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Microsoft extends AirSim to include autonomous car research - Microsoft Research

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Earlier this year, we open-sourced a research project called AirSim, a high-fidelity system for testing the safety of artificial intelligence systems. AirSim provides realistic environments, vehicle dynamics and sensing for research into how autonomous vehicles that use AI that can operate safely in the open world. Today, we are sharing an update to AirSim: We have extended the system to include car simulation, which will help advance the research and development of self-driving vehicles. The latest version is available now on GitHub as an open-source, cross-platform offering. The updated version of AirSim also includes many other features and enhancements, including additional tools for testing airborne vehicles.


Solution guide: Building connected vehicle apps with Cloud IoT Core

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GCP services, including the recently launched Cloud IoT Core provides a robust computing platform that takes advantage of Google's end-to-end security model. Device Management: To handle secure device management and communications, Cloud IoT Core makes it easy for you to securely connect your globally distributed devices to GCP and centrally manage them. Applications: Compute Engine, Container Engine and App Engine all provide computing components for a connected vehicle platform. Predictive Models: TensorFlow and Cloud Machine Learning Engine provide a sophisticated modeling framework and scalable execution environment.


What Is The Difference Between Deep Learning, Machine Learning and AI?

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Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there. Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has "learned", it can classify, with a certain probability of accuracy, passing vehicles by their make and model.


Flipboard on Flipboard

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What Is The Difference Between Deep Learning, Machine Learning and AI? Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is.


News in artificial intelligence and machine learning

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Google DeepMind announced their research project with Moorfields Eye Hospital in London aimed at the early detection of preventable eye disease (e.g. I've tracked close to a dozen new startups founded in the last 12 months applying deep learning to medical imaging, such as BayLabs, Imagia, MD.ai, AvalonAI, Behold.ai, Apple plays feature catch-up with Google, namely on its photo tagging/search capabilities and predictive keyboard, but takes a view that privacy should come first. Following from this point, the New York Times features a piece on how algorithms perpetuate intrinsic biases in their training data, drawing on examples from the police force, image classification tasks and gender discrimination. Facebook's Language Technology team, which forms part of Applied ML, was the subject of a recent expose by Forbes diving into its various initiatives.


Navistar takes command of big data for truck design, breakdown prevention

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Besides supporting internal customers in truck design and engineering, the analytics group uses advanced statistics and machine learning techniques to benefit its external customers. The model predicts failures for more than 40,000 combinations of diagnostic trouble codes (DTCs) by make, model and year of vehicle. When alerts are found for International trucks, its customer service group can address the problem directly with the fleet customer. The team used the technique to analyze the usage patterns of 100,000 vehicles by engine operating hours, miles, idling time, etc.


Optimization for machine learning and monster trucks

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When he turned two, I unwisely thought he would enjoy a monster truck rally and purchased tickets, imagining father and son duo making great memories together. Prototype approaches can be created relatively quickly, requiring publishable mathematics to prove convergence, pounded home with surprisingly good results. This is why I am excited about this Hessian-free approach, because although it is currently not mainstream, and it lacks the rock star status of stochastic gradient descent (SGD) approaches, it has the potential to save the user significant user processing time. I personally love algorithm development and enjoy spending my waking hours seeking to make algorithms faster and more robust.