Tuberculosis (TB), an infectious bacterial disease, is a significant cause of death, especially in low-income countries, with an estimated ten million new cases reported globally in $2020$. While TB is treatable, non-adherence to the medication regimen is a significant cause of morbidity and mortality. Thus, proactively identifying patients at risk of dropping off their medication regimen enables corrective measures to mitigate adverse outcomes. Using a proxy measure of extreme non-adherence and a dataset of nearly $700,000$ patients from four states in India, we formulate and solve the machine learning (ML) problem of early prediction of non-adherence based on a custom rank-based metric. We train ML models and evaluate against baselines, achieving a $\sim 100\%$ lift over rule-based baselines and $\sim 214\%$ over a random classifier, taking into account country-wide large-scale future deployment. We deal with various issues in the process, including data quality, high-cardinality categorical data, low target prevalence, distribution shift, variation across cohorts, algorithmic fairness, and the need for robustness and explainability. Our findings indicate that risk stratification of non-adherent patients is a viable, deployable-at-scale ML solution. As the official AI partner of India's Central TB Division, we are working on multiple city and state-level pilots with the goal of pan-India deployment.

This post takes a closer look at the state of multilingual AI. How multilingual are current models? What are the recent contributions and remaining challenges?

This morning, before checking in on my young son or making a coffee, I opened the Duolingo app on my phone and translated "They love smelling meat" into Italian. I've been starting my days like this for a few months now: wake up, wash face, grapple with the gerund. I usually spend between 10 and 20 minutes on it while the kettle boils or I load CBeebies or write some emails. Duolingo is a language learning app and pretty simple to use. After you've chosen which language you want to learn, you are presented with about 100 skill-sets divided by scenario or grammar (grocery shopping, the future tense and so on).

A Huawei executive said Thursday information and communications technologies, or ICT, will enable the digitalization of industry, spark innovation and make other industries green. The remarks were made at a session organized by the Global Innovation Hub (UGIH) of the United Nations Framework Convention on Climate Change (UNFCCC) at the ongoing 27th Conference of the Parties, or COP27, in Sharm El-Sheikh of Egypt. Referring to what is known as the "enabling effect", Philippe Wang, Huawei's Executive Vice President for the Northern Africa region, said ICT is "making other industries greener". "5G, Artificial Intelligence, data analytics, cloud computing – all these things will improve industrial processes in a way that cuts energy use, and lowers carbon emissions," he said. According to Philippe Wang, in the same way that ICT enables a smart streetlight to turn itself off when no one is around, 5G wireless base stations can automatically shut down when there is no data traffic, which saves energy.

Over the past few years, these machine learning systems have been tweaked and refined, undergoing multiple iterations to find their present popularity with the everyday internet user. These image generators--DALL-E and Midjourney arguably the most prominent--generate imagery from a variety of text prompts, for instance allowing people to create conceptual renditions of architectures of the future, present, and past. But as we exist in a digital landscape filled with human biases--navigating these image generators requires careful reflection. Midjourney is a particularly interesting Artificial Intelligence tool, proving popular amongst artists and designers alike for its painting-like, imaginative images created from sometimes very minimal text prompts. But the results fed back using this tool also raise complicated questions surrounding image-making and design, questions brought to the forefront when using prompts like "African architecture" to produce images.

Numerical Question Answering is the task of answering questions that require numerical capabilities. Previous works introduce general adversarial attacks to Numerical Question Answering, while not systematically exploring numerical capabilities specific to the topic. In this paper, we propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. A series of numerical capabilities are highlighted, and corresponding dataset perturbations are designed. Empirical results indicate that existing systems are severely challenged by these perturbations. E.g., Graph2Tree experienced a 53.83% absolute accuracy drop against the ``Extra'' perturbation on ASDiv-a, and BART experienced 13.80% accuracy drop against the ``Language'' perturbation on the numerical subset of DROP. As a counteracting approach, we also investigate the effectiveness of applying perturbations as data augmentation to relieve systems' lack of robust numerical capabilities. With experiment analysis and empirical studies, it is demonstrated that Numerical Question Answering with robust numerical capabilities is still to a large extent an open question. We discuss future directions of Numerical Question Answering and summarize guidelines on future dataset collection and system design.

Question-answering (QA) that comes naturally to humans is a critical component in seamless human-computer interaction. It has emerged as one of the most convenient and natural methods to interact with the web and is especially desirable in voice-controlled environments. Despite being one of the oldest research areas, the current QA system faces the critical challenge of handling multilingual queries. To build an Artificial Intelligent (AI) agent that can serve multilingual end users, a QA system is required to be language versatile and tailored to suit the multilingual environment. Recent advances in QA models have enabled surpassing human performance primarily due to the availability of a sizable amount of high-quality datasets. However, the majority of such annotated datasets are expensive to create and are only confined to the English language, making it challenging to acknowledge progress in foreign languages. Therefore, to measure a similar improvement in the multilingual QA system, it is necessary to invest in high-quality multilingual evaluation benchmarks. In this dissertation, we focus on advancing QA techniques for handling end-user queries in multilingual environments. This dissertation consists of two parts. In the first part, we explore multilingualism and a new dimension of multilingualism referred to as code-mixing. Second, we propose a technique to solve the task of multi-hop question generation by exploiting multiple documents. Experiments show our models achieve state-of-the-art performance on answer extraction, ranking, and generation tasks on multiple domains of MQA, VQA, and language generation. The proposed techniques are generic and can be widely used in various domains and languages to advance QA systems.

In this work, we focus on intrasentential code-mixing and propose several different Synthetic Code-Mixing (SCM) data augmentation methods that outperform the baseline on downstream sentiment analysis tasks across various amounts of labeled gold data. Most importantly, our proposed methods demonstrate that strategically replacing parts of sentences in the matrix language with a constant mask significantly improves classification accuracy, motivating further linguistic insights into the phenomenon of code-mixing. We test our data augmentation method in a variety of low-resource and cross-lingual settings, reaching up to a relative improvement of 7.73% on the extremely scarce English-Malayalam dataset. We conclude that the code-switch pattern in code-mixing sentences is also important for the model to learn. Finally, we propose a language-agnostic SCM algorithm that is cheap yet extremely helpful for low-resource languages.

Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river flood prediction models already exist and perform relatively well in most high-income countries. But, due to the limited availability of data, these models are lacking in low-income countries. Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset. Our framework aims to serve as a benchmark for future global river flood prediction research. To support generalizability claims we include custom data evaluation splits. Further, we propose and evaluate a novel two-path LSTM architecture (2P-LSTM) against three baseline models. Finally, we evaluate the generated models on different locations in Africa and Asia that were not part of the Caravan dataset.

We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup. Agents can communicate as part of a dynamically changing network. We explore collaboration and communication on the back of a high-impact problem. Forests are the main resource to control rising CO2 conditions. Unfortunately, the global forest volume is decreasing at an unprecedented rate. Many areas are too large and hard to traverse to plant new trees. To efficiently cover as much area as possible, here we propose a Graph Neural Network (GNN) based communication mechanism that enables collaboration. Agents can share location information on areas needing reforestation, which increases viewed area and planted tree count. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.