One sentence summary – OpenAI has launched DALL-E 3, an image generation model that integrates with its chatbot ChatGPT, allowing users to generate images directly within the chat interface and refine them using natural language prompts; the model will be available to subscribers and customers starting in October, and users retain full ownership of the generated images, although requests for images resembling living artists or public figures will be declined to respect intellectual property rights; in other AI news, MIT researchers have developed a “Multiagent Society” approach that uses multiple AI systems to generate answers, improving accuracy and reducing hallucinatory outputs.
At a glance
- OpenAI has launched DALL-E 3, an image generation model that integrates with ChatGPT.
- Users can generate images directly within the chat interface and refine them using natural language prompts.
- DALL-E 3 will be available to ChatGPT Plus subscribers and ChatGPT Enterprise customers starting in October.
- Users retain full ownership of the images generated by DALL-E 3 and can use them for reprint, sale, and merchandising purposes.
- DALL-E 3 has limitations, such as declining requests for images resembling living artists or public figures, to respect intellectual property rights.
- MIT researchers have developed a Multiagent Society approach to improve the accuracy of AI systems for answering questions.
- This approach involves using multiple AI systems and feedback from other agents to update and refine responses.
- The Multiagent Society approach achieved superior results on various benchmarks, outperforming the traditional single-agent approach.
- The process of discourse and deliberation among AI models improves their problem-solving abilities and overall performance.
- The code for the multiagent project is available on GitHub, encouraging further exploration and utilization of this approach.
The details
OpenAI, a leading artificial intelligence research lab, has announced the launch of DALL-E 3, the newest version of its image generation model.
DALL-E 3 is designed to integrate seamlessly with ChatGPT, OpenAI’s chatbot.
This integration allows users to generate images directly within the chat interface, streamlining the creative process.
DALL-E 3 also offers users the ability to refine the images it generates.
This is achieved by providing natural language prompts to ChatGPT, which in turn enhances the control and specificity of the images produced.
Starting in October, DALL-E 3 will be available to ChatGPT Plus subscribers and ChatGPT Enterprise customers.
OpenAI has stated that DALL-E 3 represents a significant improvement over its predecessor, DALL-E 2.
The new model demonstrates a better understanding of nuanced details, according to OpenAI.
One of the key features of DALL-E 3 is that users retain full ownership of the images it generates.
Users are free to use these images for reprint, sale, and merchandising purposes.
However, DALL-E 3 does have certain limitations.
The model will decline requests for images that resemble living artists or public figures.
This is part of OpenAI’s commitment to respecting intellectual property rights and avoiding potential legal issues.
OpenAI is also developing a provenance classifier.
This tool is designed to identify images created by DALL-E 3.
The aim of this development is to ensure transparency and accountability in the use and attribution of generated images.
In other AI news
researchers at MIT have developed a “Multiagent Society” approach to improve the accuracy of AI systems used for answering questions.
This method outperforms the traditional single-agent approach, reducing instances of hallucinatory outputs.
The Multiagent Society approach involves using multiple AI systems to generate answers.
Feedback from other agents is then used to update and refine these responses.
This approach, inspired by group discussions, facilitates collaborative decision-making.
This leads to improved accuracy and quality of answers.
By combining different language models within the Multiagent Society, the MIT team achieved superior results on various benchmarks.
These benchmarks included tasks related to natural language processing, mathematics, and puzzle-solving.
On the MMLU benchmark, the approach using multiple agents scored 71 for accuracy, while the sole agent approach scored 64.
The process of discourse and deliberation among AI models helps them recognize and rectify issues.
It also enhances their problem-solving abilities and verifies the precision of their responses.
This process effectively improves the overall performance of the models.
The MIT team has made the code used in the multiagent project available on GitHub.
This move encourages further exploration and utilization of this innovative approach.
The combination of DALL-E 3 and the Multiagent Society methodology could lead to new levels of accuracy, creativity, and problem-solving abilities in AI systems.
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| aibusiness.com |
|---|
| – OpenAI has unveiled DALL-E 3, the latest version of its image generation model. – DALL-E 3 will work alongside ChatGPT, OpenAI’s chatbot, allowing users to generate images without leaving the chatbot. |
| – Users can refine the generated images by providing natural language prompts to ChatGPT. – DALL-E 3 will be available to ChatGPT Plus subscribers and ChatGPT Enterprise customers starting in October. |
| – |
| The model is described as a significant improvement over DALL-E 2, with a better understanding of nuance and detail. |
| – Generated images created with DALL-E 3 will be owned by the users and can be freely used for reprint, sale, and merchandising. – Requests for images in the style of living artists or public figures will be declined by DALL-E 3. – OpenAI is working on a tool called a provenance classifier to identify images created by DALL-E 3. – Creators can choose to opt-out of having their images used in future training of OpenAI’s image generation models. |
| aibusiness.com |
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| – MIT researchers found that using multiple AI systems to debate answers to questions improves accuracy compared to using a single AI system. – |
| The process, called a “Multiagent Society,” reduces hallucinations in generated output and can be applied to existing models like OpenAI’s ChatGPT. – |
| The model generates an answer and incorporates feedback from other agents to update its response, similar to a group discussion. – |
| The method can combine different language models, improving the final answer. |
| – |
| The MIT team achieved superior results on benchmarks for natural language processing, mathematics, and puzzle solving using the Multiagent Society approach. – |
| On the MMLU benchmark, using multiple agents scored 71 in accuracy, while using a sole agent scored 64. – |
| The process allows AI models to sharpen and improve their answers by scrutinizing responses from their counterparts. |
| – Engaging in discourse and deliberation helps AI models recognize and rectify issues, enhance problem-solving abilities, and verify the precision of their responses. – |
| The code used in the multiagent project is available on GitHub. |
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