This text discusses several popular explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the explanatory methods are accepted tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree surrogate models, individual conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), partial dependence plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable application domain. Along with descriptions of these methods, this text presents real-world usage recommendations supported by a use case and public, in-depth software examples for reproducibility.

The U.S. and China are locked in an increasingly heated struggle for superpower status. Many perceived this confrontation initially only through the lenses of a trade war. However, the ZTE "saga" already indicated the issue was broader and involved a battle for supremacy over 21st century technologies and, relatedly, for international power (see When AI Started Creating AI – Artificial Intelligence and Computing Power, 7 May 2018). The technological battle increasingly looks like a fight to the death, with the offensive against Huawei, aiming notably to protect future 5G networks (Cassell Bryan-Low, Colin Packham, David Lague, Steve Stecklow And Jack Stubbs, "The China Challenge: the 5G Fight", Reuters Investigates, 21 May 2019). For Huawei and China, as well as for the world, consequences are far reaching, as, after Google "stopping Huawei's Android license", and an Intel and Qualcomm ban, the British chip designer ARM, held notably by Japanese Softbank, now stops relations with Huawei (Paul Sandle, "ARM supply halt deals fresh blow to Chinese tech giant Huawei", Reuters, 22 May 2019; "DealBook Briefing: The Huawei Backlash Goes Global", The New York Times, 23 May 2019; Tom Warren, "Huawei's Android And Windows Alternatives Are Destined For Failure", The Verge, 23 May 2019). The highly possible coming American move against Chinese Hikvision, one of the largest world producers of video surveillance systems involving notably "artificial intelligence, speech monitoring and genetic testing" would only further confirm the American offensive (Doina Chiacu, Stella Qi, "Trump says'dangerous' Huawei could be included in U.S.-China trade deal", Reuters, 23 May 2019; Ana Swanson and Edward Wong, "Trump Administration Could Blacklist China's Hikvision, a Surveillance Firm", The New York Times, 21 May 2019). China, for its part, answers to both the trade war and the technological fight with an ideologically martial mobilisation of its population along the lines of "People's War", "The Long March", and changing TV scheduling to broadcast war films (Iris Zhao and Alan Weedon, "Chinese television suddenly switches scheduling to anti-American films amid US-China trade war", ABC News, 20 May 2019; Michael Martina, David Lawder, "Prepare for difficult times, China's Xi urges as trade war simmers", Reuters, 22 May 2019). This highlights how much is as stake for the Middle Kingdom, as we explained previously ( Sensor and Actuator (4): Artificial Intelligence, the Long March towards Advanced Robots and Geopolitics).

This week, I interviewed Yuval Noah Harari, the author of three best-selling books about the history and future of our species, and Fei-Fei Li, one of the pioneers in the field of artificial intelligence. The event was hosted by the Stanford Center for Ethics and Society, the Stanford Institute for Human-Centered Artificial Intelligence, and the Stanford Humanities Center. A transcript of the event follows, and a video is posted below. Nicholas Thompson: Thank you, Stanford, for inviting us all here. I want this conversation to have three parts: First, lay out where we are; then talk about some of the choices we have to make now; and last, talk about some advice for all the wonderful people in the hall. Yuval, the last time we talked, you said many, many brilliant things, but one that stuck out was a line where you said, "We are not just in a technological crisis. We are in a philosophical crisis." So explain what you meant and explain how it ties to AI. Let's get going with a note of ...

Hours spent toiling away under the beating sun to harvest berries and fruit may soon be a thing of the past as robots look set to replace humans in the field. A £700,000 machine built by the University of Plymouth has succeeded in plucking a raspberry from a plant and carefully placing it in a punnet. The painstaking process takes a whole minute to get one berry because it requires a combination of soft robotics, clever AI and'deep learning'. It stands around six foot tall (1.8metres) and will combat a continued drop in the amount of migrant farm workers available for the arduous harvests. Fieldwork Robotics, a spin-off from the university dedicated to agricultural robots, built the machine and says it will be able to pick 25,000 fruits a day in the future.

New Zealand is a leader in government use of artificial intelligence (AI). It is part of a global network of countries that use predictive algorithms in government decision making, for anything from the optimal scheduling of public hospital beds to whether an offender should be released from prison, based on their likelihood of reoffending, or the efficient processing of simple insurance claims. But the official use of AI algorithms in government has been in the spotlight in recent years. On the plus side, AI can enhance the accuracy, efficiency and fairness of day-to-day decision making. But concerns have also been expressed regarding transparency, meaningful human control, data protection and bias.

Last week, Mona Lisa smiled. A big, wide smile, followed by what appeared to be a laugh and the silent mouthing of words that could only be an answer to the mystery that had beguiled her viewers for centuries. A great many people were unnerved. Mona's "living portrait," along with likenesses of Marilyn Monroe, Salvador Dali, and others, demonstrated the latest technology in deepfakes--seemingly realistic video or audio generated using machine learning. Developed by researchers at Samsung's AI lab in Moscow, the portraits display a new method to create credible videos from a single image.

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning presents us with a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. We outline fundamental machine learning methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that links data with modeling, experiments, and simulations. Machine learning provides a powerful information processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the non-abstained samples. We show how such a deep abstaining classifier (DAC) can be used for robust learning in the presence of different types of label noise. In the case of structured or systematic label noise -- where noisy training labels or confusing examples are correlated with underlying features of the data-- training with abstention enables representation learning for features that are associated with unreliable labels. In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise. We provide analytical results on the loss function behavior that enable dynamic adaption of abstention rates based on learning progress during training. We demonstrate the utility of the deep abstaining classifier for various image classification tasks under different types of label noise; in the case of arbitrary label noise, we show significant improvements over previously published results on multiple image benchmarks.

Over recent years, devising classification algorithms that are robust to adversarial perturbations has emerged as a challenging problem. In particular, deep neural nets (DNNs) seem to be susceptible to small imperceptible changes over test instances. In this work, we study whether there is any learning task for which it is possible to design classifiers that are only robust against polynomial-time adversaries. Indeed, numerous cryptographic tasks (e.g. encryption of long messages) are only be secure against computationally bounded adversaries, and are indeed mpossible for computationally unbounded attackers. Thus, it is natural to ask if the same strategy could help robust learning. We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating a classifier for a learning task in which computational and information theoretic adversaries of bounded perturbations have very different power. Namely, while computationally unbounded adversaries can attack successfully and find adversarial examples with small perturbation, polynomial time adversaries are unable to do so unless they can break standard cryptographic hardness assumptions. Our results, therefore, indicate that perhaps a similar approach to cryptography (relying on computational hardness) holds promise for achieving computationally robust machine learning. We also show that the existence of such learning task in which computational robustness beats information theoretic robustness implies (average case) hard problems in $\mathbf{NP}$.

Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets, and highlight directions for future research.