Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can significantly help reduce costs, and eliminate human biases and errors. However, limited sample size of financial data-sets and the need for not only good recommendations, but also explaining why a particular recommendation is being made, makes this a challenging problem. In this work we propose a representation learning based recommendation engine that works extremely well with small datasets and demonstrate how it can be coupled with a parameterized explanation generation engine to build an explainable recommendation system for investor-company matching. We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task. We also highlight how explainability helps with real-life adoption of our system.

With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the deep models' input drive the final ranking decision, thus, they cannot often be understood by human stakeholders. In this paper, we investigate the dilemma between recommendation and explainability, and show that by utilizing the contextual features (e.g., item reviews from users), we can design a series of explainable recommender systems without sacrificing their performance. In particular, we propose three types of explainable recommendation strategies with gradual change of model transparency: whitebox, graybox, and blackbox. Each strategy explains its ranking decisions via different mechanisms: attention weights, adversarial perturbations, and counterfactual perturbations. We apply these explainable models on five real-world data sets under the contextualized setting where users and items have explicit interactions. The empirical results show that our model achieves highly competitive ranking performance, and generates accurate and effective explanations in terms of numerous quantitative metrics and qualitative visualizations.

This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

Our smart devices are listening. Whether it's personally identifiable information, location data, voice recordings, or shopping habits, our smart assistants know far more than we realize. A survey on smart assistant usage conducted by Reviews.org After analyzing the terms and conditions of Alexa, Google Assistant, Siri, Bixby, and Cortana, though, it was clear that some degree of data collection is ultimately inescapable. All five services collect your name, phone number, device location, and IP address; the names and numbers of your contacts; your interaction history; and the apps you use.

Amazon has two new programs that integrate Alexa into hospitals and senior living communities, the company announced today. They're run through Alexa Smart Properties, which allows organizations to control a centralized Alexa system. "Early on in the pandemic, hospitals and senior living communities reached out to us and asked us to help them set up Alexa and voice in their communities," Liron Torres, global leader for Alexa Smart Properties, said in an interview with The Verge. Hospitals wanted ways to interact with patients without using protective equipment, and senior living communities wanted to connect residents with family members and staff, she says. The program lets senior living facilities use Amazon Echo devices to send announcements or other messages to residents' rooms.

This week, Infor is the latest to have announced the general availability of its digital assistant within Microsoft Teams. The integration will enable customers to access information from within their ERP systems. To do so, customers will interact with the Infor Coleman AI Digital Assistant app for Teams. The digital assistant bot was previously available via a web browser, the Infor Go mobile app, and Amazon Alexa for Business. Oddly, unlike many others, Infor has not yet added an integration.

The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.

Consider the task of matrix estimation in which a dataset $X \in \mathbb{R}^{n\times m}$ is observed with sparsity $p$, and we would like to estimate $\mathbb{E}[X]$, where $\mathbb{E}[X_{ui}] = f(\alpha_u, \beta_i)$ for some Holder smooth function $f$. We consider the setting where the row covariates $\alpha$ are unobserved yet the column covariates $\beta$ are observed. We provide an algorithm and accompanying analysis which shows that our algorithm improves upon naively estimating each row separately when the number of rows is not too small. Furthermore when the matrix is moderately proportioned, our algorithm achieves the minimax optimal nonparametric rate of an oracle algorithm that knows the row covariates. In simulated experiments we show our algorithm outperforms other baselines in low data regimes.

What was the motivation for adding voice and image recognition to the iPhone's SoC? If you've ever used Siri, Apple's voice assistant, you may have run into occasional problems where, instead of responding to your command, she says something along the lines of "Please wait a moment..." This is because at present, Siri uses cloud processing of voice data, and if she is unable to connect to Apple's servers through the internet, that's where the party ends. This is due to change very soon however, as this fall's release of iOS 15 will switch Siri to process your voice commands completely on the device itself. Voice assistants such as Siri, Amazon's Alexa, Google Assistant, or Microsoft's Cortana, on-device processing brings a host of benefits: Reduced latency since the data doesn't have to travel over the internet to be processed with wearable technologies Less use of bandwidth which can translate to cheaper internet bills Better privacy as the processing is all done locally and not on someone else's computer The Natural Language Processing (NLP) functionality on these smart assistants are sometimes designed as a hybrid edge and cloud solution known as "fog computing" because it's at the "edge of the cloud". In these systems, they process some data locally and more complex data in the cloud.

You might think that artificial intelligence is only something the tech giants are focused on and that it doesn't have any impact on your household or everyday life. But the reality is different. Whether you realize it or not, Artificial Intelligence is everywhere. The application of AI is not only for big sectors or finance or manufacturing, it is also impacting our daily lives. So, let's find out about the applications of AI in your daily life.