It's a modern age conundrum: for years, customers have engaged with ChatBots to resolve a whole range of queries, only to be left frustrated and dissatisfied by the experience While these solutions are adept at automating the customer service process and cutting costs, their limited functionality means they have generated little value wider business context – until now. A new generation of technologies has been developed to not only service customers more effectively, but also to optimize human resource management and employee enablement. Even now that Conversational AI (CAI) technologies have arrived on the scene to take user experience (UX) to the next level, the truth is, that many individuals will still mistake them for run-of-the-mill Bots. The result of this is that some organizations might invest in the wrong technologies, or else dismiss next-gen solutions that could boost their efficiency. Beyond just offering first-line support to customers and colleagues, or the conversational acumen of home assistants like Alexa or Siri, newer CAI technologies are capable of fielding a much more complex range of queries, which will no doubt be a great service to organizations in the remote climate.

Support agents that help users in their daily lives need to take into account not only the user's characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However, research shows that it is important to also determine the meaning of a situation, a step which we refer to as social situation comprehension. We propose using psychological characteristics of situations, which have been proposed in social science for ascribing meaning to situations, as the basis for social situation comprehension. Using data from user studies, we evaluate this proposal from two perspectives. First, from a technical perspective, we show that psychological characteristics of situations can be used as input to predict the priority of social situations, and that psychological characteristics of situations can be predicted from the features of a social situation. Second, we investigate the role of the comprehension step in human-machine meaning making. We show that psychological characteristics can be successfully used as a basis for explanations given to users about the decisions of an agenda management personal assistant agent.

THE INSTITUTE Artificial intelligence is transforming the financial services industry. The technology is being used to determine creditworthiness, identify money laundering, and detect fraud. AI also is helping to personalize services and recommend new offerings by developing a better understanding of customers. Chatbots and other AI assistants have made it easier for clients to get answers to their questions, 24/7. Although confidence in financial institutions is high, according to the Banking Exchange, that's not the case with AI.

The problem of low-rank matrix completion with heterogeneous and sub-exponential (as opposed to homogeneous and Gaussian) noise is particularly relevant to a number of applications in modern commerce. Examples include panel sales data and data collected from web-commerce systems such as recommendation engines. An important unresolved question for this problem is characterizing the distribution of estimated matrix entries under common low-rank estimators. Such a characterization is essential to any application that requires quantification of uncertainty in these estimates and has heretofore only been available under the assumption of homogenous Gaussian noise. Here we characterize the distribution of estimated matrix entries when the observation noise is heterogeneous sub-exponential and provide, as an application, explicit formulas for this distribution when observed entries are Poisson or Binary distributed.

At I/O 2021, Google reiterated its commitment to Matter with a handful of smart home-related Nest and Android updates. If you need a refresher, Matter was known as Project CHIP, or Connected Home over IP, before a rebranding this past May. It's a pact between some of the biggest companies in tech, including Google, Amazon and Apple, that aims to bring standardization to the fragmented smart home space. When it launches in the first half of 2022, it will support a variety of voice assistants and networking protocols, including Alexa, Google Assistant, Siri as well as WiFi, Thread and Bluetooth LE. At its simplest, the promise of Matter is that you'll be able to buy a new device and it will simply work with your existing smart home setup.

Artificial intelligence (AI) is transforming the landscape of 21st century marketing. Long gone are the days of throwing spaghetti on the wall and shooting in the dark to acquire new customers and to regain their business. With the amount of data growing exponentially on a daily basis, AI can help businesses scale their marketing efforts and leverage the data for actionable insights leading to greater ROI. Look up the term "marketing" and you'll find something that mentions actions or activities involving a business or company, promoting or selling products or services. Is that something that you or your company does?

When's the last time you chirped, "Hey Google" (or Siri for that matter), and asked your phone for a recommendation for good sushi in the area, or perhaps asked what time sunset would be? Most folks these days perform these tasks on a regular basis on their phones, but you may not have realized there were multiple AI (Artificial Intelligence) engines involved in quickly delivering the results for your request. In these examples, AI neural network models were used to process natural language recognition, and then also inferred what you were looking for, to deliver relevant search results from internet databases around the globe, but also targeting the most appropriate results based on your location and a number of other factors as well. These are just a couple of examples but, in short, AI or machine learning processing is a big requirement of smartphone experiences these days, from recommendation engines to translation, computational photography and more. As such, benchmarking tools are now becoming more prevalent, in an effort to measure mobile platform performance. MLPerf is one such tool that nicely covers the gamut of AI workloads, and today Qualcomm is highlighting some fairly impressive results in a recent major update to the MLCommons database.

Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models are vulnerable to poisoning attacks, compromising their integrity. In the context of recommender systems, a typical goal of such poisoning attacks is to promote the adversary's target items by interfering with the training dataset and/or process. Hence, a common practice is to subsume recommender systems under the decentralized federated learning paradigm, which enables all user devices to collaboratively learn a global recommender while retaining all the sensitive data locally. Without exposing the full knowledge of the recommender and entire dataset to end-users, such federated recommendation is widely regarded `safe' towards poisoning attacks. In this paper, we present a systematic approach to backdooring federated recommender systems for targeted item promotion. The core tactic is to take advantage of the inherent popularity bias that commonly exists in data-driven recommenders. As popular items are more likely to appear in the recommendation list, our innovatively designed attack model enables the target item to have the characteristics of popular items in the embedding space. Then, by uploading carefully crafted gradients via a small number of malicious users during the model update, we can effectively increase the exposure rate of a target (unpopular) item in the resulted federated recommender. Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium

YouTube relies heavily on AI to deliver content. The newest YouTube algorithms put a great deal of value on the average time that a person views any video, gives it a like or dislike, and comments. Similarly, the recommender system is one of the most powerful use cases of ML that is encountered by every one of us many times a day. Collaborative Filtering: This is a type where we tend to build collaborations between various users and items(videos). Matrix Factorization: It tries to dissolve both user and item vectors together thus decomposing them and providing YouTube with better comparison metrics.