Several recent works have suggested to represent semantic relations with questions and answers, decomposing textual information into separate interrogative natural language statements. In this paper, we consider three QA-based semantic tasks - namely, QA-SRL, QANom and QADiscourse, each targeting a certain type of predication - and propose to regard them as jointly providing a comprehensive representation of textual information. To promote this goal, we investigate how to best utilize the power of sequence-to-sequence (seq2seq) pre-trained language models, within the unique setup of semi-structured outputs, consisting of an unordered set of question-answer pairs. We examine different input and output linearization strategies, and assess the effect of multitask learning and of simple data augmentation techniques in the setting of imbalanced training data. Consequently, we release the first unified QASem parsing tool, practical for downstream applications who can benefit from an explicit, QA-based account of information units in a text.

An artificial intelligence agent recently flew the Lockheed Martin VISTA X-62A training aircraft for over 17 hours. VISTA (which stands for Variable In-flight Simulation Test Aircraft) can use software to simulate the performance characteristics of other aircraft. The flight took place during a testing period in December. This is the first time that AI has been engaged in such a way on a tactical aircraft, Lockheed says. The aim is to use the platform to test aircraft designs that can be flown autonomously.

As OpenAI's release of ChatGPT in late 2022 and expected release of GPT-4 in 2023 continues to garner widespread attention, there is renewed focus on both opportunities and risks presented by the use of artificial intelligence ("AI"). With this focus comes the inevitable call for regulation. At the end of 2022, the U.S. White House weighed in through what it calls an "AI Bill of Rights" for the American public, a non-binding policy document. Banks and others in financial services should take note of the particular civil rights, privacy, and other priorities expressed in this vision for the future of AI governance. In financial services, technologies deploying some element of AI are expected to increase but already abound.

Machine learning based algorithms are now in the wild, and read their environment to react to it. The past has already exhibited relatively benign forms of cascades or chain reactions between these. With the increasing interconnectivity of networks and complexity of algorithms, unpredictable chain reactions might lead to severe flash collapses. This possibility stays largely under the shadow of some less likely events such as a threatening strong artificial intelligence. Due to the sensational progress of machine learning -- and in particular neural networks -- for solving cognitive tasks, was resurrected a fear of technological annihilation by an "autonomous artificial intelligence".

Originally published on Towards AI. With ChatGPT blowing the internet, we are at a critical juncture that demands us to again ask hard questions about the impact of AI models on society, a conversation that starts but never ends. In this article, I aim to bring attention to the importance of knowing that, even though large AI models are impressive, there are often unacknowledged costs behind them. It is like saying " data is the new oil" to describe its value, but this analogy often ignores the costs of the oil and mining industries. To understand what AI is made from, we need to leave Silicon Valley and go to the place where the stuff for the AI industry is made. The term "artificial intelligence" may evoke the ideas of algorithms and data, but it is powered by the rare earth's minerals and resources that make up the computing components [1].

A startup called Istari, backed by Eric Schmidt, the former CEO of Google and chair of Alphabet, reckons some of that work can be done more effectively in the metaverse. Ishtari uses machine learning to virtually assemble and test war machines from computer models of individual components, such as the chassis and engines, that are usually marooned on separate digital drawing boards. It may sound dull, but Schmidt says it can bring a dose of tech industry innovation to US military engineering. "The Istari team is bringing internet-type usability to models and simulations," he says. "This unlocks the possibility of software-like agility for future physical systems--it is very exciting."

Britain's National Health Service has become a story of crisis. The coronavirus pandemic almost broke it -- and the hangover still might. This winter has played out against a backdrop of record waiting lists, ambulances unable to deliver patients to hospitals and picket lines of striking nurses. For a host of medical practitioners and scientists and tech firms and politicians, the NHS -- the U.K.'s biggest employer -- has finally reached a tipping point after 75 years, and the time has come to remake it. Programs and studies under way include "virtual wards" for remote care, family doctors paying energy bills for vulnerable patients, and scientists using artificial intelligence to predict the impact of cold and damp homes on children's health.

Narrative sensemaking is an essential part of understanding sequential data. Narrative maps are a visual representation model that can assist analysts to understand narratives. In this work, we present a semantic interaction (SI) framework for narrative maps that can support analysts through their sensemaking process. In contrast to traditional SI systems which rely on dimensionality reduction and work on a projection space, our approach has an additional abstraction layer -- the structure space -- that builds upon the projection space and encodes the narrative in a discrete structure. This extra layer introduces additional challenges that must be addressed when integrating SI with the narrative extraction pipeline. We address these challenges by presenting the general concept of Mixed Multi-Model Semantic Interaction (3MSI) -- an SI pipeline, where the highest-level model corresponds to an abstract discrete structure and the lower-level models are continuous. To evaluate the performance of our 3MSI models for narrative maps, we present a quantitative simulation-based evaluation and a qualitative evaluation with case studies and expert feedback. We find that our SI system can model the analysts' intent and support incremental formalism for narrative maps.

Rapid testing is essential to fighting pandemics such as COVID-19, the disease caused by the SARS-CoV-2 virus. Exhaled human breath contains multiple volatile molecules providing powerful potential for non-invasive diagnosis of diverse medical conditions. We investigated breath detection of SARS-CoV-2 infection using cavity-enhanced direct frequency comb spectroscopy (CE-DFCS), a state-of-the-art laser spectroscopic technique capable of a real-time massive collection of broadband molecular absorption features at ro-vibrational quantum state resolution and at parts-per-trillion volume detection sensitivity. Using a total of 170 individual breath samples (83 positive and 87 negative with SARS-CoV-2 based on Reverse Transcription Polymerase Chain Reaction tests), we report excellent discrimination capability for SARS-CoV-2 infection with an area under the Receiver-Operating-Characteristics curve of 0.849(4). Our results support the development of CE-DFCS as an alternative, rapid, non-invasive test for COVID-19 and highlight its remarkable potential for optical diagnoses of diverse biological conditions and disease states.

We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class labels at each bag is available. Albeit, the objective of the learner is to achieve low task loss at an individual instance level. Here we propose Easyllp: a flexible and simple-to-implement debiasing approach based on aggregate labels, which operates on arbitrary loss functions. Our technique allows us to accurately estimate the expected loss of an arbitrary model at an individual level. We showcase the flexibility of our approach by applying it to popular learning frameworks, like Empirical Risk Minimization (ERM) and Stochastic Gradient Descent (SGD) with provable guarantees on instance level performance. More concretely, we exhibit a variance reduction technique that makes the quality of LLP learning deteriorate only by a factor of k (k being bag size) in both ERM and SGD setups, as compared to full supervision. Finally, we validate our theoretical results on multiple datasets demonstrating our algorithm performs as well or better than previous LLP approaches in spite of its simplicity.