In 2002, a couple of Japanese visitors to Australia swapped passports with each other before walking through an automatic biometric border control gate being tested at Sydney airport. The facial recognition algorithm falsely matched each of them to the others' passport photo. These gentlemen were in fact part of an international aviation industry study group and were in the habit of trying to fool biometric systems then being trialed round the world. When I heard about this successful prank, I quipped that the algorithms were probably written by white people - because we think all Asians look the same. Colleagues thought I was making a typical sick joke, but actually I was half-serious.

Wine, spirits and craft beer retailer Fine Wine Delivery is using IBM's Watson artificial intelligence (AI) to help customers choose products from its range. It says, through IBM Watson, consumers are now able to access a level of expert product knowledge to "assist them in their discovery of the complex world of wine, craft beer and spirits" from their smartphone or tablet. Fine Wine Delivery operates an independent, expert tasting panel that creates tasting notes on all of its more than 2000 products. The company says it wanted to make that knowledge more accessible to customers and create an online experience that was as informative as chatting to their expert team in-store. To achieve this it worked with Auckland-based AI specialist, Spacetime to create a natural language search by ingesting the original tasting notes and using IBM Watson Virtual Assistant to provide customised advice online.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data.

InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability - glassbox models, which are machine learning models designed for interpretability (ex: linear models, rule lists, generalized additive models), and blackbox explainability techniques for explaining existing systems (ex: Partial Dependence, LIME). The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform. InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models. The MIT licensed source code can be downloaded from github.com/microsoft/interpret.

There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to their sheer size which poses a serious challenge for deployment. We aim to tackle this issue for the specific task of Visual Question Answering (VQA). A Convolutional Neural Network (CNN) is an integral part of the visual processing pipeline of a VQA model (assuming the CNN is trained along with entire VQA model). In this project, we propose an efficient and modular neural architecture for the VQA task with focus on the CNN module. Our experiments demonstrate that a sparsely activated CNN based VQA model achieves comparable performance to a standard CNN based VQA model architecture.

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.

Artificial intelligence is learning how to predict El Niño climate cycles. The hope is that the technology could be used to improve climate predictions and give policy-makers more time to prepare. El Niño can cause severe weather and devastating damage. A phase of the El Niño-Southern Oscillation, it occurs when water warms over the tropical Pacific Ocean, shifting east and increasing rainfall and cyclones over the Americas while pulling rain away from Indonesia and Australia. Strong El Niño events are associated with intense storms and flooding in some areas, and drought and fires in others.

RIYADH – Saudi Arabia alleged Wednesday an attack by drones and cruise missiles on the heart of the kingdom's oil industry was "unquestionably sponsored by Iran," naming but not directly accusing Tehran of launching the assault. Iran denies being involved in the attack claimed by Yemeni rebels, and has threatened the U.S. that it will retaliate "immediately" if Tehran is targeted in response. The news conference by Saudi military spokesman Col. Turki al-Malki comes after a summer of heightened tensions between Iran and the U.S. over President Donald Trump unilaterally withdrawing America from Tehran's 2015 nuclear deal with world powers. The U.S. alleges Iran launched the attack, which Yemen's Houthi rebels earlier claimed as a response to the yearslong Saudi-led war there that's killed tens of thousands of people. Al-Malki made a point not to directly accuse Iran of firing the weapons or launching them from inside of Iranian territory.

Sometimes, you can't see what's right in front of you. This is doubly true if what's in front of you is terabytes of data; the hundreds of thousands of text messages, images, videos, and phone records that constitute just a fraction of the evidence in a criminal investigation. Law enforcement agencies the world over are faced with this exact scenario--enormous amounts of data isolated in siloed systems. Sorting through information in search of relevant insights, the type of leads that could crack a case is often a tedious, manual process. That's where AI solutions based in Microsoft Azure can help.