With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to spend a lot of time to find the goods they want to buy. To solve this problem, the recommendation system came into being. The recommendation system is a subset of the Information Filtering System, which can be used in a range of areas such as movies, music, e-commerce, and Feed stream recommendations. The recommendation system discovers the user's personalized needs and interests by analyzing and mining user behaviors and recommends information or products that may be of interest to the user. Unlike search engines, recommendation systems do not require users to accurately describe their needs but model their historical behavior to proactively provide information that meets user interests and needs.

The future the place individuals can delegate mundane duties to a machine is just not removed from taking place. From beginning the laundry down to cooking dinner after an extended day is about to be over. Artificial Intelligence has actually helped form our web at present. After all, we will already talk with digital assistants like Apple's Siri and Amazon's Alexa for small issues round the home, like calling Uber or ordering a pizza. Things that we solely see on sci-fi films could also be nearer than you suppose.

THINK PIECE 2 of I'd blush if I could, is the first in-depth UN examination of the gendering of AI technology. Using the example of digital voice assistants such as Amazon's Alexa and Apple's Siri technology, it explains how gender imbalances in the digital sector can be'hard-coded' into technology products.

Let's get our James Bond swag on shall we? Defense departments worldwide are betting on AI to deliver the next generation advanced military technology, and the US is no different. In the US of A, this strategy is being orchestrated by the Joint Artificial Intelligence Center (JAIC), a department under the umbrella of the Department of Defense (DoD) led by Acting Director Nand Mulchandani. And he recently gave his first press conference. NLP will play a bigger role in the future of JAIC strategy .

Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model and then use maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper, we present Deep Retrieval (DR), an end-to-end learnable structure model for large-scale recommendations. DR encodes all candidates into a discrete latent space. Those latent codes for the candidates are model parameters and to be learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates. Empirically, we showed that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline.

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate the explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.

Artificial Intelligence seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These kind of problems are present in all fields, the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

This is what Elon Musk and similar people are fearful of for controlling the world. This brings us to the fact that we need more computing resources to handle the corpus of data which unfortunately is limited. Therefore, we need to work through a rule-based programming – hence the shift away from AI towards ML.

In mathematics, a type of variation called direct variation describes'a simple relationship between two variables -- such that we observe an increase of one variable when the other increases, or a decrease of the same when the other reduces'. This law works in business as well, wherein the population of the workforce determines the productivity of the company. However, recent development in artificial intelligence defies this law. In 2020, a business could have only a team of essential workers with a couple of well-utilized computers and do more than a larger company which has the help of its thousands of workers but without digital intervention or its innovative implementation whatsoever. The usefulness of computer technology in business has been paced recently in such a way that most businesses will not need to hire more workers in the future.