<span lang="EN-US">The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting using adaptive traffic light systems, instead of fixed-time traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model self-adjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.</span>

The concept of open-domain chatbots has been one of the exciting problems in the field of research for a long time now. Open-domain chatbots are a class of chatbots that are expected to carry a conversation with a human on every possible topic in every context. This class of chatbots helps realize the goal of artificial general intelligence and is on the cutting edge of innovation and research unlike the various types of closed-domain chatbots. Success at building truly open-domain chatbots will also be coupled with man-made systems passing the Turing test and paving way for the next era of human-like systems. The objective of this paper is to highlight the key differences between the classes of chatbots and go on to showcase the advancements that have been made towards achieving this open-domain standard of conversation using reinforcement learning models. In doing this, various metrics are also explained and possible baselines that can be used as inspiration for future open-domain chatbots are presented.