The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (less than 15 years), senior pedestrians (greater than 64 years), older drivers (>64 years), and other driving behaviors such as failure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally, young drivers (15-24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian alcohol/drug involvement as the most frequent item in the dark-with-streetlight condition. This crash type is particularly associated with pedestrian action (crossing intersection/midblock), driver age (55-64 years), speed limit (30-35 mph), and specific area type (business with mixed residential area). Fatal pedestrian crashes are found to be associated with roadways with high-speed limits (>50 mph) during the dark without streetlight condition. Some other risk factors linked with high-speed limit related crashes are pedestrians walking with/against the traffic, presence of pedestrian dark clothing, pedestrian alcohol/drug involvement. The research findings are expected to provide an improved understanding of the underlying relationships between pedestrian crash risk factors and specific lighting conditions. Highway safety experts can utilize these findings to conduct a decision-making process for selecting effective countermeasures to reduce pedestrian crashes strategically.

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

Why did I decide to finally write? Like most people, mass shootings have always left me speechless. Of course, I don't need to have lived in other countries to know that the approach the USA takes regarding gun-related violence is perplexingly unique. I consider myself a keen observer, so I noticed that during the lockdown of the pandemic in 2020-2021, mass shooting news reports drastically went down (even as violence overall seemed to reach new records). So, I found myself wondering whether the lockdown would help with the gun violence crisis, and save lives from the lethal virus.

Having AIs Train Robot Dogs to Balance Makes Them a Lot Cheaper Jeremy Tsu New Scientist "An AI has been used to train a small robot dog to perform cleaning tasks. The hardware cost a total of $6300, which is less than a tenth of the price tag of the well-known robot dogs built by US tech firm Boston Dynamics. This type of self-taught robotic body coordination relies on an AI training regimen that could pave the way for affordable robot dogs and possibly even humanoid robots that could be used as helpers in homes and workplaces." Google Plans Giant AI Language Model Supporting World's 1,000 Most Spoken Languages James Vincent The Verge "i'The way we get to 1,000 languages is not by building 1,000 different models. Languages are like organisms, they've evolved from one another and they have certain similarities. And we can find some pretty spectacular advances in what we call zero-shot learning when we incorporate data from a new language into our 1,000 language model and get the ability to translate [what it's learned] from a high-resource language to a low-resource language,' says [Zoubin Ghahramani, vice president of research at Google AI]. Genetically Modified Mosquitoes Cut the Insect's Number by 96 Percent Miriam Fauzia New Scientist "Although not a permanent fix, periodically releasing such mosquitoes could reduce the burden of infections including dengue, malaria, and Zika.

Elon Musk is wasting no time making extremely deep cuts at Twitter, calving off many teams doing essential work at the company in the process. News of layoffs swept the platform on Friday, showing that Twitter's billionaire owner is painting in broad strokes when it comes to trimming down the team by half. The same day that Musk complained about supposed activists impacting Twitter's ad revenue, he cut some departments outright -- actions that are sure to make advertisers all the more skittish about Musk's ability to steer a ship with a skeleton crew. As he's only owned the company for a single week, it's impossible to imagine that such sweeping layoffs won't lead to dysfunction at Twitter, from the content moderation policies sure to prove crucial for Tuesday's U.S. midterm elections to product teams keeping the platform humming. Former Twitter employees affected by the layoffs describe a chaotic situation with little official communication beyond abruptly receiving a termination letter or seeing their access to internal tools like Slack or databases suddenly revoked.

A new report from UN Climate Change says that the world might be on track for 2.5 C of warming by the end of the century, a potentially catastrophic level of warming that's far above the 1.5 C target of the 2015 Paris Agreement. I think it is time to seriously consider a specific solution in which AI can play a meaningful role: Climate geoengineering via stratospheric aerosol injection. Stratospheric aerosol injection involves spraying fine particles that reflect sunlight high in the atmosphere. By increasing the reflectivity (or albedo) of the planet, we can slow down the rate at which sunlight warms it, and thereby buy more time to reduce carbon emissions and develop mitigations. Harvard Professor David Keith explains the science behind this idea is in his book, A Case for Climate Engineering. At the current 1.1 C of warming, the world is already experiencing increased climate-related crises.

A gaggle of new AI projects are coming soon from Google, including disaster monitoring tools and a service that uses machine intelligence to generate custom videos. The company announced the array of initiatives at its AI@ event this week. The most practical development: Google is expanding its AI-powered disaster tracking and response systems. The company rolled out a wildfire tracking tool during the apocalyptic 2020 fire season. The tool aims to track wildfire movements in real time using satellite imagery, on-the-ground data, and AI predictions.

In 2015, General Qassem Suleimani, the architect of Iran's foreign military strategy, travelled to Moscow to see President Vladimir Putin. He had a pitch--and a plea. During their two-hour meeting, Suleimani rolled out large maps of the battlefield in Syria, which was in the fifth year of a civil war with disparate rebel groups and had lost control of a third of its territory to ISIS jihadis. The stakes for Russia were high. For decades, Syria had been its only steadfast ally among the twenty-two Arab countries.

Somewhere in Mabushi, a crossroad area between the inner and outer districts of Abuja, Nigeria, Uniccon Group, a two-year-old Nigerian technology firm, has built a humanoid: a 6-foot-tall multilingual human-like robot called Omeife. From an idea that was conceptualised in 2020 to a back-and-forth construction--slow wins and quick-succession learning--that stretched across two years, Omeife, built as a female Igbo character that understands and speaks eight different languages, is now a product ready to meet the world. Powered by sophisticated artificial intelligence algorithms developed in-house by the company's team of scientists, Omeife has a deep understanding of African culture and behavioural patterns. Speaking to TechCabal about the project over a call, Chuks Ekwueme, who founded the company in 2020 and serves as its CEO, revealed that the humanoid also has a real time understanding of its environment including active listening and the ability to focus on a specific conversation thread as it's happening. "It's not just multilingual, it has the ability to switch languages and interact with specific gestures--hand illustrations, smile and other bodily gestures--that match the tone of the conversation," said Ekwueme.

In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historical dialogue forms an essential part of a conversation and a precise understanding of the present phrase or sentence. However, the standard attention mechanisms typically generate weights using query and key but ignore context, forming a Bi-Attention framework, despite their great success in modeling sequence alignment. This Bi-Attention mechanism does not explicitly model the interactions between the contexts, queries and keys of target sequences, missing important contextual information and resulting in poor attention performance. Accordingly, a novel and general triple-attention (Tri-Attention) framework expands the standard Bi-Attention mechanism and explicitly interacts query, key, and context by incorporating context as the third dimension in calculating relevance scores. Four variants of Tri-Attention are generated by expanding the two-dimensional vector-based additive, dot-product, scaled dot-product, and bilinear operations in Bi-Attention to the tensor operations for Tri-Attention. Extensive experiments on three NLP tasks demonstrate that Tri-Attention outperforms about 30 state-of-the-art non-attention, standard Bi-Attention, contextual Bi-Attention approaches and pretrained neural language models1.