Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model either the node's latent spaces in which node distributions are rigid or do not effectively capture the interrelations between drugs; these limitations hinder the methods from accurately predicting drug-pair interactions. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on three multimodal networks: (1) a multimodal graph consisting of drug and protein nodes, (2) a multimodal graph constructed from a subset of the DrugBank database involving drug nodes under different interaction types, and (3) a multimodal graph consisting of drug and cell line nodes.

In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively.

We're in the middle of a global technological revolution, but dating apps have become relics of the status quo. Generative AI programs like ChatGPT offer a way to put the fun back into dating. These programs can provide relevant icebreakers while still maintaining intimacy in conversation. The people who are against using AI to help with dating are ignoring the fact that it can help accelerate the tedious initial process of online dating. For a decade, Americans have described dating apps as exhausting.

ChatGPT and the underlying AI technology are, as near as anyone can tell, the next-level toolkit for research, communication, idea generation, and a million other uses. If you haven’t introduced yourself to it yet, leave plenty of time – getting started is easy, but getting yourself to stop may not be. Those of us that were around pre-internet may liken the first-time experience to the first time we gained access to the world wide web. The Sudden, much easier access to a world of information, puts one in a place where you don’t know what to try first.ChatGPT is the AI natural language program that is disrupting major technology companies. We explore how investors, businesses, and households can benefit from the efficiencies it creates.

The past weeks have seen escalating competition between Microsoft and Google over large language models--or more precisely put, Google trying hard to protect its search business against Microsoft and OpenAI's large language models. The two tech giants are in an intensifying tug of war over how we will access information in the future, matching research with research, product with product, and investment with investment. Since OpenAI released ChatGPT in November, there has been a lot of speculation about the large language model's killer application(s). One of the topics brought up again and again is ChatGPT and other LLMs making Google Search obsolete. I'm still sticking to my previous argument that something like ChatGPT will replace Google Search.

The company confirmed OpenAI's chatbot tech was going to be integrated into both Bing and Edge at the Microsoft ChatGPT event in early February. Since then, the search engine has received more attention than it ever has. Microsoft has rolled out access to the new Bing with ChatGPT to a limited group of people. According to Microsoft corporate vice president and consumer chief marketing officer, Yusuf Mehdi (opens in new tab), there are "multiple millions" on the waitlist. Because of that, folks will have to wait until they're granted access.

Fox News host Steve Hilton delves into ChatGPT, an artificial intelligence program that could have major implications for writing-focused jobs on'The Next Revolution.' The generative artificial intelligence service ChatGPT refused to write a story about Hunter Biden in the style of The New York Post but obliged the user request when asked to do the same in the style of CNN. The striking difference in responses from the chatbot developed by OpenAI was first highlighted by The New York Post, with the paper claiming that ChatGPT was exhibiting a liberal bias. When asked to write the story about Hunter in The New York Post style, ChatGPT said it could not generate content "designed to be inflammatory or biased." "The role of a news outlet is to provide accurate and impartial reporting and to present information in a manner that is fair and balanced," the chatbot continued.

Looking to cash in on the generative AI craze, Yext, the platform for online brand management, today announced an AI-powered chatbot called Yext Chat. Taking inspiration from OpenAI's ChatGPT, Yext Chat is designed for enterprise use cases -- and differentiated, Yext claims, by a partly proprietary back end. "ChatGPT has shown the world that large language models can hold incredibly coherent, helpful conversations -- far better than any technology up to this point. But right now there is no easy way for enterprises to harness this technology," Yext president and chief operating officer Marc Ferrentino told TechCrunch in an email interview. "Yext Chat is designed for the enterprise, and enterprises need full control over what a chatbot says and does."

Write yourself or import content from any channel and integrate it to any applications or website programmatically.

I was amazed last August, when I first asked GPT-3 to write a sample article about the impact of GPT on higher education. It responded with an accurate, cogent 700-word piece in just seconds. I wrote then that "higher ed will never be the same!" However, the models and interfaces continued to improve with the GPT-3.5 and ChatGPT. We have now embarked on an ever-accelerating improvement of the technology, fueled by tens of billions of dollars of investment and a hot competition--most notably between Microsoft/Bing and Alphabet/Google.