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Nowadays, more and more companies begin to embrace the power of Machine Learning (ML) for different business processes. Most of the time, ML models are trained off-line on very big and representative training sets and are then frozen after deployment. However, this makes impossible for the model to improve and adapt to new circumstances if exposed to new training data.
Continuous Learning enable both scalability and adaptation, two essential factor for many ML and AI systems. In this page you will find everything related to the Industry applications of CL.
Current Solutions | Future Applications
In this section we provide a list of companies that exploit Continuous Learning approaches:
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Neurala uses deep learning neural network software that makes smart products (drones) more autonomous and useful. In particular, Neurala Lifelong Deep Neural Networks (Lifelong-DNN™) enable incremental learning of new objects on the fly, without the power of a server located in the cloud. Neurala accomplishes this by combining different neural network architectures).
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Amazon-Comprehend analyzes text and tells you what it finds, starting with the language, from Afrikans to Yoruba, with 98 more in between. It can identify different types of entities (people, places, brands, products, and so forth), key phrases, sentiment (positive, negative, mixed, or neutral), and extract key phrases, all from text in English or Spanish. Finally, Comprehend‘s topic modeling service extracts topics from large sets of documents for analysis or topic-based grouping
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IBM-Watson has embraced the phylosophy of Continuous Learning by providing automated monitoring of model performance, retraining, and redeployment to ensure prediction quality. IBM Watson allow data scientists and analysts to quickly build and prototype models, to monitor deployments, and to learn over time as more data become available.
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Cogitai is concerned about building artificial intelligences (AIs) that learn continually from interaction with the real world. Commerical applications and solutions are designed for learning knowledge and actions from experience by relying on continual-learning AI approaches.
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DeepMind is one of the world leader in artificial intelligence research. DeepMind reasearch has recently showed how to develop programs that can learn to solve complex problem without needing to be taught how. In this context, continuous learning approaches have been applied to Reinforcement Learning methods (for more information check out the following articles Continuous Deep Q-Learning or Learning Continuous Control Policies)
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The future AI systems will rely on continuous learning as opposed to algorithms that are trained offline. There are many applications and scenarios where continuous learning already plays a central role or can be exploited for achieving better results. Here we provide a list of applications where Continuous Learning will make the difference:
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Robotics deals with use of robots where learning approaches are generally focused on discrete, e.g. single-task, learning events. However, in many applications robots need to be able to react to unexpected events and then update their models/policies to include the just encountered data points. Nowadays, Reinforcement Learning techniques are starting to provide robots and agents with such capabilities.
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Object Recognition applications aims to recognize different categories of objects in an image. Incremental learning of new categories of objects, without forgetting previous ones, is extremely important for building lifelong autonomous systems.
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