Today, two labs separately announced programs that use diffusion models to generate designs for novel proteins with more precision than ever before. Generate Biomedicines, a Boston-based startup, revealed a program called Chroma, which the company describes as the "DALL-E 2 of biology." At the same time, a team at the University of Washington led by biologist David Baker has built a similar program called RoseTTAFold Diffusion. In a preprint paper posted online today, Baker and his colleagues show that their model can generate precise designs for novel proteins that can then be brought to life in the lab. "We're generating proteins with really no similarity to existing ones," says Brian Trippe, one of the co-developers of RoseTTAFold. These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function.

The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on how to overcome or otherwise mitigate the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.

With the announcement of GPT-4 coming soon and building along with our anticipation really getting up there, what's so incredible about DeepMind or OpenAI is how they keep tweaking and improving their inventions. Not many publications have covered with Davinci-003 is all about so I thought I'd give it a shot. On Monday, OpenAI announced a new model in the GPT-3 family of AI-powered large language models, text-davinci-003, that reportedly improves on its predecessors by handling more complex instructions and producing longer-form content. Subscribe to Artificial Intelligence Survey to keep reading this post and get 7 days of free access to the full post archives.

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.