Artificial intelligence is becoming a critical competitive factor. Economic markets are increasingly being led by companies where artificial intelligence (AI) is calling the shots. But the race for competitive advantage is not just the domain of companies and organizations. Countries are also vying with each other for AI supremacy to strengthen their industries, protect national security, or solve societal challenges. In addition to the United States, the world leaders in AI adoption, research, and development include Asian countries such as China, Singapore, and South Korea.

It is an established fact that China uses civil military fusion to develop intelligence. It is often reported that Chinese mobile phones which are freely available in India pose a threat to national security. It is said that the mobile phones provide the big data needed to develop AI based predictive models. However, it is not largely understood how all this happens. For most of us AI, big data, data theft, intelligence collection et al are imaginary issues.

Companies are increasingly relying on automation to help screen candidates in the hiring process, a trend prompting scrutiny from local governments and regulators. Nearly one in four organizations already use automation or artificial intelligence (AI) to support hiring, according to a February 2022 survey from the Society for Human Resource Management, and usage is higher--42 percent--among large employers with 5,000 or more employees. A recent report from Recode detailed Amazon's ambitions to replace some of its recruiters with AI software that can fast-track candidates to interviews without any human involvement. Today AI technology can do more than just screen resumes. Companies may also use AI tools to monitor candidates' social media presence quickly and pick up on red flags.

We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our physics-informed neural operators to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled partial differential equations. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our physics informed neural operators, and a tutorial for their use at the Data and Learning Hub for Science.

Predictive coding is a message-passing framework initially developed to model information processing in the brain, and now also topic of research in machine learning due to some interesting properties. One of such properties is the natural ability of generative models to learn robust representations thanks to their peculiar credit assignment rule, that allows neural activities to converge to a solution before updating the synaptic weights. Graph neural networks are also message-passing models, which have recently shown outstanding results in diverse types of tasks in machine learning, providing interdisciplinary state-of-the-art performance on structured data. However, they are vulnerable to imperceptible adversarial attacks, and unfit for out-of-distribution generalization. In this work, we address this by building models that have the same structure of popular graph neural network architectures, but rely on the message-passing rule of predictive coding. Through an extensive set of experiments, we show that the proposed models are (i) comparable to standard ones in terms of performance in both inductive and transductive tasks, (ii) better calibrated, and (iii) robust against multiple kinds of adversarial attacks.

Trajectory prediction is an integral component of modern autonomous systems as it allows for envisioning future intentions of nearby moving agents. Due to the lack of other agents' dynamics and control policies, deep neural network (DNN) models are often employed for trajectory forecasting tasks. Although there exists an extensive literature on improving the accuracy of these models, there is a very limited number of works studying their robustness against adversarially crafted input trajectories. To bridge this gap, in this paper, we propose a targeted adversarial attack against DNN models for trajectory forecasting tasks. We call the proposed attack TA4TP for Targeted adversarial Attack for Trajectory Prediction. Our approach generates adversarial input trajectories that are capable of fooling DNN models into predicting user-specified target/desired trajectories. Our attack relies on solving a nonlinear constrained optimization problem where the objective function captures the deviation of the predicted trajectory from a target one while the constraints model physical requirements that the adversarial input should satisfy. The latter ensures that the inputs look natural and they are safe to execute (e.g., they are close to nominal inputs and away from obstacles). We demonstrate the effectiveness of TA4TP on two state-of-the-art DNN models and two datasets. To the best of our knowledge, we propose the first targeted adversarial attack against DNN models used for trajectory forecasting.

A deepfake detector can spot fake videos of Ukraine's president Volodymyr Zelenskyy with high accuracy. This detection system could not only protect Zelenskyy, who was the target of a deepfake attempt during the early months of the Russian invasion of Ukraine, but also be trained to flag deepfakes of other world leaders and business tycoons. "We don't have to distinguish you from a billion people – we just have to distinguish you from [the deepfake made by] whoever is trying to imitate you," says Hany Farid at the University of California, Berkeley. Farid worked with Matyáš Boháček at Johannes Kepler Gymnasium in the Czech Republic to develop detection capabilities for faces, voices, hand gestures and upper body movements. Their research builds on previous work in which a system was trained to detect deepfake faces and head movements of world leaders, such as former president Barack Obama. Boháček and Farid trained a computer model on more than 8 hours of video featuring Zelenskyy that had previously been posted publicly.

A deepfake detector can spot fake videos of Ukraine's president Volodymyr Zelenskyy with high accuracy by analysing a combination of voices, facial expressions and upper body movements. This detection system could not only protect Zelenskyy, who was the target of a deepfake attempt during the early months of the Russian invasion of Ukraine, but also be trained to flag deepfakes of other world leaders and business tycoons. "We don't have to distinguish you from a billion people – we just have to distinguish you from [the deepfake made by] whoever is trying to imitate you," says Hany Farid at the University of California, Berkeley. Farid worked with Matyáš Boháček at Johannes Kepler Gymnasium in the Czech Republic to develop detection capabilities for faces, voices, hand gestures and upper body movements. Their research builds on previous work in which an AI system was trained to detect deepfake faces and head movements of world leaders, such as former president Barack Obama. Boháček and Farid trained a computer model on more than 8 hours of video featuring Zelenskyy that had previously been posted publicly.

This article provides a comprehensive overview of the main ethical issues related to the impact of Artificial Intelligence (AI) on human society. AI is the use of machines to do things that would normally require human intelligence. In many areas of human life, AI has rapidly and significantly affected human society and the ways we interact with each other. It will continue to do so. Along the way, AI has presented substantial ethical and socio-political challenges that call for a thorough philosophical and ethical analysis. Its social impact should be studied so as to avoid any negative repercussions. AI systems are becoming more and more autonomous, apparently rational, and intelligent. This comprehensive development gives rise to numerous issues. In addition to the potential harm and impact of AI technologies on our privacy, other concerns include their moral and legal status (including moral and legal rights), their possible moral agency and patienthood, and issues related to their possible personhood and even dignity. It is common, however, to distinguish the following issues as of utmost significance with respect to AI and its relation to human society, according to three different time periods: (1) short-term (early 21st century): autonomous systems (transportation, weapons), machine bias in law, privacy and surveillance, the black box problem and AI decision-making; (2) mid-term (from the 2040s to the end of the century): AI governance, confirming the moral and legal status of intelligent machines (artificial moral agents), human-machine interaction, mass automation; (3) long-term (starting with the 2100s): technological singularity, mass unemployment, space colonisation. This section discusses why AI is of utmost importance for our systems of ethics and morality, given the increasing human-machine interaction. AI may mean several different things and it is defined in many different ways. When Alan Turing introduced the so-called Turing test (which he called an'imitation game') in his famous 1950 essay about whether machines can think, the term'artificial intelligence' had not yet been introduced. Turing considered whether machines can think, and suggested that it would be clearer to replace that question with the question of whether it might be possible to build machines that could imitate humans so convincingly that people would find it difficult to tell whether, for example, a written message comes from a computer or from a human (Turing 1950). The term'AI' was coined in 1955 by a group of researchers--John McCarthy, Marvin L. Minsky, Nathaniel Rochester and Claude E. Shannon--who organised a famous two-month summer workshop at Dartmouth College on the'Study of Artificial Intelligence' in 1956. This event is widely recognised as the very beginning of the study of AI.

Interest in the possibilities afforded by algorithms and big data continues to blossom as early adopters gain benefits from AI systems that automate decisions as varied as making customer recommendations, screening job applicants, detecting fraud, and optimizing logistical routes.1 But when AI applications fail, they can do so quite spectacularly.2 Consider the recent example of Australia's "robodebt" scandal.3 In 2015, the Australian government established its Income Compliance Program, with the goal of clawing back unemployment and disability benefits that had been made inappropriately to recipients. It set out to identify overpayments by analyzing discrepancies between the annual income that individuals reported and the income assessed by the Australian Tax Office.