One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response.However, obtaining training data to teach models how to issue search queries is time and resource consuming.In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions.We prompt a large language model (PaLM) to create conversational versions of question answering datasets, and use it to improve query generation models that communicate with external search APIs to ground dialog responses.Unlike previous approaches which relied on human written dialogs with search queries, our method allows to automatically generate querybased grounded dialogs with better control and scale.Our experiments demonstrate that: (1) For query generation on the QReCC dataset, models trained on our synthetically-generated data achieve 90%-97% of the performance of models trained on the human-generated data;(2) We can successfully generate data for training dialog models in new domains without any existing dialog data as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets.(3) We perform a thorough analysis of the generated dialogs showing that humans find them of high quality and struggle to distinguish them from human-written dialogs. 1 * Work done during an internship at Google Research. 1 We publicly release all prompts and will release all generated datasets.

District-based manipulation, or gerrymandering, is usually taken to refer to agents who are in fixed location, and an external division is imposed upon them. However, in many real-world setting, there is an external, fixed division – an organizational chart of a company, or markets for a particular product. In these cases, agents may wish to move around (“reverse gerrymandering”), as each of them tries to maximize their influence across the company’s subunits, or resources are “working” to be allocated to areas where they will be most needed.In this paper we explore an iterative dynamic in this setting, finding that allowing this decentralized system results, in some particular cases, in a stable equilibrium, though in general, the setting may end up in a cycle. We further examine how this decentralized process affects the social welfare of the system.

We examine the surveying problem, where we attempt to predict how a target user is likely to respond to questions by iteratively querying that user, collaboratively based on the responses of a sample set of users. We focus on an active learning approach, where the next question we select to ask the user depends on their responses to the previous questions. We propose a method for solving the problem based on a Bayesian dimensionality reduction technique. We empirically evaluate our method, contrasting it to benchmark approaches based on augmented linear regression, and show that it achieves much better predictive performance, and is much more robust when there is missing data.

Research in artificial intelligence ranges over many subdisciplines, such as Natural Language Processing, Computer Vision, Machine Learning, and MultiAgent Systems. Recently, AI techniques have become increasingly robust and complex, and there has been enhanced interest in research at the intersection of seemingly disparate research areas. Such work is motivated by the observation that there is actually a great deal of commonality among areas, that can be exploited within subfields. One example of a successful combination is the intersection of machine learning and multiagent systems. For example ,Kearns et al. [2001] proposed an efficient graphical model-based algorithm for calculating Nash equilibria. Going in the other direction, Datta et al. [2015] showed that solution concepts from cooperative game theory can be used to uniquely characterize the influence measure of classifiers.

We demonstrate a system for predicting gaming related properties from Twitter accounts. Our system predicts various traits of users based on the tweets publicly available in their profiles. Such inferred traits include degrees of tech-savviness and knowledge on computer games, actual gaming performance, preferred platform, degree of originality, humor and influence on others. Our system is based on machine learning models trained on crowd-sourced data. It allows people to select Twitter accounts of their fellow gamers, examine the trait predictions made by our system, and the main drivers of these predictions. We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset.

We propose a system for determining properties of mathematical functions given an image of their graph representation. We demonstrate our approach for two-dimensional graphs (curves of single variable functions) and three-dimensional graphs (surfaces of two variable functions), studying the properties of convexity and symmetry. Our method uses a Convolutional Neural Network which classifies functions according to these properties, without using any hand-crafted features. We propose algorithms for randomly constructing functions with convexity or symmetry properties, and use the images generated by these algorithms to train our network. Our system achieves a high accuracy on this task, even for functions where humans find it difficult to determine the function's properties from its image.

We consider the task of predicting various traits of a person given an image of their face. We aim to estimate traits such as gender, ethnicity and age, as well as more subjective traits as the emotion a person expresses or whether they are humorous or attractive. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture, and corroborate that CNNs are promising for facial attribute prediction. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. We empirically analyze the performance of our proposed method, showing consistent improvement over the baselines across traits. We demonstrate our system on a sizeable Face Attributes Dataset (FAD), comprising of roughly 200,000 labels, for 10 most sought-after traits, for over 10,000 facial images.

We examine the relation between the emotions users express on social networks and their perceived areas of interests, based on a sample of Twitter users. Our methodology relies on training machine learning models to classify the emotions expressed in tweets, according to Ekman's six high-level emotions. We then used raters, sourced from Amazon's Mechanical Turk, to examine several Twitter profiles and to determine whether the profile owner is interested in various areas, including sports, movies, technology and computing, politics, news, economics, science, arts, health and religion. We find that the propensity of a user to express various emotions correlates with their perceived degree of interest in various areas. We present several models that use the emotional distribution of a Twitter user, as reflected by their tweets, to predict whether they are interested or disinterested in a topic or to determine their degree of interest in a topic.