On the need to strengthen the country's components and industrial materials industries, Choi said the government is committed to pouring 5 trillion won into related R&D up till 2022, with support being given to smart chips, future mobility, quantum technology, biotech and laying the foundation for a hydrogen economy.

According to the Google leadership, the company will not renew its Project Maven contract when it expires in 2019. Project Maven is the company's involvement with the U.S. military which involves the use of Artificial Intelligence to detect and identify people or objects in military drone surveillance videos. Many of the employees at Google were upset and 3,000 of them signed a petition voicing their concerns of Google's involvement with the military which could in turn be harmful for Google. They were against the development of image recognition technology which could be used by military drones to identify and track objects. It was reported on June 1 by Gizmodo that the company would not renew the Project Maven contract after June 2019.

T. L. Carroll US Naval Research Lab, Washington, DC 20375 (Dated: December 16, 2019) A reservoir computer is a complex dynamical system, often created by coupling nonlinear nodes in a network. The nodes are all driven by a common driving signal. In this work, three dimension estimation methods, false nearest neighbor, covariance and Kaplan-Yorke dimensions, are used to estimate the dimension of the reservoir dynamical system. It is shown that the signals in the reservoir system exist on a relatively low dimensional surface. Changing the spectral radius of the reservoir network can increase the fractal dimension of the reservoir signals, leading to an increase in testing error. A reservoir computer uses a complex dynamical system to perform computations. The reservoir is often created by coupling together a set of nonlinear nodes. Each node is driven by a common input signal. The time series responses from each node are then used to fit a training signal that is related to the input. The training can take place via a least squares fit, while, the connections between nodes are not altered during training, so training a reservoir computer is fast. Reservoir computers are described as "high dimensional" dynamical systems because they contain many signals, but the concept of dimension is rarely explored. A reservoir with M nodes defines an M dimensional space, but the actual signals in the reservoir may live on a lower dimensional surface. Two different dimension estimation methods are used to find the dimension of this surface. Counterintuitively, as the dimension of this surface increases, the fits to the training signal become worse. The increase in reservoir dimension can be explained by a well known effect in driven dynamical systems that causes signals in the driven system to have a higher fractal dimension than the driving signal.

--Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting outcomes of certain events such as election debates. In this work, we investigate whether crowd wisdom is useful in predicting such outcomes and whether their opinions are influenced by the experts in the field. We work in the domain of multi-label classification to perform sentiment classification of tweets and obtain the opinion of the crowd. This learnt sentiment is then used to predict outcomes of events such as: US Presidential Debate winners, Grammy A ward winners, Super Bowl Winners. We find that in most of the cases, the wisdom of the crowd does indeed match with that of the experts, and in cases where they don't (particularly in the case of debates), we see that the crowd's opinion is actually influenced by that of the experts. I NTRODUCTION Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics, discuss current issues, complain, etc. Twitter is one such popular microblogging service where users create status messages (called "tweets"). With over 400 million tweets per day on Twitter, microblog users generate large amount of data, which cover very rich topics ranging from politics, sports to celebrity gossip. Because the user generated content on microblogs covers rich topics and expresses sentiment/opinions of the mass, mining and analyzing this information can prove to be very beneficial both to the industrial and the academic community. Tweet classification has attracted considerable attention because it has become very important to analyze peoples' sentiments and opinions over social networks.

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Peter Kairouz and H. Brendan McMahan conceived, coordinated, and edited this work.

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of symbolic regression. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are lacking. We propose a framework that combines deep learning with symbolic regression via a simple idea: use a large model to search the space of small models. More specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions, and employ reinforcement learning to train the network to generate better-fitting expressions. Our algorithm significantly outperforms standard genetic programming-based symbolic regression in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate a priori constraints in situ. Understanding the mathematical relationships among variables in a physical system is an integral component of the scientific process. Symbolic regression aims to identify these relationships by searching over the space of tractable mathematical expressions to best fit a dataset.

Despite their importance, such theorems offer no explanation for the advantages of neural networks, let alone deep neural networks, over classical approximation methods, since universal approximation properties are enjoyed by polynomials (Cheney and Light, 2009) as well as single layer neural networks (Cybenko, 1989). To address this, a recent thread has emerged in the literature concerning optimal approximation with deep ReLU networks, where the error in an optimal choice of weights and biases is bounded from above using the width and depth of the neural network. For example, using the "sawtooth" function of Telgarsky (2015), Y arotsky (2017) constructed an exponentially accurate (in the number of layers) ReLU network emulator for multiplication (x,y) null xy . This construction is used to obtain upper bounds on optimal approximation based upon DNN emulation of polynomial approximation. Building on these ideas, Opschoor et al. (2019) proved that optimal approximation with deep ReLU networks can emulate adaptive hp-finite element approximation, with greater depth allowing p -refinement to obtain exponential convergence rates. An additional contribution by He et al. (2018) reinterpreted single hidden layer ReLU networks as r -adaptive piecewise linear finite element spaces.

--Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. Here we use a neural network-based architecture for symbolic regression that we call the Sequential Equation Learner (SEQL) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation. T o demonstrate the power of such systems, we study their performance on several substantially different tasks. First, we show that the neural network can perform symbolic regression and learn the form of several functions. Next, we present an MNIST arithmetic task where a separate part of the neural network extracts the digits. Finally, we demonstrate prediction of dynamical systems where an unknown parameter is extracted through an encoder . We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery. Many complex phenomena in science and engineering can be reduced to general models that can be described in terms of relatively simple mathematical equations. For example, classical electrodynamics can be described by Maxwell's equations and non-relativistic quantum mechanics can be described by the Schr odinger equation. These models elucidate the underlying dynamics of a particular system and can provide general predictions over a very wide range of conditions. On the other hand, modern machine learning techniques have become increasingly powerful for many tasks including image recognition and natural language processing, but the neural network-based architectures in these state-of-the-art techniques are black-box models that often make them difficult for use in scientific exploration.

Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model due to the imbalances in the dataset for discriminating attacks from the overall dataset. Multilayer Perceptron (MLP) technique will provide improvement in accuracy and increase the performance of detecting the attack and benign data from a balanced dataset. We have worked on the UGR'16 dataset publicly available for this work. Data wrangling has been done due to prepare test set from in the original set. We fed the neural network classifier larger input to the neural network in an increasing manner (i.e. 10000, 50000, 1 million) to see the distribution of features over the accuracy. We have implemented a GAN model that can produce samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan). We have been able to generate as many samples as necessary based on the data sample we have taken from the UGR'16. We have tested the accuracy of our model with the imbalance dataset initially and then with the increasing the attack samples and found improvement of classification performance for the latter.

Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usability, safety and validation. In order to design a comprehensive and representative taxonomy and associated descriptors we surveyed the eXplainable Artificial Intelligence literature, extracting the criteria and desiderata that other authors have proposed or implicitly used in their research. The survey includes papers introducing new explainability algorithms to see what criteria are used to guide their development and how these algorithms are evaluated, as well as papers proposing such criteria from both computer science and social science perspectives. This novel framework allows to systematically compare and contrast explainability approaches, not just to better understand their capabilities but also to identify discrepancies between their theoretical qualities and properties of their implementations. We developed an operationalisation of the framework in the form of Explainability Fact Sheets, which enable researchers and practitioners alike to quickly grasp capabilities and limitations of a particular explainable method. When used as a Work Sheet, our taxonomy can guide the development of new explainability approaches by aiding in their critical evaluation along the five proposed dimensions.