A council in England is using virtual assistants (VAs) to check it is paying staff and schools correctly. Wiltshire Council is using Microsoft technology to complete repetitive tasks such as checking payrolls in its Human Resources department. This frees up staff to work on more critical tasks that directly help the 435,000 residents in Wiltshire and the council. VAs are currently checking more than 43 payrolls each month, which cover schools and academies in the region as well as council staff. Payroll checks need to be completed before payments can be made to ensure the correct amount of money is paid to the right person or organisation.

Inc., Apple Inc. and Alphabet Inc.'s Google are partnering to lay the groundwork for better compatibility among their smart home products, the companies said on Wednesday. Zigbee Alliance, whose members include IKEA and NXP Semiconductors among others, will also contribute to the project, titled "Connected Home over IP. The group aims to make it easier for device manufacturers to build products that are compatible with smart home and voice services such as Alexa, Siri and Google Assistant. Amazon had launched a similar initiative earlier this year that allows users to access Alexa, Microsoft Corp's Cortana and multiple other voice-controlled virtual assistant services from a single device.

Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information. Compared to traditional recommender systems, CARS are, in general, able to generate more accurate recommendations. Latent factors approach accounts for a large proportion of CARS. Recently, a nonlinear Gaussian Process (GP) based factorization method was proven to outperform the state-of-the-art methods in CARS. Despite its effectiveness, GP model-based methods can suffer from over-fitting and may not be able to determine the impact of each context automatically. In order to address such shortcomings, we propose a Gaussian Process Latent V ariable Model Factorization (GPL VMF) method, where we apply an appropriate prior to the original GP model. Our work is primarily inspired by the Gaussian Process Latent V ariable Model (GPL VM), which was a nonlinear dimensionality reduction method. As a result, we improve the performance on the real datasets significantly as well as capturing the importance of each context. In addition to the general advantages, our method provides two main contributions regarding recommender system settings: (1) addressing the influence of bias by setting a nonzero mean function, and (2) utilizing real-valued contexts by fixing the latent space with real values.

We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user's training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature.

The 2019 edition of Opus Research's Decision Makers' Guide to Enterprise Intelligent Assistants report determined [24]7 AIVA to be a top solution for enterprises, and the only virtual agent solution capable of delivering across a breadth of simple FAQs to complex, conversational issues to online transactions. The Opus report presents a comprehensive assessment of 16 enterprise-grade Intelligent Assistant solution providers, with a focus on natural language processing, machine learning, AI, analytics and customer management integration to power digital self-service solutions. The report highlights [24]7 AIVA's ability to support both voice and digital channels and deliver unified self-service, calling out the company's differentiators as being a unique blend of AI and human insights, two decades of unparalleled experience in customer journeys across all channels, and proprietary insights including more than 150 patents and patent applications. "We analyzed a short-list of the leading providers in natural language processing, machine learning, AI and analytics to develop the industry's most comprehensive assessment of today's virtual agents and digital self-service solutions," said Dan Miller, lead analyst, Opus Research. An agent can take over a bot conversation at any time, and hand the conversation back to the bot to complete the interactions.

How can we tackle gender imbalance in the personalities of AI learning tools? The expected growth in use of artificial intelligence (AI) in learning applications is raising concerns about both the potential gendering of these tools and the risk that they will display the inherent biases of their developers. Well, to make it easier for us to integrate AI tools and chatbots into our lives, designers often give them human attributes. For example, applications and robots are often given a personality and gender. Unfortunately, in many cases, gender stereotypes are being perpetuated.

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

CIMON (The Crew Interactive Mobile Companion 2) has been busy working with astronauts aboard the International Space Station. The robotic assistant is now using a tone analyzer, detecting emotions during the current voyage. CIMON made its debut on the ISS in November of this year, with Space.com's "During the experiment, CIMON successfully found and recognized Gerst's face, took photos and video, positioned itself autonomously within the Columbus module using its ultrasonic sensors, and issued instructions for Gerst to perform a student-designed experiment with crystals. Weighing about 5 kilograms (11 lbs. on Earth), the 3D-printed robot designed jointly by the German space agency DLR, Airbus, and IBM works similarly to Apple's virtual assistant Siri or Amazon's Alexa. "If CIMON is asked a question or addressed, the Watson AI firstly converts this audio signal into text, which is understood, or interpreted, by the AI," explained IBM project lead Matthias Biniok in the statement. "IBM Watson not only understands content in context, [but] it can also understand the intention behind it." There is a great video here, of Gerst conversing with CIMON, and it shows the complexity of this fantastic technology. Especially regarding the amount of relevant information that it can store and relay to astronauts, making their jobs easier. The Watson team at IBM computing only added the tone analyzer to the standard set of Watson capabilities this week. However, CIMON 2 was added as a seventh crew member on SpaceX Dragon during a resupply mission last week. In addition to updated software, the robot also got a hardware upgrade, with enhanced sensitivity on its microphones, and a more advanced sense of orientation. The German Aerospace Center and Airbus are the other crew members for this CIMON project. "IBM is using its tone analyzer technology to analyze how CIMON converses with the astronauts.

Advances in artificial intelligence have renewed interest in conversational agents. Additionally to software developers, today all kinds of employees show interest in new technologies and their possible applications for customers. German insurance companies generally are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies theoretically by determining which classes of agents exist which are of interest to insurance companies, finding relevant use cases and requirements. We add two practical parts: First we develop a showcase prototype for an exemplary insurance scenario in claim management. Additionally in a second step, we create a prototype focusing on customer service in a chatbot hackathon, fostering innovation in interdisciplinary teams. In this work, we describe the results of both prototypes in detail. We evaluate both chatbots defining criteria for both settings in detail and compare the results and draw conclusions for the maturity of chatbot technology for practical use, describing the opportunities and challenges companies, especially small and medium enterprises, face.

With everything you know about Artificial intelligence now, the image of sentient machines taking over the world and ruling humanity is probably not what comes to mind whenever the subject of AI comes up. You also probably know that its applications are quite simple, and in the things around us such as: entertainment (Netflix), or internet searches (google assistant, Siri), shopping (Amazon, Alibaba), etcetera. Such b2c companies use AI to simplify their operations and make more accurate product recommendations. Others like IBM, are pushing AI's limits further with initiatives like project debater, an AI system that debates humans on topics. But a more practical application for AI in b2b is in marketing.