Robots still have some trouble handling the basics when put to the test, apparently. Roborace team SIT Acronis Autonomous suffered an embarrassment in round one of the Season Beta 1.1 race after its self-driving car abruptly drove directly into a wall. It's not certain what led to the mishap, but track conditions clearly weren't at fault -- the car had been rounding a gentle curve and wasn't racing against others at the same time. It wasn't the only car to suffer a problem, either. Autonomous Racing Graz's vehicle had positioning issues that got it "lost" on the track and cut its race short.
Online Courses Udemy - Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn Created by Lazy Programmer Inc English [Auto-generated], Spanish [Auto-generated] Students also bought Bayesian Machine Learning in Python: A/B Testing The Complete Python Course Learn Python by Doing Complete Python Developer in 2020: Zero to Mastery Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning in Python Preview this course GET COUPON CODE Description In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.
Back in 2015, when creative director Clint Hocking and his team began crafting the near-future world of Watch Dogs: Legion, some of the biggest technology companies in the world were confidently describing skies buzzing with package-delivery drones and streets alive with autonomous vehicles. Into the game they went. For a speculative fiction game about mass surveillance, that creates some problems. "Technology companies--Tesla, Amazon--had started talking publicly about pretty aggressive timelines, schedules, and regulations," Hocking said in an interview with WIRED. On October 29, Watch Dogs: Legion will release as both a game and a time capsule from 2015, back when a couple of big, stock-inflating daydreams painted a picture for 2020 that's still far from materializing.
This week Intel launched its Intel Geospatial offering. The platform provides access to a wide range of high-quality 2D and 3D geospatial data and analytic applications through Intel's ecosystem of trusted partners. The platform offers nationwide high-resolution RGB library for rich visual experience and superior analytics. This includes data from satellites, manned aircraft, and unmanned aerial vehicles (UAVs) like drones. Customers can also request data from Mobileye, Intel's autonomous vehicle subsidiary.
Recently, a team of researchers from MIT, Institute of Science and Technology Austria (IST Austria) and Technische Universität Wien (TU Wien) developed an AI system by combining brain-inspired neural computation principles and scalable deep learning architectures. The AI system is basically a brain-inspired intelligent agent that learns to control an autonomous vehicle directly from its camera inputs. The researchers discovered that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learns to map high-dimensional inputs into steering commands. One of the interesting facts of this research is that the AI agent is inspired by the neural computations known to happen in biological brains in order to achieve a remarkable degree of controllability. They took the inspiration from animals as small as the roundworms.
The human brain possesses the remarkable ability to infer depth when viewing a two-dimensional scene, even with a single-point measurement, as in viewing a photograph. However, accurate depth mapping from a single image remains a challenge in computer vision. Depth information from a scene is valuable for many tasks like augmented reality, robotics, self driving cars etc. In this blog we explore how to train a depth estimation model on NYU Depth Data set. The model gets state of the art results on this data set.
Continental AG is taking a minority stake in AEye Inc., a Dublin, California-based developer of LiDAR technology, in order to bring its autonomous vehicle technology to commercial vehicles sooner. Specifically, AEye, founded in 2013, has developed a long-range LiDAR system that can detect vehicles at a distance of more than 300 meters and pedestrians at more than 200 meters. Continental hopes the investment will enhance its current short-range LiDAR technology that is slated to go into production by the end of 2020. Then the AEye system would be deployed in a automotive passenger or commercial vehicle later this decade. "We now have optimum short-range and long-range LiDAR technologies with their complimentary sets of benefits under one roof," said Frank Petznick, head of Continental's advanced driver assistance systems, in a statement.
Daimler is clearly eager to expand its plans for self-driving trucks. The automotive giant is teaming up with Waymo to develop trucks capable of level 4 autonomy, or full self-driving in specific conditions. The early strategy will focus on a modified Freightliner Cascadia that uses Waymo Driver for navigation. This first truck will be available in the US in the "coming years," the companies said. The two would also "investigate" expanding their efforts to other brands and markets.
AMD is acquiring chip designer Xilinx for $35 billion in stock to "significantly" expand the range of products it makes and customers it reaches, particularly in high performance computing. As the Wall Street Journal noted, Xilinx's easily customizable FPGA (field-programmable gate array) chips are used in a variety of places AMD wouldn't have even considered before, from 5G systems to the F-35 to self-driving cars. The newly-bought company also specializes in adaptive systems-on-chip, accelerators and smart networking devices found in data centers, edge computing and end devices. AMD expects the Xilinx deal to take a while to wrap up. It should close by the end of 2021, the company said.
Description This course is about the fundamental concept of image processing, focusing on face detection and object detection. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation. Self-driving cars (for example lane detection approaches) relies heavily on computer vision. With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?