SRI Blog

  Rufin VanRullen,  and Ryota Kanai Paper Link Abstract Recent advances in deep learning have allowed Artificial Intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic or cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive architectures. The Global Workspace theory refers to a large-scale system integrating and distributing information among networks of specialized modules to create higher-level forms of cognition and awareness. We argue that the time is ripe to consider explicit implementations of this theory using deep learning techniques. We propose a roadmap based on unsupervised neural translation between multiple latent spaces (neural networks trained for distinct tasks, on distinct sensory inputs and/or modalities) to create a unique, amodal global latent workspace (GLW). Potential functional advantages of GLW are reviewed, along with neuroscientific implications Paper approach to a cognitive framework t

 Cedric Renggli, Luka Rimanic, Nezihe Merve Gurel, Bojan Karlas, Wentao Wu, Ce Zhang ETH Zurich Microsoft Research Paper Link ease.ml reference paper link Image courtesy 99designes Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong dependency between the quality of a machine learning model and the quality of the data used to train or perform evaluations. In this work, we demonstrate how different aspects of data quality propagate through various stages of machine learning development. By performing joint analysis of the impact of well-known data quality dimensions and the downstream machine learning process, we show that different components of a typical MLOps pipeline can be efficiently designed, providing both a technical and theoretical perspective. Courtesy: google The term “MLOps” is used when this DevOps process is specifically applied to ML. Different

  Tu Le (University of Virginia) Danny Yuxing Huang (New York University) Noah Apthorpe (Colgate University) Yuan Tian (University of Virginia) Page Link Image courtesy: kidscreen Abstract Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem (e.g., inappropriate content or information collection) . To study the risks VPAs pose to children, built a Natural Language Processing (NLP)-based system to automatically interact with VPA apps and analyze the resulting conversations to identify contents risky to children. Identified 28 child-directed apps with risky contents and maintain a growing dataset of 31,966 non-overlapping app behaviours collected from 3,434 Alexa apps. Findings suggest that although voice apps designed for children are subject to more policy requirements and intensive vetting, children are still vuln

  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 account. In