Members of the public have said there is no justification for the use of facial recognition technology in CCTV systems operated by a private developer at a 67-acre site in central London. It emerged on Monday that the property developer Argent was using the cameras "in the interests of public safety" in King's Cross, mostly north of the railway station across an area including the Google headquarters and the Central Saint Martins art school, but the precise uses of the technology remained unclear. "For law enforcement purposes, there is some justification, but personally I don't think a private developer has the right to have that in a public place," said Grant Otto, who lives in London. He questioned possible legal issues around the collection of facial data by a private entity and said he was unaware of any protections that would allow people to request their information be removed from a database, with similar rights as those enshrined in GDPR. Jack Ramsey, a tourist from New Zealand, echoed his concerns.
At Google Cloud, we love to share how we're shaping our cloud computing technology. Beyond the cloud blog, though, we know there are lots of fascinating stories from around Google. Here's a reading list of stories that grabbed our attention recently. How stuffed is your bus? See transit trends from Google Maps This post contains some fun graphics and data about the relative crowdedness of various bus and subway lines around the world (fun for us to look at, though perhaps not so much fun for those on the crowded subway cars). The trends are pulled from the aggregated, anonymized feedback data that Google Maps users can opt to give after they've used transit mode.
We each use rails, roads and airports to live our lives but striking new neon maps show the world's infrastructure in a different light - using electrifying patterns of glowing lines to show how roads, rail and ports cross the globe. Graphic designer Peter Atwood, 23, from Nova Scotia, spent three days researching into a variety of data sources to show the maps'which connect our world.' He gathered precise information from Natural Earth Data and plotted each different point on a map with a black and white'mask', which then generated a light-source. Graphic designer Peter Atwood, 23, from Nova Scotia, spent three days researching into a variety of data sources to show the maps'which connect our world.' These include Natural Earth Data, which is a public domain map dataset for designers.
Facial recognition has received widespread application to assist in the identification of criminals in public spaces and crowds. For instance, Chinese officers succeeded in arresting a person from a sea of people inside a stadium, which without AI technology would have been nearly impossible to spot this person. China has installed numerous cameras in public spaces, which helps the police monitor citizens, catch citizens committing petty crimes such as jaywalking and helping identify criminals and arrest them. Some cities like London have experimented with the use of AI in railway stations to help in increasing surveillance, and maybe more nations will come up with the same for better law enforcement.
An artificial intelligence (AI)-trained facial recognition system (FRS) has been installed at the Puratchi Thalaivar Dr. MGR Central railway station for detecting known culprits passing through the gates and alerting authorities. "For the first time, we have introduced the CCTV camera device backed by artificial intelligence. In the existing system, we capture the picture and video of any suspect. But we have to manually analyse the footage to detect their movement. The new system will automatically alert us about known culprits," said a senior police officer of the Government Railway Police (GRP).
Parking, matching, scheduling, and routing are common problems in train maintenance. In particular, train units are commonly maintained and cleaned at dedicated shunting yards. The planning problem that results from such situations is referred to as the Train Unit Shunting Problem (TUSP). This problem involves matching arriving train units to service tasks and determining the schedule for departing trains. The TUSP is an important problem as it is used to determine the capacity of shunting yards and arises as a sub-problem of more general scheduling and planning problems. In this paper, we consider the case of the Dutch Railways (NS) TUSP. As the TUSP is complex, NS currently uses a local search (LS) heuristic to determine if an instance of the TUSP has a feasible solution. Given the number of shunting yards and the size of the planning problems, improving the evaluation speed of the LS brings significant computational gain. In this work, we use a machine learning approach that complements the LS and accelerates the search process. We use a Deep Graph Convolutional Neural Network (DGCNN) model to predict the feasibility of solutions obtained during the run of the LS heuristic. We use this model to decide whether to continue or abort the search process. In this way, the computation time is used more efficiently as it is spent on instances that are more likely to be feasible. Using simulations based on real-life instances of the TUSP, we show how our approach improves upon the previous method on prediction accuracy and leads to computational gains for the decision-making process.
Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, minimising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorithm compared with other state-of-the-art methods.
Until recently, artificial intelligence was a thing for science fiction movies and books. However, we are now in the midst of an ever-changing tech world where we are advancing faster than ever before. The future of AI is unknown, but that doesn't stop people from contemplating. Here are some of the best quotes about the future of AI. "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks."
Wireless trajectory data consists of a number of (time, point) entries where each point is associated with a particular wireless device (WAP or BLE beacon) tied to a location identifier, such as a place name. A trajectory relates to a particular mobile device. Such data can be clustered `semantically' to identify similar trajectories, where similarity relates to non-geographic characteristics such as the type of location visited. Here we present a new approach to semantic trajectory clustering for such data. The approach is applicable to interpreting data that does not contain geographical coordinates, and thus contributes to the current literature on semantic trajectory clustering. The literature does not appear to provide such an approach, instead focusing on trajectory data where latitude and longitude data is available. We apply the techniques developed above in the context of the Onward Journey Planner Application, with the motivation of providing on-line recommendations for onward journey options in a context-specific manner. The trajectories analysed indicate commute patterns on the London Underground. Points are only recorded for communication with WAP and BLE beacons within the rail network. This context presents additional challenge since the trajectories are `truncated', with no true origin and destination details. In the above context we find that there are a range of travel patterns in the data, without the existence of distinct clusters. Suggestions are made concerning how to approach the problem of provision of on-line recommendations with such a data set. Thoughts concerning the related problem of prediction of journey route and destination are also provided.
Trains delayed by'leaves on the line' might soon be a thing of the past as an AI system is trialled to predict build ups on the line and warn of encroaching plants. The artificial intelligence studies footage of plants near the line taken from trains and attempts to spot when leaves change colour, indicating that they might fall. It can also warn of fallen trees and when vegetation growth might soon obstruct the path of trains and lead to delays. The project is one of 24 high-tech schemes that have today been funded a total of £7.8 million ($9.9 million) by the UK government to improve the nation's railways. Trains delayed by'leaves on the line' might soon be a thing of the past as an AI system is trialled to predict build ups on the line and warn of encroaching plants (stock image) Slippery rails -- commonly referred to as'leaves on the line' -- result when build ups on the track led to trains not being able to grip the rails properly.