SRI Blog

  Other learning paradigms are about minimization;  Reinforcement learning is about maximization . The statement quoted above has been attributed to Harry Klopf, though it might only be accurate in sentiment. The statement may sound vacuous, since minimization can be converted to maximization simply via negation of an objective. However, further reflection reveals a deeper observation. Many learning algorithms aim to mimic observed patterns, minimizing differences between model and data. Reinforcement learning is distinguished by its open-ended view. A reinforcement learning agent learns to improve its behavior over time, without a prescription for eventual dynamics or the limits of performance. If the objective takes non-negative values, minimization suggests a well-defined desired outcome while maximization conjures pursuit of the unknown Video Courtesy:bdtechtalks.com What happens when AI Plays Hide and Seek 500 Times Paper By - Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwara...

  Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning -By ZINING LIU1 , CHONG LONG , XIAOLU LU , ZEHONG HU , JIE ZHANG , YAFANG WANG Paper Link Courtesy:facebook Abstract Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers’ questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers’ requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers’ satisfaction. To achieve the optimal tradeoff between resources and customers’ satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into ac...

 -By Hugh Leather - University of Edinburgh Edinburgh,                                                   Chris Cummins -  Facebook AI Research Paper Link   image Courtesy:Embecosm Abstract Writing optimising compilers is difficult. The range of programs that may be presented to the compiler is huge and the systems on which they run are complex, heterogeneous, non-deterministic, and constantly changing. The space of possible optimisations is also vast, making it very hard for compiler writers to design heuristics that take all of these considerations into account. As a result, many compiler optimisations are out of date or poorly tuned. A retrospective of machine learning in compiler optimisation from its earliest inception, through some of the works that set themselves apart, to today’s deep learning, finishing with our visi...

-By Md Zahangir Alom , Tarek M. Taha , Chris Yakopcic , Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin , Brian C Van Essen , Abdul A S. Awwal, and Vijayan K. Asari Paper covers vast information on Deep learning, its History, techniques, and analysis. I would present a miniature version of it. For detailed info refer  Paper Link Since the 1950s, a small subset of Artificial Intelligence (AI), often called Machine Learning (ML), has revolutionized several fields in the last few decades. Neural Networks(NN) is a subfield of ML, and it was this subfield that spawned Deep Learning (DL). Since its inception, DL has been creating ever larger disruptions, showing outstanding success in almost every application domain. Fig. 1 shows, the taxonomy of AI. DL (using either deep architecture of learning or hierarchical learning approaches) is a class of ML developed largely from 2006 onward. Learning is a procedure consisting of estimating the model parameters so t...