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#artificialintelligence

The media loves to frame matters of technology in quite dystopian terms, so it's perhaps not surprising that coverage of autonomous vehicles has tended to focus on both the safety implications of the technology and the number of driving jobs that might be lost. It's a narrative that seems to demand perfection from the technology before it can be rolled out en-masse on public roads. Such thinking has distinct dangers of its own, with a recent from the RAND Corporation highlighting how delaying the launch of autonomous technology until perfection is achieved will cost many thousands of lives per year. The report argues that even a 10% improvement on human drivers could save thousands of deaths on our roads, thus rendering it morally questionable whether it's wise to demand technology that is 90% and above better than human drivers. The figures were arrived at by examining hundreds of possible futures and the changing safety requirements for autonomous vehicle introduction, with estimated road fatalities extrapolated for each potential scenario.


Developing the AI future

#artificialintelligence

Artificial Intelligence (AI) is starting to change how many businesses operate. The ability to accurately process and deliver data faster than any human could is already transforming how we do everything from studying diseases and understanding road traffic behaviour to managing finances and predicting weather patterns. For business leaders, AI's potential could be fundamental for future growth. With so much on offer and at stake, the question is no longer simply what AI is capable of, but where AI can best be used to deliver immediate business benefits. According to Forrester, 70% of enterprises will be implementing AI in some way over the next year.


Advance in perception and motion planning for autonomous vehicles Electric Vehicles Research

#artificialintelligence

AEye Inc has introduced iDAR, a new form of intelligent data collection that enables rapid, dynamic perception and path planning. AEye's iDAR is designed to intelligently prioritize and interrogate co-located pixels (2D) and voxels (3D) within a frame, enabling the system to target and identify objects within a scene 10-20x more effectively than LiDAR-only products. Additionally, iDAR is capable of overlaying 2D images on 3D point clouds for the creation of True Color LiDAR. Its embedded AI capabilities enable iDAR to utilize thousands of existing and custom computer vision algorithms, which add intelligence that can be leveraged by path planning software. The introduction of iDAR follows AEye's September demonstration of the first 360 degree, vehicle-mounted, solid-state LiDAR system with ranges up to 300 meters at high resolution.


Why use RBF Learning rather than Deep Learning in an industrial environment

#artificialintelligence

One of today's most overused buzzword is "Artificial Intelligence". Both technical and general press is full of articles talking about machines that drive autonomous cars and invent new languages. Machine Learning is an essential part of the AI puzzle and Deep Learning is one of the most popular approaches to implement Machine Learning. Interestingly, Deep Learning is not new. Geoffrey Hinton demonstrated the use of back-propagation of errors for training multi-layer neural networks in 1986, more than 30 years ago.


Artificial Intelligence and Supercomputers to Help Alleviate Urban Traffic Problems

#artificialintelligence

Look above the traffic light at a busy intersection in your city and you will probably see a camera. These devices may have been installed to monitor traffic conditions and provide visuals in the case of a collision. But can they do more? Can they help planners optimize traffic flow or identify sites that are most likely to have accidents? And can they do so without requiring individuals to slog through hours of footage?


Artificial intelligence and supercomputers to help alleviate urban traffic problems

#artificialintelligence

Look above the traffic light at a busy intersection in your city and you will probably see a camera. These devices may have been installed to monitor traffic conditions and provide visuals in the case of a collision. But can they do more? Can they help planners optimize traffic flow or identify sites that are most likely to have accidents? And can they do so without requiring individuals to slog through hours of footage?


AI innovation will trigger the robotics network effect

#artificialintelligence

Anyone who has thought about scaling a business or building a network is familiar with a dynamic referred to as the "network effect." The more buyers and sellers who use a marketplace like eBay, for example, the more useful it becomes. Well, the data network effect is a dynamic in which increased use of a service actually improves the service, such as how machine-learning models generally grow more accurate as a result of training from larger and larger volumes of data. Autonomous vehicles and other smart robots rely on sensors that generate increasingly massive volumes of highly varied data. This data is used to build better AI models that robots rely on to make real-time decisions and navigate real-world environments.


Gaming Machine Learning

Communications of the ACM

Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe." All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles.


Robotics, Positioning and AI for Mining, Construction Safety and Autonomous Vehicles

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Researchers from our group at QUT and the Australian Centre for Robotic Vision have had six papers accepted to the upcoming Australasian Conference on Robotics and Automation to be held at The University of Technology Sydney. This year the conference trialed a dual submission process with the IEEE International Conference on Robotics and Automation, meaning work can be presented at both conferences but only published in the proceedings of one. The papers cover ongoing research in our lab spanning topics including robotics, positioning and AI for applications in mining, construction safety and autonomous vehicles. I'll give an overview here of the research we're doing, and a wrap up at the end. Despite very high safety standards, work sites of all varieties around Australia still cause large numbers of injuries and occasional fatalities.


Watch: This motorcycle-riding robot is no match for one of the most successful racers of all time

ZDNet

We all know the robots are coming. That probably inspires some complicated feelings. So, it's comforting when a three-year development effort to make a robot that can set a speed record results in a human victory... by a wide margin. Yamaha and robotics developer SRI have been working on a humanoid that can ride an unmodified motorcycle. The goal was to beat the lap times of one of the most successful motorcycle racers of all time, Valentino Rossi.