Join the channel membership: https://www.youtube.com/c/AIPursuit/join Subscribe to the channel: https://www.youtube.com/c/AIPursuit?sub_confirmation=1 Support and Donation: Paypal ⇢ https://paypal.me/tayhengee Patreon ⇢ https://www.patreon.com/hengee BTC ⇢ bc1q2r7eymlf20576alvcmryn28tgrvxqw5r30cmpu ETH ⇢ 0x58c4bD4244686F3b4e636EfeBD159258A5513744 Doge ⇢ DSGNbzuS1s6x81ZSbSHHV5uGDxJXePeyKy Wanted to own BTC, ETH, or even Dogecoin? Kickstart your crypto portfolio with the largest crypto market Binance with my affiliate link: https://accounts.binance.com/en/register?ref=27700065 ----------------------------------------------------------------------------------------- The video is reposted for educational purposes. Source: https://vimeo.com/437692128 Authors: Tsung-Hsien Wen, Pei-Hao Su, Paweł Budzianowski, Iñigo Casanueva and Ivan Vulić Date: 3-NOV-2019 Location: SkyCity, AsiaWorld-Expo, HONG KONG A fundamental long-term goal of conversational AI is to merge two main dialogue system paradigms into a standalone multi-purpose system. Such a system should be capable of conversing about arbitrary topics (Paradigm 1: open-domain dialogue systems), and simultaneously assist humans with completing a wide range of tasks with well-defined semantics such as restaurant search and booking, customer service applications, or ticket bookings (Paradigm 2: task-based dialogue systems). The recent developmental leaps in conversational AI technology are undoubtedly linked to more and more sophisticated deep learning algorithms that capture patterns in increasing amounts of data generated by various data collection mechanisms. The goal of this tutorial is therefore twofold. First, it aims at familiarising the research community with the recent advances in algorithmic design of statistical dialogue systems for both open-domain and task-based dialogue paradigms. The focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. In theory, learning end-to-end from data offers seamless and unprecedented portability of dialogue systems to a wide spectrum of tasks and languages. From a practical point of view, there are still plenty of research challenges and opportunities remaining: in this tutorial we analyse this gap between theory and practice, and introduce the research community with the main advantages as well as with key practical limitations of current end-to-end dialogue learning. The critical requirement of each statistical dialogue system is the data at hand. The system cannot provide assistance for the task without having appropriate task-related data to learn from. Therefore, the second major goal of this tutorial is to provide a comprehensive overview of the current ... https://www.youtube.com/watch?v=nopXtXmhjM0

If you would like to support the channel, please join the membership: https://www.youtube.com/c/AIPursuit/join Subscribe to the channel: https://www.youtube.com/c/AIPursuit?sub_confirmation=1 The video is reposted for educational purposes and encourages involvement in the field of AI research. Source: ... https://www.youtube.com/watch?v=93Jh0DEMzn4