When soldiers look through the sights of their assault rifles with the Elbit System's new artificial intelligence data platform, their view is transformed to resemble a first-person shooter video game. Shooters push buttons on a grip to toggle among layers of information about their surroundings, including motion detection, range, ammunition levels and more data that's just a click away. ARCAS, which the Israel-based company is featuring at the DSEI conference in London, incorporates a microcomputer in the weapon to process data and provide a graphical user interface to display the information in the rifle's electro-optical sight and through an optional helmet-mounted eyepiece. The demo used ARCAS systems mounted on M-4s, with testers shooting at stationary targets. The use of ideas from the gaming world is clear when putting the sight up to the eye.

A new form of "influence government", which uses sensitive personal data to craft campaigns aimed at altering behaviour has been "supercharged" by the rise of big tech firms, researchers have warned. National and local governments have turned to targeted advertisements on search engines and social media platforms to try to "nudge" the behaviour of the country at large, the academics found. The shift to this new brand of governance stems from a marriage between the introduction of nudge theory in policymaking and an online advertising infrastructure that provides unforeseen opportunities to run behavioural adjustment campaigns. Some of the examples found by the Scottish Centre for Crime and Criminal Justice (SCCCJ) range from a Prevent-style scheme to deter young people from becoming online fraudsters to tips on how to light a candle properly. While targeted advertising is common across business, one researcher argues that the government using it to drive behavioural change could create a perfect feedback loop.

More than 500 websites have promoted misinformation about the coronavirus – including debunked claims about vaccines, according to a firm that rates the credibility of websites. NewsGuard announced Wednesday that, of the more than 6,700 websites it has analyzed, 519 have published false information about COVID-19. Some of the sites publish dubious health information or political conspiracy theories, while others were "created specifically to spread misinformation about COVID-19," the company says on its website. "It's become virtually impossible for people to tell the difference between a generally reliable site and an untrustworthy site," Gordon Crovitz, co-founder of NewsGuard, told USA TODAY in an exclusive interview. "And that is why there is such a big business in publishing this information."

We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. For example, the 6B-parameter GPT-J model was 17% less truthful than its 125M-parameter counterpart. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.

The Automatic Dependent Surveillance Broadcast protocol is one of the latest compulsory advances in air surveillance. While it supports the tracking of the ever-growing number of aircraft in the air, it also introduces cybersecurity issues that must be mitigated e.g., false data injection attacks where an attacker emits fake surveillance information. The recent data sources and tools available to obtain flight tracking records allow the researchers to create datasets and develop Machine Learning models capable of detecting such anomalies in En-Route trajectories. In this context, we propose a novel multivariate anomaly detection model called Discriminatory Auto-Encoder (DAE). It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase (e.g. climbing, cruising or descending) during its training.To illustrate the DAE's efficiency, an evaluation dataset was created using real-life anomalies as well as realistically crafted ones, with which the DAE as well as three anomaly detection models from the literature were evaluated. Results show that the DAE achieves better results in both accuracy and speed of detection. The dataset, the models implementations and the evaluation results are available in an online repository, thereby enabling replicability and facilitating future experiments.

With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.

The field of artificial intelligence (AI), regarded as one of the most enigmatic areas of science, has witnessed exponential growth in the past decade including a remarkably wide array of applications, having already impacted our everyday lives. Advances in computing power and the design of sophisticated AI algorithms have enabled computers to outperform humans in a variety of tasks, especially in the areas of computer vision and speech recognition. Yet, AI's path has never been smooth, having essentially fallen apart twice in its lifetime ('winters' of AI), both after periods of popular success ('summers' of AI). We provide a brief rundown of AI's evolution over the course of decades, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another 'winter'.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirty-Fifth Conference on Artificial Intelligence was held virtually from February 8-9, 2021. There were twenty-six workshops in the program: Affective Content Analysis, AI for Behavior Change, AI for Urban Mobility, Artificial Intelligence Safety, Combating Online Hostile Posts in Regional Languages during Emergency Situations, Commonsense Knowledge Graphs, Content Authoring and Design, Deep Learning on Graphs: Methods and Applications, Designing AI for Telehealth, 9th Dialog System Technology Challenge, Explainable Agency in Artificial Intelligence, Graphs and More Complex Structures for Learning and Reasoning, 5th International Workshop on Health Intelligence, Hybrid Artificial Intelligence, Imagining Post-COVID Education with AI, Knowledge Discovery from Unstructured Data in Financial Services, Learning Network Architecture During Training, Meta-Learning and Co-Hosted Competition, ...

When you think about Artificial Intelligence (AI) and war, you might find yourself thinking about killer robots, like those we have seen in movies such as The Terminator. In reality, AI and warfare looks quite different from these popularized images, and today we see many countries around the world exploring the use of AI and implementing AI systems into their militaries and defense programs. With this increased interest in AI, there has also been a growing debate about the ethics and legality of using AI in warfare. While there are many concerning aspects about AI being utilized in warfare, one that is particularly troubling, but has also received less attention, is that of biased AI systems. Certain lessons can be learnt by looking at examples of biased AI in non-military settings. It has become increasingly clear from a number of investigations and studies that the biases that exist within our society will also become embedded into AI.

Watch now: Bloomington police discuss Jelani Day remains identified Kourtney Kardashian and Megan Fox team up for sexy SKIMS campaign Watch now: Jelani Day's mother calls on local police for answers Watch now: Mother of missing ISU graduate student Jelani Day pleads for her son's return Watch now: Pritzker speaks about Illinois electric vehicle expansion Watch now: ISU president talks about graduate worker negotiations Watch now: Bloomington police discuss impact of Jelani Day case Watch now: Jelani Day's mother speaks about her missing son Watch now: Bloomington Police discuss Jelani Day identification Can the Philadelphia Phillies make the playoffs? Watch now: Coroner confirms remains found in Illinois river are Jelani Day's Watch now: Autumnal Equinox marks beginning of fall Watch now: Andy Dalton's fate hanging in balance with his knee Watch now: Durbin says push for immigration reform isn't over Watch now: Conor McGregor mocked for first pitch at Cubs game Watch now: Jelani Day's mother calls on local police for answers Watch now: Jelani Day's mother speaks about her missing son Can the Philadelphia Phillies make the playoffs? Watch now: Coroner confirms remains found in Illinois river are Jelani Day's Watch now: Durbin says push for immigration reform isn't over