Counting and identifying characteristics of crowds can provide organizations with a lot of valuable insights. Yet challenges like image distortion, density, and different camera angles can make analyzing images accurately very challenging. Ganes Kesari, Co-founder and Head of Analytics at Gramener, joins the Intel on AI podcast to discuss how Gramener has created a crowd counting solution that can overcome those challenges and produce a very rapid and accurate analysis of images. He talks about how Gramener has utilized this solution for several AI for good projects including a joint effort with Microsoft* to count Antarctic penguin colonies. Ganes explains how their solution used convolutional neural networks (CNNs) using density-based estimations to deliver a more accurate penguin count than traditional manual counting methods.

Deep learning is a fascinating sub field of machine learning that creates artificially intelligent systems inspired by the structure and function of the brain. The basis of these models are bio-inspired artificial neural networks that mimic the neural connectivity of animal brains to carry out cognitive functions such as problem solving. A field with the most impressive results of neuromorphic computing is that of visual image analysis. Similar to how our brains learn to recognize objects in order to make predictions and act upon them, artificial intelligence must be shown millions of pictures before they are able to generalize them in order to make their best educated guesses for images they have never seen before. Professor Cheol Seong Hwang from the Department of Material Science and Engineering at Seoul National University and his research team have developed a method to accelerate the image recognition process by combining the inherent efficiency of resistive random access memory (ReRAM) and cross-bar array structures, two of the most commonly used hardware. Many of us have performed a reversed image search to find information based on a certain image in order to browse similar results.

The field of machine learning has experienced significant growth in the past two decades as new algorithms and techniques have been developed and new research and applications have emerged. This series reflects the latest advances and applications in machine learning and pattern recognition through the publication of a broad range of reference works, textbooks, and handbooks. The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of machine learning, pattern recognition, computational intelligence, robotics, computational/statistical learning theory, natural language processing, computer vision, game AI, game theory, neural networks, and computational neuroscience. We are also willing to consider other relevant topics, such as machine learning applied to bioinformatics or cognitive science, which might be proposed by potential contributors.

The organisation of tomorrow will be built around data, and it will require artificial intelligence to make sense of all that data. Artificial intelligence is a broad discipline with the objective to develop intelligent machines. AI consists of several subfields: Machine learning (ML), a subset of AI that enables machines to learn from data. Reinforcement learning, which is a subset of ML and focuses on artificial agents that use trial and error to improve itself. And deep learning, also a subset of ML that aims to mimic the human brain to detect patterns in large datasets and benefit from those patterns.

--This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories. The proposed multi-layer neural network architecture encodes transferable knowledge extracted from a large annotated dataset of base categories. This architecture is then applied to novel categories containing only a few samples. The transfer of knowledge is carried out at the feature-extraction and the classification levels distributed across the two training stages. In the first-training stage, we introduce the relative feature to capture the structure of the data as well as obtain a low-dimensional discriminative space. Secondly, we account for the variable variance of different categories by using a network to predict the variance of each class. Classification is then performed by computing the Maha-lanobis distance to the mean-class representation in contrast to previous approaches that used the Euclidean distance. In the second-training stage, a category-agnostic mapping is learned from the mean-sample representation to its corresponding class-prototype representation. This is because the mean-sample representation may not accurately represent the novel category prototype. Finally, we evaluate the proposed network structure on four standard few-shot image recognition datasets, where our proposed few-shot learning system produces competitive performance compared to previous work. We also extensively studied and analyzed the contribution of each component of our proposed framework. For the past decade, deep convolutional neural networks (CNN) have produced excellent results in visual recognition tasks such as object recognition, scene classification, etc. [1]- [3]. A CNN learns to recognize a large quantity of visual categories by training on a large collection of annotated images using a gradient-descent technique [4]. Although the training procedure is computationally intensive, it can be parallelized using a Graphics Processing Unit (GPU). Even after a long training period, the CNN can only recognize a fixed set of image categories. To learn to recognize novel categories, one has to collect new training data and retrain the CNN model with further adjustments. Unfortunately, in some cases, there might not be enough labeled data available for training a novel category. This work was supported in part by the National Science Foundation under Grant IIS-1813935. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We also gratefully acknowledge the support of NVIDIA Corporation for the donation of a TIT AN XP GPU used for this research. Object categories follow a long tailed distribution with a lot of rare classes and very few common classes. In such a long-tailed distribution, only a few object categories occur frequently.

In order to accurately diagnose a patient's condition, Artificial Intelligence (AI) requires data mining method and pattern recognition. Neural networks process information by identifying patterns and data previously loaded into the system. Global artificial intelligence market includes 35% share of drug discovery and medical imaging which is likely to go up to 40% that will amount to USD 2.5 billion by 2024. This is possible because of AI's ability to recognize drug targets and help to instantly model, discover, recognize and monitor molecules. Medical imaging, on the other hand, is one of the main areas of application that has led to a better diagnosis of cancer and tumors using AI.

ABSTRACT The V oxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or'in the wild' data. It consisted of: (i) a publicly available speaker recognition dataset from Y ouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and workshop held at Interspeech 2019 in Graz, Austria. This paper outlines the challenge and provides its baselines, results and discussions. Index T erms-- speaker verification, unconstrained conditions 1. INTRODUCTION The V oxCeleb Speaker Recognition Challenge (V oxSRC) 2019 was the first of a new series of speaker recognition challenges that are intended to be hosted annually. V oxSRC 2019 consisted of: (i) a publicly available speaker recognition dataset with speech segments'in the wild', together with ground truth annotations and standardised evaluation software; and (ii) a public challenge and workshop held at Interspeech 2019 in Graz, Austria.

Facial recognition tools have significant error rates that differ by race. An AI hiring tool from Amazon "learned" gender bias against women and favored male candidates. We know diversity bias is rampant in artificial intelligence. But decisions made based on prejudiced AI systems aren't just an ethical dilemma; they're a financial one. The more unbiased a system, the more likely it is to maximize profits, make better hiring or selling recommendations and provide accurate risk predictions.

This story began in Madrid, Spain. The winter was comming, and a team of four young enthusiasts started a project. The initial idea was to create an app to learn Sign Language, not only because it is an interesting aspect of our society, but for those 34 million children with disabling hearing loss that need to learn it to communicate. The beauty of technology is that it can be used to help others too. We aimed to do exactly that.

The automatic identification of complex features in images has already become a reality thanks to artificial neural networks. Some examples of software exploiting this technique are Facebook's automatic tagging system, Google's image search engine and the animal and plant recognition system used by iNaturalist. We know that these networks are inspired by the human brain, but their working mechanism is still mysterious. New research, conducted by SISSA in association with the Technical University of Munich and published for the 33rd Annual NeurIPS Conference, proposes a new approach for studying deep neural networks and sheds new light on the image elaboration processes that these networks are able to carry out. Similar to what happens in the visual system, neural networks used for automatic image recognition analyse the content progressively, through a chain of processing stages.