Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval and recommendation. Since knowledge graph can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic reasoning, neural reasoning and the neural-symbolic reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.

For the jailbreak mission, I used the "spy" character, whom I obtained as the reward for "freeing" the Westminster district of propaganda. Just as an aside, the Westminster mission showed the reward as a silhouette of a man with a beret, but the spy turned out to be a middle-aged woman in a sharp blazer armed with a silencer. A fellow reviewer told me he got an older black gentleman in a suit for that same mission, which indicates that even the more specialized characters are randomized. This might be worth keeping in mind for players looking for characters they think might look or dress or move a certain type of way.

Enterprises seeing real success with artificial intelligence have something in common: they are capable of learning quickly from their successes or failures and re-applying those lessons into the mainstream of their businesses. Of course, there's nothing new about the ability to rinse, learn and repeat, which has been a fundamental tenet of business success for ages. But because AI is all about real-time, nanosecond responsiveness to a range of things, from machines to markets, the ability to leap and learn at a blinding pace has taken on a new urgency. At this moment, only 10% of companies are seeing financial benefits from their AI initiatives, a survey of 3,000 executives conducted by Boston Consulting Group and MIT Sloan Management Review finds. There is a lot of AI going around: more than half, 57%, piloting or deploying AI -- up from 46% in 2017.

Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with on-line adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric Learning and natural language processing. In this paper we provide a comprehensive literature review and we propose a general Contrastive Representation Learning framework that simplifies and unifies many different contrastive learning methods. We also provide a taxonomy for each of the components of contrastive learning in order to summarise it and distinguish it from other forms of machine learning. We then discuss the inductive biases which are present in any contrastive learning system and we analyse our framework under different views from various sub-fields of Machine Learning. Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in Reinforcement Learning are also presented. Finally, we discuss the challenges and some of the most promising future research directions ahead.

Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised learning perspective. Next, we provide a comprehensive review of 46 state-of-the-art robust training methods, all of which are categorized into seven groups according to their methodological difference, followed by a systematic comparison of six properties used to evaluate their superiority. Subsequently, we summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics. Finally, we present several promising research directions that can serve as a guideline for future studies.

General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment. Affordances as perceived valences of action possibilities may be cast into predicted policy-relative goodness and modelled as GVFs. A systematic explication of this connection shows that GVFs and especially their deep learning embodiments (1) realize affordance prediction as a form of direct perception, (2) illuminate the fundamental connection between action and perception in affordance, and (3) offer a scalable way to learn affordances using RL methods. Through a comprehensive review of existing literature on recent successes of GVF applications in robotics, rehabilitation, industrial automation, and autonomous driving, we demonstrate that GVFs provide the right framework for learning affordances in real-world applications. In addition, we highlight a few new avenues of research opened up by the perspective of "affordance as GVF", including using GVFs for orchestrating complex behaviors.

Enterprises seeing real success with artificial intelligence have something in common: they are capable of learning quickly from their successes or failures and re-applying those lessons into the mainstream of their businesses. Of course, there's nothing new about the ability to rinse, learn and repeat, which has been a fundamental tenet of business success for ages. But because AI is all about real-time, nanosecond responsiveness to a range of things, from machines to markets, the ability to leap and learn at a blinding pace has taken on a new urgency. At this moment, only 10% of companies are seeing financial benefits from their AI initiatives, a survey of 3,000 executives conducted by Boston Consulting Group and MIT Technology Review finds. There is a lot of AI going around: more than half, 57%, piloting or deploying AI -- up from 46% in 2017.

Recent advances in machine learning, deep learning techniques, and sensors are greatly impacting how humans and computers and robots interact. For instance, surface electromyography sensors combined with deep-learning-based algorithms are currently being used to operate robotic prosthetic limbs or 3D pose estimation methods to control an avatar in Virtual Reality. Thus, the combination of sensors and machine learning techniques is enabling a range of novel and interesting applications. This Special Issue is intended to cover cutting-edge applications and research on new sensors, machine learning methods or their combination to perform human–computer and human–robot interaction. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics will also be considered.

Planning to invest in a mobile app? Here are the top 15 AI/ML/VR/AR app development ideas that ensure your success in 2020–21! With the availability of around 5 million apps existing in the app stores, the trends of developing ordinary mobile apps are just fading away. The increasing usage of mobile applications with each passing year also pushes the demand for innovative technologies to meet future mobile app users' demands. And Artificial Intelligence and Machine Learning (AI & ML) have become the most influencing technologies in the field of mobile app development and creating a plethora of opportunities for startups in 2021.