![]() ![]() ![]() Versatility is achieved by intelligent amalgamation of Meta Learning along with related techniques such as reinforcement learning (finding suitable actions to maximize a reward), transfer learning (re-purposing a trained model for a specific task on a second related task), and active learning (learning algorithm chooses the data it wants to learn from). What machine learning algorithms need to do is develop versatility – the capability of doing many different things. Or an AI navigation controller won’t be able to hold a perfect human-like conversation. Currently, a Go Player, will not be able to navigate the roads or find new places. However, now that AI and machine learning is possibly being integrated in everyday tasks, we need a single AI system to solve a variety of problems. Meta Learning: Making a versatile AI agentĬurrent AI Systems excel at mastering a single skill, playing Go, holding human-like conversations, predicting a disaster, etc. To explain in simple terms, meta-learning is how the algorithm learns how to learn. Similar concepts, when applied to the machine learning theory states that a meta learning algorithm uses prior experience to change certain aspects of an algorithm, such that the modified algorithm is better than the original algorithm. If we go by the social psychology definition, meta learning is the state of being aware of and taking control of one’s own learning. Meta Learning, an original concept of cognitive psychology, is now applied to machine learning techniques. ![]()
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