We all enjoy sharing jokes with friends, hoping a witty one might elicit a smile--or maybe even a belly laugh. A lawyer opened the door of his BMW, when, suddenly, a car came along and hit the door, ripping it off completely. When the police arrived at the scene, the lawyer was complaining bitterly about the damage to his precious BMW. "Officer, look what they've done to my Beeeeemer!" he whined. "You lawyers are so materialistic, you make me sick!" retorted the officer.

From the way we count our steps to the measures we take to get noticed online, Silicon Valley has transformed the everyday life of the average American. How and what platform we choose to date hasn't escaped this reality. Users of online dating apps, stemming from websites that became less socially acceptable among younger generations, are at the mercy of "swipes" to find love, a casual encounter or simply to boost their egos. But which app daters use may lead to unintentional assumptions – clichés, even – about why they chose a particular matchmaking platform, ranging from how they present themselves on their profiles to what kind of connection they are seeking. Almost half of U.S. online users have met or know someone who has met a romantic partner on a dating website or app. Tinder is currently the leader among online dating services, according to one study of 1,000 adults.

Google is launching a number of updates to its G Suite tools today that, among other things, brings to Google Docs an AI grammar checker, smarter spellchecking and, soon, spelling autocorrect. The company is also launching the ability for G Suite users to use the Google Assistant to read out a calendar schedule and, maybe even more importantly, create, cancel and reschedule events. Google is also adding new accessibility features to the Assistant for use during meetings. In addition, Google yesterday announced that Smart Compose would soon come to G Suite, too. It's maybe no surprise that Google is adding its new grammar suggestions to Docs.

Although you haven't figured it out, we usually use artificial intelligence (AI) in Digital Marketing to optimize user experience and automate several everyday processes, without dispensing with the human factor, which is still essential for making strategic decisions. In this post we tell you what applications have artificial intelligence in the different areas of Digital Marketing. Artificial intelligence allows you to create useful and personalized content for users of your website. Mass media such as BBC, The New York Times, Reuters or The Washington Post use this technology to write soft news, such as, sports results, weather forecasts, etc., from data sources with the aim of journalists have more time to produce quality information that requires interpretation and contextualization. In addition, AI is used to make recommendations based on the tastes and browsing habits, same as Amazon and Netflix do, offering relevant suggestions for products that may interest to their users.

Technology is playing an increasingly important role in the travel sector. It not only impacts the products and services companies provide, but how they're offered as well. Travelers benefit from improved customer service throughout their journey. Customer-facing roles are as important as ever. The best companies invest in technology to help their employees provide top-notch service.

One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowledge states of masses of learners for significantly longer periods of time (ideally, a lifetime). This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner's knowledge from implicit data in the form of engagement with open educational resources. We i) use a text ontology based on Wikipedia to automatically extract knowledge components of educational resources and, ii) propose a set of online Bayesian strategies inspired by the well-known areas of item response theory and knowledge tracing. Our proposal, TrueLearn, focuses on recommendations for which the learner has enough background knowledge (so they are able to understand and learn from the material), and the material has enough novelty that would help the learner improve their knowledge about the subject and keep them engaged. We further construct a large open educational video lectures dataset and test the performance of the proposed algorithms, which show clear promise towards building an effective educational recommendation system. Introduction One-on-one tutoring has shown learning gains of the order of two standard deviations (Corbett 2001). Machine learning now promises to provide such benefits of high quality personalised teaching to anyone in the world in a cost effective manner (Piech et al. 2015). Meanwhile, Open Educational Resources (OERs), defined as teaching, learning and research material available in the public domain or published under an open license (UNESCO 2019), are growing at a very fast pace.

Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.

We started collaborating with the Play store to help develop and improve systems that determine the relevance of an app with respect to the user. In this post, we'll explore some of the cutting-edge machine learning techniques we developed to achieve this. Today, Google Play's recommendation system contains three main models: a candidate generator, a reranker, and a model to optimise for multiple objectives. The candidate generator is a deep retrieval model that can analyse more than a million apps and retrieve the most suitable ones. For each app, a reranker, i.e. a user preference model, predicts the user's preferences along multiple dimensions.

Low-rank approximations of data matrices have become an important tool in Machine Learning in the field of bio-informatics, computer vision, text processing, recommender systems, and others. They allow for embedding high dimensional data in lower dimensional spaces which mitigate effects due to noise, uncover latent relations, or facilitate further processing. In general, MF is a process to find two factor matrices, P R, k m and Q R, k n, to describe a given m-by-n training matrix R in which some entries may be missing. MF can be found in many applications, but we only use collaborative filtering in recommender systems as examples. It is based on the assumption that the entries of R are the historical users' preferences for merchandises, and the task on hand is to predict unobserved user behavior (i.e., missing entries in R) to make a suitable recommendation. In this blog, I discuss about different types of matrix factorization techniques for real-time recommendation engines and their corresponding Python libraries.

FOX Business' Maria Bartiromo reports on the growth of artificial intelligence and the impact it could have on jobs. It sounds like a plot of a science-fiction novel: A future that doesn't need human beings. But is it more reality than fantasy? Most people use artificial intelligence and probably don't even realize they're using it -- whether it's asking Siri a question or trying to avoid a traffic jam using GPS or even using our faces as a password to access our smartphones. Artificial intelligence is defined as the capability of a machine to imitate intelligent human behavior.