Artificial intelligence may soon be deciding who lives or dies. The US Army wants to build smart missiles that will use AI to select their targets, out of reach of human oversight. The project has raised concerns that the missiles will be a form of lethal autonomous weapon – a technology many people are campaigning to ban. The US Army's project is called Cannon-Delivered Area Effects Munition (C-DAEM). Companies will bid for the contract to build the weapon, with the requirements stating it should be able to hit "moving and imprecisely located armoured targets" …

Facebook has become the latest company to admit that human contractors listened to recordings of users without their knowledge, a practice the company now says has been "paused". Citing contractors who worked on the project, Bloomberg News reported on Tuesday that the company hired people to listen to audio conversations carried out on Facebook Messenger. The practice involved users who had opted in Messenger to have their voice chats transcribed, the company said. The contractors were tasked with re-transcribing the conversations in order to gauge the accuracy of the automatic transcription tool. "Much like Apple and Google, we paused human review of audio more than a week ago," a Facebook spokesperson told the Guardian.

The fingerprints of over 1 million people, as well as facial recognition information, unencrypted usernames and passwords, and personal information of employees, was discovered on a publicly accessible database for a company used by the likes of the UK Metropolitan police, defence contractors and banks. Suprema is the security company responsible for the web-based Biostar 2 biometrics lock system that allows centralised control for access to secure facilities like warehouses or office buildings. Biostar 2 uses fingerprints and facial recognition as part of its means of identifying people attempting to gain access to buildings. Last month, Suprema announced its Biostar 2 platform was integrated into another access control system – AEOS. AEOS is used by 5,700 organisations in 83 countries, including governments, banks and the UK Metropolitan police.

In this Thursday, July 11, 2019, photograph, United States Department of Agriculture intern Alex Olsen prepares to place down a drone at a research farm northeast of Greeley, Colo. After a brief, snaking flight above the field, the drone landed and the researchers removed a handful of memory cards. Back at their computers, they analyzed the images for signs the corn was stressed from a lack of water. This U.S. Department of Agriculture station outside Greeley and other sites across the Southwest are experimenting with drones, specialized cameras and other technology to squeeze the most out of every drop of water in the Colorado River – a vital but beleaguered waterway that serves an estimated 40 million people. Should they still be able to use it?

A computer code can simulate a system's propagation of variation from random inputs to output measures of quality. Our aim here is to estimate a critical output tail probability or quantile without a large Monte Carlo experiment. Instead, we build a statistical surrogate for the input-output relationship with a modest number of evaluations and then sequentially add further runs, guided by a criterion to improve the estimate. We compare two criteria in the literature. Moreover, we investigate two practical questions: how to design the initial code runs and how to model the input distribution. Hence, we close the gap between the theory of sequential design and its application.

The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena. To aid in interpreting and diagnosing the novel aspect of these selected observations, we recommend the use of novelty detection methods that generate explanations. In the context of large image data sets, these explanations should highlight what aspect of a given image is new (color, shape, texture, content) in a human-comprehensible form. We propose DEMUD-VIS, the first method for providing visual explanations of novel image content by employing a convolutional neural network (CNN) to extract image features, a method that uses reconstruction error to detect novel content, and an up-convolutional network to convert CNN feature representations back into image space. We demonstrate this approach on diverse images from ImageNet, freshwater streams, and the surface of Mars.

Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applicable to any input domain. One such body of work, deep Taylor decomposition, propagates relevance from the model output distributively onto its input and thus is not restricted to image processing models. However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks. We instead propose a discriminative method that produces a na\"ive representation of both the spatial and temporal relevance of a frame as two separate objects. This new discriminative relevance model exposes relevance in the frame attributed to motion, that was previously ambiguous in the original explanation. We observe the effectiveness of this technique on a range of samples from the UCF-101 action recognition dataset, two of which are demonstrated in this paper.

HPCwire: Walk us through the program, give us a sense of what these AI and science town halls are all about and what they are trying to accomplish? RS: If you remember back in 2007, we had three town hall meetings – at Argonne, Berkeley and Oak Ridge – that launched the whole DOE Exascale project and so forth. At that time the idea was to get people together and ask them, for exascale, what if we could build these faster machines, what would you do with them. It was a way to get people thinking about the possibility of that and of course it took long time to get the exascale computing program going. With these town halls we are kind of asking a variation on that question. Now we're asking the question of what's the opportunity for AI in science or the application of science, particularly in the context of DOE, but more broadly because DOE's got a lot of collaborations with NIH and other agencies. So really asking the fundamental question of what do we have to do in the AI space to make it relevant for science. The point of the town halls – three in the labs and one in Washington in October – is go get people thinking about what opportunities there are in different scientific domains for breakthrough science that can be accomplished by leveraging AI and working AI into simulation, and bringing AI into big data, bringing AI to the facility and so forth. So that's the concept; it's really to get the community moving.

A few years ago, consulting giant McKinsey predicted that artificial intelligence and automation would eliminate a 73 million jobs by 2030. That's a scary number of jobs, and a prediction that led many professionals to evaluate their own skills and research ways to future-proof their careers from the coming automation/AI job apocalypse. In the absence of an elder expert giving you one word of advice, like "Plastics!" what can an IT professional (or a new graduate) do to ensure that the robots don't come to take your job? That's been a key question over the past few years, and one without that simple one-word answer. Experts have said that jobs focused on implementing and managing artificial intelligence and automation would be great avenues for job-seekers to pursue.

Chinese search engine Sogou is creating artificial-intelligence lookalikes to read popular novels in authors' voices. It announced "lifelike" avatars of Chinese authors Yue Guan and Bu Xin Tian Shang Diao Xian Bing - created from video recordings - at the China Online Literature conference. Last year, Sogou launched two AI newsreaders, which are still used by the government's Xinhua news agency. Appetite for audiobooks in China is on the rise, mirroring trends in the West. Chinese think tank iiMedia expects the market to more than double between 2016 and 2020, to 7.8bn Chinese yuan (£900m) a year. It is now a simple process to use text-to-speech technology to quickly generate an audio version of a book, using digitised, synthetic voices.