If we could talk to our younger selves, what would we say, what advice would we impart and how would it feel? Well, one woman has an idea after she created an artificial intelligence chatbot of herself as a child by training it to learn what she was like based on a diary written when she was young. 'Creative coder' Michelle Huang used source material from 10 years' worth of entries and combined it with the OpenAI language model Generative Pre-trained Transformer 3 (GPT-3). She told people on Twitter that she created the AI system so that she'could engage in real-time dialogue' with her'inner child'. Ms Huang (left) used source material from 10 years' worth of entries and combined it with the OpenAI language model Generative Pre-trained Transformer 3. Pictured right is her as a child AI creation: Michelle Huang created an artificial intelligence chatbot of herself as a child by training it to learn what she was like based on a diary written when she was young.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. OpenAI released today ChatGPT -- a new language model for chat.

AWS has announced that Stability AI, a community-driven, open-source artificial intelligence (AI) company, has selected AWS as its preferred cloud provider to build and scale its AI models for image, language, audio, video, and 3D content generation. Stability AI uses Amazon SageMaker (AWS's end-to-end machine learning service), as well as AWS's proven compute infrastructure and storage, to accelerate its work on open-source generative AI models. In addition, Stability AI will collaborate with AWS to make its open-source tools and models available to students, researchers, startups, and enterprises around the world. Stability AI offers generative AI models that create text, images, audio, video, code, and more from simple text instructions. Generative AI or foundational models--models that are adaptable to a variety of tasks in domains such as language, image, audio, and video--require a high-performance compute cluster with thousands of GPUs or AWS Trainium chips, advanced expertise, and months of training.

For more than 30,000 years we have been the only art-making species on Earth, give or take the odd paint-throwing Neanderthal or chimpanzee. Art is the oldest and most spectacular triumph of human consciousness, from Lascaux to the Sistine Chapel. But a new generation of artificial intelligence (AI) art software may be about to end that. It will whip you up a Picasso or a Turner in an instant, or apply their styles to any theme you picture, from Liz Truss dancing in a supermarket to a brawl in a 1970s disco. Stable Diffusion and competitors such as DALL-E 2 go far beyond previous claims for AI art.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. As GPT-4 rumors fly around NeurIPS 2022 this week in New Orleans (including whispers that details about GPT-4 will be revealed there), OpenAI has managed to make plenty of news in the meantime. On Monday, the company announced a new model in the GPT-3 family of AI-powered large language models, text-davinci-003, part of what it calls the "GPT-3.5 series," that reportedly improves on its predecessors by handling more complex instructions and producing higher-quality, longer-form content. Unlike davinci-002, which uses supervised fine-tuning on human-written demonstrations and highly scored model samples to improve generation quality, davinci-003 is a true reinforcement learning with human feedback (RLHF) model." Meanwhile, today OpenAI launched an early demo of ChatGPT, another part of the GPT-3.5 series that is an interactive, conversational model whose dialogue format "makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests."

ChatGPT appears to address some of these problems, but it is far from a full fix--as I found when I got to try it out. This suggests that GPT-4 won't be either. In particular, ChatGPT--like Galactica, Meta's large language model for science, which the company took offline earlier this month after just three days--still makes stuff up. There's a lot more to do, says John Shulman, a scientist at OpenAI: "We've made some progress on that problem, but it's far from solved." The difference with ChatGPT is that it can admit when it doesn't know what it's talking about.

At present, the potential for generative Artificial Intelligence--the variety of predominantly advanced machine learning that analyzes content to produce strikingly similar new content--is boundless. These technologies have transcended Natural Language Generation, in which they achieved much of their early renown via paradigms such as Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer 3 (GPT3), and deep neural networks. Although it's still utilized to create verbal summaries of documents and analytics results, generative AI is now widely employed to compose poetry, music, visual arts, and many other things once thought relegated to the realm of human ingenuity. Still, generative AI's benefits of automation, time-to-action, and scalability are the very reasons organizations rely on AI in the first place. Prudent companies will adopt these advantages within broader frameworks for mitigating the shortfalls of advanced machine learning to provide tangible business value for decision support, customer satisfaction, workload optimization, and cost reductions.

OpenAI API adds'text-davinci-003' to its list of main GPT-3 models, which can do all tasks other models can do while also ensuring high quality, longer output, and better instruction-following. Davinci is the most competent and can perform all tasks the other models can, often with fewer instructions. It works specifically well with tasks requiring in-depth knowledge of the subject matter, such as summarising texts for a specific audience and creative content development. However, the new capabilities of Davinci also require more computing resources leading to higher costs per API call and lesser speed than other models. For example, it is good at deducing solutions to various logical problems and outlining character motivations.

The Generative AI space is moving so fast it's hard to keep everything straight. At Every, we've found ourselves furiously researching the space to try to understand the technology behind this explosion in creativity. Here are three of our favorite resources to understand the most important parts of this technology wave: neural networks, transformer models, and diffusion models. Want to understand the fundamental building blocks of deep learning models? Watch this YouTube series to get some intuition for how neural networks work--and why they're so powerful. Go deeper: Read this free online book, Neural Networks and Deep Learning by Michael Nielsen to get your hands dirty building a simple neural network yourself. Transformer models are the innovation that led to GPT-3 and the current explosion of innovation in AI. Read this article to understand how they work--and how they use something called

Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using the three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image sharpening approaches.