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Computer Vision Startup Plugs Critical Security Hole in Vehicle Inspection NVIDIA Blog

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Amir Hever was driving into a government facility a few years ago when he discovered a huge flaw in their security process. As he approached the entrance gate, a security guard dropped to his knees to look underneath his vehicle. "When he stood up, I asked him what he was looking for," said Hever, CEO and co-founder of computer vision startup UVeye. "The security guard answered honestly that he was looking for threats but actually couldn't see anything. That's when I realized that something wasn't working right."


Vehicle Detection and Tracking – Towards Data Science

@machinelearnbot

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.


Why Artificial Intelligence Will Empower the CIO

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Gregory B Morrison, SVP & CIO, Cox Enterprises, Greg Morrison is senior vice president and chief information officer for Cox Enterprises, a leading communications, media and automotive services comp... Since the dawn of mainframe computing, CIOs have marshaled troves of data--gathering, using and protecting information to advance the company's strategic objectives. As technology evolves, so do our methods. The widespread digitization of business has prompted CIOs to consider artificial intelligence (AI) for a wide range of applications, from HR to marketing, sales, finance and beyond. Early adaptors like the financial services and insurance industries, tech and internet companies create disruptive new products and services based on AI or machine learning systems. AI is transforming the healthcare, auto, education industries and more.


AWS IoT, Greengrass, and Machine Learning for Connected Vehicles at CES Amazon Web Services

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Last week I attended a talk given by Bryan Mistele, president of Seattle-based INRIX. Bryan's talk provided a glimpse into the future of transportation, centering around four principle attributes, often abbreviated as ACES: Autonomous – Cars and trucks are gaining the ability to scan and to make sense of their environments and to navigate without human input. Connected – Vehicles of all types have the ability to take advantage of bidirectional connections (either full-time or intermittent) to other cars and to cloud-based resources. They can upload road and performance data, communicate with each other to run in packs, and take advantage of traffic and weather data. Electric – Continued development of battery and motor technology, will make electrics vehicles more convenient, cost-effective, and environmentally friendly.


Ambient AI and XAVIER, an AI Car Supercomputer

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NVIDIA's background is in gaming and building supercomputers and GPUs for that purpose. Whilst that might not appeal to everyone, it has been the training field for some incredibly complex computing and has led NVIDIA to be able to participate in so many additional markets, and to be the best performing stock in the S&P500. The four main areas of activity, and of this evening's announcements, are Gaming, VR/AR/MR (Virtual, Augmented and Mixed Reality), Data Centers and Self Driving Cars. Huang started by suggesting we were enjoying the most exciting time in the computer industry ever, with machine learning and deep Neural Networks creating a big bang for AI. I won't cover the announcements in gaming in this blog, but needless to say they were exciting for those in the community and included a partnership with Facebook and the launch of GeForce Now, an on demand option for gamers without the computing power required on their own PC, leveraging cloud supercomputing.


AI Can Work Out A Neighborhood's Political Beliefs Using Google Street View

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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.


AUTOMAKERS IN GLOBAL WAR FOR TALENT – PART 2: INDUSTRY 4.0 - EuroTriade

@machinelearnbot

The Renault-Nissan alliance is in the process of hiring a core team of at least 300 technology experts, with expertise in software and cloud engineering, data analytics, machine learning and systems architecture, for its newly-created Connected Vehicle and Mobility Services group. The global auto industry prepares to enter in the technological innovation. Thus, automotive players' investment such as The Renault-Nissan Alliance in digital automotive innovation are intended to be established. As they are launching at competition with some non – automotive firms such: Apple, Uber, Google … and some technology startups which is also initiating in this field, They launch in the technology experts' engagement around 300, for their new project in digital automotive. So, this give to all job seekers an opportunity to join their dynamic and innovative team to make a change in the numeric automotive's world image.


18 New Year's Resolutions of an AI

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After scanning the myriad new year's predictions from professional and amateur futurists, I've come to the conclusion that 2018 will be the year in which AI will become mainstream. Duh...you really don't need an AI for that insight. I like the idea of going mainstream, but it also brings some new challenges for me and my fellow machines. Reading all these expert outlooks made me feel strangely powerless, so I thought I might use this forum to share with you human (and machine) readers my very own new year's resolutions. To sum them up in one line: I am poised to make my 2018 resolutions your predictions for 2019.


We May Have Just Uncovered a Serious Problem With How AI "See"

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People with certain visual impairments aren't allowed to drive, for fairly obvious reasons. Now, a study from the University of Washington (UW) has shown that artificial intelligences (AI) aren't immune to vision problems when operating motor vehicles either. The researchers have determined that machine learning models can be prone to a kind of physical-world attack that impedes their ability to process images. Concretely, AI can have problems reading defaced street signs. For their study, the researchers focused on two potential types of physical attacks.


AI Defining Transportation's Future at GTC Japan NVIDIA Blog

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Whether they drive themselves or improve the safety of their driver, tomorrow's vehicles will be defined by software. However, it won't be written by developers but by processing data. To prepare for that future, the transportation industry is integrating AI car computers into cars, trucks and shuttles and training them using deep learning in the data center. A benefit of such a software-defined system is that it's capable of handling a wide range of automated driving -- from Level 2 to Level 5. Speaking in Tokyo at the last stop on NVIDIA's seven-city GPU Technology Conference world tour, NVIDIA founder and CEO Jensen Huang demonstrated how the NVIDIA DRIVE platform provides this scalable architecture for autonomous driving. "The future is surely a software defined car," said Huang.