This article discusses recent trends and concepts in developing new kinds of artificial intelligence (AI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence, which to date have been under-discussed. We describe various aspects of multiple human intelligence and learning styles, which may impact on a variety of AI problem domains. Using the concept of multiple intelligence rather than a single type of intelligence, we categorize and provide working definitions of various AI depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human actors and each other, but also to efficiently exchange knowledge with abilities of cooperation, collaboration and even co-creating something new and valuable and have meta-learning capacities. Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

A numerical approach is developed for detecting the equivalence of deep learning architectures. The method is based on generating Mixed Matrix Ensembles (MMEs) out of deep neural network weight matrices and {\it conjugate circular ensemble} matching the neural architecture topology. Following this, the empirical evidence supports the {\it phenomenon} that difference between spectral densities of neural architectures and corresponding {\it conjugate circular ensemble} are vanishing with different decay rates at the long positive tail part of the spectrum i.e., cumulative Circular Spectral Difference (CSD). This finding can be used in establishing equivalences among different neural architectures via analysis of fluctuations in CSD. We investigated this phenomenon for a wide range of deep learning vision architectures and with circular ensembles originating from statistical quantum mechanics. Practical implications of the proposed method for artificial and natural neural architectures discussed such as the possibility of using the approach in Neural Architecture Search (NAS) and classification of biological neural networks.

Energy theft causes large economic losses to utility companies around the world. In recent years, energy theft detection approaches based on machine learning (ML) techniques, especially neural networks, become popular in the research literature and achieve state-of-the-art detection performance. However, in this work, we demonstrate that the well-perform ML models for energy theft detection are highly vulnerable to adversarial attacks. In particular, we design an adversarial measurement generation algorithm that enables the attacker to report extremely low power consumption measurements to the utilities while bypassing the ML energy theft detection. We evaluate our approach with three kinds of neural networks based on a real-world smart meter dataset. The evaluation result demonstrates that our approach can significantly decrease the ML models' detection accuracy, even for black-box attackers.

With the increasing demand of AI professionals in the industry, the demand for Artificial Intelligence softwares that caters to their needs has also exploded. A lot of exciting, new prospects are there to fulfill this demand. In this article, we will discuss the 10 best AI softwares you must know. The IBM Watson Studio is a platform for developers, data analysts to integrate deep learning algorithms into their own business needs. It has applications in distributed computing and also is capable of designing neural architecture with the help of deep learning algorithms. It is highly robust in nature and can learn even from a small dataset.

This article talks about the concept of adversarial examples as applied to NLP (natural language processing). The terminology can be confusing at times, so we'll begin with an overview of the language used to talk about adversarial examples and adversarial attacks. We'll conclude with some thoughts on the future of this area of research. An adversarial example is an input designed to fool a machine learning model [1]. An adversarial example crafted as a change to a benign input is known as an adversarial perturbation.

Artificial intelligence (AI) is changing advertising, enabling marketers to communicate with their target audience more personally. With the latest in OpenAI GPT-3, AI is getting its due attention for writing emotive TV scripts, and targeting smart ads using facial recognition. In each of these scenarios, AI is helping advertising professionals with intelligence backed big data, offering much more granularity than the legacy systems. While creative Artificial Intelligence makes for exciting headlines, some of the best AI technology work quietly in the background. By using Predictive Analytics, OTT platforms like Prime and Netflix can offer better recommendations to their users, allowing them to make informed choices from their subscriptions. This results in great customer satisfaction and increased value proposition.

Automation has been gradually transforming the workplace for years (think Excel spreadsheets or chatbots). As artificial intelligence (AI), machine learning and deep learning systems that can learn from each other become more prevalent and smarter (think Alexa or IBM Watson), they continue to replace more manual, repetitive job tasks. Consequently, automation and robots are changing more jobs globally at breakneck speed. A McKinsey Global Institute report suggests that between 400 million to 800 million jobs worldwide will be lost due to automation by 2030. The report claims that the U.S. could lose between 16 to 54 million jobs by 2030. The pace at which robots are entering our workforce is staggering.

Not sure where this quote originally came from, it is sometimes kind of half-attributed to Albert Einstein. Anyway, this post is my attempt of explaining (to myself and others) how the neural…

AutoML, the application of machine learning to create new automation tools, is branching out to new use cases, making itself useful for particularly tedious data science tasks when training speech recognition models. Among the latest attempts at automating the data science workflow is an AutoML tool from Deepgram, offering what the speech recognition vendor claims is a new model training framework for machine transcription. The startup's investors include Nvidia GPU Ventures and In-Q-Tel, the venture arm of the U.S. intelligence community. Deepgram's flagship platform scans audio data to train a speech recognition tool. Its deep learning tool uses a hybrid convolutional/recurrent neural network approach, training models via GPU accelerators.