In an effort to avoid reinforcing widespread hype about the capabilities of state-of-the-art language technology, researchers have developed practices in framing and citation that serve to deemphasize the field's successes. Though well-meaning, these practices often yield misleading or even false claims about the limits of our best technology. This is a problem, and it may be more serious than it looks: It limits our ability to mitigate short-term harms from NLP deployments and it limits our ability to prepare for the potentially enormous impacts of more distant future advances. This paper urges researchers to be careful about these claims and suggests some research directions and communication strategies that will make it easier to avoid or rebut them.

In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining the flexibility and modularity on the definitions of complex event rules, (iii) allowing the system to be trained in an end-to-end manner and (iv) being robust against noisily labelled data. Our approach makes use of DeepProbLog to create a neuro-symbolic architecture that combines a neural network to process the subsymbolic data with a probabilistic logic layer to allow the user to define the rules for the complex events. We demonstrate that our approach is capable of detecting complex events from an audio stream. We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.

In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs. In this work, we show that showing controlled counterfactual image-question examples are more effective at improving the mental model of users as compared to simply showing random examples. We compare a generative approach and a retrieval-based approach to show counterfactual examples. We use recent advances in generative adversarial networks (GANs) to generate counterfactual images by deleting and inpainting certain regions of interest in the image. We then expose users to changes in the VQA system's answer on those altered images. To select the region of interest for inpainting, we experiment with using both human-annotated attention maps and a fully automatic method that uses the VQA system's attention values. Finally, we test the user's mental model by asking them to predict the model's performance on a test counterfactual image. We note an overall improvement in users' accuracy to predict answer change when shown counterfactual explanations. While realistic retrieved counterfactuals obviously are the most effective at improving the mental model, we show that a generative approach can also be equally effective.

Gaussian processes (GP) are a widely-adopted tool used to sequentially optimize black-box functions, where evaluations are costly and potentially noisy. Recent works on GP bandits have proposed to move beyond random noise and devise algorithms robust to adversarial attacks. In this paper, we study this problem from the attacker's perspective, proposing various adversarial attack methods with differing assumptions on the attacker's strength and prior information. Our goal is to understand adversarial attacks on GP bandits from both a theoretical and practical perspective. We focus primarily on targeted attacks on the popular GP-UCB algorithm and a related elimination-based algorithm, based on adversarially perturbing the function $f$ to produce another function $\tilde{f}$ whose optima are in some region $\mathcal{R}_{\rm target}$. Based on our theoretical analysis, we devise both white-box attacks (known $f$) and black-box attacks (unknown $f$), with the former including a Subtraction attack and Clipping attack, and the latter including an Aggressive subtraction attack. We demonstrate that adversarial attacks on GP bandits can succeed in forcing the algorithm towards $\mathcal{R}_{\rm target}$ even with a low attack budget, and we compare our attacks' performance and efficiency on several real and synthetic functions.

Extreme events with potential deadly outcomes, such as those organized by terror groups, are highly unpredictable in nature and an imminent threat to society. In particular, quantifying the likelihood of a terror attack occurring in an arbitrary space-time region and its relative societal risk, would facilitate informed measures that would strengthen national security. This paper introduces a novel self-exciting marked spatio-temporal model for attacks whose inhomogeneous baseline intensity is written as a function of covariates. Its triggering intensity is succinctly modeled with a Gaussian Process prior distribution to flexibly capture intricate spatio-temporal dependencies between an arbitrary attack and previous terror events. By inferring the parameters of this model, we highlight specific space-time areas in which attacks are likely to occur. Furthermore, by measuring the outcome of an attack in terms of the number of casualties it produces, we introduce a novel mixture distribution for the number of casualties. This distribution flexibly handles low and high number of casualties and the discrete nature of the data through a {\it Generalized ZipF} distribution. We rely on a customized Markov chain Monte Carlo (MCMC) method to estimate the model parameters. We illustrate the methodology with data from the open source Global Terrorism Database (GTD) that correspond to attacks in Afghanistan from 2013-2018. We show that our model is able to predict the intensity of future attacks for 2019-2021 while considering various covariates of interest such as population density, number of regional languages spoken, and the density of population supporting the opposing government.

Science fiction movies like "Blade Runner" and "The Terminator" have defined the perception of artificial intelligence within popular culture. For most people, the term AI conjures up images of a dystopian future dominated by humanoid robots that have taken over the world. This common conception leads to the dismissal of the technology as impossible, or at least faroff in the future. Few people realize that we are already delving into a world dominated by AI, and it's nothing like "The Terminator." The actual risks posed by artificial intelligence have nothing to do with killer robots; they relate to the machine-learning algorithms that recommend content on the internet.

October 14, 2021 – On September 7, U.S. Air Force Chief Software Officer Nicolas Chaillan resigned in a post on Linked in titled, "It is time to say Goodbye!" "I realize more clearly than ever before," he wrote, "that in 20 years from now, our children...will have no chance competing in a world where China has the drastic advantage of population over the US." Chaillan told'Fox & Friends First' Thursday that his goal in resigning was not to admit defeat, but to implore people to understand that'we're running out of time.' "If we do not take action right now, we will be facing a situation where we will not be able to catch up." "I resigned," he said, "because I wanted to raise the alarm and ensure we take action before it is too late." The issue, Chaillan believes, lies in part with top U.S. officials: "The reports coming out from the Pentagon saying that China is catching up...when really it is now a real threat to our democracy" he said. STEPHEN MOORE: BIDEN THINKS CLIMATE CHANGE IS A BIGGER THREAT THAN CHINA. Chaillan joined the Pentagon as Chief Software Officer in 2019, the first to hold that title. There, he quickly identified a key difference between the way the U.S. and China interact with their private-sector technology companies.

The US military may be getting a dog-like quadruped robot armed with a sniper rifle. The robot, developed by Ghost Robotics of Philadelphia, is a new version of its Vision series of legged robots. The US Air Force is currently testing an unarmed version of these robots for use as perimeter security at the Tyndall Air Force Base in Florida. Ghost Robotics displayed the armed version at the annual meeting of the Association of the United States Army held in Washington DC this week. The robot is fitted with a Special Purpose Unmanned Rifle pod from Sword Defense, with a powerful 6.5mm sniper rifle.

Boston Dynamics, the company most commonly associated with robot dogs, prohibits the weaponization of its Spot devices. One of them, Ghost Robotics, showed off a version of its Q-UGV device that many will have been dreading. It's a robot dog with a gun attached to it. Ghost Robotics has made robot dogs for the military, and it displayed this deadly model at the Association of the United States Army's 2021 annual conference in Washington DC this week. A company called Sword International built the "special purpose unmanned rifle" (or SPUR) module. According to The Verge, it has a thermal camera for nighttime operation, an effective range of 1.2km (just under three quarters of a mile) and a 30x optical zoom.

In one song by English pop band Busted, 'not much has changed' by the year 3000, 'but they live underwater'. If carbon dioxide emissions continue to rise, the Amazon will be barren, the American Midwest tropical and India too hot to live in come the year 2500. This is the gloomy warning of a research team led from Canada's McGill University who modelled future climates under three greenhouse gas mitigation scenarios. They then illustrated their worst case scenarios -- alongside sketches of life in the year 1500 AD and the present -- to highlight how alien the future Earth could be. The findings, they said, highlight the importance of not stopping climate predictions at the 2100 benchmark, as is common, but also considering the ongoing impacts.