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Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
Development in the field of Single Board Computers (SBC) have been increasing for several years. They provide a good balance between computing performance and power consumption which is usually required for mobile platforms, like application in vehicles for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). However, there is an ever-increasing need of more powerful and efficient SBCs which can run power intensive Deep Neural Networks (DNNs) in real-time and can also satisfy necessary functional safety requirements such as Automotive Safety Integrity Level (ASIL). ProAI is being developed by ZF mainly to run powerful and efficient applications such as multitask DNNs and on top of that it also has the required safety certification for AD. In this work, we compare and discuss state of the art SBC on the basis of power intensive multitask DNN architecture called Multitask-CenterNet with respect to performance measures such as, FPS and power efficiency. As an automotive supercomputer, ProAI delivers an excellent combination of performance and efficiency, managing nearly twice the number of FPS per watt than a modern workstation laptop and almost four times compared to the Jetson Nano. Furthermore, it was also shown that there is still power in reserve for further and more complex tasks on the ProAI, based on the CPU and GPU utilization during the benchmark.
In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of CondenseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency.
Perception of the lane boundaries is crucial for the tasks related to autonomous trajectory control. In this paper, several methodologies for lane detection are discussed with an experimental illustration: Hough transformation, Blob analysis, and Bird's eye view. Following the abstraction of lane marks from the boundary, the next approach is applying a control law based on the perception to control steering and speed control. In the following, a comparative analysis is made between an open-loop response, PID control, and a neural network control law through graphical statistics. To get the perception of the surrounding a wireless streaming camera connected to Raspberry Pi is used. After pre-processing the signal received by the camera the output is sent back to the Raspberry Pi that processes the input and communicates the control to the motors through Arduino via serial communication.
Bojarski, Mariusz, Chen, Chenyi, Daw, Joyjit, Değirmenci, Alperen, Deri, Joya, Firner, Bernhard, Flepp, Beat, Gogri, Sachin, Hong, Jesse, Jackel, Lawrence, Jia, Zhenhua, Lee, BJ, Liu, Bo, Liu, Fei, Muller, Urs, Payne, Samuel, Prasad, Nischal Kota Nagendra, Provodin, Artem, Roach, John, Rvachov, Timur, Tadimeti, Neha, van Engelen, Jesper, Wen, Haiguang, Yang, Eric, Yang, Zongyi
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway. This system represents a departure from the classical approach for self-driving in which the process is manually decomposed into a series of modules, each performing a different task. In PilotNet, on the other hand, a single deep neural network (DNN) takes pixels as input and produces a desired vehicle trajectory as output; there are no distinct internal modules connected by human-designed interfaces. We believe that handcrafted interfaces ultimately limit performance by restricting information flow through the system and that a learned approach, in combination with other artificial intelligence systems that add redundancy, will lead to better overall performing systems. We continue to conduct research toward that goal. This document describes the PilotNet lane-keeping effort, carried out over the past five years by our NVIDIA PilotNet group in Holmdel, New Jersey. Here we present a snapshot of system status in mid-2020 and highlight some of the work done by the PilotNet group.
Take a joyride through a 3D urban neighborhood that looks like Tokyo, or New York, or maybe Rio de Janeiro -- all imagined by AI. We've introduced at this week's NeurIPS conference AI research that allows developers to render fully synthetic, interactive 3D worlds. While still early stage, this work shows promise for a variety of applications, including VR, autonomous vehicle development and architecture. The tech is among several NVIDIA projects on display here in Montreal. Attendees huddled around a green and black racing chair in our booth have been wowed by the demo, which lets drivers navigate around an eight-block world rendered by the neural network.
Wherever you look these days, you can find AI affecting your life in one way or another. Whether it's the Netflix recommendation system or self driving cars, the use of deep learning is becoming ever more prevalent throughout our lives and is starting to make increasingly more crucial decisions. Since AI is becoming ingrained in our lives, you'd expect it to be safe and fool proof, right? The potential exists for bad actors to trick deep learning systems into misinterpreting the input on purpose causing it to give a wrong answer. We present a method for preventing these intentional misclassifications to help maintain trust in complex AI systems.
In a clear demonstration of why AI leadership demands the best compute capabilities, NVIDIA today unveiled the world's 22nd fastest supercomputer -- DGX SuperPOD -- which provides AI infrastructure that meets the massive demands of the company's autonomous-vehicle deployment program. The system was built in just three weeks with 96 NVIDIA DGX-2H supercomputers and Mellanox interconnect technology. Delivering 9.4 petaflops of processing capability, it has the muscle for training the vast number of deep neural networks required for safe self-driving vehicles. Customers can buy this system in whole or in part from any DGX-2 partner based on our DGX SuperPOD design. AI training of self-driving cars is the ultimate compute-intensive challenge.
In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.
The recent boom in artificial intelligence has produced impressive results in a somewhat surprising realm: the world of image and video generation. The latest example comes from chip designer Nvidia, which today published research showing how AI-generated visuals can be combined with a traditional video game engine. The result is a hybrid graphics system that could one day be used in video games, movies, and virtual reality. "It's a new way to render video content using deep learning," Nvidia's vice president of applied deep learning, Bryan Catanzaro, told The Verge. "Obviously Nvidia cares a lot about generating graphics [and] we're thinking about how AI is going to revolutionize the field."