ODIN is an innovative approach that addresses the problem of dataset constraints by integrating generative AI models. Traditional zero-shot learning methods are constrained by the training dataset. To fundamentally overcome this limitation, ODIN attempts to mitigate the dataset constraints by generating on-demand datasets based on user requirements. ODIN consists of three main modules: a prompt generator, a text-to-image generator, and an image post-processor. To generate high-quality prompts and images, we adopted a large language model (e.g., ChatGPT), and a text-to-image diffusion model (e.g., Stable Diffusion), respectively. We evaluated ODIN on various datasets in terms of model accuracy and data diversity to demonstrate its potential, and conducted post-experiments for further investigation. Overall, ODIN is a feasible approach that enables Al to learn unseen knowledge beyond the training dataset.

Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality images that are faithful to a given reference text, known as a "prompt". These systems have immediately received lots of attention from researchers, creators, and common users. Despite the plenty of efforts to improve the generative models, there is limited work on understanding the information needs of the users of these systems at scale. We conduct the first comprehensive analysis of large-scale prompt logs collected from multiple text-to-image generation systems. Our work is analogous to analyzing the query logs of Web search engines, a line of work that has made critical contributions to the glory of the Web search industry and research. Compared with Web search queries, text-to-image prompts are significantly longer, often organized into special structures that consist of the subject, form, and intent of the generation tasks and present unique categories of information needs. Users make more edits within creation sessions, which present remarkable exploratory patterns. There is also a considerable gap between the user-input prompts and the captions of the images included in the open training data of the generative models. Our findings provide concrete implications on how to improve text-to-image generation systems for creation purposes.

For search engines and enterprise writing assistance, the top contender is OpenAI, which yesterday announced the latest model of its language model, GPT-4. GPT-4 is now available on ChatGPT Plus and as an API, for which developers can join a waitlist. It's throwing a new weapon into the AI war, in which organizations jostle to provide the best, most flexible writing AI. OpenAI demonstrated the new natural language model with a challenge: "Explain the plot of Cinderella in a sentence where each word has to begin with the next letter in the alphabet from A to Z, without repeating any letters." It's a neat riddle to show the AI can perform some reasoning along with producing straightforward text, but what does it do in the office?

You can add Duolingo to the growing list of companies jumping on the generative AI craze. On Wednesday, the company announced Duolingo Max, a new, more expensive subscription tier that comes with access to a pair of GPT-4 features. The first of those, "Explain My Answer," allows you to ask Duo, a chatbot named after the company's owl mascot, to spell out why your answer to a question was right or wrong, with the option to ask for additional clarification if you need more help. The second feature, Roleplay, allows you to practice the skills you've learned through Duolingo in a handful of scenarios. Duolingo says no two conversations will be exactly the same, even when you rehearse a situation more than once, and users can earn experience points by completing the practice sessions.

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis. Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance. Here, the authors propose HydrAMP, an extended conditional variational autoencoder. HydrAMP generated antimicrobial peptides with high activity against bacteria, including multidrug-resistant species.

Are you looking for a new way to unleash your creativity and create stunning, one-of-a-kind art? Look no further than AI art generators! With the power of generative AI, you can create art that's truly unique and exciting, without having to spend hours drawing or painting. In this post, we'll explore the best free AI art generators available today, from the popular Deep Dream Generator and Artbreeder to the cutting-edge Urza's AI and MidJourney. Each generator offers its own unique features and capabilities, allowing you to explore the vast potential of generative AI and push the boundaries of what's possible in art.

The artificial intelligence research lab OpenAI on Tuesday launched the newest version of its language software, GPT-4, an advanced tool for analyzing images and mimicking human speech, pushing the technical and ethical boundaries of a rapidly proliferating wave of AI. OpenAI's earlier product, ChatGPT, captivated and unsettled the public with its uncanny ability to generate elegant writing, unleashing a viral wave of college essays, screenplays and conversations -- though it relied on an older generation of technology that hasn't been cutting-edge for more than a year. GPT-4, in contrast, is a state-of-the-art system capable of creating not just words but describing images in response to a person's simple written commands. When shown a photo of a boxing glove hanging over a wooden seesaw with a ball on one side, for instance, a person can ask what will happen if the glove drops, and GPT-4 will respond that it would hit the seesaw and cause the ball to fly up. The buzzy launch capped months of hype and anticipation over an AI program, known as a large language model, that early testers had claimed was remarkably advanced in its ability to reason and learn new things.

GPT-4 didn't give me an existential crisis. But it exacerbated the dizzy and vertiginous feeling I've been getting whenever I think about A.I. lately. And it has made me wonder whether that feeling will ever fade, or whether we're going to be experiencing "future shock" -- the term coined by the writer Alvin Toffler for the feeling that too much is changing, too quickly -- for the rest of our lives. For a few hours on Tuesday, I prodded GPT-4 -- which is included with ChatGPT Plus, the $20-a-month version of OpenAI's chatbot, ChatGPT -- with different types of questions, hoping to uncover some of its strengths and weaknesses. I asked GPT-4 to help me with a complicated tax problem.

I ALWAYS know there is something fishy going on with a new tech product when journalists start desperately reaching out to science fiction authors to explain it for them. Such is the case with ChatGPT, an artificial intelligence chatbot from San Francisco company OpenAI, which has become one of the world's most widely used apps in just a few short months. So many news outlets were asking science fiction writers to weigh in on AI's capabilities that the Science Fiction and Fantasy Writers Association had to issue a special media statement on its website, linking to dozens of authors' …

The company behind the ChatGPT app that churns out essays, poems or computing code on command released Tuesday a long-awaited update of its artificial intelligence (AI) technology that it said would be safer and more accurate than its predecessor. GPT-4 has been widely awaited ever since ChatGPT burst onto the scene in late November, wowing users with its capabilities that were based on an older version of OpenAI's technology, known as a large language model. "We've created GPT-4, the latest milestone in OpenAI's effort in scaling up deep learning," a company blog said, adding that the AI technology "exhibits human-level performance" on some professional and academic tasks. The company said the model is "more creative and collaborative than ever before" and would "solve difficult problems with greater accuracy" than its earlier versions. With its update, text responses from GPT-4 will be more accurate, and--in future--will come from both image and text inputs in a major leap forward for the technology, though this aspect has not yet been released.