Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs. On the other hand, the explanation of GNNs (GNNExplainer) provides a better understanding of a trained GNN model by generating a small subgraph and features that are most influential for its prediction. In this paper, we first perform empirical studies to validate that GNNExplainer can act as an inspection tool and have the potential to detect the adversarial perturbations for graphs. This finding motivates us to further initiate a new problem investigation: Whether a graph neural network and its explanations can be jointly attacked by modifying graphs with malicious desires? It is challenging to answer this question since the goals of adversarial attacks and bypassing the GNNExplainer essentially contradict each other. In this work, we give a confirmative answer to this question by proposing a novel attack framework (GEAttack), which can attack both a GNN model and its explanations by simultaneously exploiting their vulnerabilities. Extensive experiments on two explainers (GNNExplainer and PGExplainer) under various real-world datasets demonstrate the effectiveness of the proposed method.

Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.

Modern AI Masterclass: Build 6 Real World AI Applications, Harness the power of AI to solve practical, real-world problems in Finance, Tech, Art and Healthcare Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team,Mitchell Bouchard PREVIEW THIS COURSE - GET COUPON CODE Description Artificial Intelligence (AI) revolution is here! "Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43. Deep Learning, one of the segments analyzed and sized in this study, displays the potential to grow at over 42. AI is the science that empowers computers to mimic human intelligence such as decision making, reasoning, text processing, and visual perception. AI is a broader general field that entails several sub-fields such as machine learning, robotics, and computer vision.

The manufacturing of products often requires careful quality control, especially when health and safety depend on the quality of the product. For example, in the case of medical pills, small physical defects may not only impact the appearance of the product, but can result in incorrect dosages. To help automate the detection of such defects, we set out to build an image recognition model in PerceptiLabs that could identify defective pills by analyzing images. A model like this could potentially help pharmaceutical companies, pharmacists, or medical practitioners identify physically defective pill products. To train our model, we used the images from the Pill defect dataset.

At 135 billion parameters, the neural network is described as the largest "universal" artificial intelligence that the company has running in production. The largest neural network built to date, OpenAI LLC's GPT-3 natural language processing model, has 175 billion parameters.

AI is a complex and ever-evolving field where organisations and individuals are constantly focused 0n finding novel solutions to pressing challenges. The year has been full of path-breaking innovations which have pushed the boundaries and made way for better outcomes. In this article, we list the top ten AI innovations of 2021 so far. OpenAI and Microsoft's GitHub Copilot is an AI-based tool for programmers to write better code. The programmer can describe a function to the Copilot in plain English as a comment, and the machine will convert it to actual code.

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.

Recent years have demonstrated the potential of deep multi-agent reinforcement learning (MARL) to train groups of AI agents that can collaborate to solve complex tasks – for instance, AlphaStar achieved professional-level performance in the Starcraft II video game, and OpenAI Five defeated the world champion in Dota2. These successes, however, were powered by huge swaths of computational resources; tens of thousands of CPUs, hundreds of GPUs, and even TPUs were used to collect and train on a large volume of data. This has motivated the academic MARL community to develop MARL methods which train more efficiently. DeepMind's AlphaStar attained professional level performance in StarCraft II, but required enormous amounts of computational power to train. Research in developing more efficient and effective MARL algorithms has focused on off-policy methods – which store and re-use data for multiple policy updates – rather than on-policy algorithms, which use newly collected training data before each update to the agents' policies.

Wu Dao 2.0 has surpassed OpenAI's GPT-3 in so many ways. China could grow to monopolise the language modelling world. Artificial intelligence models have become a strong informal indicator of national and continental progress. Wu Dao 2.0 means enlightenment. It is dubbed as China's first homegrown super-scale intelligent model system, and was led by BAAI Research Academic Vice President and Tsinghua University Professor Tang Jie.

AI is becoming popular in real life. Many applications are using computer vision by implementing Convolutional Neural network algorithms. Agriculture apps are using CNN based techniques to analyze the crop images for crop's health and viability of seeds. Self driving cars are using them in moving car and other vehicle detection and classification. Video analysis software uses CNN for finding the automobiles, road blocks, and human beings on the road.