The Cameras for Allsky Meteor Surveillance (CAMS) project, funded by NASA starting in 2010, aims to map our meteor showers by triangulating meteor trajectories detected in low-light video cameras from multiple locations across 16 countries in both the northern and southern hemispheres. Its mission is to validate, discover, and predict the upcoming returns of meteor showers. Our research aimed to streamline the data processing by implementing an automated cloud-based AI-enabled pipeline and improve the data visualization to improve the rate of discoveries by involving the public in monitoring the meteor detections. This article describes the process of automating the data ingestion, processing, and insight generation using an interpretable Active Learning and AI pipeline. This work also describes the development of an interactive web portal (the NASA Meteor Shower portal) to facilitate the visualization of meteor radiant maps. To date, CAMS has discovered over 200 new meteor showers and has validated dozens of previously reported showers.

Nowadays, autonomous cars are gaining traction due to their numerous potential applications on battlefields and in resolving a variety of other real-world challenges. The main goal of our project is to build an autonomous system using DeepRacer which will follow a specific person (for our project, a soldier) when they will be moving in any direction. Two main components to accomplish this project is an optimized Single-Shot Multibox Detection (SSD) object detection model and a Reinforcement Learning (RL) model. We accomplished the task using SSD Lite instead of SSD and at the end, compared the results among SSD, SSD with Neural Computing Stick (NCS), and SSD Lite. Experimental results show that SSD Lite gives better performance among these three techniques and exhibits a considerable boost in inference speed (~2-3 times) without compromising accuracy.

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, NewsAgendas, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.

The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here, we performed a direct comparison between human reasoners and a large language model (the text-davinci-003 variant of GPT-3) on a range of analogical tasks, including a non-visual matrix reasoning task based on the rule structure of Raven's Standard Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings; preliminary tests of GPT-4 indicated even better performance. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.

With a rising attention for the issue of PM2.5 or PM0.3, particulate matters have become not only a potential threat to both the environment and human, but also a harming existence to instruments onboard International Space Station (ISS). Our team is aiming to relate various concentration of particulate matters to magnetic fields, humidity, acceleration, temperature, pressure and CO2 concentration. Our goal is to establish an early warning system (EWS), which is able to forecast the levels of particulate matters and provides ample reaction time for astronauts to protect their instruments in some experiments or increase the accuracy of the measurements; In addition, the constructed model can be further developed into a prototype of a remote-sensing smoke alarm for applications related to fires. In this article, we will implement the Bi-GRU (Bidirectional Gated Recurrent Unit) algorithms that collect data for past 90 minutes and predict the levels of particulates which over 2.5 micrometer per 0.1 liter for the next 1 minute, which is classified as an early warning

Data scarcity has been the main factor that hinders the progress of event extraction. To overcome this issue, we propose a Self-Training with Feedback (STF) framework that leverages the large-scale unlabeled data and acquires feedback for each new event prediction from the unlabeled data by comparing it to the Abstract Meaning Representation (AMR) graph of the same sentence. Specifically, STF consists of (1) a base event extraction model trained on existing event annotations and then applied to large-scale unlabeled corpora to predict new event mentions as pseudo training samples, and (2) a novel scoring model that takes in each new predicted event trigger, an argument, its argument role, as well as their paths in the AMR graph to estimate a compatibility score indicating the correctness of the pseudo label. The compatibility scores further act as feedback to encourage or discourage the model learning on the pseudo labels during self-training. Experimental results on three benchmark datasets, including ACE05-E, ACE05-E+, and ERE, demonstrate the effectiveness of the STF framework on event extraction, especially event argument extraction, with significant performance gain over the base event extraction models and strong baselines. Our experimental analysis further shows that STF is a generic framework as it can be applied to improve most, if not all, event extraction models by leveraging large-scale unlabeled data, even when high-quality AMR graph annotations are not available.

Ukraine has warned Vladimir Putin that more drone attacks coming -- just hours after a flying bot smashed into one of Moscow's skyscrapers for the second time in as many days. Although Kyiv refuses to officially take responsibility for such attacks inside Russia, this latest skirmish is considered to be part of a wider offensive aimed at shifting the focus of the conflict to the Kremlin's doorstep. Experts say the way Kyiv is looking to do this is with the help of drones in the air and by sea -- a'cheap', expendable technology which has been revolutionising modern warfare over the past two decades. It is certainly turning attention from the First World War-style trench warfare that has been raging throughout Ukraine since the conflict broke out - and there's a reason the rest of the world is watching. Here, MailOnline looks at how drones are changing the face of future conflict, and why Ukraine is ratcheting up the use of them in an attempt to win the propaganda war and turn the tide of Putin's invasion.

Although there is no clear evidence on where these drones are launched from, they "spell trouble for the Russian authorities," Pavel Aksenov of the BBC's Russian service says. "If launched from behind the front line, it implies the weakness of Russian air defences. If the drones were launched within Russian territory, it suggests that the security services lack control over their own country."

The Chinese government is battling a new public health concern: mpox. The World Health Organization reports that China is currently experiencing the world's fastest increase in cases of mpox (formerly known as monkeypox)--and it needs to act fast to contain the spread. The countries that have successfully contained mpox outbreaks have mostly done so thanks to proactive measures like vaccination campaigns. The problem is, the Chinese government has barely started to take action. Generative AI is making it ridiculously easy to manipulate people's images.

The UK intelligence agencies are lobbying the government to weaken surveillance laws they argue place a "burdensome" limit on their ability to train artificial intelligence models with large amounts of personal data. The proposals would make it easier for GCHQ, MI6 and MI5 to use certain types of data, by relaxing safeguards designed to protect people's privacy and prevent the misuse of sensitive information. Privacy experts and civil liberties groups have expressed alarm at the move, which would unwind some of the legal protection introduced in 2016 after disclosures by Edward Snowden about intrusive state surveillance. The UK's spy agencies are increasingly using AI-based systems to help analyse the vast and growing quantities of data they hold. Privacy campaigners argue rapidly advancing AI capabilities require stronger rather than weaker regulation.