The anthropologist and famed love expert Helen Fisher seemed ready to dash into oncoming traffic. We were on a sidewalk in Manhattan, opposite the American Museum of Natural History, and nowhere near a safe place to cross the street. She wanted me to stare down the yellow cabs and charge off the curb, though she knew I wouldn't do it: I'd recently taken the personality questionnaire she wrote 17 years ago for a dating website, which produced the insight that I am a cautious, conventional rule follower. She, however, is an "explorer"--she has visited 111 countries, including North Korea--but also, being high in estrogen, a "negotiator" who will use the crosswalk for my benefit. "I am horribly empathetic," she told me. I look into baby carriages and worry about their future with love." This is how Fisher, the 77-year-old chief scientific adviser for Match.com and one of the best-known, most-often-quoted experts on romance and "mate choice," understands life: Personality is a cocktail of ...

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.

What exactly is the Fourth Industrial Revolution -- and why should you care? The Fourth Industrial Revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's the collective force behind many products and services that are fast becoming indispensable to modern life. Think GPS systems that suggest the fastest route to a destination, voice-activated virtual assistants such as Apple's Siri, personalized Netflix recommendations, and Facebook's ability to recognize your face and tag you in a friend's photo. As a result of this perfect storm of technologies, the Fourth Industrial Revolution is paving the way for transformative changes in the way we live and radically disrupting almost every business sector.

Savvy investors will want to check out stocks that capitalize on artificial intelligence -- using computers to simulate human thinking, reasoning and problem solving. Companies that use AI technology often do so by using software to process huge amounts of data. As the pandemic of 2020 forced lockdowns, many businesses took advantage of AI to interact with the public, as face-to-face contact was limited. That trend seems likely to continue. In a November market forecast, Gartner Inc. predicted the market for AI would grow 21.3%, to $62.5 billion, in 2022.

Let's take your daily life and make it better with AI! You're most likely using apps like Siri or Google Assistant, but do you know the benefits? AIs will assist in tasks that would otherwise require human effort- saving time for everyone. The benefits of using AI are all around you. You just need to take the time to explore them and see what they can do for you! This is an experiment AI tool from Google itself.

I have a love-hate relationship with my alarm. I rely on it to ensure I get up in the morning and perform important tasks throughout the day, but when it sounds in the morning, I glare at it with the fury of a thousand suns. A good alarm clock can make the waking experience less jarring -- with cool features like ramp-up lighting or pleasing nature sounds. But it doesn't stop there; the best alarm clocks have additional features like voice assistants and interactive displays that make your alarm clock more useful than ever. Picking the best alarm clock for you can be tough, especially with so many features, price points and brands.

Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.

The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting increases the energy efficiency of residential buildings by nudging residents to change their energy consumption behavior. This paper introduces an activity prediction-based framework for the utility-based context-aware multi-agent recommendation system that generates an activity shifting schedule for a 24-hour time horizon to either focus on CO2 emissions or energy cost savings. In particular, we design and implement an Activity Agent that uses hourly energy consumption data. It does not require further sensorial data or activity labels which reduces implementation costs and the need for extensive user input. Moreover, the system enhances the utility option of saving energy costs by saving CO2 emissions and provides the possibility to focus on both dimensions. The empirical results show that while setting the focus on CO2 emissions savings, the system provides an average of 12% of emissions savings and 7% of cost savings. When focusing on energy cost savings, 20% of energy costs and 6% of emissions savings are possible for the studied households in case of accepting all recommendations. Recommending an activity schedule, the system uses the same terms residents describe their domestic life. Therefore, recommendations can be more easily integrated into daily life supporting the acceptance of the system in a long-term perspective.

A Music Recommendation System based on Emotion, Age, and Ethnicity is developed in this study, using FER-2013 and ``Age, Gender, and Ethnicity (Face Data) CSV'' datasets. The CNN architecture, which is extensively used for this kind of purpose has been applied to the training of the models. After adding several appropriate layers to the training end of the project, in total, 3 separate models are trained in the Deep Learning side of the project: Emotion, Ethnicity, and Age. After the training step of these models, they are used as classifiers on the web application side. The snapshot of the user taken through the interface is sent to the models to predict their mood, age, and ethnic origin. According to these classifiers, various kinds of playlists pulled from Spotify API are proposed to the user in order to establish a functional and user-friendly atmosphere for the music selection. Afterward, the user can choose the playlist they want and listen to it by following the given link.