Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.

Fake news spread widely on social media in various domains, which lead to real-world threats in many aspects like politics, disasters, and finance. Most existing approaches focus on single-domain fake news detection (SFND), which leads to unsatisfying performance when these methods are applied to multi-domain fake news detection. As an emerging field, multi-domain fake news detection (MFND) is increasingly attracting attention. However, data distributions, such as word frequency and propagation patterns, vary from domain to domain, namely domain shift. Facing the challenge of serious domain shift, existing fake news detection techniques perform poorly for multi-domain scenarios. Therefore, it is demanding to design a specialized model for MFND. In this paper, we first design a benchmark of fake news dataset for MFND with domain label annotated, namely Weibo21, which consists of 4,488 fake news and 4,640 real news from 9 different domains. We further propose an effective Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to aggregate multiple representations extracted by a mixture of experts. The experiments show that MDFEND can significantly improve the performance of multi-domain fake news detection. Our dataset and code are available at https://github.com/kennqiang/MDFEND-Weibo21.

This post was originally featured on the 30SecondsToFly blog in 2017. Ready Player One, Ernest Cline's first novel (soon to be adapted to film by Steven Spielberg), portrays a futuristic dystopian society that consumed all the energetic resources on earth and spends most of its time emerged in a Virtual Reality platform, the Oasis. Besides the brilliant references and tributes to the '80s presented in the book, the author also makes some propositions about the role Artificial Intelligence will play in our lives in a (not so) distant future. Instead of going to the physical stores to complain about a product or a service, the customers only have to put on their VR headsets and introduce their requests and complaints to a virtual assistant (who is operated anywhere else in the world by another human being). In one of the book's chapters, an interaction between a customer and an IT assistant takes place inside of the Oasis.

Artificial Intelligence Can Predict Which People Who Attend Memory Clinics Will Develop Dementia Within 2 Years With 92% Accuracy.

In Star Trek: Discovery the answer appears to be yes, which means an old rule from The Original Series has suddenly been reversed. In the big Discovery mid-season finale "...But to Connect," David Cronenberg's Dr. Kovich returns to pass judgment over the sentient shipboard AI known as Zora (Annabelle Wallis). Here's how this episode references The Next Generation and also reaches back to one famous Original Series story about an AI gone berserk. Although much of the Discovery mid-season finale focuses on the actions the Federation will or won't take to retaliate against the unknown species that created the Dark Matter Anomaly, the bigger change is the fact that the status quo of the sentient starship computer, Zora, has taken an uplifting turn. Because Zora has achieved total sentience, Kovich is brought in to assess her.

A growing number of fitness apps use artificial intelligence software to personalise workouts.

Social media has become an effective platform to generate and spread fake news that can mislead people and even distort public opinion. Centralized methods for fake news detection, however, cannot effectively protect user privacy during the process of centralized data collection for training models. Moreover, it cannot fully involve user feedback in the loop of learning detection models for further enhancing fake news detection. To overcome these challenges, this paper proposed a novel decentralized method, Human-in-the-loop Based Swarm Learning (HBSL), to integrate user feedback into the loop of learning and inference for recognizing fake news without violating user privacy in a decentralized manner. It consists of distributed nodes that are able to independently learn and detect fake news on local data. Furthermore, detection models trained on these nodes can be enhanced through decentralized model merging. Experimental results demonstrate that the proposed method outperforms the state-of-the-art decentralized method in regard of detecting fake news on a benchmark dataset.

It was a very good year for Google, but especially for its mobile division. The jury's still out on whether hardware sales will match the quality of its latest phones but in all cases the phones we saw from Google in 2021 deserve respect. Google's custom-developed chip Tensor is probably the company's biggest win. It enables advancements in machine learning and computational photography that Google wouldn't have been able to achieve by using an off-the-shelf Qualcomm chip. The processor has two Cortex-X1 cores, instead of the usual one, which means the Tensor is plenty powerful.

Nucleai's software uses computer vision and machine learning algorithms to … During his time in the intelligence corps, he worked in a defense …

The ethical and strategic use of artificial intelligence at centres of teaching and learning, which support faculty in troubleshooting and innovating …