention.
A typical workflow presents the model with a sequence of tasks or data streams, updates its parameters as new information arrives, and applies a forgetting-control approach during those updates. The system then needs to consider both newly acquired performance and retention of earlier capabilities. This workflow is suited to operational settings where conditions evolve after deployment.
Engineers may choose continual learning when a system must adapt to changing conditions over time and cannot rely on complete retraining for every new situation. The approach is especially relevant to deployed systems that encounter sequential information, such as autonomous vehicles, adaptive robots, predictive-maintenance tools, and monitoring systems. Its value comes from combining adaptation with preservation of earlier knowledge.
In engineering, continual learning supports systems that face evolving operating conditions. Adaptive robots can incorporate new situations, autonomous vehicles can respond to changing environments, and predictive-maintenance or monitoring systems can update their learned behavior as conditions develop. Across these applications, the central outcome is an ability to accommodate new information while retaining previously acquired capabilities rather than discarding them.