You may have to pay to speak to a real person when you agree to hybrid human-robo management. Technically, you are always in charge of your finances, but you may not be willing to hand over your portfolio's reigns to a robot. A robo-advisor may not be a great fit if you want a more hands-on approach to online guidance. Even an algorithm is still the most sophisticated computer algorithm. It can't sit with you, it can't explain anything to you, and it can't listen to your future dreams.

Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-$N$ linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset.

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The scene plays out like a thriller: You pull out your phone, and you see an ad for AirPods. Wait a minute, you think. Didn't I just have a conversation about AirPods with my friend? Like, a real conversation, spoken aloud? Is my phone… listening to me?

"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).

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.