Large language models (LLMs), like OpenAI ChatGPT and Google LaMDA, are impressive, being competent in many aspects. At the same time, LLMs are incompetent in many ways. LLMs are evolving, and new players are joining. What further progress may be possible? Moreover, we may ask a question relevant to almost all players in the world of LLMs, from students, researchers, engineers, entrepreneurs, venture capitalists, officers, to the public crowd: Where is the boundary for large language models?

Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusion models gradually synthesize images in an iterative fashion while conditioning on text prompts. We find that their synthesis behavior qualitatively changes throughout this process: Early in sampling, generation strongly relies on the text prompt to generate text-aligned content, while later, the text conditioning is almost entirely ignored. This suggests that sharing model parameters throughout the entire generation process may not be ideal. Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages. To maintain training efficiency, we initially train a single model, which is then split into specialized models that are trained for the specific stages of the iterative generation process. Our ensemble of diffusion models, called eDiff-I, results in improved text alignment while maintaining the same inference computation cost and preserving high visual quality, outperforming previous large-scale text-to-image diffusion models on the standard benchmark. In addition, we train our model to exploit a variety of embeddings for conditioning, including the T5 text, CLIP text, and CLIP image embeddings. We show that these different embeddings lead to different behaviors. Notably, the CLIP image embedding allows an intuitive way of transferring the style of a reference image to the target text-to-image output. Lastly, we show a technique that enables eDiff-I's "paint-with-words" capability. A user can select the word in the input text and paint it in a canvas to control the output, which is very handy for crafting the desired image in mind. The project page is available at https://deepimagination.cc/eDiff-I/

Generative AI techniques like ChatGPT, DALL-e and Codex can generate digital content such as images, text, and the code. Recent progress in large-scale AI models has improved generative AI's ability to understand intent and generate more realistic content. In recent years, Artificial Intelligence Generated Content (AIGC) has gained much attention beyond the computer science community, where the whole society is interested in the various content generation products built by large tech companies. Technically, AIGC refers to, given human instructions which could help teach and guide the model to complete the task, using Generative AI algorithms to form a content that satisfies the instruction. This generation process usually comprises two steps: extracting intent information from human instructions and generating content according to the extracted intentions.

Artificial Intelligence (AI) has been revolutionizing the way we interact with technology, and chatbots are one of the most prominent examples of this trend.

Microsoft Corp. today introduced Dynamics 365 Copilot, which takes the company's recent integration of OpenAI LLC's ChatGPT chatbot for search and puts it to work for business users. The new AI assistant will make use of ChatGPT's "generative AI" feature, which can comprehend user-written natural speech and instantly produce new content or responses. Microsoft is integrating the technology into applications where it can be quickly used to automate data entry, create email content for clients, and summarize meeting notes. By including it in customer service tools, users will be able to quickly create emails and chats powered by AI that can be handled entirely by a virtual agent and switch to a human when the AI is no longer able to handle it. Check Out The New Enterprisetalk Podcast.

In this week's real-time analytics news: Several companies announced generative AI offerings or enhancements to their product lines. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important real-time analytics news items our staff came across this week. Salesforce launched Einstein GPT, a generative AI CRM technology, which delivers AI-created content across every sales, service, marketing, commerce, and IT interaction. Einstein GPT will infuse Salesforce's proprietary AI models with generative AI technology from an ecosystem of partners and real-time data from the Salesforce Data Cloud.

CyberGuy explains how ChatGPT's functions can help you in your day to day life. ChatGPT has kept growing more and more in popularity since OpenAI released it back in November. Now, the chatbot has Chrome extensions that you can add to your browser to make accessing the feature that much easier. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER By now, you may have heard of ChatGPT. It is a computer program developed by the artificial intelligence laboratory OpenAI that simulates human conversation and provides helpful and informative responses.

BigTech companies already dominate in GenAI infrastructure with their cloud services and hardware chips. Microsoft and Google are well-positioned in the US cloud market, while Baidu and Alibaba are well-positioned in China. Their massive supercomputer cloud infrastructure is engineered to run GenAI's complex, expensive, large text, visual, and audio Foundational Models. There are already many developers using their cloud AI API services and tools to build apps, and this trend is expected to accelerate as entrepreneurs rush to address virtually limitless GenAI use cases. Amazon has been quiet on Foundational Models, so a big question is how will they respond. GenAI uses massive amounts of computational power to generate creative outputs.

AI Web Designer is a web building tool powered by OpenAI's GPT API. It allows you to create and design static web pages with ease. The tool is programmed using ReactJS and is designed to help individuals and teams build web pages faster. Due to the limitations of OpenAI's API, AI Web Designer cannot handle large documents with too much content. The API has a maximum token limit of 4,000 per request, which means that the AI cannot generate large sections of code, or respond to the user when the document is too big.

Collaboration is an essential function of multiplayer game such as a MOBA, and Soccer game. In the case of Reinforcement Learning, the transition probabilities should be stationary in order to be trained well. Due to this point, famous early study of the OpenAI tried to apply a additional method to deal with the fluctuating transition probabilities. However, recent research of the DeepMind for MARL say that multiple agent game also can be converged to the Nash Equilibrium despite of unstable transition probability. In theory such multi-agent systems may continue to explore forever.