A startup's ads for controversial embryo tests hit the New York City subway. One day this fall, I watched an electronic sign outside the Broadway-Lafayette subway station in Manhattan switch seamlessly between an ad for makeup and one promoting the website Pickyourbaby.com, Inside the station, every surface was wrapped with more ads--babies on turnstiles, on staircases, on banners overhead. To his mind, one should be as accessible as the other. Nucleus is a young, attention-seeking genetic software company that says it can analyze genetic tests on IVF embryos to score them for 2,000 traits and disease risks, letting parents pick some and reject others. This is possible because of how our DNA shapes us, sometimes powerfully.

Breakthroughs, discoveries, and DIY tips sent every weekday. Subway commuters in Shenzhen, China, may soon need to make room for a fleet of chunky, snack-carrying delivery robots. Earlier this week, more than three dozen autonomous, four-wheeled delivery robots boarded and exited active subway trains, and eventually delivered packages to several 7-Eleven convenience stores. Although this demonstration was only a preliminary test and took place during off-peak hours, the company behind the subway-riding robots believes they could soon help stock shelves at around 100 7-Eleven locations. The initiative is part of a broader effort in China and other countries to normalize the presence of delivery robots operating in public spaces.

In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowledge-intensive questions. We define three sub-tasks: (i) Single Audio Question Answering (s-AQA), where questions are answered based on a single audio sample, (ii) Multi-Audio Question Answering (m-AQA), which requires reasoning over multiple audio samples, and (iii) Retrieval-Augmented Audio Question Answering (r-AQA), which involves retrieving relevant audio to answer the question. We benchmark large audio language models (LALMs) on these sub-tasks and observe suboptimal performance. To address this, we propose a generic framework that can be adapted to any LALM, equipping them with knowledge reasoning capabilities. Our framework has two components: (i) Audio Entity Linking (AEL) and (ii) Knowledge-Augmented Audio Large Multimodal Model (KA2LM), which together improve performance on knowledge-intensive AQA tasks. To our knowledge, this is the first work to address advanced audio understanding via knowledge-intensive tasks like Audiopedia.

Social media has become an important platform for people to express their opinions towards transportation services and infrastructure, which holds the potential for researchers to gain a deeper understanding of individuals' travel choices, for transportation operators to improve service quality, and for policymakers to regulate mobility services. A significant challenge, however, lies in the unstructured nature of social media data. In other words, textual data like social media is not labeled, and large-scale manual annotations are cost-prohibitive. In this study, we introduce a novel methodological framework utilizing Large Language Models (LLMs) to infer the mentioned travel modes from social media posts, and reason people's attitudes toward the associated travel mode, without the need for manual annotation. We compare different LLMs along with various prompting engineering methods in light of human assessment and LLM verification. We find that most social media posts manifest negative rather than positive sentiments. We thus identify the contributing factors to these negative posts and, accordingly, propose recommendations to traffic operators and policymakers.

Los Angeles will utilize AI-powered scanners at Union Station over the next month in an effort to stop passengers with hidden weapons from boarding the rails. Commuters descending to underground platforms for the A, B and D lines (formally known as the Blue, Red and Purple lines) will enter into the testing ground for Metro's 30-day pilot program, which went into effect on Tuesday, though the scanners will not run every day. The program arrives amid growing concern over passenger safety, with Metro recording an uptick in arrests this year for riders carrying concealed weapons. The roughly 6-foot-tall Evolv Technology scanners use artificial intelligence to pinpoint on a person's body where they could possibly be carrying a weapon, according to the company's website. All weapons are banned on the Metro system, and it is illegal to carry a concealed firearm without a permit in California.

Transit officials are looking at facial recognition technology and fare gates as they scramble to find a way to secure the Metro system after a grandmother was fatally stabbed on the subway this week. The suspect arrested in the killing of 67-year-old Mirna Soza had once been banned from the system by a court order. Distressed by her death and a rash of attacks, the Los Angeles County Metropolitan Transportation Authority's board on Thursday asked its staff to look into a litany of measures to beef up security on the sprawling system, including creating a protocol for communication among law enforcement agencies, examining the feasibility of facial recognition devices, and securing station gates. "Our agency has grappled with a very real and unacceptable level of violence, illicit drug use sales and overdoses, and a blatant disregard for the law, our code of conduct and, quite frankly, basic human decency," said board member and Los Angeles County Supervisor Kathryn Barger, who initiated the effort. "Until we completely reverse security reality on our system, I'm concerned that we will never come back."

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. When I get home from work at 6:00, the light is fading, and I see my sons and their little friend playing in the street, two white boys and a Black boy throwing a foam football back and forth. I pull around the corner and they scatter, Oliver running one way while Jameson and the neighbor kid run the other. At the last minute, though, Jameson changes his mind, dropping the football and dashing across to his brother's side of the street. I slam to a halt, the bumper almost touching him. My heart throbs in my jaw: so close. Then, just as I release the brake, the neighbor kid runs across, too, and I have to stomp to a stop a second time. Don't any of you have common sense?" Through the unrolled window I see them all staring at me with wide eyes. "What is wrong with your ...

Due to the unfamiliarity to particular words(or proper nouns) for ingredients, non-native English speakers can be extremely confused about the ordering process in restaurants like Subway. Thus, We developed a dialogue system, which supports Chinese(Mandarin)1 and English2 at the same time. In other words, users can switch arbitrarily between Chinese(Mandarin) and English as the conversation is being conducted. This system is specifically designed for Subway ordering3. In BilDOS, we designed a Discriminator module to tell the language is being used in inputted user utterance, a Translator module to translate used language into English if it is not English, and a Dialogue Manager module to detect the intention within inputted user utterances, handle outlier inputs by throwing clarification requests, map detected Intention and detailed Keyword4 into a particular intention class, locate the current ordering process, continue to give queries to finish the order, conclude the order details once the order is completed, activate the evaluation process when the conversation is done.

When you sit back and think about the changes that have been made in technology over the past few years, it is quite amazing. It was not that long ago that home phones were more common than mobile phones. However, now almost everyone has a mobile phone and many people do not have a landline. The same can be said for newer technologies such as Google Nest Audio or Amazon Alexa. Households that own a smart speaker state that using it has become an essential part of their day.

So as a kid growing up in the 70's I loved science fiction movies. As a kid, I imagined the future as frightening and weird. Every day would be like Black Friday at Walmart. Who can forget "Mad Max"? Riveting tales of a dystopian earth where lawlessness reigns and survival becomes a daily challenge.