We have seen how COVID-19 mounted pressure on businesses to fast-track their digital transformation journeys by months and, in several cases, by years. The arrival of the pandemic made them reconsider technologies they had at their fingertips – artificial intelligence (AI) in particular – and harness them to boost productivity, address supply chain issues, and seamlessly deliver products and services. Organizations have realized the indispensability of integrating AI into their digital strategy and this article will focus on addressing common AI adoption challenges. Artificial Intelligence is a revolutionary technology that saves time, energy, and money. It is no longer confined to science textbooks or science-fiction fantasies; it has countless applications in the real world.

There will always be interactions between machines and humans. When the machine has a high level of autonomy and the human-machine relationship is close, there will be underpinning, implicit assumptions about behavior and mutual trust. The performance of the Human-Machine team will be maximized when a partnership is formed that is based on providing mutual benefits. Designing systems that include human-machine partnerships requires an understanding of the rationale of any such relationship, the balance of control, and the nature of autonomy. Essential first steps are to understand the nature of human-machine cooperation, to understand synergy, interdependence, and discord within such systems, and to understand the meaning and nature of "collective intelligence."

The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really fast in last decade and have been one of the most effective platforms for people to communicate and share information with each other. Due to the "word of mouth" effects, information usually can spread rapidly on these social media platforms. Therefore, it is important to study the mechanisms driving the information diffusion and quantify the consequence of information spread. A lot of efforts have been focused on this problem to help us better understand and achieve higher performance in viral marketing and advertising. On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models. Compared to traditional models, GRL methods are often shown to be more effective. In this paper, we present a comprehensive review for existing works using GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods. Deep learning method is further classified into six small classes: convolutional neural networks, graph convolutional networks, graph attention networks, graph neural networks, recurrent neural networks, and reinforcement learning. We compare the performance of these different models and discuss their strengths and limitations. Finally, we outline the challenges and future chances for popularity prediction problem.

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.

Robotics and Artificial Intelligence till now have been used mainly for commercial work and yet to dominate defence sector in India. There is a huge potential in this segment and newly formed Task-Force on Artificial Intelligence by Indian government is a step towards economic transformation. Robotics and Artificial Intelligence (AI) is combination of technology and cognitive intelligence for simulation, processing of information and knowledge to build capability in a machine to imitate human behaviour. It is a transformative technology that has tremendous applications in the social, economic and military fields. Till some time back we had robots operated by a human or through a set of programming to perform repetitive task.

Around a year and a half ago, Yann LeCun realized he had it wrong. LeCun, who is chief scientist at Meta's AI lab and one of the most influential AI researchers in the world, had been trying to give machines a basic grasp of how the world works--a kind of common sense--by training neural networks to predict what was going to happen next in video clips of everyday events. But guessing future frames of a video pixel by pixel was just too complex. Now, after months figuring out what was missing, he has a bold new vision for the next generation of AI. In a draft document shared with MIT Technology Review, LeCun sketches out an approach that he thinks will one day give machines the common sense they need to navigate the world.

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their adaptation to tabular data for inference or data generation tasks remains challenging. To facilitate further progress in the field, this work provides an overview of state-of-the-art deep learning methods for tabular data. We categorize these methods into three groups: data transformations, specialized architectures, and regularization models. For each of these groups, our work offers a comprehensive overview of the main approaches. Moreover, we discuss deep learning approaches for generating tabular data, and we also provide an overview over strategies for explaining deep models on tabular data. Thus, our first contribution is to address the main research streams and existing methodologies in the mentioned areas, while highlighting relevant challenges and open research questions. Our second contribution is to provide an empirical comparison of traditional machine learning methods with eleven deep learning approaches across five popular real-world tabular data sets of different sizes and with different learning objectives. Our results, which we have made publicly available as competitive benchmarks, indicate that algorithms based on gradient-boosted tree ensembles still mostly outperform deep learning models on supervised learning tasks, suggesting that the research progress on competitive deep learning models for tabular data is stagnating. To the best of our knowledge, this is the first in-depth overview of deep learning approaches for tabular data; as such, this work can serve as a valuable starting point to guide researchers and practitioners interested in deep learning with tabular data.

"This idea that we're going to just scale up the current large language models and eventually human-level AI will emerge--I don't believe this at all, not for one second." Yann LeCun, chief scientist at Meta's artificial intelligence (AI) lab and one of the world's most influential AI researchers, has a bold new vision for the next generation of AI. In a draft document shared with MIT Technology Review, LeCun sketches out an approach that he thinks will one day give machines the common sense they need to navigate the world. "Getting machines to behave like humans and animals has been the quest of my life," he says. LeCun thinks that animal brains run a kind of simulation of the world, which he calls a world model.

Machine learning is a broad topic. Deep learning, in particular, is a way of using neural networks for machine learning. Neural network is probably a concept older than machine learning, dated back to 1950s. Unsurprisingly, there were many libraries created for it. In the following, we will give an overview of some of the famous libraries for neural network and deep learning.

This special issue highlights the applications, practices and theory of artificial intelligence in the domain of cyber security. In the past few decades there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, block-chain, and virtualization etc.) for solving complex and intricate problems arising in the domain of cyber security. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The primary objective of this topical collection is to bring forward thorough, in-depth, and well-focused developments of artificial intelligence technologies and their applications in cyber security domain, to propose new approaches, and to present applications of innovative approaches in real facilities. AI can be both a blessing and a curse for cybersecurity.