Picture a machine learning system that relies on crowdsourced data labelers to help rank music recommendations. Labellers are all different and this difference may manifest in their labels. The answer depends on many things, but one of them is who you are asking. Bias means different things to different people. The other day I watched a very interesting discussion along these lines between a lawyer (Jake Goldenfein) and a data scientist (Danula Hettiachchii). It seemed like my colleagues had fundamentally different ideas about bias.

The Portal Plus is an expensive device with a $349 price tag that belies its capabilities. Although the Portal Plus boasts a big screen and some fun tools, it's far more limited than, say, the current-generation Amazon Echo Show 10 and Google Nest Hub Max. In fact, the big screen is arguably the sole reason to choose the Portal Plus over Facebook's $199 Portal Go, a nearly identical device save for its 10-inch display and battery-powered portability. Whether or not you'll be truly satisfied with either one, though, depends on what you want--and whether you have a Facebook or WhatsApp account, one of which is required to use any Portal. The Portal Plus' base is also its speaker.

Artificial intelligence is a brain created by humans that can think and make decisions for itself, often to assist humans and improve our everyday lives. Think of it as when you're just born baby, you see many things, but don't know what to do with it. But with the help of your parents, teachers, and friends you learn how to talk, eat, and think for yourself. AI without code is literally that. It's the developers job to teach the program how to think and real with the data given.

Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fastai and Python. Recommender system is a promising approach to boost sales to the next level by suggesting the right products to the right customers. This course starts by showing you the main solutions of recommender systems in the industry and the hypotheses behind the main solutions. You'll then learn how to build collaborative filtering models with fastai, and exercise the trained model on test datasets. As you advance, you'll visualize latent features, interpret weights and biases, and check what similar users/Items are from the model's perspective.

Welcome to the first course in Term 2 as part of the series "MBA in Artificial Intelligence Digital Marketing". Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining, and functioning when it is trying to solve problems. These kinds of problems are present in all fields, the most emerging ones, and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning, and problems along with the ability to manipulate things.

Version control tracks and manages changes in a collection of related entities. It records changes and modifications over time, so you can recall, revert, compare, reference, and restore anything you want. Version control is also known as source control or revision control. Each version is associated with a timestamp, and the ID of the person making the changes in documents, computer programs, files, etc. Version control prevents conflicts in concurrent work, and enables a platform for better decision-making and fostering compatibility. Version Control Systems (VCM) run as stand-alone software tools that implement a systematic approach to track, record, and manage changes made to a codebase. In this article, we're going to explore what version control means from different perspectives. This version control system consists of a local database on your computer that stores every file change as a patch (difference between files in a unique format).

NVIDIA has been steadily advancing its AI assistant technology in recent months, and now it's clear just how all the pieces fit together. The company has introduced Omniverse Avatar (for 3D assistant creation) and Riva (custom AI voice creation) platforms that, combined, lead to surprisingly realistic virtual personas with relatively little effort -- or, in one case, deliberately unrealistic. In one demo, used to highlight NVIDIA's AI-powered Maxine toolkit, the company created an Omniverse Avatar from a woman's photo and used Riva to train the voice based on that woman, convert text to speech and translate to different languages. The digital stand-in looks and sounds much like the real person (aside from a couple of stiff-sounding translations), and can even turn its head while maintaining natural-looking eye contact. As you might imagine, this could lead to more relatable virtual helpers at kiosks and websites.

Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage and act on them. These digital tools -- such as task management applications -- provide a unique opportunity to study and understand tasks and their connection to the real world, and through intelligent assistance, help people be more productive. By logging signals such as text, timestamp information, and social connectivity graphs, an increasingly rich and detailed picture of how tasks are created and organized, what makes them important, and who acts on them, can be progressively developed. Yet the context around actual task completion remains fuzzy, due to the basic disconnect between actions taken in the real world and telemetry recorded in the digital world. Thus, in this paper we compile and release a novel, real-life, large-scale dataset called MS-LaTTE that captures two core aspects of the context surrounding task completion: location and time. We describe our annotation framework and conduct a number of analyses on the data that were collected, demonstrating that it captures intuitive contextual properties for common tasks. Finally, we test the dataset on the two problems of predicting spatial and temporal task co-occurrence, concluding that predictors for co-location and co-time are both learnable, with a BERT fine-tuned model outperforming several other baselines. The MS-LaTTE dataset provides an opportunity to tackle many new modeling challenges in contextual task understanding and we hope that its release will spur future research in task intelligence more broadly.

Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs and preferences of individual users. In conversational recommendation approaches, these needs and preferences are acquired by the system in an interactive, multi-turn dialog. A common approach in the literature to drive such dialogs is to incrementally ask users about their preferences regarding desired and undesired item features or regarding individual items. A central research goal in this context is efficiency, evaluated with respect to the number of required interactions until a satisfying item is found. This is usually accomplished by making inferences about the best next question to ask to the user. Today, research on dialog efficiency is almost entirely empirical, aiming to demonstrate, for example, that one strategy for selecting questions is better than another one in a given application. With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation. This model, which is designed to cover a range of application scenarios, allows us to investigate the efficiency of conversational approaches in a formal way, in particular with respect to the computational complexity of devising optimal interaction strategies. Through such a theoretical analysis we show that finding an efficient conversational strategy is NP-hard, and in PSPACE in general, but for particular kinds of catalogs the upper bound lowers to POLYLOGSPACE. From a practical point of view, this result implies that catalog characteristics can strongly influence the efficiency of individual conversational strategies and should therefore be considered when designing new strategies. A preliminary empirical analysis on datasets derived from a real-world one aligns with our findings.

WhatsApp is the new buzzword for digital conversations as the post-pandemic world gets more digital-savvy and contactless shopping emerges as the new trend. According to a Statista report, WhatsApp is the most popular messaging platform with more than 2 billion active users outranking Facebook Messenger at 1.3 billion and WeChat at 1.2 billion users. Not only this, it has become the world's third-largest social networking platform after Facebook and YouTube. Given the popularity and demand of this new messaging channel, businesses too have started to turn their ships, the WhatsApp way. Also, recent innovations in artificial intelligence (AI) and machine learning (ML) make WhatsApp more profitable for businesses.