The era of artificial intelligence (AI) is officially here. The AI market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2025, at a CAGR of 36.62% between 2018 and 2025, according to a recent report. AI's phenomenal growth across different industries is being fueled by unprecedented computing power, ever-increasing amounts of data--billions of gigabytes every day--and sophisticated deep-learning algorithms. According to the AI Index report, the number of active U.S. startups developing AI systems has increased 14 times whereas the annual VC investment into such startups has increased only 6 times since 2000. Moreover, the share of jobs requiring AI skills in the U.S. has grown 4.5 times since 2013.
Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there.
CES showcases the tech trends that will shape the year ahead. See the most important products that will impact businesses and professionals. NVIDIA, as I've written about several times, is the company that started in gaming and graphics but which has rapidly transformed into an organization focused on AI. Nope, NVIDIA is swinging for the fences, leveraging its GPU technology, deep learning, its Volta architecture, its Cuda GPU programming platform and a dizzying array of partnerships to move beyond mere tech and become an industrial powerhouse. CEO and Founder Jensen Huang gave the Sunday night keynote at CES, an prized time slot once dominated by Microsoft.
Deep neural networks (DNNs) have enabled great progress in a variety of application areas, including image processing, text analysis, and speech recognition. DNNs are also being incorporated as an important component in many cyber-physical systems. However, recent research has shown that DNNs are vulnerable to adversarial examples: Adding carefully crafted adversarial perturbations to the inputs can mislead the target DNN into mislabeling them during run time. There have been several techniques proposed to generate adversarial examples and to defend against them. In this blog post we will briefly introduce state-of-the-art algorithms to generate digital adversarial examples, and discuss our algorithm to generate physical adversarial examples on real objects under varying environmental conditions.
Artificial Intelligence (AI) is starting to change how many businesses operate. The ability to accurately process and deliver data faster than any human could is already transforming how we do everything from studying diseases and understanding road traffic behaviour to managing finances and predicting weather patterns.
Look above the traffic light at a busy intersection in your city and you will probably see a camera. These devices may have been installed to monitor traffic conditions and provide visuals in the case of a collision. But can they do more? Can they help planners optimize traffic flow or identify sites that are most likely to have accidents? And can they do so without requiring individuals to slog through hours of footage?