Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation (IR) for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under standard i.i.d., out-of-distribution, and low-resource settings validate GraphQ IR's superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.

People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due to their intuitive and powerful storytelling capabilities. However, creating such montages usually involves a lot of manual searches, clicks, and selections that are time-consuming and cumbersome, adversely affecting user experiences. To alleviate this, we propose task-oriented dialogs for montage creation as a novel interactive tool to seamlessly search, compile, and edit montages from a media collection. To the best of our knowledge, our work is the first to leverage multi-turn conversations for such a challenging application, extending the previous literature studying simple media retrieval tasks. We collect a new dataset C3 (Conversational Content Creation), comprising 10k dialogs conditioned on media montages simulated from a large media collection. We take a simulate-and-paraphrase approach to collect these dialogs to be both cost and time efficient, while drawing from natural language distribution. Our analysis and benchmarking of state-of-the-art language models showcase the multimodal challenges present in the dataset. Lastly, we present a real-world mobile demo application that shows the feasibility of the proposed work in real-world applications. Our code and data will be made publicly available.

News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.

After weeks of denial, Iran has confirmed it supplied deadly unmanned drones to Russia for use in its ongoing war with Ukraine. On Saturday, Iranian Foreign Minister Hossein Amirabdollahian told IRNA, Iran's state-run news agency, that reports of continued drone shipments were false and that Iran had not sent drones to Russia since before the invasion began in February. "This fuss made by some Western countries that Iran has provided missiles and drones to Russia to help the war in Ukraine - the missile part is completely wrong. The part about drones is correct, we did provide a limited number of drones to Russia in the months before the start of the war in Ukraine," said Amirabdollahian. The admission from Amirabdollahian comes just weeks after Iran's U.N. representative gave a striking denial to past allegations of drone shipments.

Mars rovers Opportunity and Spirit departed Earth in 2003. Upon successfully touching down on the red planet, they were only expected to last about 90 days. The scientists and engineers at NASA were flabbergasted that the pair survived for many years. In his latest documentary, "Good Night Oppy," director Ryan White examines the doting relationship between the control room crew members – people from across the globe – and their robotic progeny. It's a story of gumption: When a machine gets mired in quicksand 140 million miles away, how do you rescue it?

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Question: I have very little exposure to data analytics. However, I am told that auto ML (automated machine learning) is the new opportu.

Self-Driving Car with JavaScript Course – Neural Networks and Machine Learning. freeCodeCamp.org•1.8M views.

Palantir seeks to utilize AI and machine learning (ML) in these software packages.

Picture the corniest sci-fi '80s TV you can imagine: filled with cheesy one-liners, fast cars, a tough action hero, and retro technology that probably felt cool at the time but now seems incredibly dated. Now, what if I told you that same show may have predicted a 21st-century technology that could revolutionize the world? That show is none other than Knight Rider, a 1980s NBC TV show featuring former detective Michael Knight, who takes on bad guys with the help of a superpowered artificially intelligent car known as Knight 2000, or KITT. As a self-driving car, KITT beat out Elon Musk's Tesla and other autonomous vehicles by decades -- even if only on the small screen. But is the portrayal of KITT on Knight Rider something more than science fiction concocted by Hollywood screenwriters?