-- This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars. In this paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets. Our contributions are threefold, 1) we created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor scenes, that we call Foggy Synscapes and plan to release publicly 2) we show that with this data we outperform previous approaches on real-world foggy test data 3) we show that a combination of our data and previously used data can even further improve the performance on real-world foggy data. The last years have seen tremendous progress in tasks relevant to autonomous driving [1].

Machine learning models, especially neural networks (NNs), have achieved outstanding performance on diverse and complex applications. However, recent work has found that they are vulnerable to Trojan attacks where an adversary trains a corrupted model with poisoned data or directly manipulates its parameters in a stealthy way. Such Trojaned models can obtain good performance on normal data during test time while predicting incorrectly on the adversarially manipulated data samples. This paper aims to develop ways to detect Trojaned models. We mainly explore the idea of meta neural analysis, a technique involving training a meta NN model that can be used to predict whether or not a target NN model has certain properties. We develop a novel pipeline Meta Neural Trojaned model Detection (MNTD) system to predict if a given NN is Trojaned via meta neural analysis on a set of trained shadow models. We propose two ways to train the meta-classifier without knowing the Trojan attacker's strategies. The first one, one-class learning, will fit a novel detection meta-classifier using only benign neural networks. The second one, called jumbo learning, will approximate a general distribution of Trojaned models and sample a "jumbo" set of Trojaned models to train the meta-classifier and evaluate on the unseen Trojan strategies. Extensive experiments demonstrate the effectiveness of MNTD in detecting different Trojan attacks in diverse areas such as vision, speech, tabular data, and natural language processing. We show that MNTD reaches an average of 97% detection AUC (Area Under the ROC Curve) score and outperforms existing approaches. Furthermore, we design and evaluate MNTD system to defend against strong adaptive attackers who have exactly the knowledge of the detection, which demonstrates the robustness of MNTD.

Modern deep learning methods provide an effective means to learn good representations. However, is a good representation itself sufficient for efficient reinforcement learning? This question is largely unexplored, and the extant body of literature mainly focuses on conditions which permit efficient reinforcement learning with little understanding of what are necessary conditions for efficient reinforcement learning. This work provides strong negative results for reinforcement learning methods with function approximation for which a good representation (feature extractor) is known to the agent, focusing on natural representational conditions relevant to value-based learning and policy-based learning. For value-based learning, we show that even if the agent has a highly accurate linear representation, the agent still needs to sample exponentially many trajectories in order to find a near-optimal policy. For policy-based learning, we show even if the agent's linear representation is capable of perfectly representing the optimal policy, the agent still needs to sample exponentially many trajectories in order to find a near-optimal policy. These lower bounds highlight the fact that having a good (value-based or policy-based) representation in and of itself is insufficient for efficient reinforcement learning. In particular, these results provide new insights into why the existing provably efficient reinforcement learning methods rely on further assumptions, which are often model-based in nature. Additionally, our lower bounds imply exponential separations in the sample complexity between 1) value-based learning with perfect representation and value-based learning with a good-but-not-perfect representation, 2) value-based learning and policy-based learning, 3) policy-based learning and supervised learning and 4) reinforcement learning and imitation learning.

Learn the basics of deep learning - a machine learning technique that uses neural networks to learn and make predictions - through computer vision projects, tutorials, and real world, hands-on exploration with a physical device. AWS DeepLens lets you run deep learning models locally on the camera to analyze and take action on what it sees.

The rapid adoption of AI technologies shows that they cater to the needs of businesses. Today, Artificial Intelligence and Machine Learning are seen as part of the everyday life of large organizations in various fields. The rapid pace of AI development – achievements in unmanned aerial vehicles, the ability to beat humans at chess and poker, automated customer service and analytical systems – shows that AI is a revolutionary technology designed to change the way people use devices and conduct business. The appearance of intelligent machines capable of solving difficult problems in a way that only humans could previously hasn't gone unnoticed. From the day of its invention until now, the AI technology has been evolving and improving.

See a larger version of the cover image here. Prediction rules in deep learning are based on a forward, recursive computation through several layers. Implicit deep learning rules go much beyond, by relying on the solution of an implicit (or, "fixed-point") equation that has to be numerically solved in order to make the prediction: for a given input vector u, the predicted vector y is of the form Here, the so-called "state" n-vector x, which contains the hidden features of the model, is not expressed explicitly; rather it is implicitly defined via the "fixed-point" (or, equilibrium) equation x ϕ(Ax Bu). At first glance, the above models seem very specific. Perhaps surprisingly, they include a special case most known neural network architectures, including standard feedforward networks, CNNs, RNNs, and many more.

SingularityNET, a startup that bills itself as a "decentralized artificial intelligence" company, said today it's working with networking giant Cisco Systems Inc. on an ambitious project to create more advanced AI technologies that will soon be able to surpass humans in their ability to learn and perform new tasks. The partnership is a strong validation of SingularityNET's technologies, which include a blockchain-based, decentralized marketplace for AI algorithms, and various deep neural net models for computer vision and language understanding. Its biggest project, however, is a customized version of the OpenCog Advanced General Intelligence engine, an architecture for robot and virtual embodied cognition that defines a set of interacting components designed to give rise to "human-equivalent AGI." AGI is an emerging new field within AI, or some might even say a more superior form of it. Traditional AI generally refers to the ability of a machine to imitate human cognition, such as learning and problem-solving. But most AI models remain quite primitive, specialized on training machines at a single task, whether that's image recognition, playing chess or studying medical data.

Sign in to report inappropriate content. "This video is about NVIDIA, HPE Edgeline and Apollo systems that help factories in leveraging AI (Artificial Intelligence) to identify real-time data patterns that people might miss.

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. Python provides a great way for machine learning newcomers to begin their hands-on practice, or for experienced practitioners to augment their growing battery of tools. Python's popularity has recently grown even further since it is the most common way to access leading deep learning solutions such as TensorFlow. Note that this workshop day does not cover deep learning, since it serves first-time users by covering a broader, foundational range of traditional machine learning methods. However, this training does provide helpful groundwork for the "Hands-On Deep Learning in the Cloud" workshop scheduled for later in the same week.

The Johns Hopkins hospital launches capacity command center to enhance hospital operations. U.S. healthcare leaders expect widespread adoption of artificial intelligence by 2023. Can machine-learning improve cardiovascular risk prediction using routine clinical data? Radiology in 2018: Are you working with ai or being replaced by AI? Radiology. Robotic invasion of operation theatre and associated anaesthetic issues: A review.