Learning has been essential to our existence. And, continuous learning helps an individual avoid stagnation in any profession and ensures that one keeps on moving forward towards reaching his goal and potential. The same also goes for machine models that are backed by AI’s machine learning algorithm. Enter continual learning. Continual learning, also called lifelong learning or online machine learning, is a fundamental idea in machine learning in which models continuously learn and evolve based on the input of increasing amounts of data while retaining previously learned knowledge. In practice, this refers to supporting a model’s ability to autonomously learn and adapt in production as new data comes in. Just like us, this concept too, is based on mimicking humans’ ability to learn incrementally by acquiring, fine-tuning, and transferring knowledge and skills throughout their lifespan.

What is Machine Learning?

Machine Learning came into existence in 1946 when Polish scientist Stanislaw Ulam was looking for solutions in an attempt to figure out the probability of winning a game of solitaire. Today it is defined as an application of artificial intelligence where a computer/machine learns from past experiences (input data) and makes future predictions. This allows the machine learning models to make assumptions, test them, and learn autonomously, without being explicitly programmed. If implemented correctly, ML shall unlock the ability to empower organizations to modernize the way they function.

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