Best listening experience is on Chrome, Firefox or Safari. The irony of artificial intelligence is how much human brainpower is required to build it. For three years, our next guest had been on loan from the University of Massachusetts, to the Defense Advanced Research Projects Agency. There's she headed up several DARPA artificial intelligence projects. Now she's been awarded a high honor, the Meritorious Public Service Medal.

Uncovering evidence for historical theories and identifying patterns in past events has long been hindered by the labour-intensive process of inputting data from artefacts and handwritten records. The adoption of artificial intelligence and machine learning techniques is speeding up such research and drawing attention to overlooked information. But this approach, known as "digital humanities", is in a battle for funding against more future-focused applications of AI. "There is a lot of interest in digital humanities, but there is not a lot of money," says Ilan Shimshoni, professor of computer vision and machine learning at the University of Haifa in Israel, where he works on archaeological projects that include reassembling artefacts from photos of fragments. "If you want to do an analysis of Facebook you'll get much more money than if you want to look at ancient Greek artefacts." Archaeological puzzles may not seem as urgent as computer science projects in healthcare, finance and other industries, but applying algorithmic techniques to historical research can improve AI's capabilities, says Ayellet Tal, an archaeological and computer science researcher at Israel's Technion University.

Organising ethical debates has long been an efficient way for industry to delay and avoid hard regulation. Europe now needs strong, enforceable rights for its citizens, writes Green MEP Alexandra Geese. If the rules are too weak, there is a too great a risk that our rights and freedoms will be undermined: This currently applies to all applications of artificial intelligence, which up to now have only been based on non-binding ethical principles and values. In this legislation, Europe has the chance to adopt a legal framework for AI with clear rules. We need strong instruments to protect our fundamental rights and democracy.

The statistical and computational performance of sparse principal component analysis (PCA) can be dramatically improved when the principal components are allowed to be sparse in a rotated eigenbasis. For this, we propose a new method for sparse PCA. In the simplest version of the algorithm, the component scores and loadings are initialized with a low-rank singular value decomposition. Then, the singular vectors are rotated with orthogonal rotations to make them approximately sparse. Finally, soft-thresholding is applied to the rotated singular vectors. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. Our sparse PCA framework is versatile; for example, it extends naturally to the two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We identify the close relationship between sparse PCA and independent component analysis for separating sparse signals. We provide empirical evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications---sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of Twitter accounts, we demonstrate the usefulness of sparse PCA in exploring modern multivariate data.

The existence of adversarial examples capable of fooling trained neural network classifiers calls for a much better understanding of possible attacks, in order to guide the development of safeguards against them. It includes attack methods in the highly challenging non-interactive blackbox setting, where adversarial attacks are generated without any access, including queries, to the target model. Prior works in this setting have relied mainly on algorithmic innovations derived from empirical observations (e.g., that momentum helps), and the field currently lacks a firm theoretical basis for understanding transferability in adversarial attacks. In this work, we address this gap and lay the theoretical foundations for crafting transferable adversarial examples to entire function classes. We introduce Adversarial Examples Games (AEG), a novel framework that models adversarial examples as two-player min-max games between an attack generator and a representative classifier. We prove that the saddle point of an AEG game corresponds to a generating distribution of adversarial examples against entire function classes. Training the generator only requires the ability to optimize a representative classifier from a given hypothesis class, enabling BlackBox transfer to unseen classifiers from the same class. We demonstrate the efficacy of our approach on the MNIST and CIFAR-10 datasets against both undefended and robustified models, achieving competitive performance with state-of-the-art BlackBox transfer approaches.

Accurately forecasting county level COVID-19 confirmed cases is crucial to optimizing medical resources. Forecasting emerging outbreaks pose a particular challenge because many existing forecasting techniques learn from historical seasons trends. Recurrent neural networks (RNNs) with LSTM-based cells are a logical choice of model due to their ability to learn temporal dynamics. In this paper, we adapt the state and county level influenza model, TDEFSI-LONLY, proposed in Wang et a. [l2020] to national and county level COVID-19 data. We show that this model poorly forecasts the current pandemic. We analyze the two week ahead forecasting capabilities of the TDEFSI-LONLY model with combinations of regularization techniques. Effective training of the TDEFSI-LONLY model requires data augmentation, to overcome this challenge we utilize an SEIR model and present an inter-county mixing extension to this model to simulate sufficient training data. Further, we propose an alternate forecast model, {\it County Level Epidemiological Inference Recurrent Network} (\alg{}) that trains an LSTM backbone on national confirmed cases to learn a low dimensional time pattern and utilizes a time distributed dense layer to learn individual county confirmed case changes each day for a two weeks forecast. We show that the best, worst, and median state forecasts made using CLEIR-Net model are respectively New York, South Carolina, and Montana.

Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.

The recently proposed Thermodynamic Variational Objective (TVO) leverages thermodynamic integration to provide a family of variational inference objectives, which both tighten and generalize the ubiquitous Evidence Lower Bound (ELBO). However, the tightness of TVO bounds was not previously known, an expensive grid search was used to choose a "schedule" of intermediate distributions, and model learning suffered with ostensibly tighter bounds. In this work, we propose an exponential family interpretation of the geometric mixture curve underlying the TVO and various path sampling methods, which allows us to characterize the gap in TVO likelihood bounds as a sum of KL divergences. We propose to choose intermediate distributions using equal spacing in the moment parameters of our exponential family, which matches grid search performance and allows the schedule to adaptively update over the course of training. Finally, we derive a doubly reparameterized gradient estimator which improves model learning and allows the TVO to benefit from more refined bounds. To further contextualize our contributions, we provide a unified framework for understanding thermodynamic integration and the TVO using Taylor series remainders.

Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the climate changes, precipitation and rainfall-runoff pattern variations become more extreme, and accurate training data that can account for the resulting distributional shifts become more scarce. In this work we present a novel family of hydrologic models, called HydroNets, which leverages river network structure. HydroNets are deep neural network models designed to exploit both basin specific rainfall-runoff signals, and upstream network dynamics, which can lead to improved predictions at longer horizons. The injection of the river structure prior knowledge reduces sample complexity and allows for scalable and more accurate hydrologic modeling even with only a few years of data. We present an empirical study over two large basins in India that convincingly support the proposed model and its advantages.

"Made in China" used to mean cheap and poor quality, and probably involving the theft of intellectual property somewhere along the line. That perception has been out of date for many years now. Counterfeiting by Chinese manufacturers is still a major problem in some industries, but the best Chinese companies are world leaders in quality and in innovation. European telecoms utilities are alarmed by Trump's demand that they exclude Huawei components from their 5G rollout programmes: if they comply, their 5G services will be late and expensive. China's two mobile payments giants, Alibaba's Alipay and Tencent's WeChat Pay, both have many more active users than PayPal and Apple Pay combined.