Results


Self-driving cars are NOT safe 'while in the wild', says the co-founder of Google's DeepMind

Daily Mail

The co-founder of Google's DeepMind has slammed self-driving cars for not being safe enough, saying current early tests on public roads are irresponsible. Demis Hassabis has urged developers to be cautious with the new technology, saying it is difficult to prove systems are safe before putting them on public roads. The issue of AI in self-driving cars has flared up this year following the death of a women hit but a self-driving Uber in March. The accident was the first time a pedestrian was killed on a public road by an autonomous car, which had previously been praised as the safer alternative to a traditional car. Speaking at the Royal Society in London, Dr Hassabis said current driverless car programmes could be putting people's lives in danger.


27 Incredible Examples Of Artificial Intelligence (AI) And Machine Learning In Practice

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There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.


Vehicle Detection and Tracking using Machine Learning and HOG

#artificialintelligence

I am into my first term of Udacity's Self Driving Car Nanodegree and I want to share my experience regarding the final project of Term 1 i.e. The complete code can be found here. The basic objective of this project is to apply the concepts of HOG and Machine Learning to detect a Vehicle from a dashboard video. Sure, the Deep Learning implementations like YOLO and SSD that utilize convolutional neural network stand out for this purpose but when you are a beginner in this field, its better to start with the classical approach. The most important thing for any machine learning problem is the labelled data set and here we need to have two sets of data: Vehicle and Non Vehicle Images.


Artificial Intelligence: Science fiction to science fact - Connected Magazine

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Artificial intelligence is quickly growing in importance in the'smart building' sector. Paul Skelton looks at the road ahead for a complex technology. When Mark Chung received an unexpectedly high $500 monthly electricity bill, he turned to his utility for help and answers. However, despite'smart' meters being installed in his home, they were no help. So Mark – an electrical engineer trained at Stanford University – took matters into his own hands.


Insurance 2030--The impact of AI on the future of insurance

#artificialintelligence

The industry is on the verge of a seismic, tech-driven shift. A focus on four areas can position carriers to embrace this change. Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter.


Insurance 2030--The impact of AI on the future of insurance

#artificialintelligence

The industry is on the verge of a seismic, tech-driven shift. A focus on four areas can position carriers to embrace this change. Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter.


27 Incredible Examples Of AI And Machine Learning In Practice

#artificialintelligence

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.


Microsoft/AirSim

#artificialintelligence

AirSim is a simulator for drones, cars and more built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want. Our goal is to develop AirSim as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way.


The 5 Most Amazing AI Advances in Autonomous Driving

#artificialintelligence

The very idea of a driverless vehicle rolling around on the streets seems incredible. And yet, we may be close to seeing such vehicles on the road around the world, thanks to artificial intelligence (AI), among other driving forces. In the recent past, there have been some amazing advances in autonomous vehicle technology which indicate the dream is inching toward fruition. It seems that the framework of autonomous vehicles has been almost finalized. Subject to legal and administrative approvals, driverless vehicles will be a common sight on the roads soon.


Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge

arXiv.org Artificial Intelligence

Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually. One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed. Moreover, the merging process involves interactions with other vehicles whose behaviors are sometimes hard to predict but may influence the merging vehicle optimal actions. To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment. Specifically, we apply a Long Short-Term Memory (LSTM) architecture to model the interactive environment, from which an internal state containing historical driving information is conveyed to a Deep Q-Network (DQN). The DQN is used to approximate the Q-function, which takes the internal state as input and generates Q-values as output for action selection. With this DRL architecture, the historical impact of interactive environment on the long-term reward can be captured and taken into account for deciding the optimal control policy. The proposed architecture has the potential to be extended and applied to other autonomous driving scenarios such as driving through a complex intersection or changing lanes under varying traffic flow conditions.