Researchers at the University of California, Berkeley have developed an “intrinsic curiosity model” to make the learning algorithm of machines function even when there is a deficit in strong feedback signal. The model developed by this team visions the AI software controlling a virtual agent in video games to maximize its understanding of the environment and factors influencing the environment. Preceding researches have seen numerous attempts to provide AI agents’ curiosity, but this time the trick has proved rewarding.
This trick aims to solve the drawbacks in techniques pertaining to core machine learning and may even help us by making machines better equipped at solving obscure real world problems.
Among the numerous machine learning techniques developed in the past decade, Reinforcement Learning has brought an exceptional change in the manner in which machines accomplish their tasks. As a result this technique is now employed to instill machines with skills that might be impossible to code manually. However, Reinforcement Learning comes with limitations. As pointed out by a PhD student of UC Berkeley, it requires huge amount of training to be able to learn a task. Furthermore the program doesn’t work for computer games in which the benefits of certain behaviors aren’t immediately obvious. That’s where curiosity could come in.
Rigorous research on artificial curiosity is being carried out for quite some time now. What’s interesting is that these ideas are transitioning from “exotic’’ to ‘’mainstream’’ in both AI and neuroscience. To learn more about the current advances in the field of Artificial Intelligence follow us on Dexlab Analytics. We are a premier institute providing Machine Learning Certification in Gurgaon. Interested in a career in Data Analyst? To learn more about Machine Learning Using Python and Spark To learn more about Data Analyst with Advanced excel course To learn more about Data Analyst with SAS Course To learn more about Data Analyst with R Course To learn more about Big Data Course
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