I wanted to talk to Moore about some of the AI basics -- like how the School of Computer Science defines artificial intelligence. That may seem simplistic, but the term is used so broadly that I think it's worth taking the time to make sure we all know what we're talking about when we talk about AI. So, our conversation started with a definition, it moved to CMU's AI stack, which I'll explain in a minute and which could help CIOs wrap their heads around this sprawling term.
To carry out their experiments, the team trained their model in TensorFlow, employing a public dataset of road signs. While the dataset of a few thousand training examples was relatively small, the results plainly show the potential vulnerabilities of deep learning artificial neural networks used in autonomous driving systems when real objects are modified. "Unlike prior work, […] here we focus on evasion attacks where attackers can only modify the testing data instead of training data (poisoning attack)," explained the researchers. "In evasion attacks, an attacker can only change existing physical road signs. Here we assume that an attacker gains access to the classifier after it has been trained ('white-box' access)."
Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic." One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to building machines that, in effect, act like neurons in a neural network as they learn while they're fed more information. In this course, you'll start with the basics of building a linear regression module in Python, and progress into practical machine learning issues that will provide the foundations for an exploration of Deep Learning. Access 20 lectures & 2 hours of content 24/7 Use a 1-D linear regression to prove Moore's Law Learn how to create a machine learning model that can learn from multiple inputs Apply multi-dimensional linear regression to predict a patient's systolic blood pressure given their age & weight Discuss generalization, overfitting, train-test splits, & other issues that may arise while performing data analysis The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer.
You can add one more name to the constantly expanding list of companies that want a slice of that autonomous driving pie, as a new company named Drive.ai The new company, which also announced that it has added former General Motors Vice Chairman and Board Member Steve Girsky to its Board of Directors, is looking to put its stamp on the self-driving space with its own deep learning algorithms. These full stack deep learning algorithms, Drive.ai CEO Sameep Tandon says that the team at Drive.ai has been working on these deep learning applications since the company was founded in 2015. For now, the company says it will offer a retrofitted system that can be used in existing vehicle fleets.
Autonomous cars use a variety of technologies like radar, lidar, odometry and computer vision to detect objects and people on the road, prompting it to adjust its trajectory accordingly. To tackle this problem, electrical engineers from University of California, San Diego used powerful machine learning techniques in a recent experiment that incorporated so-called deep learning algorithms in a pedestrian-detection system that performs in near real-time, using visual data only. The findings, which were presented at the International Conference on Computer Vision in Santiago, Chile, are an improvement over current methods of pedestrian detection, which uses something called cascade detection. This traditional form of classification architecture in computer vision takes a multi-stage approach that first breaks down an image into smaller image windows. These sub-images are then processed by whether they contain the presence of a pedestrian or not, using markers like shape and color.
In the late 00's some clever academics rebranded a subset of neural network techniques to'Deep Learning', which just means a stack of different nets on top of one another, forming a sort of computationally-brilliant lasagne. When I say'machine learning' in this blogpost, I'm referring to some kind of neural network technique.) Robotics has just started to get into neural networks. This has already sped up development. This year, Google demonstrated a system that teaches robotic arms to learn how to pick up objects of any size and shape.