MOUNTAIN VIEW, Calif., Aug 16 (Reuters) - Alphabet Inc's (GOOGL.O) Google is combining the eyes and arms of physical robots with the knowledge and conversation skills of virtual chatbots to help its employees fetch soda and chips from breakrooms with ease. The mechanical waiters, shown in action to reporters last week, embody an artificial intelligence breakthrough that paves the way for multipurpose robots as easy to control as ones that perform single, structured tasks such as vacuuming or standing guard. Google robots are not ready for sale. They perform only a few dozen simple actions, and the company has not yet embedded them with the "OK, Google" summoning feature familiar to consumers. While Google says it is pursuing development responsibly, adoption could ultimately stall over concerns such as robots becoming surveillance machines, or being equipped with chat technology that can give offensive responses, as Meta Platforms Inc (META.O) and others have experienced in recent years.

In a short period of about 5–10 years, Humanity has met AI. And over this period, some Humans have grown skeptical, pessimistic, and jealous towards AI. And, they have a reason to be. Create enough hunger and we're just monkeys. I look at AI in a very optimistic fashion.

Late last week, Google research scientist Fei Xia sat in the center of a bright, open-plan kitchen and typed a command into a laptop connected to a one-armed, wheeled robot resembling a large floor lamp. The robot promptly zoomed over to a nearby countertop, gingerly picked up a bag of multigrain chips with a large plastic pincer, and wheeled over to Xia to offer up a snack. The most impressive thing about that demonstration, held in Google's robotics lab in Mountain View, California, was that no human coder had programmed the robot to understand what to do in response to Xia's command. Its control software had learned how to translate a spoken phrase into a sequence of physical actions using millions of pages of text scraped from the web. That means a person doesn't have to use specific preapproved wording to issue commands, as can be necessary with virtual assistants such as Alexa or Siri.

As recommender systems become increasingly sophisticated and complex, they often suffer from lack of fairness and transparency. Providing robust and unbiased explanations for recommendations has been drawing more and more attention as it can help address these issues and improve trustworthiness and informativeness of recommender systems. However, despite the fact that such explanations are generated for humans who respond more strongly to messages with appropriate emotions, there is a lack of consideration for emotions when generating explanations for recommendations. Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning. In this paper, we propose a novel method based on a multi-head transformer, called Emotion-aware Transformer for Explainable Recommendation (EmoTER), to generate more robust, fair, and emotion-enhanced explanations. To measure the linguistic quality and emotion fairness of the generated explanations, we adopt both automatic text metrics and human perceptions for evaluation. Experiments on three widely-used benchmark datasets with multiple evaluation metrics demonstrate that EmoTER consistently outperforms the existing state-of-the-art explanation generation models in terms of text quality, explainability, and consideration for fairness to emotion distribution. Implementation of EmoTER will be released as an open-source toolkit to support further research.

Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users' recommendations are drastically altered by minor perturbations in other unrelated users' interactions. We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data. We develop a method, CASPER, which uses cascading effect to identify the minimal and systematical perturbation to induce higher instability in a recommender system. Experiments on four datasets show that recommender models are overly sensitive to minor perturbations introduced randomly or via CASPER - even perturbing one random interaction of one user drastically changes the recommendation lists of all users. Importantly, with CASPER perturbation, the models generate more unstable recommendations for low-accuracy users (i.e., those who receive low-quality recommendations) than high-accuracy ones.

If you've been in the market for a large smart display, it might be worth taking a look at Amazon's Echo Show 15, which is currently on sale. You can snap one up for $190, which is $60 off the regular price of $250. Amazon released the device last year and we gave it a score of 78 in our review. We admired the large, bright screen and the picture frame design. We found the widgets (which include ones for headlines, weather, calendar, sticky notes, recipe suggestions and package delivery tracking) to be handy.

Debiased recommender models have recently attracted increasing attention from the academic and industry communities. Existing models are mostly based on the technique of inverse propensity score (IPS). However, in the recommendation domain, IPS can be hard to estimate given the sparse and noisy nature of the observed user-item exposure data. To alleviate this problem, in this paper, we assume that the user preference can be dominated by a small amount of latent factors, and propose to cluster the users for computing more accurate IPS via increasing the exposure densities. Basically, such method is similar with the spirit of stratification models in applied statistics. However, unlike previous heuristic stratification strategy, we learn the cluster criterion by presenting the users with low ranking embeddings, which are future shared with the user representations in the recommender model. At last, we find that our model has strong connections with the previous two types of debiased recommender models. We conduct extensive experiments based on real-world datasets to demonstrate the effectiveness of the proposed method.

Rammas, DEWA's virtual AI employee, is available on seven channels for customer service. These include DEWA's smart app (iOS and Android), DEWA's website and social media account on Facebook, Amazon's Alexa, and Google Assistant, robots, and WhatsApp Business. Rammas provides round-the-clock multiple services and answers enquiries in English and Arabic. DEWA employs AI to enhance customer and employee happiness as well as improve the performance of its systems through'Rammas for You' and'Rammas at Work' and'Powered by Rammas'. Rammas has answered more than 6.4 million enquiries since its inception in the first quarter of 2017 and until the end of June 2022.

In a world dominated by artificial intelligence, data, and ever-advancing connectivity technologies, it's hard to leave the'Internet of Things' out of a list of innovative and game changing technologies. In fact, IoT may be one of the most important technologies out there right now, as it is responsible for the success of many other technologies, like machine learning. As the market landscape evolves over the next several years, it's critical for businesses to monitor how things are changing. Some of the most successful businesses are the ones who think creatively about evolving technologies. Coming up with ideas for innovative ways to use and combine these technologies together isn't possible without keeping an eye on these trends.

If you ask Alexa, Amazon's smart speaker, that she is spying on you. She replies, "I only send audio back to Amazon when I hear you say the wake word. For more information and to view Amazon's privacy notice, visit the help section of your Alexa app." This reply sounds so naive and gives little hint of the sea of data Amazon's smart speaker captures and stores. To understand the data collection size of'Echo' speaker, Dave Bryant, who has created and sold a multi-million dollar e-commerce store, requested all of his personal data from Amazon.