Anytime you speak to Alexa, it records your voice and entire conversation. Amazon then stores those voice recordings forever, unless you opt to delete these in a more timely manner. If you already own an Amazon Alexa, you know how easy they make your day-to-day life. You can ask questions, avoid your phone altogether, or utilize the different skills you can set up and create new routines. How much information are you allowing Alexa and Amazon to collect from you?

Care patients could see at home visits replaced by a call from an AI-powered voice assistant in a new British trial. Dubbed'Siri for care', a human-like virtual assistant will ring patients once a week to ask a list of automated questions. An algorithm will then analyse the answers and alert carers if there are any deteriorations in health so they can arrange a doctor's visit. Similar trials in Europe have reduced A&E visits by 55 per cent, according to the tech company behind it. The new technology will be tested out on patients in domiciliary care for those who are living independently but who rely on helpers to visit them regularly.

Amazon is rolling out new Echo Show accessibility features today. The company announced new gestures, text-to-speech (TTS) features and caption settings using the device's screen to help customers with disabilities. The Echo Show 8 (2nd Gen) and Echo Show 10 (3rd Gen) now let you dismiss timers with a gesture. If a cooking timer goes off while your hands are messy, you can raise your palm towards the camera to silence it. Of course, Alexa already supported dismissing timers with voice, but the hand-wave accommodates people with speech disabilities (or those who don't feel like talking to a computer).

Erica, which was launched in 2018, garnered 1 billion client interactions and has helped 32 million users. Bank of America expects Erica, early next year, to connect clients to banking agents regarding new products and services, such as mortgages, credit cards and deposit accounts. The pandemic saw a growing number of older customers use digital banking tools, including Erica, Gopalkrishnan said. The bank now wants to build on enhanced adoption by improving the flow of transactions, including the seamless handoff from Erica to human agents and vice versa. "We realized, at some point, people go, 'I'm done with that chat, I need to talk to a human,'" Gopalkrishnan said.

"Receiving proactive service leads to a 9% increase in a customer's value enhancement score," said Eric Keller, senior research director in the Gartner Customer Service & Support practice. The goal of customer success is to ensure that your client receives measurable value in a reasonable time frame with the help of CCAI.

Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback. As a principled way of incorporating market constraints and user incentives in the design, we consider our objectives to be two-fold: maximal social welfare with minimal instability. To maximize social welfare, our proposed framework enhances the quality of recommendations by exploring allocations that optimistically maximize the rewards. To minimize instability, a measure of users' incentives to deviate from recommended allocations, the algorithm prices the items based on a scheme derived from the Walrasian equilibria. Though it is known that these equilibria yield stable prices for markets with known user preferences, our approach accounts for the inherent uncertainty in the preferences and further ensures that the users accept their recommendations under offered prices. To the best of our knowledge, our approach is the first to integrate techniques from combinatorial bandits, optimal resource allocation, and collaborative filtering to obtain an algorithm that achieves sub-linear social welfare regret as well as sub-linear instability. Empirical studies on synthetic and real-world data also demonstrate the efficacy of our strategy compared to approaches that do not fully incorporate all these aspects.

Business processes that involve AI-powered automation have been gaining importance and market share in recent years. These business processes combine the characteristics of classical business process management, goal-driven chatbots, conversational recommendation systems, and robotic process automation. In the new context, prescriptive process monitoring demands innovative approaches. Unfortunately, data logs from these new processes are still not available in the public domain. We describe the main challenges in this new domain and introduce a synthesized dataset that is based on an actual use case of intelligent process automation with chatbot orchestration. Using this dataset, we demonstrate crowd-wisdom and goal-driven approaches to prescriptive process monitoring.

Today, recommender systems have played an increasingly important role in shaping our experiences of digital environments and social interactions. However, as recommender systems become ubiquitous in our society, recent years have also witnessed significant fairness concerns for recommender systems. Specifically, studies have shown that recommender systems may inherit or even amplify biases from historical data, and as a result, provide unfair recommendations. To address fairness risks in recommender systems, most of the previous approaches to date are focused on modifying either the existing training data samples or the deployed recommender algorithms, but unfortunately with limited degrees of success. In this paper, we propose a new approach called fair recommendation with optimized antidote data (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset. Toward this end, we formulate our antidote data generation task as a mathematical optimization problem, which minimizes the unfairness of the targeted recommender systems while not disrupting the deployed recommendation algorithms. Extensive experiments show that our proposed antidote data generation algorithm significantly improve the fairness of recommender systems with a small amounts of antidote data.

Bixby is an intelligent personal assistant which is only available as a built-in application on Samsung flagship devices and wearables. This application uses Natural Language Processing and Knowledge-Based AI to perform tasks on these devices using multimodal inputs and additional contextual information, including but not limited to making phone calls, sending text messages, setting up meetings, opening apps, setting alarms and timers, getting directions, answering general questions, providing information about restaurants and other businesses, etc. The Natural Language Processing and Knowledge-Based AI team aims to create a delightful experience for Bixby customers by making Bixby understand the intent behind any type of request quickly and accurately, and to proactively adapt to the users' needs. You will collaborate closely with experts in Machine Learning, Natural Language Processing and Knowledge-Based AI, and contribute to advancing the state of the art in virtual assistants. As a Senior Knowledge-Based AI Engineer you will primarily focus on building the Natural Language Understanding and Proactive Behavior platform for Bixby by working with Product Managers / Subject Matter Experts, Lab Leaders, Linguistic Experts, brainstorm different ideas, research, build POCs and propose solutions that cater to the broader business needs.

It took a couple of months, but the first smart home devices to support the Matter standard are finally ready. As promised in November, Eve Systems is updating the Eve Energy plug and two sensors, the Eve Door & Window and Eve Motion, with Matter support. The free firmware lets the previously HomeKit-only hardware talk to any controller that supports the new technology, including Samsung's SmartThings app as well as upcoming support through Amazon Alexa and Google Home. As The Verge notes, the update currently requires the not-yet-official iOS and iPadOS 16.2 upgrades that could arrive this week, and you'll have to enlist in an early access program. You won't need Apple products for much longer, though.