Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. However, we argue that the current TOD benchmarks are limited to surrogate real-world scenarios and that the current TOD models are still a long way to cover the scenarios. In this position paper, we first identify current status and limitations of SF-TOD systems. After that, we explore the WebTOD framework, the alternative direction for building a scalable TOD system when a web/mobile interface is available. In WebTOD, the dialogue system learns how to understand the web/mobile interface that the human agent interacts with, powered by a large-scale language model.

Viz.ai, a healthcare startup that's using artificial intelligence (AI) to help medical professionals spot early signs of stroke, has raised $50 million in a series B round of funding led by Greenoaks, with participation from Alphabet's VC arm GV and Kleiner Perkins. In the U.S. alone, someone has a stroke every 40 seconds, according to data from the Centers for Disease Control and Prevention (CDC), culminating in some 140,000 deaths each year -- or 1 in every 20 deaths. Moreover, those who survive a stroke often suffer long-term disability as a result. As with many medical conditions, early stroke detection is the key to treating and negating the impact of strokes, but they can be difficult to diagnose. And even then, coordinating treatment among the various specialists can cause unnecessary delays.