Meta, the owner of Facebook, has developed a powerful language model that's its answer to OpenAI's insanely popular ChatGPT. OpenAI's ChatGPT has paved the way for the popularity of artificial intelligence-powered systems, demonstrating the immense power of language models trained on varying amounts of datasets, each containing gigabytes of data. Following the public release of ChatGPT were announcements from other companies joining the AI race, Google revealed more details about its various AI-projects, while Microsoft rolled out Bing Chat, an AI-powered search engine tool. Now Meta has thrown its hat into the ring with a new announcement about a language model called LLamA. While Meta's language model isn't publicly available, but users can request access to the download file.

The latest technological advances suggest that the "AI revolution" will deliver socioeconomic turmoil, massive wealth redistribution – or both. Recent months have seen a lot of conversation about the dangers and opportunities presented by the latest advances in artificial intelligence (AI) technology. Most of this has been stirred up by ChatGPT, a generative AI chatbot created by the Microsoft-backed OpenAI and launched to the public in late 2022. The software's impressive capabilities – formulating articulate, (usually) accurate responses to complex questions, and creating text often indistinguishable from that of a human writer – have reignited debates about "the robots taking over." Much of the public appears divided into two camps: the technophiles, excited about "upgrading" our already symbiotic relationship with computers; and the modern-day Luddites, foes of progress who fear these new machines just as their predecessors feared textile mechanization.

Azure OpenAI Service is now available in preview, and ChatGPT is available in preview as well. With Azure OpenAI Service, over 1,000 customers are applying the most advanced AI models--including Dall-E 2, GPT-3.5, Codex, and other large language models backed by the unique supercomputing and enterprise capabilities of Azure--to innovate in new ways. Since ChatGPT was introduced late last year, we've seen a variety of scenarios it can be used for, such as summarizing content, generating suggested email copy, and even helping with software programming questions. Now with ChatGPT in preview in Azure OpenAI Service, developers can integrate custom AI-powered experiences directly into their own applications, including enhancing existing bots to handle unexpected questions, recapping call center conversations to enable faster customer support resolutions, creating new ad copy with personalized offers, automating claims processing, and more. Today, we are thrilled to announce that ChatGPT is available in preview in Azure OpenAI Service.

Artificial intelligence has become the tech industry's shiny new toy, with expectations it'll revolutionize trillion-dollar industries from retail to medicine. But the creation of every new chatbot and image generator requires a lot of electricity, which means the technology may be responsible for a massive and growing amount of planet-warming carbon emissions. Microsoft Corp., Alphabet Inc.'s Google and ChatGPT maker OpenAI use cloud computing that relies on thousands of chips inside servers in massive data centers across the globe to train AI algorithms called models, analyzing data to help them "learn" to perform tasks. The success of ChatGPT has other companies racing to release their own rival AI systems and chatbots or building products that use large AI models to deliver features to anyone from Instacart shoppers to Snap users to CFOs. AI uses more energy than other forms of computing, and training a single model can gobble up more electricity than 100 US homes use in an entire year.

Auchincloss, who spent time working as a project manager at a cybersecurity startup before running for Congress, said three trends had to converge to lead to the current breakthrough in generative AI: "You had the academic research that created the algorithms and deep learning, you had Moore's law that got semiconductors efficient and powerful enough to actually be the cloud computing resources to train these models, which was not even feasible 10 years ago. And you've got the internet-scale quality and quantity of data."

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.