The thrust for increased rating capacity of wind turbines has resulted into larger generators, longer blades, and taller towers. Presently, up to 16 MW wind turbines are being offered by wind turbines manufacturers which is nearly a 60 percent increase in the design capacity over the last five years. Manufacturing of these turbines involves assembling of gigantic sized components. Due to the frequent design changes and the variety of tasks involved, conventional automation is not possible making it a labor-intensive activity. However the handling and assembling of large components are challenging the human capabilities. The article proposes the use of mobile robotic assistants for partial automation of wind turbines manufacturing. The robotic assistant can result into reducing production costs, and better work conditions. The article presents development of a robot assistant for human operators to effectively perform assembly of wind turbines. The case is from a leading wind turbines manufacturer. The developed system is also applicable to other cases of large component manufacturing involving intensive manual effort.

A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri, and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented, and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone, and many other places. They have recently undergone a massive transformation and run on operating systems that are fueled by artificial intelligence (A.I.). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using A.I. to make sense of the situation.

"Google fires engineer who contended its AI technology was sentient." A new discovery (or debacle) is reported practically every week, sometimes exaggerated, sometimes not. Policymakers struggle to know what to make of AI and it's hard for the lay reader to sort through all the headlines, much less to know what to be believe. Here are four things every reader should know. First, AI is real and here to stay.

What can we do in 24 hours? What happens in our lives between sunrise and sunset? What happens in 24 hours around the world? On average, in 24 hours, I will experience 104,000 heartbeats, I'll take a breath about 23,000 times, I'll walk about 8,000 steps on average, and in the shower, I'll spend about 12 minutes. My body will shed and create up to 50 trillion new cells, and I usually spend 20 minutes in the bathroom. There will be a 0.35 mm growth in my hair, and I will also lose somewhere between 40 and 100 hairs at the same time, and on average, I'll speak for roughly 48,000 words.

Could an artificial intelligence (AI) be sentient? A recent opinion piece in the New York Times claims that AIs are not sentient.(1) The article raises an interesting question but does not adequately answer it, in part because it conflates sentience and intelligence and in part because its language is confused. There is no evidence this technology is sentient or conscious -- two words that describe an awareness of the surrounding world. This sentence is little more than a tautology, since in English "conscious" and "aware" mean the same thing.(2)

To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervision signals. The strategy of sampling negative items at each time step is less explored. Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative. As a result, the model will inaccurately learn user preferences toward items. Identifying informative negatives is challenging because informative negative items are tied with both dynamically changed interests and model parameters (and sampling process should also be efficient). To this end, we propose to Generate Negative Samples (items) for SR (GenNi). A negative item is sampled at each time step based on the current SR model's learned user preferences toward items. An efficient implementation is proposed to further accelerate the generation process, making it scalable to large-scale recommendation tasks. Extensive experiments on four public datasets verify the importance of providing high-quality negative samples for SR and demonstrate the effectiveness and efficiency of GenNi.

With recent technological developments, artificial intelligence is quickly evolving and becoming more advanced to offer a higher level of accommodation. It's continued to become more versatile and now has many different uses in all types of businesses. There are a few important facts to understand when it comes to machine learning and AI assistants in business as it's integrated into every industry. Although everyone has heard of AI, not everyone understands its potential and how it can be integrated into different organizations. Artificial intelligence consists of software that mimics human interaction and engages in activities.

Today's Narrow Artificial Intelligence, "AI," is showing up in everything we do including language, vision, robotics, and game playing. Can human-level Artificial General Intelligence, "AGI," be far away?

Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities like last.fm or movielens. Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation. However, some solutions are directly inherited from GCN without justifications, which is difficult to alleviate the sparsity, ambiguity, and redundancy issues introduced by tags, thus adding to difficulties of training and degrading recommendation performance. In this work, we aim to simplify the design of GCN to make it more concise for TRS. We propose a novel tag-aware recommendation model named Light Folksonomy Graph Collaborative Filtering (LFGCF), which only includes the essential GCN components. Specifically, LFGCF first constructs Folksonomy Graphs from the records of user assigning tags and item getting tagged. Then we leverage the simple design of aggregation to learn the high-order representations on Folksonomy Graphs and use the weighted sum of the embeddings learned at several layers for information updating. We share tags embeddings to bridge the information gap between users and items. Besides, a regularization function named TransRT is proposed to better depict user preferences and item features. Extensive hyperparameters experiments and ablation studies on three real-world datasets show that LFGCF uses fewer parameters and significantly outperforms most baselines for the tag-aware top-N recommendations.

Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.