The "Artificial Intelligence and Machine Learning 2020, Volume 1" report has been added to ResearchAndMarkets.com's offering. This survey gives a comprehensive view of the attitudes, adoption patterns and intentions of artificial intelligence and machine learning developers worldwide. This series focuses on tools, methodologies, and concerns related to implementing machine learning, deep learning, image recognition, pattern recognition and other forms of artificial intelligence as well as efficiently storing, handling, and analyzing large datasets and databases from a wide range of sources. Artificial intelligence is permeating software development in many ways and many industries, which necessitates a thorough knowledge of how developers are doing this. This volume includes research and analysis covering topics such as developer demographics and firmographics, artificial intelligence landscape, methods and approaches, resources and services, conversational systems, speech and image recognition, enterprise AI, security, platform adoption, API frameworks, tools and languages, technology adoption, hardware, hardware optimization, parallelism, and high-performance computing, purchasing and influencers, challenges and barriers to success, AI as it relates to IoT, the Cloud, and containerization and more.

Abstract: Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user's set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks. Keywords: Intelligent Systems, AI, Trust, Explainable AI, Knowledge Management, Knowledge Patterns 1. INTRODUCTION uncommon and have been constructed in uncommon ways. Such techniques, a class to which systems that we now Human expertise in many aspects is largely based on call intelligent systems belong to, produce results of high prior knowledge and familiar patterns, which have either complexity (e.g.

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high resolution images. Our method may be interfaced with any downstream neural network, is able to aggregate information from different patches in a flexible way, and allows the whole model to be trained endto-end Figure 1: Examples of large images where patch extraction using backpropagation. We show results for traffic allows (top-left) to focus on details for fine-grained recognition, sign recognition, inter-patch relationship reasoning, and (bottom-left) to reason across patches, and (right) to fine-grained recognition without using object/part bounding efficiently capture very localized information.

Got a stack of Magic: The Gathering cards sitting somewhere in storage? With the game's "Modern" format, chances are you might be sitting on at least a couple of ones that could be worth selling. One of the most popular places to buy and sell trading cards online is eBay. What keeps most people parting with their collections is that it can be time-consuming to list every individual card. But eBay has a plan to speed up the process. In an announcement that flew under our radar until Gizmodo picked it up this morning, eBay said it's updating its Android and iOS app with image recognition capabilities.

A multi-decade exploration into the theoretical foundations of artificial and natural general intelligence, which has been expressed in a series of books and papers and used to guide a series of practical and research-prototype software systems, is reviewed at a moderate level of detail. The review covers underlying philosophies (patternist philosophy of mind, foundational phenomenological and logical ontology), formalizations of the concept of intelligence, and a proposed high level architecture for AGI systems partly driven by these formalizations and philosophies. The implementation of specific cognitive processes such as logical reasoning, program learning, clustering and attention allocation in the context and language of this high level architecture is considered, as is the importance of a common (e.g. typed metagraph based) knowledge representation for enabling "cognitive synergy" between the various processes. The specifics of human-like cognitive architecture are presented as manifestations of these general principles, and key aspects of machine consciousness and machine ethics are also treated in this context. Lessons for practical implementation of advanced AGI in frameworks such as OpenCog Hyperon are briefly considered.

Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Deep Convolutional Neural Networks in deep learning take an hour or day to train the mode if the dataset we are playing is vast. The approach is we reuse the weights of the pre-trained model, which was trained for some standard Computer Vision datasets such as Image classification (Image Net). Extensive deep Convolutional networks for large-scale image classification are available in Keras, which we can directly import and can be used with their pre-trained weights. Let's now understand how to use VGG16 pre-trained on 10,000 categories(Image Net) for the Distracted driver Detection dataset.

Machine learning is a field of computational science which first emerged in the 1950s. However, our ability to effectively harness the power of machine learning techniques was only truly realised in the 1990s. In ecology, the earliest adoption of machine learning came about in the early 2000s, when regression tree algorithms were applied to spatial data to predict species distributions. This was quickly adapted in the field of marine ecology to study the distribution of many pelagic species. Since that time, machine learning algorithms have been adapted and applied in various studies in the marine environment, from population models, image recognition, and experimental studies.

The paper trained their networks by crops from the renowned ImageNet image recognition dataset. Although it is beneficial to train models in large amounts of data, the dataset found to be too heavy and I decided to use the tf_flowers dataset, consisting of 3,670 images which might seem too small but were just enough for a toy dataset to evaluate and compare the performance of each training method of the paper. We use the tensorflow_datasets module for loading the tf_flowers dataset and take the first 600 images as a validation dataset. We then define a function to map each image from the dataset to (128, 128) crops and a (32, 32) low-resolution copy of it. We can apply this function to our dataset by train_data.map(build_data,

Speaker recognition refers to audio biometrics that utilizes acoustic characteristics for automatic speaker recognition. These systems have emerged as an essential means of verifying identity in various scenarios, such as smart homes, general business interactions, e-commerce applications, and forensics. However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance. Various complicated neural architectures are presented to address speaker recognition in the wild but neglect the requirements of storage and computation. To address this issue, we propose a neural architecture search-based efficient time-delay neural network (EfficientTDNN) to improve inference efficiency while maintaining recognition accuracy. The proposed EfficientTDNN contains three phases. First, supernet design is to construct a dynamic neural architecture that consists of sequential cells and enables network pruning. Second, progressive training is to optimize randomly sampled subnets that inherit the weights of the supernet. Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and efficiency. Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately $10^{13}$ subnets and achieves 1.66% EER and 0.156 DCF$_{0.01}$ with 565M MACs. Comprehensive investigation suggests that the trained supernet generalizes cells unseen during training and obtains an acceptable balance between accuracy and efficiency.

The Image Recognition has over 300 startups that comprise of companies offering software that can identify places, people, objects and actions in images or digital videos. This includes companies offering services like image recognition software facial recognition software, object recognition software amd optical character recognition. Image Recognition is one of the most active sectors for investors, with an overall funding of USD 8.2B in 150 companies. It is also interesting to note that more than half of the funding has been raised in the last 3 years (2018-2020). Plug and Play Tech Center, Deep Learning, Y Combinator, Capital Factory and Alibaba Group are amongst the most active investors in this sector, by number of investments. Applications, facial recognition, offline retail, shelf management and security systems are some of the top business models attracting major funding.