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) …
The fight against videos altered by the use of artificial intelligence just got a new ally. According to researchers at UC Berkeley and the University of Southern California, a new algorithm can help spot whether a video has been manipulated via a process known as'deepfaking.' Counter-intuitively, the tool that scientists say will aid them in their crusade against faked videos happens to be the very same tool that helps make the videos in the first place: artificial intelligence. The fight against videos altered by the use of artificial intelligence just got a new ally. Pictured is a grab from a deep fake video where Steve Buscemi's face is superimposed over Jennifer Lawrence's body Deepfakes are so named because they utilize deep learning, a form of artificial intelligence, to create fake videos.
A programming technique that works on the same principle as disease-preventing vaccinations could safeguard machine learning systems from malicious cyber-attacks. The technique was developed by the digital specialist arm of Australia's national science agency, the CSIRO, and presented recently at an international conference on machine learning, held in Long Beach, California, US. Machine learning systems, or neural networks, are becoming increasingly prevalent in modern society, where they are pressed into service across a wide range of areas, including traffic management, medical diagnosis, and agriculture. They are also critical components in autonomous vehicles. They operate from an initial training phase, in which they are fed tens of thousands of possible iterations of a given task.
The story of Artificial Intelligence (AI) and Machine Learning (ML) is all about hope and hype. On the one hand, there's a technology that promises to revolutionize fields as diverse as agriculture, manufacturing, education, and healthcare. On the other, there's so much media attention that it gets impossible to cut through the hype and, proverbially speaking, separate the wheat from the chaff. And though making heads or tails of it all is difficult, DevOps is for sure poised to capitalize on the opportunities that AI and ML offer, such as automation of tasks, data analysis, and improvement of efficiency. DevOps generates tons of data.
As AI algorithms--and the computing power that drives them--improve year-on-year, their ability to positively transform the world in which we live is unquestionable. In fact, PwC predicts that AI could contribute up to $15.7 trillion to the global economy by 2030. Indeed, as many as one-in-five (20 percent) of the 1,000 US organisations recently surveyed by PwC had plans to implement AI enterprise-wide in 2019. The PwC research also reveals how companies are increasingly initiating AI models at the very core of their production processes, in a bid to enhance operational decision-making and provide forward-looking intelligence to people in every function throughout the business. To many, this move to AI is no surprise.
This story was co-published with ProPublica. Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren't enough to set off a small microphone in the ceiling that was supposed to detect aggression. A few days later, at the Staples Pathways Academy in Westport, Connecticut, junior Sami D'Anna inadvertently triggered the same device with a less spooky sound--a coughing fit from a lingering chest cold.
The field of artificial intelligence is exploding with projects such as IBM Watson, DeepMind's AlphaZero, and voice recognition used in virtual assistants including Amazon's Alexa, Apple's Siri, and Google's Home Assistant. Because of the increasing impact of AI on people's lives, concern is growing about how to take a sound ethical approach to future developments. Building ethical artificial intelligence requires both a moral approach to building AI systems and a plan for making AI systems themselves ethical. For example, developers of self-driving cars should be considering their social consequences including ensuring that the cars themselves are capable of making ethical decisions. Here are some major issues that need to be considered.
Helsingin Sanomat responded to the bafflement by stating that the election engine's algorithm was built to recommend parties, not single candidates, that best correspond to a citizen's political views. This arrangement left perfectly suited candidates to the sidelines of the engine's suggestions. As a result of public discussion about the engine's function the newspaper published its algorithm for everyone to see. It also modified the algorithm based on the suggested improvements it received. Meeri Haataja was thrilled about the conversation.
This article summarizes the lessons learned after two years of our team engaging with dozens of enterprise clients from different industries including manufacturing, financial services, retail, entertainment, and healthcare, among others. What are the most common ML problems faced by the enterprise? What is beyond training an ML model? How to address data preparation? How to scale to large datasets?
Decisions made by complex algorithms impact all areas of our lives: the ads we see, the social status updates we read, the medications we are prescribed, how much an insurance policy will cost and whether or not we get a mortgage for a new home. Automated decisions help us cope with our fast-paced lifestyles. They are quick, they may feel relevant and they can be convenient. For example, while shopping on Amazon, you get suggestions for similar products that an algorithm has chosen based on other peoples' purchasing habits. Most of us aren't thinking about why we're shown some products instead of others.
When Conor Sprouls, a customer service representative in the call center of insurance giant MetLife talks to a customer over the phone, he keeps one eye on the bottom-right corner of his screen. There, in a little blue box, A.I. tells him how he's doing. The program flashes an icon of a speedometer, indicating that he should slow down. A heart icon pops up. For decades, people have fearfully imagined armies of hyper-efficient robots invading offices and factories, gobbling up jobs once done by humans.