Physicists are increasingly developing artificial intelligence and machine learning techniques to advance our understanding of the physical world but there is a rising concern about the bias in such systems and their wider impact on society at large. In 2011, during her undergraduate degree at Georgia Institute of Technology, Ghanaian-US computer scientist Joy Buolamwini discovered that getting a robot to play a simple game of peek-a-boo with her was impossible – the machine was incapable of seeing her dark-skinned face. Later, in 2015, as a Master's student at Massachusetts Institute of Technology's Media Lab working on a science–art project called Aspire Mirror, she had a similar issue with facial analysis software: it detected her face only when she wore a white mask. Buolamwini's curiosity led her to run one of her profile images across four facial recognition demos, which, she discovered, either couldn't identify a face at all or misgendered her – a bias that she refers to as the "coded gaze". She then decided to test 1270 faces of politicians from three African and three European countries, with different features, skin tones and gender, which became her Master's thesis project "Gender Shades: Intersectional accuracy disparities in commercial gender classification" (figure 1).

A manager hires a new employee, and offers to pay her $1,000 a day. She replies: "I'll do you one better. Why don't you pay me one penny on my first day, and double my pay every day from there until the month is over?" Sensing a bargain, the manager agrees. Such is the price of failing to respect exponential growth.

Artificial intelligence (AI) has the potential to affect ever more aspects of military and civilian life as part of the fourth industrial revolution. Countries are racing for global AI dominance, and whoever'wins' shall reap the economic and geopolitical power expected to result. However, AI-enhanced technologies could pose new security risks that have not been encountered before. Ultimately, this paper argues that the UK and the EU should approach outsourcing critical communications infrastructure with caution and take recent security concerns involving China more seriously. The UK Project on Nuclear Issues 2020 papers can be found here and recording can be viewed here.

Facial recognition is becoming an important tool for many applications involving people and cameras. As we continue to exit out of this global pandemic, facial recognition and more broadly facial analytics will continue to build in importance, even as the associated technical challenges and required capabilities grow. There have been various significant advances in leveraging some of the latest applications in AI technology for facial recognition. According to research published by the Centre for Strategic and International Studies (CSIS), facial recognition systems have nearly absolute precision in ideal conditions, reaching a 99.97 per cent recognition accuracy level. It has to be noted, however, that conditions were far from ideal in many scenarios, and with the uncontrolled environment of CCTV on the streets, the accuracy is commonly lower even with more advanced algorithmic techniques.

This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations including business emails, panel discussions, and work channels. Unlike open-domain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge. In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions, from 10,259 selected conversations with both human-written and machine-generated questions. We segment long conversations into chunks, and use a question generator and dialogue summarizer as auxiliary tools to collect multi-hop questions. The dataset has two testing scenarios, chunk mode and full mode, depending on whether the grounded chunk is provided or retrieved from a large conversational pool. Experimental results show that state-of-the-art QA systems trained on existing QA datasets have limited zero-shot ability and tend to predict our questions as unanswerable. Fine-tuning such systems on our corpus can achieve significant improvement up to 23.6% and 13.6% in both chunk mode and full mode, respectively.

Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are trained across sets of time series have shown a huge potential in providing accurate forecasts compared with the traditional univariate forecasting models that work on isolated series. However, there are currently no comprehensive time series archives for forecasting that contain datasets of time series from similar sources available for the research community to evaluate the performance of new global forecasting algorithms over a wide variety of datasets. In this paper, we present such a comprehensive time series forecasting archive containing 20 publicly available time series datasets from varied domains, with different characteristics in terms of frequency, series lengths, and inclusion of missing values. We also characterise the datasets, and identify similarities and differences among them, by conducting a feature analysis. Furthermore, we present the performance of a set of standard baseline forecasting methods over all datasets across eight error metrics, for the benefit of researchers using the archive to benchmark their forecasting algorithms.

We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination therapies in an inductive and interpretable manner. Though deep learning algorithms are widely used in the drug synergy prediction problem, it is still an open problem to formulate the prediction model with biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI collaboration in healthcare systems. To address the challenges, we propose a deep graph neural network, IDSP (Interpretable Deep Signaling Pathways), to incorporate the gene-gene as well as gene-drug regulatory relationships in synergic drug combination predictions. IDSP automatically learns weights of edges based on the gene and drug node relations, i.e., signaling interactions, by a multi-layer perceptron (MLP) and aggregates information in an inductive manner. The proposed architecture generates interpretable drug synergy prediction by detecting important signaling interactions, and can be implemented when the underlying molecular mechanism encounters unseen genes or signaling pathways. We test IDWSP on signaling networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data. The experimental results demonstrated that 1) IDSP can learn from the underlying molecular mechanism to make prediction without additional drug chemical information while achieving highly comparable performance with current state-of-art methods; 2) IDSP show superior generality and flexibility to implement the synergy prediction task on both transductive tasks and inductive tasks. 3) IDSP can generate interpretable results by detecting different salient signaling patterns (i.e. MoS) for different cell lines.

There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allow the swap of depleted for full batteries in minutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov Decision Process model for the stochastic scheduling, allocation, and inventory replenishment problem (SAIRP), which determines when and how many batteries are charged, discharged, and replaced over time. We present theoretical proofs for the monotonicity of the value function and monotone structure of an optimal policy for special SAIRP cases. Due to the curses of dimensionality, we develop a new monotone approximate dynamic programming (ADP) method, which intelligently initializes a value function approximation using regression. In computational tests, we demonstrate the superior performance of the new regression-based monotone ADP method as compared to exact methods and other monotone ADP methods. Further, with the tests, we deduce policy insights for drone swap stations.

Machine learning for anomaly detection has become a widely researched field in cybersecurity. Inherent to today's operating environment is the practice of adversarial machine learning, which attempts to circumvent machine learning models. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection in the network intrusion detection system setting, as well as leverage an ensemble approach to supervised learning of the anomaly detection problem. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments. Our results indicate that the unsupervised and graph-based methods were outperformed in detecting anomalies (malicious activity) by the supervised stacking ensemble method with two levels. This model consists of three different classifiers in the first level, followed by either a Naive Bayes or Decision Tree classifier for the second level. We see that our model maintains an F1-score above 0.97 for malicious samples across all tested level two classifiers. Notably, Naive Bayes is the fastest level two classifier averaging 1.12 seconds while Decision Tree maintains the highest AUC score of 0.98.

The US Air Force conducted a flight test that paves the way for AI-piloted fighter jets to man the skies. The military group flew its Skyborg Autonomy Core System (ACS) for two hours and 10 minutes over Florida and Gulf of Mexico on April 29. The technology is a combination of hardware and software designed to act as a brain for a drone, allowing it to conduct operations without human interference. Fitted to a Kratos UTAP-22 tactical unmanned vehicle, the ACS demonstrated basic aviation capabilities and responded to navigational commands, while reacting to geo-fences, adhering to aircraft flight envelopes and demonstrating coordinated maneuvering. The US Air Force conducted a flight test that paves the way for AI-piloted fighter jets to man the skies.