Recording Industry Association of America CEO Mitch Glazier says the Human Artistry Campaign aims to protect professional creators' rights to their performances, voices and likenesses after AI creates Drake and The Weeknd songs. A pilot program in the U.K. to enhance police capabilities via artificial intelligence has proven successful but could pave the way for a slide into a future of "predictive policing," experts told Fox News Digital. "Artificial intelligence is a tool, like a firearm is a tool, and it can be useful, it can be deadly," Christopher Alexander, CCO of Liberty Blockchain, told Fox News Digital. "In terms of the Holy Grail here, I really think it is the predictive analytics capability that if they get better at that, you have some very frightening capabilities." British police in different communities have experimented with an artificial intelligence-powered (AI) system to help catch drivers committing violations, such as using their phones while driving or driving without a seat belt.

Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when hateful discourse may arise. Using graph transformer networks, coupled with modelling attention and BERT-level natural language processing, our approach can capture context and anticipate upcoming anti-social behaviour. In this paper, we offer a detailed qualitative analysis of this solution for hate speech detection in social networks, leading to insights into where the method has the most impressive outcomes in comparison with competitors and identifying scenarios where there are challenges to achieving ideal performance. Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images. This suggests avenues for extending our model to be more comprehensive. A key insight is that the focus on reasoning about the concept of context positions us well to be able to support multi-modal analysis of online posts. We conclude with a reflection on how the problem we are addressing relates especially well to the theme of dynamic change, a critical concern for all AI solutions for social impact. We also comment briefly on how mental health well-being can be advanced with our work, through curated content attuned to the extent of hate in posts.

Non-governmental organizations for environmental conservation have a significant interest in monitoring conservation-related media and getting timely updates about infrastructure construction projects as they may cause massive impact to key conservation areas. Such monitoring, however, is difficult and time-consuming. We introduce NewsPanda, a toolkit which automatically detects and analyzes online articles related to environmental conservation and infrastructure construction. We fine-tune a BERT-based model using active learning methods and noise correction algorithms to identify articles that are relevant to conservation and infrastructure construction. For the identified articles, we perform further analysis, extracting keywords and finding potentially related sources. NewsPanda has been successfully deployed by the World Wide Fund for Nature teams in the UK, India, and Nepal since February 2022. It currently monitors over 80,000 websites and 1,074 conservation sites across India and Nepal, saving more than 30 hours of human efforts weekly. We have now scaled it up to cover 60,000 conservation sites globally.

Critical infrastructures like water treatment facilities and power plants depend on industrial control systems (ICS) for monitoring and control, making them vulnerable to cyber attacks and system malfunctions. Traditional ICS anomaly detection methods lack transparency and interpretability, which make it difficult for practitioners to understand and trust the results. This paper proposes a two-phase dual Copula-based Outlier Detection (COPOD) method that addresses these challenges. The first phase removes unwanted outliers using an empirical cumulative distribution algorithm, and the second phase develops two parallel COPOD models based on the output data of phase 1. The method is based on empirical distribution functions, parameter-free, and provides interpretability by quantifying each feature's contribution to an anomaly. The method is also computationally and memory-efficient, suitable for low- and high-dimensional datasets. Experimental results demonstrate superior performance in terms of F1-score and recall on three open-source ICS datasets, enabling real-time ICS anomaly detection.

In this article I propose an approach for defining replicability for prediction rules. Motivated by a recent NAS report, I start from the perspective that replicability is obtaining consistent results across studies suitable to address the same prediction question, each of which has obtained its own data. I then discuss concept and issues in defining key elements of this statement. I focus specifically on the meaning of "consistent results" in typical utilization contexts, and propose a multi-agent framework for defining replicability, in which agents are neither partners nor adversaries. I recover some of the prevalent practical approaches as special cases. I hope to provide guidance for a more systematic assessment of replicability in machine learning.

Motion planning of autonomous agents in partially known environments with incomplete information is a challenging problem, particularly for complex tasks. This paper proposes a model-free reinforcement learning approach to address this problem. We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task. The LTL formula is then converted to a limit-deterministic generalized B\"uchi automaton (LDGBA). The problem is redefined as finding an optimal policy on the product of PL-POMDP with LDGBA based on model-checking techniques to satisfy the complex task. We implement deep Q learning with long short-term memory (LSTM) to process the observation history and task recognition. Our contributions include the proposed method, the utilization of LTL and LDGBA, and the LSTM-enhanced deep Q learning. We demonstrate the applicability of the proposed method by conducting simulations in various environments, including grid worlds, a virtual office, and a multi-agent warehouse. The simulation results demonstrate that our proposed method effectively addresses environment, action, and observation uncertainties. This indicates its potential for real-world applications, including the control of unmanned aerial vehicles (UAVs).

Bryan Stern and Mark Geist discusses helping Americans out of a war zone after being left behind by the Biden admin. The U.S. has evacuated its first group of American citizens and permanent residents from Sudan since war broke out in the capital weeks ago. The land evacuation started Friday with efforts to bus a large group of Americans to the Red Sea via Port Sudan. Officials revealed Saturday that unmanned aircraft provided armed overwatch as a bus convoy carried 200 to 300 Americans over 500 miles. Smoke is seen in Khartoum, Sudan, Wednesday, April 19, 2023.

The most interesting TV I've watched recently did not come from a conventional television channel, nor even from Netflix, but from TV coverage of parliament. It was a recording of a meeting of the AI in weapons systems select committee of the House of Lords, which was set up to inquire into "how should autonomous weapons be developed, used and regulated". The particular session I was interested in was the one held on 20 April, during which the committee heard from four expert witnesses – Kenneth Payne, who is professor of strategy at King's College London; Keith Dear, director of artificial intelligence innovation at the computer company Fujitsu; James Black from the defence and security research group of Rand Europe; and Courtney Bowman, global director of privacy and civil liberties engineering at Palantir UK. An interesting mix, I thought – and so it turned out to be. Autonomous weapons systems are ones that can select and attack a target without human intervention. It is believed (and not just by their boosters) that these systems could revolutionise warfare, and may be faster, more accurate and more resilient than existing weapons systems.

Ransomware gangs have long sought pain points where their extortion demands have the greatest leverage. Now an investigation from NBC News has made clear what that merciless business model looks like when it targets kids: One ransomware group's giant leak of sensitive files from the Minneapolis school system exposes thousands of children at their most vulnerable, complete with behavioral and psychological reports on individual students and highly sensitive documentation of cases where they've allegedly been abused by teachers and staff. But first, WIRED contributor Kim Zetter broke the news this week that the Russian hackers who carried out the notorious SolarWinds espionage operation were detected in the US Department of Justice's network six months earlier than previously reported--but the DOJ didn't realize the full scale of the hacking campaign that would later be revealed. Meanwhile, WIRED reporter Lily Hay Newman was at the RSA cybersecurity conference in San Francisco, where she brought us stories of how security researchers disrupted the operators of the Gootloader malware who sold access to victims' networks to ransomware groups and other cybercriminals, and how Google Cloud partnered with Intel to hunt for and fix serious security vulnerabilities that underlie critical cloud servers. She also captured a warning in a talk from NSA cybersecurity director Rob Joyce, who told the cybersecurity industry to "buckle up" and prepare for big changes to come from AI tools like ChatGPT, which will no doubt be wielded by both attackers and defenders alike.

Turkish defence firm Baykar aims to begin production of its new unmanned combat aerial vehicle next year which is already attracting international interest, its chairman Selcuk Bayraktar said. Named "Kizilelma", the drone expands the company's product range from slow, ground attack drones to fast and agile autonomous ones that work alongside fighter jets. "It is designed to be a highly autonomous, under human purview of course, air-to-air combat vehicle," said Bayraktar, who led the design of the 15-metre-long (49 feet) jet-powered weapon. "In a sense, the Kizilelma expresses a whole new future for combat aviation." Baykar has come to prominence internationally in recent years because of the company's light drone TB-2, which has been used in Ukraine, Azerbaijan, and North Africa and has been a huge export success, catapulting the firm to becoming one of the largest Turkish defence exporters.