A driverless car running on the road is like a screenshot from a sci-fi movie. However, fiction is becoming a reality, and thanks to #Artificial Intelligence (AI). AI technology complements the concept of self-driving cars. Elon Musk had in 2017 that all cars will be #autonomous in 10 years without any steering wheel. We are very close to bringing this estimate to reality in just 4 years.
With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise. Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.
Technologies powered by artificial intelligence, such as chatbots and personalized shopping suggestions, have become more common in recent years, leading many consumers to embrace artificial intelligence. Such human-centered AI analyzes data through the lens of human behavior, which in turn allows companies to better understand their customer base. As this technology develops and becomes more integrated into our daily lives, the future of human-centered AI is looking brighter than ever. Below, the members of Forbes Technology Council share 13 exciting future uses of human-centered AI to keep an eye on. Because we have the opportunity to teach and train the AI of the future, we have a unique opportunity to define AI for all.
Imagine we want to train a self-driving car in New York so that we can take it all the way to Seattle without tediously driving it for over 48 hours. We hope our car can handle all kinds of environments on the trip and send us safely to the destination. We know that road conditions and views can be very different. It is intuitive to simply collect road data of this trip, let the car learn from every possible condition, and hope it becomes the perfect self-driving car for our New York to Seattle trip. It needs to understand the traffic and skyscrapers in big cities like New York and Chicago, more unpredictable weather in Seattle, mountains and forests in Montana, and all kinds of country views, farmlands, animals, etc.
Hi All - This event was originally going to be held during GDC week back in March but had to be postponed. Excited to be hosting this event virtually during GDC Summer on Aug 4th. Games have always been at the forefront of AI & they serve as a good testing bed for AI before we put it to use in the real world. Therefore, its natural to look into gaming to peek into new techniques being discovered in AI. What started with self-learning AI in games has now translated into solving real-world problems in computer vision, natural language processing, & self-driving cars.
Baidu is on the cusp of making self-driving cars more practical. The Chinese tech giant has revealed that its Apollo Computing Unit, billed as the "world's first production-ready" autonomous driving computer, is ready for use on the streets. It's an unassuming box (see below), but it can handle massive amounts of data from five cameras and 12 ultrasonic radars. It's based on Xilinx processors and boasts microcontrollers from chip maker Infineon. You'll see the ACU in use quickly. It will power Apollo Valet Parking, a team-up with WM Motor that will automatically pick you up and otherwise streamline valet service without requiring drivers.
This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.
"Innovation" is a word that says everything. Yet, it means nothing without any insights behind it. Companies use the term constantly to appear forward-thinking. However, by falling back on this vague and overused vernacular, they can fail to identify what makes them new and exciting. This means that as marketing budgets grow in size and importance, companies must become better innovation storytellers.
Today almost all the industries are making benefits from machine learning including automobiles, health care, finance, etc. Machine learning helps these industries by automating procedures, reducing processing time, providing more accurate and faster decisions. It works by developing procedures that take input data and then by applying statistical analysis on the data, it predicts an output. The term machine learning was coined in 1959 by Arthur Samuel, an American pioneer in the field of artificial intelligence and computer gaming. Artificial Intelligence (AI) is a broad area of science which performs simulates human abilities. Machine learning is a subset of AI.
From The Love Bug through to Westworld, movies and TV shows have evolved self-driving vehicles to create unforgettable moments. But consumers remain wary of their real-life counterparts as they start to roll out onto roads around the world. Vanarama has visualised the 20 most iconic on-screen autonomous vehicles from 1960s to present day in an infographic timeline. We're getting closer to traveling from A to B in autonomous cars by the day, with a projected market of $615bn by 2026 (up from $27bn in 2017) including auto manufacturers such as BMW, Audi, Toyota to more disruptive tech-led businesses like Tesla, Google, Uber. A recent study by trend analysts ResearchAndMarkets has predicted that the global autonomous market is likely to reach a value of $615bn by 2026.