The New York Police Department (NYPD) is implementing a new security measure at the Times Square subway station. It's deploying a security robot to patrol the premises, which authorities say is meant to "keep you safe." We're not talking about a RoboCop-like machine or any human-like biped robot -- the K5, which was made by California-based company Knightscope, looks like a massive version of R2-D2. Albert Fox Cahn, the executive director of privacy rights group Surveillance Technology Oversight Project, has a less flattering description for it, though, and told The New York Times that it's like a "trash can on wheels." K5 weighs 420 pounds and is equipped with four cameras that can record video but not audio.
New York City's busiest subway station has a new crew member -- an almost 400-pound robocop unveiled by NYC Mayor Eric Adams. 'We're committed to exploring innovative tools to continue to make this city the safest big city in America, and this robot K5, it has the potential to serve as an important tool in our toolbox,' Adams said Friday as he unveiled the machine. The robot, a product of California-based autonomous security robots developer Knightscope, has four cameras it can use to shoot video and moves at 3mph. It will roam the Times Square -- 42 street subway station alongside a human officer for two weeks as part of a test run from city hall. After that, it is expected to patrol the mezzanine level of the station for two months.
Researchers have developed arrays of AI-controlled cameras and microphones to identify animals and birds and to monitor their movements in the wild – technology, they say, that should help tackle Britain's growing biodiversity problem. The robot monitors have been tested at three sites and have captured sounds and images from which computers were able to identify specific species and map their locations. Dozens of different birds were recognised from their songs while foxes, deer, hedgehogs and bats were pinpointed and identified by AI analysis. No human observers are involved. "The crucial point is the scale of the operation," said Anthony Dancer, a conservation specialist at the Zoological Society of London (ZSL).
Drone footage from Coconino County Emergency Management shows the aftermath of a train derailment in Williams, Arizona. A drone video captured the aftermath of a massive train derailment in Arizona involving a freight train that emergency officials say was "carrying a variety of new cars, vans and trucks." The train, operated by BNSF, derailed around midnight Wednesday in Williams, located outside of Flagstaff, according to Coconino County Emergency Management. "A total of 23 cars derailed and sustained heavy damage. The train cars involved were carrying a variety of new cars, vans and trucks," Coconino County officials said.
The London Metropolitan Police have been condemned by writers, journalist unions and activists for questioning and detaining a French publisher under the United Kingdom's Terrorism Act. Ernest Moret, foreign rights manager for popular science fiction author Alain Damasio as well as Editions La Fabrique, was on his way to the London Book Fair when he was stopped by police officers on Monday evening. Editions La Fabrique, in a joint statement with the British publishing house Verso Books, said police officers pulled Moret aside for questioning under Schedule 7 of the Terrorism Act after he arrived at London's St Pancreas railway station. The legislation gives police officers the power of stopping, questioning and detaining people to determine if they were involved in the "preparation or instigation of acts of terrorism", read a Metropolitan police's definition. The officers said Moret took part in demonstrations in France against a controversial pension reform, the publishers said in their statement.
The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focusing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users' path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London underground network, where a tap-in/tap-out system tracks the starting/exit time and location of all journeys in a day. A set of synthetic simulations and real data provided by Transport For London are used to validate and test the model on the predictions of observable and unobservable quantities.
The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focussing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London Underground network, where a tap-in/tap-out system tracks the start/exit time and location of all journeys in a day.
About Indian Railways Indian Railways is the state-owned railway company of India, which is owned and operated by the Indian government. It is the fourth-largest railway network in the world and is responsible for providing transportation services to millions of passengers and freight across the country. Indian Railways was first established in 1853 when the first train ran from Bombay (now Mumbai) to Thane. Since then, it has grown to become a major contributor to the Indian economy, providing employment to over 1.3 million people, facilitating the transportation of goods and people, and promoting tourism. The railway network of Indian Railways is divided into 18 zones, each headed by a general manager. The zones are further divided into divisions, which are responsible for the management of train services and infrastructure in their respective areas.
The Google employee who claimed last June his company's A.I. model could already be sentient, and was later fired by the company, is still worried about the dangers of new A.I.-powered chatbots, even if he hasn't tested them himself yet. Blake Lemoine was let go from Google last summer for violating the company's confidentiality policy after he published transcripts of several conversations he had with LaMDA, the company's large language model he helped create that forms the artificial intelligence backbone of Google's upcoming search engine assistant, the chatbot Bard. Lemoine told the Washington Post at the time that LaMDA resembled "a 7-year-old, 8-year-old kid that happens to know physics" and said he believed the technology was sentient, while urging Google to take care of it as it would a "sweet kid who just wants to help the world be a better place for all of us." To be sure, while A.I. applications are almost certain to influence how we work and go about our daily lives, the large language models powering ChatGPT, Microsoft's Bing, and Google's Bard cannot feel emotions and are not sentient. They simply enable chatbots to predict what word to use next based on a large trove of data.
However, the increasing prominence of AI has implications for every corner of the economy. From retail to transport, here's how AI promises to usher in a wave of change across industries. Monitoring weather patterns, managing pests and disease, working out the need for extra irrigation, or even which crops to grow where: many farmers believe agriculture is fertile ground for artificial intelligence. Many food producers are using AI to collect and analyse data in their efforts to improve productivity and profitability. AI's capacity for combining and analysing large datasets is already supplying farmers with real-time information on how to improve the health of their crops and increase yields.