Disinformation campaigns are not new--think of wartime propaganda used to sway public opinion against an enemy. What is new, however, is the use of the internet and social media to spread these campaigns. The spread of disinformation via social media has the power to change elections, strengthen conspiracy theories, and sow discord. Steven Smith, a staff member from MIT Lincoln Laboratory's Artificial Intelligence Software Architectures and Algorithms Group, is part of a team that set out to better understand these campaigns by launching the Reconnaissance of Influence Operations (RIO) program. Their goal was to create a system that would automatically detect disinformation narratives as well as those individuals who are spreading the narratives within social media networks.

Turkey aims to be among the first countries to have an entirely artificial intelligence (AI)-controlled unmanned warplane, with plans for it to take to the Turkish skies in 2023, President Recep Tayyip Erdoğan said Wednesday. The success of Turkish unmanned aerial vehicles (UAV) in the field has produced results that "require war strategies to be rewritten," the president said. Erdoğan was speaking at the ruling Justice and Development Party's (AK Party) parliamentary group meeting in the capital Ankara. The president added that currently a total of 180 Bayraktar TB2 unmanned combat aerial vehicles (UCAVs) are operated in four countries, including Turkey. Previously, Turkish drone magnate Baykar's Chief Technology Officer Selçuk Bayraktar said the maiden flight of the prototype of the country's domestically-made unmanned fighter jet is scheduled for 2023.

While people are used to paying attention to the tone and meaning of the words that have been heard, developed AI-based algorithms are being taught to detect connections between words themselves and to see the irony and intentional lies. Now intelligence officers/ agencies are ready to prove the ability of artificial intelligence to detect the human art of sarcasm. The tool, funded in part by the U.S. military, lets them analyze the social media content (including comments, posts, forum talks) while ignoring posts that shouldn't be taken seriously. A couple of scientists from the University of Central Florida conclude the following: "Сertain words in specific combinations can be a predictable indicator of sarcasm in a social media post, even if there isn't much context". To simplify, there are some words - "triggers", that get more attention from the system and help it to identify sarcasm. Thus, words as'just', 'again', 'totally', or exclamation mark make it easier to recognize the suggestion of sarcasm in text.

Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.

Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant information or identify the instruction flows to complete a task, which is prone to failures. If such texts are structured, we can readily visualize instruction-flows, reason or infer a particular step, or even build automated systems to help novice agents achieve a goal. However, this structure recovery task is a challenge because of such texts' diverse nature. This paper proposes to identify relevant information from such texts and generate information flows between sentences. We built a large annotated procedural text dataset (CTFW) in the cybersecurity domain (3154 documents). This dataset contains valuable instructions regarding software vulnerability analysis experiences. We performed extensive experiments on CTFW with our LM-GNN model variants in multiple settings. To show the generalizability of both this task and our method, we also experimented with procedural texts from two other domains (Maintenance Manual and Cooking), which are substantially different from cybersecurity. Our experiments show that Graph Convolution Network with BERT sentence embeddings outperforms BERT in all three domains

A multidisciplinary team from the Idaho and Argonne National Laboratories, Kairos Power, and Curtiss-Wright, along with support from academics, have developed digital twin nuclear reactors. By using a US$5.2 million grant from the Department of Energy's Advanced Research Projects Agency-Energy, the scientists and engineers have engaged a physics-based machine learning process to construct and later maintain the digital twin reactors. By grounding the machine learning algorithm in actual physics, the artificial intelligence model generates predictions that are more robust and reliable when compared to more abstract models. The complex nature of this approach provides two layers of problem-solving simultaneously. First, a machine learning-driven predictive maintenance system actively avoids unexpected outages while optimizing maintenance, and predicts mechanical failure before prototypical mechanical stress indicates as much.

Artemis was the twin sister of Apollo and goddess of the Moon in Greek mythology. NASA has chosen her to personify its path back to the Moon, which will see astronauts return to the lunar surface by 2024 - including the first woman and the next man. Artemis 1, formerly Exploration Mission-1, is the first in a series of increasingly complex missions that will enable human exploration to the Moon and Mars. Artemis 1 will be the first integrated flight test of NASA's deep space exploration system: the Orion spacecraft, Space Launch System (SLS) rocket and the ground systems at Kennedy Space Center in Cape Canaveral, Florida. Artemis 1 will be an uncrewed flight that will provide a foundation for human deep space exploration, and demonstrate our commitment and capability to extend human existence to the Moon and beyond.

Roughly three months ago, Evanna Hu realized something had to change. For months, she and other Asian Americans working in the national security field had heard a startling number of anecdotes about a climate of fear and hostility toward people of Asian heritage. Approval for security clearances were "taking a lot longer" for some government employees and contractors. At the State Department, more Asian American diplomats are facing restrictions on where they can serve and what positions they can hold--a process that has grown so dispiriting that one employee told CNN, "It helps immensely to change one's last name." For Hu, the chief executive of an artificial intelligence company that does business with the Defense Department, she began noticing microaggressions and "not-so-micro aggressions" in her interactions with government officials.

Like a bolt from the blue, the Navy has a new modernization priority -- Project Overmatch, a campaign to accelerate delivery of artificial intelligence, machine learning and tools needed to allow the fleet to disperse forces, mass fires, integrate unmanned ships and, in the view of service leaders, maintain maritime dominance in the future. The project aims to begin delivering the Naval Operational Architecture (NOA), a lackluster name for a breathtaking effort whose results will determine nothing less than the service's future ability to establish and sustain sea control by integrating network infrastructure, data and analytic tools to provide decision-advantage in a fight. "Beyond recapitalizing our undersea nuclear deterrent, there is no higher developmental priority in the U.S. Navy," Chief of Naval Operations Adm. Mike Gilday wrote in Oct. 1, 2020, memo to Rear Adm. Douglas Small establishing Project Overmatch. "Your goal is to enable a Navy that swarms the sea, delivering synchronized lethal and nonlethal effects from near and far, every axis and every domain." Small, who in addition to heading Project Overmatch is head of Naval Information Warfare Systems Command, was further tasked by the CNO "to develop the networks, infrastructure, data architecture, tools, and analytics that support the operational and developmental environment that will enable our sustained maritime dominance." The two-star admiral says he has committed the memo to memory and, for good measure, carries a copy at all times.

In March this year, the UK government announced an assertive agenda on artificial intelligence (AI) by launching a UK Cyber Security Council and revealing plans to publish a National Artificial Intelligence Strategy (the UK Strategy). The details of the UK Strategy will be released later this year, but at this point we understand that it will focus in particular on promoting growth of the economy through widespread use of AI with, at the same time, an emphasis on ethical, safe, and trustworthy development of AI--including through the development of a legislative framework for AI which will promote public trust and a level playing field. Shortly after the UK government's announcement, the EU Commission published a proposed EU-wide AI legislative framework (the EU Regulation) which is part of the Commission's overall "AI package". The EU Regulation is focused on ensuring the safety of individuals and the protection of fundamental human rights, and categorises AI into unacceptable, high- or low-risk use cases. The EU Regulation proposes to protect users "where the risks that the AI systems pose are particularly high". The definition and categories of high-risk use cases of AI are broad, and capture many if not most use cases that relate to individuals, including AI use in the context of biometric identification and categorisation of natural persons, management of critical infrastructure, and employment and worker management.