Like many stories about people trying to help fix the internet, this one begins in the aftermath of 2016. From his home in Ireland, Conor Brady had watched the Brexit vote and the election of Donald Trump with disbelief. In his view, the prominence of false stories during each election--whether about Muslim immigrants or Hillary Clinton's health--was the direct consequence of a hollowed-out news industry without the resources to check the spread of disinformation. At the time, Conor's son, Neil--also a former journalist--was working as a digital policy analyst at the Institute of International and European Affairs, researching neural networks and machine learning. The two got to thinking.

The direction of AI development is not preordained. It can be altered to increase human productivity, create jobs and shared prosperity, and protect and bolster democratic freedoms--if we modify our approach. The direction of AI development is not preordained. It can be altered to increase human productivity, create jobs and shared prosperity, and protect and bolster democratic freedoms--if we modify our approach. Artificial Intelligence (AI) is not likely to make humans redundant. Nor will it create superintelligence anytime soon. But like it or not, AI technologies and intelligent systems will make huge advances in the next two decades--revolutionizing medicine, entertainment, and transport; transforming jobs and markets; enabling many new products and tools; and vastly increasing the amount of information that governments and companies have about individuals. Should we cherish and look forward to these developments, or fear them? Current AI research is too narrowly focused on making advances in a limited set of domains and pays insufficient attention to its disruptive effects on the very fabric of society. There are reasons to be concerned. Current AI research is too narrowly focused on making advances in a limited set of domains and pays insufficient attention to its disruptive effects on the very fabric of society. If AI technology continues to develop along its current path, it is likely to create social upheaval for at least two reasons. For one, AI will affect the future of jobs. Our current trajectory automates work to an excessive degree while refusing to invest in human productivity; further advances will displace workers and fail to create new opportunities (and, in the process, miss out on AI's full potential to enhance productivity). For another, AI may undermine democracy and individual freedoms. Each of these directions is alarming, and the two together are ominous. Shared prosperity and democratic political participation do not just critically reinforce each other: they are the two backbones of our modern society.

The Way of the Future, a church founded by a former Google and Uber engineer, is now a thing of the past. It's been a few months since the world's first AI-focused church shuttered its digital doors, and it doesn't look like its founder has any interest in a revival. But it's a pretty safe bet we'll be seeing more robo-centric religious groups in the future. Perhaps, however, they won't be about worshipping the machines themselves. The world's first AI church "The Way of the Future," was the brainchild of Anthony Levandowski, a former autonomous vehicle developer who was convicted on 33 counts of theft and attempted theft of trade secrets. In the wake of his conviction, Levandowski was sentenced to 18 months in prison but his sentence was delayed due to COVID and, before he could be ordered to serve it, former president Donald Trump pardoned him.

In a recent survey, "2021 Thriving in an AI World," KPMG found that across every industry--manufacturing to technology to retail--the adoption of artificial intelligence (AI) is increasing year over year. Part of the reason is digital transformation is moving faster, which helps companies start to move exponentially faster. But, as Cliff Justice, US leader for enterprise innovation at KPMG posits, "Covid-19 has accelerated the pace of digital in many ways, across many types of technologies." Justice continues, "This is where we are starting to experience such a rapid pace of exponential change that it's very difficult for most people to understand the progress." But understand it they must because "artificial intelligence is evolving at a very rapid pace." Justice challenges us to think about AI in a different way, "more like a relationship with technology, as opposed to a tool that we program," because he says, "AI is something that evolves and learns and develops the more it gets exposed to humans." If your business is a laggard in AI adoption, Justice has some cautious encouragement, "[the] AI-centric world is going to accelerate everything digital has to offer." Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review.

The Australian government is strengthening the country's digital economy through new investments in artificial intelligence (AI), cyber security and digital government services, among other areas. The investments, aimed at bolstering Australia's competitiveness in the global technology sector, are part of the government's 2021-2022 budget, which was unveiled last week. The centrepiece of the budget is arguably the A$1.2bn Digital Economy Strategy, a set of policies and actions the government is taking to grow Australia's future as a leading digital economy by 2030. But to put that expenditure into perspective, it is less than half of the A$2.6bn earmarked for a single 6km road project in Adelaide – even as it is welcomed by some quarters of the technology industry.

China's space agency has released the first photos taken by the Zhurong rover on Mars, showing parts of its lander and the red planet itself. The Tianwen-1 mission arrived at its destination on May 15th, making China the second nation to successfully soft-land on Mars after the US. One of the photos is a colored image (above) taken by the navigation camera mounted at the rear of the rover. It features Zhurong's solar panels and unfolded antennae, along with a view of the planet's red soil and rocks. The other photo (below) is a black-and-white image taken by an obstacle avoidance camera installed in front of the rover. It was captured using a wide-angle lens, so it not only shows a ramp from the lander extending to the surface of the planet, but also the Martian horizon.

A new report from AI research firm Adversa looked at a number of measurements of the adoption of AI systems, from the number and types of research papers on the topic, to government initiatives that aim to provide policy frameworks for the technology. They found that AI is being rapidly adopted but often without the necessary defenses needed to protect AI systems from targeted attacks. So-called adversarial AI attacks include bypassing AI systems, manipulating results, and exfiltrating the data that the model is based on. These sorts of attacks are not yet numerous, but have happened, and will happen with greater frequency, says Eugene Neelou, co-founder and chief technology officer of Adversa. "Although our research corpus is mostly collected from academia, they have attack cases against AI systems such as smart devices, online services, or tech giant's APIs," he says.

This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorithm to infer causal relations among time-history data measured during each representative volume element (RVE) simulation through a directed acyclic graph (DAG). With multiple plausible sets of causal relationships estimated from multiple RVE simulations, the predictions are propagated in the derived causal graph while using a deep neural network equipped with dropout layers as a Bayesian approximation for uncertainty quantification. We select two representative numerical examples (traction-separation laws for frictional interfaces, elastoplasticity models for granular assembles) to examine the accuracy and robustness of the proposed causal discovery method for the common material law predictions in civil engineering applications.

Neural topic models have triggered a surge of interest in extracting topics from text automatically since they avoid the sophisticated derivations in conventional topic models. However, scarce neural topic models incorporate the word relatedness information captured in word embedding into the modeling process. To address this issue, we propose a novel topic modeling approach, called Variational Gaussian Topic Model (VaGTM). Based on the variational auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian in decoder to incorporate word relatedness. Furthermore, to address the limitation that pre-trained word embeddings of topic-associated words do not follow a multivariate Gaussian, Variational Gaussian Topic Model with Invertible neural Projections (VaGTM-IP) is extended from VaGTM. Three benchmark text corpora are used in experiments to verify the effectiveness of VaGTM and VaGTM-IP. The experimental results show that VaGTM and VaGTM-IP outperform several competitive baselines and obtain more coherent topics.

We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform evaluates NLP models directly instead of relying on self-reported metrics or predictions on a single dataset. Under this paradigm, models are submitted to be evaluated in the cloud, circumventing the issues of reproducibility, accessibility, and backwards compatibility that often hinder benchmarking in NLP. This allows users to interact with uploaded models in real time to assess their quality, and permits the collection of additional metrics such as memory use, throughput, and robustness, which -- despite their importance to practitioners -- have traditionally been absent from leaderboards. On each task, models are ranked according to the Dynascore, a novel utility-based aggregation of these statistics, which users can customize to better reflect their preferences, placing more/less weight on a particular axis of evaluation or dataset. As state-of-the-art NLP models push the limits of traditional benchmarks, Dynaboard offers a standardized solution for a more diverse and comprehensive evaluation of model quality.