You can now get Google Assistant to stop talking with just one word: "Stop." That's it -- you don't even have to say "Hey, Google" before that. The official Google Twitter account has announced the small but necessary quality-of-life improvement for the company's speakers and smart displays. It sometimes takes a while (and several repeated attempts) to get Assistant's attention with a "Hey, Google" if it suddenly goes off without you wanting it to or if you absolutely have to cut it off mid-spiel. This new feature solves that problem.

Here's a fun thought experiment; picture the amount of personal data you think tech companies keep on you. Now, realize it's actually way more than that (hmm, maybe this isn't that fun). Even as privacy and security become more talked about in consumer tech, the companies behind our favorite products are collecting more and more of our data. Well, if you want to know the information, say, Amazon has on you, there is a way to find out. To be clear, data collection is far from an Amazon-specific problem; it's pretty much par for the course when it comes to tech companies.

The amount of time we spend doing repetitive work is mind-boggling, with manual computer tasks and data entry taking up a good portion of an office worker's day. A recent survey indicates that people estimate they waste five hours each week on tasks that should be automated. According to McKinsey, the number is even higher, with at least one-third of job activities deemed automatable in about 60% of occupations. Whether it's data collection, approvals, or updates, many tasks don't require creativity or intuition, essential attributes that serve to increase job satisfaction. Instead, the monotony of the work lowers satisfaction, leading to lower productivity and other inefficiencies.

A previous version of this paper, entitled "Personalized Recommendation of PoIs to People with Autism," was published in the Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (Genoa, Italy, 2020), 163–172.

Recommender systems are among the most pervasive machine learning applications on the Internet. Social media, audio and video streaming, news, and e-commerce are all heavily driven by the data-intensive personalization they enable, leveraging information drawn from the behavior of large user bases to offer a myriad of recommendation services. Point of Interest (PoI) recommendation is the task of recommending locations (business, cultural sites, natural areas) for a user to visit. This is a well-established sub-field within recommender systems, and as a domain of application, it provides a good introduction to the challenges of applying personalized recommendation in practical contexts. An effective PoI recommender must consider a user's interests and preferences, as in any personalized system, but also practical aspects of travel: weather, congestion, hours of operation, seasonality, to name a few.

At a Glance: So far we have gone through the pre-processing pipeline of the Criteo Dataset using the standard NVTabular toolkit present inside Merlin SDKs. This was a part of the ETL processing and it is present under Part 1. Followed by this, in Part 2, we have gone through the training procedures using the HugeCTR architecture. We have explored 4 standard state-of-the-art architectures using HugeCTR training toolkits inside Merlin. In this section, we are going to explore the inference implementation which is the final deployment process using the Triton Inference Server. Nvidia's Merlin contains 3 crucial components.

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty. In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders' explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply -- or need to be adapted -- to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy-based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.

To many, Artificial Intelligence (AI), is still in the realm of science fiction: decades away, and far removed from our current reality. However, increasingly, we are interfacing with platforms and systems powered by AI, such as smart watches and virtual assistants, such as Siri and Alexa. More importantly, in the coming decade, AI will drive and underpin a broad range of processes, and consequently change the ways we live and work. This episode is also available on SoundCloud, Apple iTunes, Google Play Music, Spotify, Amazon Music (NEW!) and on Stitcher! Over the past few months, there have been some new developments in the AI space that highlight the fact that AI is developing rapidly.

Those looking to add to their Google home setup can get the biggest Nest smart display for less today. Adorama has a one-day-only sale that knocks $60 off the Nest Hub Max, bringing it down to $169. That's even cheaper than we saw it a couple of weeks ago when a bunch of Nest gadgets were discounted across the web, and it's $11 less than the device's Black Friday price last year. The Nest Hub Max earned a score of 86 from us when it first came out in 2019 and it remains a good option for those that rely on the Google Assistant and want a larger home hub with advanced smart features. It has a spacious 10-inch HD touchscreen on which you can do things like take a Zoom call, watch YouTube or Netflix and control all of the smart lights, thermostats and other gadgets in your home.

The human activity of tagging content such as text, images, and videos, so that machine learning models can recognize them and use them to generate predictions, is known as data annotation. When we label data elements, ML models accurately understand what they are going to process and retain that information to automatically process the available information, based on existing knowledge, to make decisions. Data annotation refers to the process of attributing, tagging, or labeling data. To summarize, data labeling and data annotation are both concerned with labeling or tagging relevant information/metadata in a dataset so that machines can understand what they are. The dataset can take any form, such as an image, an audio file, video footage, or even text.