The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that naÏvely increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.

If I had to describe Microsoft Designer in just one sentence, I'd say it's like a fusion of Canva and ChatGPT. The Microsoft Designer application is a highly advanced graphic design tool available within the Microsoft 365 suite. This versatile platform allows users to create customized invitations, digital postcards, and other visually appealing materials, leveraging the power of artificial intelligence technology similar to DALL-E 2. According to a recent Microsoft release, Designer enables users to generate unique visual content by simply describing the image they wish to create, empowering even novice users to produce professional-grade designs with minimal effort. If you are already a member then you can sign in and if you are not a member then you can enter your email and wait for the invitation. When we logged in to Microsoft design we will see that screen on the left side we can enter the text that we want to generate.

GPT-4 is coming next week: at an approximately one-hour hybrid information event entitled "AI in Focus - Digital Kickoff" on 9 March 2023, four Microsoft Germany employees presented Large Language Models (LLM) like GPT series as a disruptive force for companies and their Azure-OpenAI offering in detail. The kickoff event took place in the German language, news outlet Heise was present. Rather casually, Andreas Braun, CTO Microsoft Germany and Lead Data & AI STU, mentioned what he said was the imminent release of GPT-4. The fact that Microsoft is fine-tuning multimodality with OpenAI should no longer have been a secret since the release of Kosmos-1 at the beginning of March. "We will introduce GPT-4 next week, there we will have multimodal models that will offer completely different possibilities – for example videos," Braun said.

By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired by classical program synthesis and the human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic instruction generation and selection. In our method, we treat the instruction as the "program," optimized by searching over a pool of instruction candidates proposed by an LLM in order to maximize a chosen score function. To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. Experiments on 24 NLP tasks show that our automatically generated instructions outperform the prior LLM baseline by a large margin and achieve better or comparable performance to the instructions generated by human annotators on 19/24 tasks. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. We show that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts. Please check out our webpage at https://sites.google.com/view/automatic-prompt-engineer.

As this season's fashion weeks wrap up in London, Milan, New York, and Paris, brands are working to produce and sell the designs they've just showcased on runways--and they're starting next season's collections. In the future, it's entirely possible that those designs will blend the prowess of a creative director with the power of generative artificial intelligence (AI), helping to bring clothes and accessories to market faster, selling them more efficiently, and improving the customer experience. By now, you've likely heard of OpenAI's ChatGPT, the AI chatbot that became an overnight sensation and sparked a digital race to build and release competitors. ChatGPT is only one consumer-friendly example of generative AI, a technology comprising algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Rather than simply identifying and classifying information, generative AI creates new information by leveraging foundation models, which are deep learning models that can handle multiple complex tasks at the same time.

It took less than a week for OpenAI's ChatGPT to reach a million users, and it crossed the 100 million user mark in under two months. The interest and excitement around this technology has been remarkable. Users around the world are seeing potential for applying these large language models to a broad range of scenarios. In the context of enterprise applications, the question we hear most often is "how do I build something like ChatGPT that uses my own data as the basis for its responses?" It integrates the enterprise-grade characteristics of Azure, the ability of Cognitive Search to index, understand and retrieve the right pieces of your own data across large knowledge bases, and ChatGPT's impressive capability for interacting in natural language to answer questions or take turns in a conversation.

OpenAI has just announced an enterprise version of its popular generative AI product, ChatGPT. But in this case, OpenAI is a fast follower -- not the first-to-market. Cohere, a Toronto-based company with close ties to Google, is already bringing generative AI to businesses. I spoke with Cohere's President and COO, Martin Kon, about how its machine learning models are being used within enterprise companies. Cohere is only a few years old, but it has an impressive pedigree.

More than half of people cannot identify whether words were written by AI chatbots such as ChatGPT, new research has shown - and Generation Z are the worst. Researchers found that 53 percent of people failed to spot the difference between content produced by a human, an AI, or an AI edited by a human. Among young people aged 18-24, just four in 10 - where people aged 65 and over were able to spot AI content more than half the time correctly. It comes amid fears ChatGPT and similar bots could threaten the jobs of white-collar workers. Robert Brandl, CEO and Founder of web tool reviewing company Tooltester, who conducted the latest survey, told DailyMail.com:

ChatGPT has cast long shadows over the media as the latest form of disruptive technology. For some, ChatGPT is a harbinger of the end of academic and scientific integrity, and a threat to white collar jobs and our democratic institutions. How concerned should we be about generative artificial intelligence (AI)? The developers of ChatGPT describe it as "a model… which interacts in a conversational way" while also calling it a "horrible product" for its inconsistent results. It can write emails, summarize documents, review code and provide comments, translate documents, create content, play games, and, of course, chat.

Surprise, Discord is partnering with OpenAI to integrate ChatGPT throughout the app. There's a chatbot, obviously, but the company also plans to use machine learning in a handful of more novel and potentially useful ways. Starting next week, the company will begin rolling out a public experiment that will augment Clyde, the built-in bot Discord employs to notify users of errors and respond to their slash commands, with conversational capabilities. Judging from the demo it showed off, Discord envisions people turning to Clyde for information they would have obtained from Google in the past. For instance, you might ask the chatbot for the local time in the place where someone on your server lives to decide if it would be appropriate to message them.