Neural network architecture design is one of the key hyperparameters in solving problems using deep learning and computer vision. Various neural networks are compared on two key factors i.e. accuracy and computational requirement. In general, as we aim to design more accurate neural networks, the computational requirement increases.

The spotlight in the IT sector has completely shifted to Artificial Intelligence and its subsets in the past few years. Machine Learning, Deep Learning, Automation and others have garnered significant attention from the businesses as well as IT experts. However, with great attention, a certain amount of confusion has also crept in. There are people who still do not figure out the key differences between artificial intelligence, robotic process automation, deep learning, and machine learning. In this article, we'll throw some light on the key differences between AI & RPA i.e.

According to Gartner Inc. AI is going to be one of the biggest the trends. It will be the number one driver of business change with reports suggesting the market size to reach $20 billion by 2022. While with over 400 people killed in road accidents every day and over 1.3 million people killed in India over the past one decade. There is a dire need to improve road safety in India. Artificial Intelligence can prominently play a role in bridging the gap by improving efficiency in driving patterns.

It's no secret that I'm an advocate of edge-based computing, and after a number of years where cloud computing has definitely been in ascendency, the swing back towards the edge is now well underway. Driven, not by the Internet of Things as you might perhaps expect, but by the movement of machine learning out of the cloud. Until recently most of the examples we've seen, such as the Neural Compute Stick or Google's AIY Projects kits, were based around custom silicon like Intel's Movidius chip. However, recently Arm quietly released its CMSIS-NN library, a neural network library optimised for the Cortex-M-based microcontrollers. Machine learning development is done in two stages.

The AICS 2019 Challenge, sponsored by the Crowdstrike Foundation and organized by the MIT Lincoln Laboratory, tasked researchers from around the world to devise a system that can classify several types of extremely harmful malware which have adapted to evade IT security measures and can remain undetected inside computer systems for years. Shouhuai Xu, director of UTSA's Laboratory for Cybersecurity Dynamics and professor in the UTSA Department of Computer Science, formed and led an international team that created a framework using deep neural networks to classify and detect the malware. The MIT Lincoln Laboratory made the challenge extra difficult because "white-hat" hackers had access to a limited training data set with an unbalanced number of malwares. Moreover, each competing team had to propose a real-world solution after being given access to the MIT testing data for just one week. "The Challenge is as realistic as what a cyber defender would encounter in the wild, because little information about the'attacks' is given to us," said Xu. "This exercise mimics what happens in the real world."

While reviewing documents is just one of several parts of the job of a lawyer, this data further proves the Hard Trend that I implore everyone to pay attention to in the years to come. Artificial intelligence is here to stay, and by using machine learning and deep learning techniques, new A.I. systems are learning how to think better and better every day. So the question remains: Are you anticipating how A.I. can be used to automate tasks and do things that might seem impossible today -- in other words, disrupt your industry? Are you starting to learn more about A.I. so that you can become a positive disruptor rather than become the disrupted?

In this paper, we propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way to address this ill-posed seismic inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong non-uniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model as well as the time-varying property of seismic data. To approach these challenges, we propose an end-to-end Seismic Inversion Networks (SeisInvNet for short) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup and global context of its corresponding seismic profile. Then from enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our proposed SeisInv dataset according to various evaluation metrics, and the inversion results are more consistent with the target from the aspects of velocity value, subsurface structure and geological interface. In addition to the superior performance, the mechanism is also carefully discussed, and some potential problems are identified for further study.

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is twofold, firstly we present a structured and comprehensive overviewof research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into different categories based on the underlying assumptions and approach adopted. Within each category, we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. Besides, for each category, we also present the advantages and limitations and discuss the computational complexity of the techniques inreal application domains. Finally, we outline open issues in research and challenges faced while adopting deep anomaly detection techniques for real-world problems.

By achieving their purposes through interactions with the physical world, Cyber Physical Systems (CPS) pose new challenges. Indeed, the evolution of the physical systems they control with transducers can be affected by surrounding physical processes over which they have no control and which may potentially hamper the achievement of their purposes. While it is illusory to hope for a comprehensive model of the physical environment at design time to anticipate and remove faults that may occur once these systems are deployed, it becomes necessary to evaluate their degree of effectiveness in vivo.In this paper, the degree of effectiveness is formally defined and generalized in the context of the measure theory and the mathematical properties it has to comply with are detailed. The measure is developed in the context of the Transferable Belief Model (TBM), an elaboration on the Dempster Shafer Theory (DST) of evidence so as to handle epistemic and aleatory uncertainties respectively pertaining the users expectations and the natural variability of the physical environment. This theoretical framework has several advantages over the probability and the possibility theories. (1) It is built on the Open World Assumption (OWA), (2) it allows to cope with dependent and possibly unreliable sources of information. The TBM is used in conjunction with the Input Output Hidden Markov Modeling framework (IOHMM) to specify the expected evolution of the physical system controlled by the CPS and the tolerances towards uncertainties. The measure of effectiveness is obtained from the forward algorithm, leveraging the conflict entailed by the successive combinations of the beliefs obtained from observations of the physical system and the beliefs corresponding to its expected evolution. The conflict, inherent to OWA, is meant to quantify the inability of the model at explaining observations.

Vision-based deep reinforcement learning (RL), similar to deep learning, typically obtains a performance benefit by using high capacity and relatively large convolutional neural networks (CNN). However, a large network leads to higher inference costs (power, latency, silicon area, MAC count). Many inference optimization have been developed for CNNs. Some optimization techniques offer theoretical efficiency, but designing actual hardware to support them is difficult. On the other hand, "distillation" is a simple general-purpose optimization technique which is broadly applicable for transferring knowledge from a trained, high capacity, teacher network to an untrained, low capacity, student network. "DQN distillation" extended the original distillation idea to transfer information stored in a high performance, high capacity teacher Q-function trained via the Deep Q-Learning (DQN) algorithm. Our work adapts the DQN distillation work to the actor-critic Proximal Policy Optimization algorithm. PPO is simple to implement and has much higher performance than the seminal DQN algorithm. We show that a distilled PPO student can attain far higher performance compared to a DQN teacher. We also show that a low capacity distilled student is generally able to outperform a low capacity agent that directly trains in the environment. Finally, we show that distillation, followed by "fine-tuning" in the environment, enables the distilled PPO student to achieve parity with teacher performance. In general, the lessons learned in this work should transfer to other actor-critic RL algorithms.