It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that blockchain is decentralized and is not controlled by anyone. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.

Kai-Fu Lee became a legend in artificial intelligence research and the tech world because of his groundbreaking work the past three decades with Apple, Microsoft, and Google. But Lee says cancer has radically changed the way he views technology, his life, and the world of medicine. In September 2013, the former head of Google China was given a diagnosis of stage IV follicular non-Hodgkin's lymphoma. The cancer diagnosis put his career and life on the line. Then, it put his career and life in a new light.

As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)--with over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You'll hear success stories about machine learning on AWS firsthand from customers and partners such as Sony, Moody's, NFL, Intuit, 21st Century Fox, Toyota, and more. This year's re:Invent also includes the AI Summit, where thought leaders in the academic community will share their perspectives on the future of AI. Here are a few highlights of this year's lineup from the re:Invent session catalog to help you plan your event agenda.

Artificial Intelligence is not a buzzword anymore. As of 2018, it is a well-developed branch of Big Data analytics with multiple applications and active projects. Here is a brief review of the topic. AI is the umbrella term for various approaches to big data analysis, like machine learning models and deep learning networks. We have recently demystified the terms of AI, ML and DL and the differences between them, so feel free to check this up.

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of only 1,512,868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized terms of the training time and data over-fitting, respectively.

Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide in-depth experiment results comparing the effectiveness of the major works in each category.

KANSAS CITY, Kan. – Companies around the globe are leveraging innovative technologies and artificial intelligence to make more informed decisions and better run their businesses. This week, Dairy Farmers of America, a national cooperative owned by dairy farm families across the U.S., announced an investment in SomaDetect, a dairy technology startup that will help farmers utilize artificial intelligence to more closely monitor the health of their herd and improve milk quality. "This is a potentially game-changing technology for our farmers and the industry as it allows dairy farmers to know the health of each cow and quality of milk in real time," said David Darr, president, farm services at DFA. "With access to better data, our farmers can make more knowledgeable decisions, which is a huge value." With SomaDetect's technology, farmers can easily evaluate components of interest in raw milk, including fat, protein, somatic cells, progesterone and trace antibiotics. While the technology continues to be refined for commercialization, the platform provides cost-effective, instant analysis, which enables farmers to make rapid and proactive decisions related to the overall health and management of their cows.

In this Book we argue that the fruitful interaction of computer vision and belief calculus is capable of stimulating significant advances in both fields. From a methodological point of view, novel theoretical results concerning the geometric and algebraic properties of belief functions as mathematical objects are illustrated and discussed in Part II, with a focus on both a perspective 'geometric approach' to uncertainty and an algebraic solution to the issue of conflicting evidence. In Part III we show how these theoretical developments arise from important computer vision problems (such as articulated object tracking, data association and object pose estimation) to which, in turn, the evidential formalism is able to provide interesting new solutions. Finally, some initial steps towards a generalization of the notion of total probability to belief functions are taken, in the perspective of endowing the theory of evidence with a complete battery of estimation and inference tools to the benefit of all scientists and practitioners.

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep learning community is the application of multi-task models that enable single deep neural networks to perform more than one task at the same time, for example classification and translation tasks. These powerful novel models are capable of transferring knowledge among different tasks or training sets and therefore could open up the legal domain for many deep learning applications. In this paper, we investigate the transfer learning capabilities of such a multi-task model on a classification task on the publicly available Kaggle toxic comment dataset for classifying illegal comments and we can report promising results.