Currently I'm searching for a Reinforcement Learning toolkit for autonomous driving to test the influence of several safety aspects during learning as a reward function. So far I have tested OpenAI Gym with the "Neon racer" environment, which does not provide those information. Are there any other toolkits you would suggest me for this purpose?

In the past few years, deep learning is all the fuss in the tech industry. To keep up on things I like to get my hands dirty implementing interesting network architectures I come across in article readings. Few months ago I came across a very nice article called Siamese Recurrent Architectures for Learning Sentence Similarity.It offers a pretty straightforward approach to the common problem of sentence similarity. Named MaLSTM ("Ma" for Manhattan distance), its architecture is depicted in figure 1 (diagram excludes the sentence preprocessing part). Notice that since this is a Siamese network, it is easier to train because it shares weights on both sides.

We implement several strategies to minimize memory footprint and GPU usage. To reduce memory footprint, we allocate the intermediate layers of our neural networks by analyzing the compute graph. This allows us to alias multiple layers to the same buffer. While being fully deterministic, this technique reduces memory footprint without impacting the performance or allocations fragmentation, and can be used on either the CPU or GPU. For Vision, the detector runs 5 networks (one for each image pyramid scale as shown in Figure 2). These 5 networks share the same weights and parameters, but have different shapes for their input, output, and intermediate layers. To reduce footprint even further, we run the liveness-based memory optimization algorithm on the joint graph composed by those 5 networks, significantly reducing the footprint.

Amazon yesterday announced its ONNX-MXNet package to import Open Neural Network Exchange (ONNX) deep learning models into Apache MXNet, signifying the company is on-board with Facebook and Microsoft in efforts to open-source AI. With the ONNX-MXNet Python package, developers running models based on open-source ONNX will be able to run them on Apache MXNet. Basically, this allows AI developers to keep models but switch networks, as opposed to starting from scratch. If you can imagine a thousand start ups and another thousand universities all creating at the bleeding edge of machine learning technology, but unable to share work due to'format' issues, you won't be very far off from the state of things without initiatives like ONNX. With Facebook and Microsoft all-in on the idea of open-source AI platforms, and now Amazon joining them, it's looking like ONNX is the path forward.

Disclaimer: I have been working on analog and mixed-signal neuromorphic micro-chips during my PhD, and in the last 10 years have switched to Deep Learning and fully digital neural networks. For a complete list of our works, see here. An older publication from regarding this topic is here (from our group). Neuromorphic or standard digital for computing neural networks: which one is better? This is a long question to answer.

I consistently hear from customers that one of their biggest challenges is how to best manage and learn from the ever-increasing amount of data they collect daily. It's a significant contributor to why the artificial intelligence (AI) market is forecasted to increase from more than $640 million in 2016 to nearly $37 billion in 2025, with AI workloads growing at an estimated annual rate of 52%1. The rapid growth of data and new technology advancements has made it economically viable to adopt machine learning to disrupt new markets, improve operations, and pave a competitive advantage. Working with our strategic technology partners, we're able to bring these powerful capabilities to organizations of all sizes and industries in more ways than ever before. At this week's Supercomputing 2017 conference, we unveiled THREE new solutions that converge our HPC and data analytics expertise along with next generation strategic partner technology and equip organizations to unlock faster, better and deeper data insights.

IBM announced new software to deliver faster time to insight for high performance data analytics (HPDA) workloads, such as Spark, Tensor Flow and Caffé, for AI, Machine Learning and Deep Learning. Based on the same software, which will be deployed for the Department of Energy's CORAL Supercomputer Project at both Oak Ridge and Lawrence Livermore, IBM will enable new solutions for any enterprise running HPDA workloads. New to this launch is Deep Learning Impact (DLI), a set of software tools to help users develop AI models with the leading open source deep learning frameworks, like TensorFlow and Caffe. The DLI tools are complementary to the PowerAI deep learning enterprise software distribution already available from IBM. Also new is web access and simplified user interfaces for IBM Spectrum LSF Suites, combining a powerful workload management platform with the flexibility of remote access.

Imaging in three dimensions rather than two offers numerous advantages for machines working in the factories of the future by granting them a whole new perspective to view the world. Combined with embedded processing and deep learning, this new perspective could soon allow robots to navigate and work in factories autonomously by enabling them to detect and interact with objects, anticipate human movements and understand given gesture commands. Certain challenges must first be overcome to unlock this promising potential, however, such as ensuring standardisation across large sensing ecosystems and increasing widespread understanding of what 3D vision can do within industry. Three-dimensional imaging can be achieved by a variety of formats, each using different mechanics to capture depth information. Imaging firm Framos was recently announced as a supplier of Intel's RealSense stereovision technology, which uses two cameras and a special purpose ASIC processor to calculate a 3D point cloud from the data of the two perspectives.

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Apple has published its latest machine learning journal entry with a new article detailing the challenges of implementing facial detection features while maintaining a high level of privacy. Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently on-device. Apple's iCloud Photo Library is a cloud-based solution for photo and video storage.