It's little wonder that investors were clamoring to plow money into OpenAI. Alongside an announcement that the company had raised 6.6 billion in funding, OpenAI revealed that "every week, over 250 million people around the world use ChatGPT to enhance their work, creativity, and learning." That's a sharp rise since late August, when OpenAI said the chatbot had 200 million weekly users -- double the number it had last November. As of June, 350 million people were using OpenAI's tools each month, according to internal documents obtained by The New York Times. It's unclear how many people are paying for access versus those using the free tier.

This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce a quality of experience (QoE) metric to measure user experience, which considers the MEC system's latency, user attention levels, and preferred resolutions. Then, a QoE maximization problem is formulated for resource allocation to ensure the highest possible user experience,which is cast as a reinforcement learning problem, aiming to learn a generalized policy applicable across diverse user environments for all MEC servers. To learn the generalized policy, we propose a framework that employs federated learning (FL) and prompt-based sequence modeling to pre-train a common decision model across MEC servers, which is named FedPromptDT. Using FL solves the problem of insufficient local MEC data while protecting user privacy during offline training. The design of prompts integrating user-environment cues and user-preferred allocation improves the model's adaptability to various user environments during online execution.

Load balancing (LB) is a challenging issue in the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendly LB solution with near-optimal network performance, at the cost of a training process. The state-of-the-art (SOTA) learning-aided LB methods, however, need retraining when the network environment (especially the number of users) changes, significantly limiting its practicability. In this paper, a novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed, which conducts AP selection for one target user upon the condition of other users. Also, an adaptive mechanism is developed to map a smaller number of users to a larger number through splitting their data rate requirements, without affecting the AP selection result for the target user. This enables the proposed method to handle different numbers of users without the need for retraining. Results show that A-TCNN achieves a network throughput very close to that of the testing dataset, with a gap less than 3%. It is also proven that A-TCNN can obtain a network throughput comparable to two SOTA benchmarks, while reducing the runtime by up to three orders of magnitude.

Facebook beat Wall Street revenue projections and announced that its user numbers were in line with estimates in the wake of a user data privacy scandal. Up to 87m users saw their data end up in the possession of political consulting firm Cambridge Analytica, which worked for Donald Trump's presidential campaign. Facebook has since been scrambling to mollify angry politicians and reassure users that it will safeguard their personal information. Amid that turmoil, observers were keenly watching the company's user figures to assess the potential damage and see if the scandal would suppress Facebook's long-term growth. Its North American user numbers were already flagging at the end of 2017, and since then a number of users have vowed to quit the platform, among them some prominent technology executives, as the #DeleteFacebook movement gained steam.

Over the past year, Verto Analytics has published critical research identifying and tracking rapidly-emerging trends in consumer behavior, particularly on mobile devices. Hannu Verkasalo, CEO, Verto Analytics, introduces the conditions under which AI driven apps are influential, from the rise of multitasking to the continued influence of cross-device behavior on digital usage. One thing is clear, says the report: "consumer habits are changing faster than before, aided by an increasingly novel technologies and shorter device innovation cycles. The prevalence of Internet access, the rise of social media, e-commerce, and most recently, mobile apps, have all shaped how consumers behave with digital devices. Within the past few years, we've witnessed the rise of AI-powered apps, which harness cloud-based natural language processing (NLP) and machine learning to power a more sophisticated wave of apps and services," continues the introduction.

You might not have heard of iFlyTek. The company is hardly a household name in its domestic market of China, either. But it has a vice-like grip on over 80 percent of the speech technology market in the People's Republic, heading an ecosystem of over 10,000 partners and developers and with user numbers in the hundreds of millions. The company was founded in 1999 by Liu Qingfeng and five other students from the University of Science and Technology of China, widely recognized as one of the nation's preëminent research institutions. They took advantage of research conducted at the university's National Intelligent Computer R&D Center and the Human-Machine Speech Communication Laboratory.