Overall this is more powerful, flexible, and accurate than the previous best systems. It still is easy to find holes in it, with with some patience and willingness to iterate, you can make some amazing images. In conclusion, generating a lot of images from a new state-of-the-art image generation system is fun, thanks for reading. If there's interest, I can also explore in-painting and Here are a few more gratuitous pics! Is that more or less cool than the actual statue they built in Miami? The concept of beauty, according to DE2, is mostly women putting on makeup, which I can't post due to restrictions on posting faces. These are really realistic, capturing ethnicity and expressing emotion, totally unlike the poker players from earlier. But there's this one pastoral scene, which is nice. This last one I edited out some floating writing on the left, and asked it to generate [a girl in a beautiful serene forest].

Machine learning systems called DALL-E, GPT and PaLM are making a … it be the end of human creativity and the start of a deep-fake nightmare?

Artificial intelligence research lab OpenAI made headlines again, this time with DALL-E 2, a machine learning model that can generate stunning images from text descriptions. DALL-E 2 builds on the success of its predecessor DALL-E and improves the quality and resolution of the output images thanks to advanced deep learning techniques. The announcement of DALL-E 2 was accompanied by a social media campaign by OpenAI's engineers and its CEO, Sam Altman, who shared wonderful photos created by the generative machine learning model on Twitter. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits. It also provides an outlook of how generative deep learning models might finally unlock new creative applications for everyone to use.

According to OpenAI co-founder & chief scientist Ilya Sutskever, DALL-E, a neural net, can accept any phrase and create a picture out of it. This contained ideas that we would not have encountered in the classroom. DALL-E, a new and improved version of the company's prior application, has just been released. As stated on OpenAI's site, DALL-E 2 can produce graphics and art based on a natural language descriptions. One thing to note is that a photo-realistic picture of an astronaut riding a horse may be created by an artificial intelligence system.

Today we did a research launch of DALL•E 2, a new AI tool that can create and edit images from natural language instructions. Most importantly, we hope people love the tool and find it useful. For me, it's the most delightful thing to play with we've created so far. I find it to be creativity-enhancing, helpful for many different situations, and fun in a way I haven't felt from technology in a while. But I also think it's noteworthy for a few reasons: We offer this for code and now image generation; both of these will get a lot better.

DeepMind's latest paper dismantles the tired trend of building larger and larger models to improve performance. The company has found a key aspect of scaling large language models that no one has ever applied before. OpenAI, Google, Microsoft, Nvidia, Facebook, and even DeepMind themselves, all big tech companies committed to creating powerful language models, are doing it wrong: Making models larger is neither the best nor the most efficient approach. Increasing model size as a proxy for increasing performance was established in 2020 by Kaplan and others at OpenAI. They found a power law between those variables and concluded that, as more budget is available to train models, the majority should be allocated to making them bigger.

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Depending on the theory of intelligence to which you subscribe, achieving "human-level" AI will require a system that can leverage multiple modalities -- e.g., sound, vision and text -- to reason about the world. For example, when shown an image of a toppled truck and a police cruiser on a snowy freeway, a human-level AI might infer that dangerous road conditions caused an accident. Or, running on a robot, when asked to grab a can of soda from the refrigerator, they'd navigate around people, furniture and pets to retrieve the can and place it within reach of the requester. But new research shows signs of encouraging progress, from robots that can figure out steps to satisfy basic commands (e.g., "get a water bottle") to text-producing systems that learn from explanations. In this revived edition of Deep Science, our weekly series about the latest developments in AI and the broader scientific field, we're covering work out of DeepMind, Google and OpenAI that makes strides toward systems that can -- if not perfectly understand the world -- solve narrow tasks like generating images with impressive robustness.

OpenAI has unveiled a new AI tool that turns text into images -- and the results are stunning. Named DALL-E 2, the system is the successor to a model unveiled last year. While its predecessor generated some impressive outputs, the new version is a major upgrade. DALL-E-2 adds enhanced textual comprehension, faster image generation, and four times greater resolution. "When approaching DALL-E 2 we focused on improving the image resolution quality and improving latency, rather than building a bigger system," OpenAI researcher Aditya Ramesh told TNW.

On March 6th, 2022, OpenAI released DALL-E 2: their "new AI system that can create realistic images and art from a description in natural language". I don't say this lightly: this new AI system is not just on-par with human artists. It is definitively better than humans in almost every sense of the word. The model has access to hundreds of thousands of distinct art styles, has a keen understanding of context, and can create incredible works in under fifteen seconds that would have otherwise taken a human days or weeks. This is not an exaggeration.