Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. However, general representations of crystals to explore the vast material search space remain limited. We introduce a material discovery framework that uses natural language embeddings derived from language models as representations of compositional and structural features. The discovery framework consists of a joint scheme that first recalls relevant candidates, and next ranks the candidates based on multiple target properties. The contextual knowledge encoded in language representations conveys information about material properties and structures, enabling both representational similarity analysis for recall, and multi-task learning to share information across related properties. By applying the framework to thermoelectrics, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces. The recommended materials are corroborated by first-principles calculations and experiments, revealing novel materials with potential high performance. Our framework provides a task-agnostic means for effective material recommendation and can be applied to various material systems.

Click-Through Rate (CTR) prediction is one of the main tasks of the recommendation system, which is conducted by a user for different items to give the recommendation results. Cross-domain CTR prediction models have been proposed to overcome problems of data sparsity, long tail distribution of user-item interactions, and cold start of items or users. In order to make knowledge transfer from source domain to target domain more smoothly, an innovative deep learning cross-domain CTR prediction model, Domain Adversarial Deep Interest Network (DADIN) is proposed to convert the cross-domain recommendation task into a domain adaptation problem. The joint distribution alignment of two domains is innovatively realized by introducing domain agnostic layers and specially designed loss, and optimized together with CTR prediction loss in a way of adversarial training. It is found that the Area Under Curve (AUC) of DADIN is 0.08% higher than the most competitive baseline on Huawei dataset and is 0.71% higher than its competitors on Amazon dataset, achieving the state-of-the-art results on the basis of the evaluation of this model performance on two real datasets. The ablation study shows that by introducing adversarial method, this model has respectively led to the AUC improvements of 2.34% on Huawei dataset and 16.67% on Amazon dataset.

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Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Artificial intelligence may seem like an emerging technology bound for regular use by humans in the distant future, but there are various machine learning products that millions of people already use in their daily lives. Machine learning technology is featured in a variety of everyday technologies, such as search engines, online shopping algorithms, navigation systems, and smartphones. Popular AI products can help you get from one destination to the next, search for facts about your favorite movie, or help you shop for a particular product online.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. It might be some time before we see the futuristic concept of artificial intelligence that is depicted in science fiction novels and films come about in real life, but AI is still all around us. Most homes have some form of voice assistant gadget, such as an Alexa smart home device or Siri assistant on an iPhone. These machines have developed the ability to learn and respond in a way similar to humans' cognitive abilities, all thanks to artificial intelligence algorithms. Alexa and Siri are applications powered by artificial intelligence.

People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For example, the low-rating items are always treated as negative feedback while they actually could provide positive information about user intent. To this end, in this paper, we propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations. The first-level disentanglement is for separating the influence factors of intent and preference, while the second-level disentanglement is performed to build independent sparse preference representations under individual intent with limited computational complexity. Specifically, we employ two variational autoencoder networks, intent recognition network and preference decomposition network, to learn the intent and preference factors, respectively. In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences. Finally, extensive experiments on three real-world datasets and four evaluation metrics clearly validate the effectiveness and the interpretability of DDCF.

Human intelligence's adaptability is remarkable, allowing us to adjust to new tasks and multi-modal environments swiftly. This skill is evident from a young age as we acquire new abilities and solve problems by imitating others or following natural language instructions. The research community is actively pursuing the development of interactive "embodied agents" that can engage in natural conversations with humans and assist them with real-world tasks. These agents must possess the ability to promptly request feedback in case communication breaks down or instructions are unclear. Additionally, they must demonstrate proficiency in learning new vocabulary specific to a given domain. In this paper, we made the following contributions: (1) a crowd-sourcing tool for collecting grounded language instructions; (2) the largest dataset of grounded language instructions; and (3) several state-of-the-art baselines. These contributions are suitable as a foundation for further research.

The creator economy refers to a rapidly growing online-platform-facilitated economy that brings together content creators and users, allowing creators to earn revenue from their creations [Banks and Humphreys, 2008, Bhargava, 2022, El Sanyoura and Anderson, 2022, Radionova and Trots, 2021, Schram, 2020]. These platforms monetize the content created by content creators through various means, including paid audience partnerships, ad revenue, tipping platforms, and product sales provided by the users. The creator economy can be viewed as a three-party game linking users, platform, and content creators. On the one hand, we can model the interactions between the platform and the content creator as a principal-agent relationship, focusing on the need to incentivize the production of high-quality content by the content creator. The platform would like to collect better content to enhance the desirability of the platform. The content creator wishes to gain profit on the platform from their content. The two sides develop agreements in the form of a contract, which specifies how much the platform would pay under the different possible kinds of content. By learning the intent and interest of the content creator, the platform is able to identify a better way to incentivize participation and share the profit with the content creator. The overall framework is contract theory, which is a branch of the theory of incentives [Bolton and Dewatripont, 2004, Faure-Grimaud et al., 2001, Grossman and Hart, 1992, Salanié, 2005].

Ensuring financial protection and access to needed healthcare is integral to achieving Universal Health coverage (UHC) which is integral to the achievement of Sustainable Development Goal (SDG) 3. The uptake of health insurance has been poor in Nigeria, and this has been due to a lot of challenges which include access to healthcare facilities, beliefs, low level of awareness about health insurance, policy challenges, poverty, and where to get required information (2-4). A significant step to improving this includes improved awareness, access to information and tools to support decision making (5). Recommender systems are designed to assist individuals to deal with a vast array of choices, it takes advantage of several sources of information to predict options and preferences around specific items (6-8). Recommender systems enhance the user experience by giving fast and coherent suggestions. Artificial intelligence (AI) based recommender systems have gained popularity in helping individuals find movies, books, music and different types of products on the internet including diverse applications in healthcare (9-12). It has also been used in the insurance industry to support decision making on insurance products (13). Recommender systems are in three main categories which include: collaborative filtering, content-based and hybrid filtering (9). Collaborative filtering method uses the data from other users rating of items to make recommendation for a user for those items.

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter. Specifically, we compute the total updates by the exponentially decaying Average of the squared Updates (AU) on a parameter from the corresponding task.Based on this novel metric, we observe that many parameters in existing MTL methods, especially those in the higher shared layers, are still dominated by one or several tasks. The dominance of AU is mainly due to the dominance of accumulative gradients from one or several tasks. Motivated by this, we propose a Task-wise Adaptive learning rate approach, AdaTask in short, to separate the \emph{accumulative gradients} and hence the learning rate of each task for each parameter in adaptive learning rate approaches (e.g., AdaGrad, RMSProp, and Adam). Comprehensive experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks, resulting SOTA average task-wise performance. Analysis on both synthetic and real-world datasets shows AdaTask balance parameters in every shared layer well.