"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn't look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General ...
Finding a car that fits your preferences can be a very time-consuming task and may drive you crazy. On the other hand, with approximately 1.5 million cars on our platform, vehicle descriptions that are constantly changing and users that are still exploring may also drive us as the solution provider ...
Artificial intelligence is taking the automobile industry by storm while all the major automobile players are utilizing their resources and technology to come up with the best. The beauty of devices with artificial intelligence is that it tries to learn from sensory inputs like real sounds and imag...
The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training. Microsoft and Amazon Web Services' (AWS) Gluon is an open source project that eliminates some of the difficult work required to develop artificial intelligence (AI) systems. It provides training algorithms and neural network models, two important components of a deep learning system, that developers can use to develop their own ML systems. Google's ML engine is part of its cloud platform and is offered as a managed service for developers to build ML models that work on any type of data, of any size. Similar to Gluon, Google's service provides pre-trained models for developers to generate their own tailored ML models.
This is the Udacity's Self-Driving Car Engineer Nanodegree Program final project for the 1st Term. To write a software pipeline to identify vehicles in a video from a front-facing camera on a car. In my implementation, I used a Deep Learning approach to image recognition. Specifically, I leveraged the extraordinary power of Convolutional Neural Networks (CNNs) to recognize images. However, the task at hand is not just to detect a vehicle's presence, but rather to point to its location.
This little vignette from my childhood highlights one of the first ways that machines "learn" – through training. Give a machine algorithm authoritative sources, dictionaries or large collections of words in common usage for example, and then ask the machine to tell you if something "looks" right. It will be highly accurate, according to the creators of such methods. If you ask how they measure accuracy, they will tell you that it is a comparison of the results of the algorithm as compared to the training sets. If the training is "right," then accuracy can be measured by comparison.
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.
Built to bring AI to every aspect of the driving experience -- and provide a technological path forward for the 320-plus companies and organizations working with us on autonomous vehicles -- our first DRIVE Xavier autonomous machine processors are up and running. The first samples of our Xavier processors, initially announced a little more than a year ago, are being delivered to customers this quarter. Xavier will power the NVIDIA DRIVE software stack, now expanded to a trio of AI platforms covering every aspect of the experience inside next-generation automobiles. With more than 9 billion transistors, Xavier is the most complex system on a chip ever created, representing the work of more than 2,000 NVIDIA engineers over a four-year period, and an investment of $2 billion in research and development. It's built around a custom 8-core CPU, a new 512-core Volta GPU, a new deep learning accelerator, new computer vision accelerators and new 8K HDR video processors.
Artificial intelligence (AI) can obtain unbelievably accurate insights into a neighborhood's inhabitants – from their income and level of education to their ethnic background and political beliefs – just by looking at images from Google Street View. If, for example, you wanted to see whether an area voted Republican or Democrat, the AI algorithm would be able to correctly tell you with over 80 percent accuracy, namely based on the types of vehicles riding on the road. The deep-learning algorithm was developed by a team of computer scientists based at Stanford University. Their study was published in the Proceedings of the National Academy of Sciences. Throughout this process, it used an object recognition algorithm to clock tens of millions of houses, landscape features like shrubberies, and – most importantly – vehicles.