We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at https://goo.gl/mZuqAV.

Many studies have been made to improve the AVs' ability of environment recognition and vehicle control, while the attention paid to decision making is not enough though the decision algorithms so far are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following (CF), is trained within this framework. In addition, theoretical analysis and experiments were conducted on setting reward function for accelerating training using DRL. The results show that on the premise of driving comfort, the efficiency of the trained AV increases 7.9% compared to the classical traffic model, intelligent driver model (IDM). Later on, on a more complex three-lane section, we trained the integrated model combines both CF and lane-changing (LC) behavior, the average speed further grows 2.4%. It indicates that our framework is effective for AV's decision-making learning. Keywords: Automated vehicle; Decision making; Deep reinforcement learning; Reward function 1. Introduction The automated vehicles have captured the public attention in recent years, especially after Google announced its automated driving program in 2010, for its advantages of alleviating the traffic congestion, liberating drivers' attention and conserving energy. The tasks involved in achieving autonomous driving can be divided into three modules: environment recognition, decision making and vehicle control. Among them, the vehicle control has no obvious differences between AV and manual driven vehicle.

In this paper, we propose the Cross-Domain Adversarial Auto-Encoder (CDAAE) to address the problem of cross-domain image inference, generation and transformation. We make the assumption that images from different domains share the same latent code space for content, while having separate latent code space for style. The proposed framework can map cross-domain data to a latent code vector consisting of a content part and a style part. The latent code vector is matched with a prior distribution so that we can generate meaningful samples from any part of the prior space. Consequently, given a sample of one domain, our framework can generate various samples of the other domain with the same content of the input. This makes the proposed framework different from the current work of cross-domain transformation. Besides, the proposed framework can be trained with both labeled and unlabeled data, which makes it also suitable for domain adaptation. Experimental results on data sets SVHN, MNIST and CASIA show the proposed framework achieved visually appealing performance for image generation task. Besides, we also demonstrate the proposed method achieved superior results for domain adaptation. Code of our experiments is available in https://github.com/luckycallor/CDAAE.

Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation ("I am here") and a representation of the goal ("I am going there"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. A video summarizing our research and showing the trained agent in diverse city environments as well as on the transfer task is available at: https://sites.google.com/view/streetlearn.

Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.

There's a discussion going on about the topic we are covering today: what's the difference between AI and machine learning and deep learning. Very frequently, press coverage and even practitioners of analytics use the terms Artificial Intelligence and Machine Learning interchangeably. However, these three concepts do not represent the same. In this video, we are going to break this down for you, giving you examples of use cases making the difference between ai and machine learning and deep learning more clear. Any device that perceives its environment and takes actions to maximize its chances of success, can be said to have some kind of artificial intelligence, more frequently referred to as A.I.

Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks. We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification.

Using deep learning, we set out to bridge the gap in image quality between inexpensive mobile phone-based microscopes and gold-standard bench-top microscopes that use high-end lenses,

The ability to take one person's face or expression and superimpose it onto a video of another person has recently become possible. In particular, pornographic videos called "deepfakes" have emerged on websites such as Reddit and 4Chan showing famous individuals' faces superimposed onto the bodies of actors. This phenomenon has significant implications. At the very least, it has the potential to undermine the reputation of people who are victims of this kind of forgery. It poses problems for biometric ID systems.

We also thank the four anonymous reviewers for their constructive criticism, which led to significant improvements of ANNA-PALM. We thank E. Rensen and C. Weber for help with experiments and suggestions, B. Lelandais for help with PALM image processing, J.-B. Arbona for polymer simulations and J. Parmar for suggestions that led to the name ANNA-PALM. We thank the IT service of Institut Pasteur, including J.-B. This work was funded by Institut Pasteur, Agence Nationale de la Recherche grant (ANR 14 CE10 0018 02), Fondation pour la Recherche Médicale (Equipe FRM, DEQ 20150331762), and the Région Ile de France (DIM Malinf).