The result is that the massive computer power so harnessed helps us to analyse what has happened in the past and, with the use of predictive analytics techniques, opens a window leading to accurate predictions. Undoubtedly, artificial intelligence is fast becoming a major technology for prescriptive analytics, the step beyond predictive analytics that helps us determine how to implement and/or optimise optimal decisions. In business applications, it can assess future risks and quantify probabilities, giving us insights into how to improve market penetration, customer satisfaction, security analysis, trade execution and fraud detection and prevention, while proving indispensable in land and air-traffic control, national security and defence, not to mention a host of healthcare applications such as patientspecific treatments for diseases and illnesses. Typically, the giant search engine firm Google is a pioneer in the field of artificial intelligence, developing self-driving automobiles, smartphone assistants and other examples of machine learning, while it is no secret that Facebook founder Mark Zuckerberg and actor Ashton Kutcher recently invested 40 million in a project focusing on developing artificial brains. In science fiction films such as Matrix, we have seen how futuristic devices will facilitate facial recognition, interpret human comments and perform complex language translations.
You don't have to look far to find statistics and predictions on the future impact of artificial intelligence (AI). But while self-driving cars and augmented reality headsets have excited consumers, enterprise headlines have focused more on the risk that it poses to workers. Analyst giant Forrester have claimed that 16% of jobs in the U.S. will be lost to artificial intelligence by 2025. Meanwhile, a recent report from PwC stated 30% of jobs in the UK were under threat from AI breakthroughs, putting 10 million British workers at risk of being'replaced by robots' in the next 15 years. We shouldn't expect a wide-scale revolution of robot workers across the entire workplace, of course.
A novel model developed by MIT and Microsoft researchers identifies instances in which autonomous systems have "learned" from training examples that don't match what's actually happening in the real world. Engineers could use this model to improve the safety of artificial intelligence systems, such as driverless vehicles and autonomous robots. The AI systems powering driverless cars, for example, are trained extensively in virtual simulations to prepare the vehicle for nearly every event on the road. But sometimes the car makes an unexpected error in the real world because an event occurs that should, but doesn't, alter the car's behavior. Consider a driverless car that wasn't trained, and more importantly doesn't have the sensors necessary, to differentiate between distinctly different scenarios, such as large, white cars and ambulances with red, flashing lights on the road.
Drive.ai is a Silicon Valley startup working on a kit to retrofit your ride If Drive.ai is a success, your first self-driving car might already be parked in the driveway. The Silicon Valley start-up, founded recently by a team of former Stanford University Artificial Intelligence Lab products, is working on a software kit that can be used to retrofit existing vehicles. "We started Drive.ai because we believe there's a real opportunity to make our roads, our commutes, and our families safer," the company announced in a statement on its blog, citing a statistic that more than one million people die each year worldwide in automobile accidents caused by human error. At its foundation, Drive.ai is looking to use deep learning -- which its founders consider the most effective form of artificial intelligence ever developed -- to key a breakthrough in a field that giant companies such as Google and General Motors have been trying to master for years. "Unlike other forms of AI, which involve programming many sets of rules, a deep learning algorithm learns more like a human brain.
We grew up watching movies like The Terminator, Star Wars, and The Matrix; weaving our AI dreams since our childhood. The term'Artificial Intelligence' was coined in 1956 by John McCarthy, but it is in the recent years that AI has experienced a resurgence as we are now being introduced to its real-world applications. Today, artificial intelligence is all around us, at times we don't even realize it; all of us at some point have been assisted by Siri or Google Assistant, have heard about a self-driving car, and have definitely received product and movie recommendations from Amazon and Netflix respectively. AI is already a part of our daily lives and its realm is likely to grow in the coming years. Now, terms like'Machine Learning' and'Deep Learning' have also started gaining ground.