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) …
In May 2017, researchers at Google Brain announced the creation of AutoML, an artificial intelligence (AI) that's capable of generating its own AIs. More recently, they decided to present AutoML with its biggest challenge to date, and the AI that can build AI created a "child" that outperformed all of its human-made counterparts.
Big Data has already made fundamental changes to the way businesses operate. There are huge advantages for companies who can derive value from their data, but these opportunities come with challenges, too. For some, this is the challenge of acquiring data from new sources. For others, it is the task of building a scalable infrastructure that can manage the data in aggregate. For a brave few, it means extracting value from the data by implementing advanced analytic techniques and tools.
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.
In this tutorial, we're going to cover the implementation of the TensorFlow Object Detection API into the realistic simulation environment that is GTAV. Due to the realistic representations that occur inside of GTAV, we can use object detectors that were made for the real-world, and still see success. For example, we can detect cars, people, stop signs, trucks, and stop lights. Note: Since this last text-based writeup, I have posted quite a few video updates to the self-driving car model, namely covering the changes to the model to handle higher resolution, color, waypoint following, and joystick inputs. If you would like to see these updates, check out the YouTube playlist starting here, or you can check out for text-based writeups on the changes psyber.io.
Check out the Strata Business Summit at the Strata Data Conference in New York City, Sept. 25-28, 2017, to learn more from data-driven businesses--including American Express, BBC Worldwide, and LinkedIn. Early price ends August 11. Driverless cars aren't the only application for deep learning on the road: neural networks have begun to make their way into every corner of the automotive industry, from supply-chain management to engine controllers. In this installment of our ongoing series on artificial intelligence (AI) and machine learning (ML) in the enterprise, we speak with Dimitar Filev, executive technical leader at Ford Research & Advanced Engineering, who leads the team focused on control methods and computational intelligence. Ford research lab has been conducting systematic research on computational intelligence--one of the branches of AI--for more than 20 years.
Ford researchers developed and implemented, in mass-produced cars, an innovative misfire detection system--a neural-net-based classifier of crankshaft acceleration patterns for diagnosing engine misfire (undesirable combustion failure that has a negative impact on performance and emissions). In our supply chain, neural networks are the main drivers behind the inventory management system recommending specific vehicle configurations to dealers, and evolutionary computing algorithms (in conjunction with dynamic semantic network-based expert systems) are deployed in support of resource management in assembly plants. We can expect in the near future a wide range of novel deep-learning-based features and user experiences in our cars and trucks, innovative mobility solutions, and intelligent automation systems in our manufacturing plants. Building centers of excellence in AI and ML was not too challenging since, as I mentioned earlier, we had engineers and researchers with backgrounds and experience in conventional neural networks, fuzzy logic, expert systems, Markov decision processes, evolutionary computing, and other main areas of computational intelligence.
Traditional intelligent algorithms generally use shallow learning models to handle situations with large amounts of data in complex classifications. Some of the most direct benefits that deep learning algorithms can bring include achieving comparable or even better-than-human pattern recognition accuracy, strong anti-interference capabilities, and the ability to classify and recognise thousands of features. With this large amount of quality training data, human, vehicle, and object pattern recognition models will become more and more accurate for video surveillance use. The deep learning model requires a large amount of samples, making a large amount of calculations inevitable.
By making use of technological advances like deep neural networks, today's computers can therefore learn a number of things; from recognizing a user's voice, to recognizing images of house cats, with high rates of success. Meanwhile, the medical industry is making use of image recognition in order to detect tumours from MRI scans, while the manufacturing industry is also benefiting from machine learning and improved image recognition technology. This rules out semi-supervised anomaly detection for many manufacturing situations, as this type of learning method requires the system to possess image samples and correct answer outputs for good parts and defective parts. As companies and research labs continue to explore the human brain, they are simultaneously creating opportunities for deep learning technology and neural networks to develop in tandem.
As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. This will help with making actual design decisions and identifying the right design patterns, including situations when no directly applicable solution exists and you must transfer ideas across domains. In rare cases, machine learning might enable a computer to perform tasks that humans simply can't perform because of speed requirements or the scale of data.