Microsoft-backed OpenAI has kept its hit ChatGPT app off-limits to users in China, but the app is attracting huge interest in the country, with firms rushing to integrate the technology into their products and launch rival solutions. While residents in the country are unable to create OpenAI accounts to access the artificial intelligence-powered (AI) chatbot, virtual private networks and foreign phone numbers are helping some bypass those restrictions. At the same time, the OpenAI models behind the ChatGPT programme, which can write essays, recipes and complex computer code, are relatively accessible in China and increasingly being incorporated into Chinese consumer technology applications from social networks to online shopping. The tool's surging popularity is rapidly raising awareness in China about how advanced U.S. AI is and, according to analysts, just how far behind tech firms in the world's second-largest economy are as they scramble to catch up. "There is huge excitement around ChatGPT. Unlike the metaverse which faces huge difficulty in finding real-life application, ChatGPT has suddenly helped us achieve human-computer interaction," said Ding Daoshi, director of Beijing-based internet consultancy Sootoo.

San Francisco, February 14: Microsoft has started rolling out the new ChatGPT-powered Bing on desktops to early testers worldwide, and it will also be available on Android and iOS in the coming weeks, the media reported. The tech giant is working on a "substantial optimised interface" for Bing.com's Chat UI for Android and iOS, which includes all-new OpenAI-powered content, reports Windows Latest, citing sources. In an email sent out to testers, Microsoft confirmed that the mobile experience is not yet ready, according to the report. "We don't have a mobile experience ready yet -- we are actively working on it and will have it ready soon. Until then, please continue to use the new Bing on desktop and download the Bing app from your favourite app store to ensure you are ready for the amazing experience when the mobile version is ready," the company said in the email.

Generative AI models like ChatGPT are so shockingly good that some now claim that AIs are not only equals of humans but often smarter. They toss off beautiful artwork in a dizzying array of styles. They churn out texts full of rich details, ideas, and knowledge. The generated artifacts are so varied, so seemingly unique, that it's hard to believe they came from a machine. We're just beginning to discover everything that generative AI can do.

The public has rapidly become fascinated with the power of a new artificial intelligence technology -- ChatGPT -- a chatbot developed by the research and deployment company OpenAI and launched late last year. Already it's demonstrated the ability to serve up detailed answers to complex questions while using the information it processes and feedback from users to improve its ability to respond. ChatGPT has proven to be versatile, with users using the technology to compose music, debug computer code, write restaurant reviews, generate advertising copy and answer test questions. It's able to deliver its responses in a conversational way, and has sparked excitement about its potential, along with some concerns with how it might be used. But what exactly is ChatGPT and what does it say about the state of AI now, and in the future?

There is no denying that Artificial Intelligence (AI) has been making significant strides in recent years. In fact, there is enough evidence to suggest that computers are getting better at natural language processing and machine learning with each successive generation. However, the thought of an AI-based tool finding its way into mainstream usage as soon as 2023 was far-fetched for even the most ardent AI enthusiasts. Thanks to recent developments in the field of generative AI, many of these people are now being forced to think otherwise. The newfound global interest in AI could be attributed to the immense popularity of ChatGPT -- a chatbot released by Microsoft-backed AI-focused research lab OpenAI late last year.

In the last article in this series on AI and machine learning, we started our discussion of neural networks by using TensorFlow. We also got familiar with our first data set by using Keras. In this seventh article in the AI series, we will continue exploring neural networks and the use of data sets for training models. We will also introduce yet another powerful Python library for machine learning called scikit-learn. But we will begin by discussing two sensational applications that will show us the power of AI and machine learning. OpenAI is an artificial intelligence (AI) research laboratory which does a lot of research in the fields of AI and machine learning. Elon Musk is one of the founding members of this organisation.

In recent years, increasingly complex computational models are being built to describe physical systems which has led to increased use of surrogate models to reduce computational cost. In problems related to Structural Health Monitoring (SHM), models capable of both handling high-dimensional data and quantifying uncertainty are required. In this work, our goal is to propose a conditional deep generative model as a surrogate aimed at such applications and high-dimensional stochastic structural simulations in general. To that end, a conditional variational autoencoder (CVAE) utilizing convolutional neural networks (CNNs) is employed to obtain reconstructions of spatially ordered structural response quantities for structural elements that are subjected to stochastic loading. Two numerical examples, inspired by potential SHM applications, are utilized to demonstrate the performance of the surrogate. The model is able to achieve high reconstruction accuracy compared to the reference Finite Element (FE) solutions, while at the same time successfully encoding the load uncertainty.

This paper delves into the fascinating field of AI-generated art and explores the various deep neural network architectures and models that have been utilized to create it. From the classic convolutional networks to the cutting-edge diffusion models, we examine the key players in the field. We explain the general structures and working principles of these neural networks. Then, we showcase examples of milestones, starting with the dreamy landscapes of DeepDream and moving on to the most recent developments, including Stable Diffusion and DALL-E 2, which produce mesmerizing images. A detailed comparison of these models is provided, highlighting their strengths and limitations. Thus, we examine the remarkable progress that deep neural networks have made so far in a short period of time. With a unique blend of technical explanations and insights into the current state of AI-generated art, this paper exemplifies how art and computer science interact.

As text-to-image systems continue to grow in popularity with the general public, questions have arisen about bias and diversity in the generated images. Here, we investigate properties of images generated in response to prompts which are visually under-specified, but contain salient social attributes (e.g., 'a portrait of a threatening person' versus 'a portrait of a friendly person'). Grounding our work in social cognition theory, we find that in many cases, images contain similar demographic biases to those reported in the stereotype literature. However, trends are inconsistent across different models and further investigation is warranted.

Diffusion models achieve state-of-the-art performance in image and audio generating tasks (Song and Ermon, 2019; Dathathri et al., 2019; Song et al., 2020b; Ho et al., 2020) and are one of the fundamental building blocks of the more advanced image synthesis system, e.g., DALL-E-2 (Ramesh et al., 2022) and stable diffusion (Rombach et al., 2022). A standard diffusion model (Sohl-Dickstein et al., 2015; Ho et al., 2020) consists of a forward process and a backward process: In the forward process, a data point is sequentially corrupted by Gaussian random noises and in the limit the data distribution is transformed into white noise; In the backward process, a denoising neural network is trained to sequentially remove the added noise in the data and restore the clean data point. Using the trained denoising network for the backward process, one can generate diverse and high fidelity samples by first sampling from the standard Gaussian distribution and then progressively removing noises.