Alphabet's DeepMind division is partnering with Unity to accelerate machine learning and artificial intelligence (AI) research. The collaboration will focus on "virtual environments" that DeepMind and others can use to test and visualize experimental algorithms. Otherwise, little is known about the partnership. Today's announcement is basically a broad agreement, or statement of intent, between the two companies. "I couldn't be more excited to be collaborating with Unity, creating virtual environments for developing and testing the kind of smart, flexible algorithms we need to tackle real-world problems," Demis Hassabis, co-founder and CEO of DeepMind said in a press release light on detail.

At Zalando Research, as in most AI research departments, we realize the importance of experimenting and quickly prototyping ideas. With datasets getting bigger it thus becomes useful to know how to train deep learning models quickly and efficiently on the shared resources we have. TensorFlow's Estimators API is useful for training models in a distributed environment with multiple GPUs. Here, we'll present this workflow by training a custom estimator written with tf.keras for the tiny Fashion-MNIST dataset, and then show a more practical use case at the end. Note: there's also a cool new feature the TensorFlow team has been working on, (which at the time of writing is still in master), that lets you train a tf.keras model without first needing to convert it to an Estimator, with just a couple lines of additional code!

Human ears have no trouble sussing out the sentiment of songs -- say, the melancholia in Eric Clapton's "Tears in Heaven," or the passion in Led Zeppelin's "Whole Lotta Love." In an attempt to get the latter to perform a bit more like the former, researchers at Deezer developed an artificially intelligent system that can associate certain tracks with moods. They describe their work in a new paper ("Music Mood Detection Based on Audio Lyrics With Deep Neural Nets") published on the preprint server Arxiv.org. "[A]utomatic music mood detection has been an active field of research … for the past twenty years," they wrote. "It consists of automatically determining the emotion felt when listening to a track. In this work, we focus on the task of multimodal mood detection based on the audio signal and the lyrics of the track."

Our workdays are getting noisier. Industry research shows that employees at larger organizations use an average of 36 cloud services at work, including tools for productivity, project management, communication, and storage. This information overload is a key source of pain for people at work--and a prime opportunity to leverage the help of machine intelligence. When we talk about machine intelligence at Dropbox, we mean the whole range of applied machine learning, from simple linear classifiers to advanced deep learning networks. For many years we've been building a world-class machine learning team, improving our platform behind the scenes.

This new offering is available to service providers such as security integrators, telecoms and alarm and monitoring companies for reselling to their customers. IvedaAI includes a powerful self-contained server with artificial intelligence (AI) software, capable of searching a combination of objects from dozens to thousands of cameras in less than one second. Video analytics have been around for many years, but adoption has been slow because of inaccuracies and high cost. IvedaAI employs 30 patents in AI, big data analytics and cloud computing. It applies a deep learning algorithm (trained, not programmed), automates processes and uses natural language.

From the word'reinforcement' we get the idea of building patterns or belief system with positive feedback. With reinforcement learning we aim to create algorithms that helps an agent in achieving maximum performance in a given environment with proper rewards. Let's consider two situations giving an abstract insight for RL. Let's first define concept of agent and environment for understanding technical details about RL. Environment is the universe of agent which changes state of agent with given action performed on it.

Machine learning security is an emerging concern for companies, as recent research by teams from Google Brain, OpenAI, US Army Research Laboratory and top universities has shown how machine learning models can be manipulated to return results fitting the attacker's desire. One area of significant finding has been in image recognition models. Image recognition is one of the stalwarts of machine learning and deep learning systems, allowing for superhuman performance on classification tasks and enabling proofs of concept in autonomous vehicles. Recent highly successful research showing the exploitation of image recognition models, specifically convolutional neural networks, is especially troubling for autonomous vehicles as attackers could theoretically take control of vehicles, or at least cause them to lose control. Advancements by Geoffrey Hinton and team address a few of the key problems plaguing convolutional neural networks, or CNNs, (more on that below), however, definitive research has not yet been performed to check if they fix the security problems. I'll outline several security issues that exist in current algorithmic deployments and then walk through some steps to take in order to provide assurance over algorithmic integrity.

Traditional machine learning models have always been very powerful to handle structured data and have been widely used by businesses for credit scoring, churn prediction, consumer targeting, and so on. The success of these models highly depends on the performance of the feature engineering phase: the more we work close to the business to extract relevant knowledge from the structured data, the more powerful the model will be. When it comes to unstructured data (images, text, voice, videos), hand engineered features are time consuming, brittle and not scalable in practice. That is why Neural Networks become more and more popular thanks to their ability to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.

Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world." Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Abstract--Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive selforganization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work. The idea that networks should learn to drive themselves is gaining traction [11], taking inspiration from self-driving cars where driving and related functionality do not require human intervention. The networking community wants to build a similar cognitive control in networks where networks are able to configure, manage, and protect themselves by interacting with the dynamic networking environment.We refer to such networks as cognitive self-organizing networks CSON. The expected complexity and heterogeneity of CSON makes machine learning (ML) a reasonable choice for realizing this ambitious goal. Recently artificial intelligence (AI) based CSON have attained a lot of attention in industry and academia.