As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user's pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularization-based tied-weight scheme is designed to perform robust joint training of the two-stage CRANet model. We finally experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. Our source code is available at https://github.com/akaxlh/CRANet.

Compared with the traditional collaborative filtering methods, the graph convolution network can explicitly model the interaction between the nodes of the user-item bipartite graph and effectively use higher-order neighbors, which enables the graph neural network to obtain more effective embeddings for recommendation, such as NGCF And LightGCN. However, its representations is very susceptible to the noise of interaction. In response to this problem, SGL explored the self-supervised learning on the user-item graph to improve the robustness of GCN. Although effective, we found that SGL directly applies SimCLR's comparative learning framework. This framework may not be directly applicable to the scenario of the recommendation system, and does not fully consider the uncertainty of user-item interaction.In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a supervised contrastive learning framework to pre-train the user-item bipartite graph, and then fine-tune the graph convolutional neural network. Specifically, we will compare the similarity between users and items during data preprocessing, and then when applying contrastive learning, not only will the augmented views be regarded as the positive samples, but also a certain number of similar samples will be regarded as the positive samples, which is different from SimCLR who treats other samples in a batch as negative samples. We term this learning method as Supervised Contrastive Learning(SCL) and apply it on the most advanced LightGCN. In addition, in order to consider the uncertainty of node interaction, we also propose a new data augment method called node replication.

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

Early in my career, working as a data analyst, I, like many people, dreamed of doing something significant and valuable for people. I wanted to be creative. I wanted to feel the results of my work, not just study the data. I worked at several startup companies for ten years before co-founding an e-commerce recommendation services company in 2012. In 2020, during the COVID pandemic, I went on sabbatical for a year -- wrote a book, released it on Amazon, and left the successful company (positive cash flow, 150 employees in Russia, Europe, and South America) entirely in the summer of 2021.

Google unveiled Wednesday at CES 2022 a range of new ways to keep its Android devices connected -- and that includes cars. As more vehicles go electric and automakers evolve into software developers, expect to see more plays directed at turning cars into connected devices. Take Volvo Cars, for instance. The automaker and Google announced at CES 2022 new content and services that will be coming to future Volvo vehicles, including the ability to download and use the YouTube app via Google Play Store and the ability to communicate with the Google Home ecosystem. New Volvo car models are equipped with an Android Automotive operating system and have embedded voice-controlled Google Assistant, Google Play Store, Google Maps and other Google services into its infotainment system.

AI stocks may be excellent long-term investments. The global artificial intelligence market is on track to hit more than $554 billion in total revenue by 2024, according to market researcher IDC. AI, automation and robotics are disrupting virtually every major industry. From machine learning to the "internet of things," autonomous vehicles, virtual assistants and smart home appliances, companies that aren't embracing AI and incorporating it into their business models risk going obsolete. Countless companies stand to benefit from AI, but a handful of companies have business models focused specifically on automation.

AI or artificial intelligence is an often-misused term. Artificial Intelligence simply means intelligence in machines. This contrasts with natural intelligence, which is found in humans and other natural organisms. AI gained its name and became a formal field of research in 1956. Initial work led to new tools for solving mathematical problems.

Google unveiled at CES on Wednesday a range of new ways to keep its Android devices connected, and that includes cars. As more vehicles go electric and automakers evolve into software developers, we can only expect to see more plays directed at turning cars into connected devices. One exemplar of this phenomenon is Volvo Cars, which will launch a direct integration with the Google Home ecosystem in the coming months, both Volvo and Google announced on Wednesday. The integration should allow car owners to turn their car on and off, control the temperature and get car information like battery life by issuing voice commands to Google Assistant-enabled home and mobile devices. Once customers pair their Volvo car to their Google account, they also can talk directly to Google while in their car.

The technology boom amidst the pandemic has already hit 2022, creating another record with Apple. This week, the company was valued at $3 trillion, the first US company to reach this growth. This follows Apple's tremendous market growth that has risen by 38% since the start of 2021 and tripled in value in under four years. The Guardian estimated the valuation is equivalent to the combined value of Boeing, Coca-Cola, Disney, Exxon-Mobil, McDonald's, Netflix and Walmart. While the growth has not been sustained, the company has surely been a disruptor in the technology market with their breakthrough innovations for decades.

Amazon have teamed up with Cisco and Lockheed Martin to develop an artificial intelligence system -- dubbed'Callisto' -- for NASA's Artemis I lunar mission. Callisto will combine into one interface the voice control technology of Amazon's Alexa virtual assistant with Cisco's Webex video conferencing system. Lockheed Martin engineers, meanwhile, will be leading the development of the custom-built system and its integration into their Orion space capsule design. Unlike Alexa, Callisto will contain technology and software that allows it to respond to commands in-flight without reliance on an internet connection. The video conferencing system, in contrast, will require a communications system to function -- and will operate via NASA's Deep Space Network.