There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD. However, they are difficult to reproduce because key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method provides a real improvement, if any. We propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into BIDS format, ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types, classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available at: https://gitlab.icm-institute.org/aramislab/AD-ML.

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships. Most existing methods, however, assume a known cause (e.g. a new drug) and focus on identifying from data the contexts of heterogeneous effects of the cause (e.g. patient groups with different responses to the new drug). There is no approach to efficiently detecting directly from observational data context specific causal relationships, i.e. discovering the causes and their contexts simultaneously. In this paper, by taking the advantages of highly efficient decision tree induction and the well established causal inference framework, we propose the Tree based Context Causal rule discovery (TCC) method, for efficient exploration of context specific causal relationships from data. Experiments with both synthetic and real world data sets show that TCC can effectively discover context specific causal rules from the data.

Given a text description, most existing semantic parsers synthesize a program in one shot. However, in reality, the description can be ambiguous or incomplete, solely based on which it is quite challenging to produce a correct program. In this paper, we investigate interactive semantic parsing for If-Then recipes where an agent can interact with users to resolve ambiguities. We develop a hierarchical reinforcement learning (HRL) based agent that can improve the parsing performance with minimal questions to users. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.

Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e.g., captions) have been modified to provide visual substantiation of the rumor/falsehood. This type of modification is referred to as image repurposing, in which often an unmanipulated image is published along with incorrect or manipulated metadata to serve the actor's ulterior motives. We present the Multimodal Entity Image Repurposing (MEIR) dataset, a substantially challenging dataset over that which has been previously available to support research into image repurposing detection. The new dataset includes location, person, and organization manipulations on real-world data sourced from Flickr. We also present a novel, end-to-end, deep multimodal learning model for assessing the integrity of an image by combining information extracted from the image with related information from a knowledge base. The proposed method is compared against state-of-the-art techniques on existing datasets as well as MEIR, where it outperforms existing methods across the board, with AUC improvement up to 0.23.

As the World Economic Forum publishes the most extensive and in-depth report yet on artificial intelligence's transformation of the financial services sector, Internet of Business editor Chris Middleton presents a 3,500-word breakdown of the document's key findings and highlights. The financial services sector is in the vanguard of deploying artificial intelligence (AI) worldwide. However, the technology has the potential to be either a transformative and beneficial force, or a destabilising, even existential threat to the global financial system, according to the World Economic Forum. This risks of economic contagion spreading via the technology are real, it says. The WEF has published an in-depth report, The New Physics of Financial Services: Understanding how artificial intelligence is transforming the financial ecosystem, produced in collaboration with C-level executives, analysts, and technology specialists from across every part of the industry. The 166-page sector analysis finds that the bonds that have historically held financial institutions together are weakening as a result of new technologies. This is creating new threats, new opportunities, and new centres of gravity where emerging and established capabilities are being combined in unexpected ways.

The Nature Morte gallery in New Delhi opens its doors to a unique, one of kind show today. Titled'Gradient Descent', the show is the country's first Artificial Intelligence (AI) art exhibition. Curated by 64/1, a Bengaluru-based curation and research collective (founded by Raghava KK and Karthik Kalyanaraman that focuses on raising awareness on AI's place in the realm of contemporary art), 'Gradient Descent' showcases the works of seven, carefully picked artists from the US, Japan, Germany, Turkey, India, UK and New Zealand. Each of these artists, equipped with a strong foundational background in artificial neural networking, has collaborated with AI to produce art. Conceptualised and planned meticulously since February, the show will run till 15 September.

Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains. In this manuscript, we develop a novel method, transfer latent representation (TLR), to learn a better latent space. Specifically, we design an objective function based on a simple linear autoencoder to derive the latent representations of both domains. The encoder in the autoencoder aims to project the data of both domains into a robust latent space. Besides, the decoder imposes an additional constraint to reconstruct the original data, which can preserve the common properties of both domains and reduce the noise that causes domain shift. Experiments on cross-domain tasks demonstrate the advantages of TLR over competing methods.

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data. Beneath the surface of the roiling sea of data we're generating hide exceptional business and sales opportunities. But the problem is there's now more information available than limited human resources and legacy tools can handle. Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at.

In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of actual labels for examples. The proposed algorithms solve a convex optimization problem at every trial. We find exact solution to those optimization problems to determine the updated parameters. We propose support class algorithm (SCA) which finds the active constraints using the KKT conditions of the optimization problems. These active constrains form support set which determines the set of thresholds that need to be updated. We derive update rules for PA, PA-I and PA-II. We show that the proposed algorithms maintain the ordering of the thresholds after every trial. We provide the mistake bounds of the proposed algorithms in both ideal and general settings. We also show experimentally that the proposed algorithms successfully learn accurate classifiers using interval labels as well as exact labels. Proposed algorithms also do well compared to other approaches.