"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics. The IoT is imminent – and so are the security challenges it will inevitably bring. Get up to speed on IoT security basics and learn how to devise your own IoT security strategy in our new e-guide.
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."
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.
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.
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.
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.
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.
Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.
A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers. In this article, we focus on the adaptive cruise control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine-learning algorithms with demographic information and expert advice into existing automated assistive systems.
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.