If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A smart city is a municipality that uses information and communication technologies (ICT) to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare. While the exact definition varies, the overarching mission of a smart city is to optimize city functions and drive economic growth while improving quality of life for its citizens using smart technology and data analysis. Value is given to the smart city based on what they choose to do with the technology, not just how much technology they may have. Several major characteristics are used to determine a city's smartness. A smart city's success depends on its ability to form a strong relationship between the government -- including its bureaucracy and regulations -- and the private sector.
With its bright-yellow armored body, grippy tank-like treads, plow nose and water cannon, the Los Angeles Fire Department's latest piece of equipment looks more like a Star Wars sidekick than a firefighting assistant. But this mini robot tanker is an inferno buster that pack a powerful punch of water or foam and can go where firefighters otherwise can't. The LAFD on Tuesday became the first fire department in the nation to acquire the Robotics Systems 3, a droid on steroids.LAFD Chief Ralph Terrazas said firefighters put their lives on the line when battling blazes. This year, 11 LAFD crew members were severely injured when a fireball engulfed four downtown buildings after a massive explosion that was ignited by hazardous materials.Now, firefighters can use RS3 as a safer alternative in battling potentially explosive blazes and it allows the department to get inside a burning building when humans could not dare enter safely. "I can afford to lose one of these wonderful machines, I cannot afford to lose a firefighter," Terrazas said, admiring the control pad that remotely operates the firefighting drone.On Tuesday morning, as flames engulfed a pair of industrial textile buildings in downtown L.A., firefighters …
Previously, facial recognition technology was reserved for the movies and was a thing of fiction. However, much like other biometric solutions that have seen improvement and progress, facial recognition technology also steadily became a reality. Over the past decade, it has not only been developed and perfected; it is being deployed around the world as well. However, not as rapidly as other biometric technologies did – which include fingerprint, iris recognition, hand geometry, and DNA. Before we discuss the history and gradual evolution of facial recognition technology, there is a need to have an understanding of how this technology works and why there was a need for it in the first place?
One of the most common problems when working with classification tasks is imbalanced data where one class is dominating over the other. For example, in the Credit Card fraud detection task, there will be very few fraud transactions (positive class) when compared with non-fraud transactions (negative class). Sometimes, it is even possible that 99.99% of transactions will be non-fraud and only 0.01% of transactions will be fraud transactions. You can have a class imbalance problem on binary classification tasks as well as multi-class classification tasks. However, the techniques we are going to learn here can be applied to both.
Generally machine learning algorithms seem to assume you have a lot of data. What techniques can be used to train classifiers where you only have a small number of examples of one of the classes? As an example problem, in fraud detection systems you may only have a few examples of the fraud but you can obtain a very large number of examples of not fraud. Or if you want to produce a text classifier that can find movies you might like by reading the synopsis on IMDB, you may only have a couple of dozen examples of movies that are interesting but can potentially list thousands of uninteresting movies.
In this blog we will be doing a project based on image classification where our problem statement describe us to classifies the images into two categories i.e. Emergency & Non-Emergency vehicle which is a binary classification problem and we will be solving using neural network. Before diving deep into this project I would recommend you all to please go through the basics of working with images in my deep learning blog. I am sure you will like it and then proceed ahead with exciting theory and practical's coming ahead. Coming to emergency vehicles there can various types like police cars, ambulance or even fire trucks.
The Boston Fire Department started to use emerging technology to fight fires in the last couple of years. In collaboration with Karen Panetta, an IEEE fellow and dean of Graduate Education at Tufts University's School of Engineering, the department is using AI for object recognition. The goal is to be able to use a drone or robot that can locate objects in a burning building. Panetta worked with the department to develop prototype technology that leverages IoT sensors and AI in tandem with robotics to help first responders "see" through blazes to detect and locate objects – and people. The AI technology she developed analyzes data coming from sensors that firefighters wear, and it recognizes objects that can be navigated in a fire.
Decisions on where to send police patrol cars, which foster parents to investigate, and who gets released on bail before trial are some of the most important, life-or-death decisions made by our government. And, increasingly, those decisions are being automated. The last eight years have seen an explosion in the capability of artificial intelligence, which is now used for everything from arranging your news feed on Facebook to identifying enemy combatants for the U.S. military. The automated decisions that affect us the most are somewhere in the middle. A.I.'s big feature is essentially pattern matching.
Technology companies provide much of the critical infrastructure of the modern state and develop products that affect fundamental rights. Search and social media companies, for example, have set de facto norms on privacy, while facial recognition and predictive policing software used by law enforcement agencies can contain racial bias. In this episode of Deep Tech, Marietje Schaake argues that national regulators aren't doing enough to enforce democratic values in technology, and it will take an international effort to fight back. Schaake--a Dutch politician who used to be a member of the European parliament and is now international policy director at Stanford University's Cyber Policy Center--joins our editor-in-chief, Gideon Lichfield, to discuss how decisions made in the interests of business are dictating the lives of billions of people. Also this week, we get the latest on the hunt to locate an air leak aboard the International Space Station--which has grown larger in recent weeks. Elsewhere in space, new findings suggest there is even more liquid water on Mars than we thought. It's located in deep underground lakes and there's a chance it could be home to Martian life. Space reporter Neel Patel explains how we might find out. Back on Earth, the US election is heating up. Data reporter Tate Ryan-Mosley breaks down how technologies like microtargeting and data analytics have improved since 2016. Check out more episodes of Deep Tech here. Gideon Lichfield: There's a situation playing out onboard the International Space Station that sounds like something out of Star Trek… But there is an air leak in the space station.
The threat landscape we operate in today is changing all the time. Around the world, pressures on law enforcement bodies remain incredibly high as they face the challenge of rising international threat levels and a backdrop of intense political, social and economic uncertainty. It is a challenge that demands a considered, proactive and dynamic response. It's clear that new technologies, such as Artificial Intelligence (AI), can dramatically improve the effectiveness of today's physical and cyber security systems and help us to better defend against a wide-spectrum of threats. Specifically speaking, for physical security systems to be effective, they must have the full support of the public.