wjfox wrote: ↑Mon Mar 04, 2024 8:20 amTrump supporters target black voters with faked AI images
1 hour ago
Donald Trump supporters have been creating and sharing AI-generated fake images of black voters to encourage African Americans to vote Republican.
BBC Panorama discovered dozens of deepfakes portraying black people as supporting the former president.
Mr Trump has openly courted black voters, who were key to Joe Biden's election win in 2020.
But there's no evidence directly linking these images to Mr Trump's campaign.
The co-founder of Black Voters Matter, a group which encourages black people to vote, said the manipulated images were pushing a "strategic narrative" designed to show Mr Trump as popular in the black community.
Can You Tell AI-generated People from Real Ones? March 6, 2024
Introduction:
(Eurekalert) If you recently had trouble figuring out if an image of a person is real or generated through artificial intelligence (AI), you're not alone.
A new study from University of Waterloo researchers found that people had more difficulty than was expected distinguishing who is a real person and who is artificially generated.
The Waterloo study saw 260 participants provided with 20 unlabeled pictures: 10 of which were of real people obtained from Google searches, and the other 10 generated by Stable Diffusion or DALL-E, two commonly used AI programs that generate images.
Participants were asked to label each image as real or AI-generated and explain why they made their decision. Only 61 per cent of participants could tell the difference between AI-generated people and real ones, far below the 85 per cent threshold that researchers expected.
"People are not as adept at making the distinction as they think they are," said Andreea Pocol, a PhD candidate in Computer Science at the University of Waterloo and the study's lead author.
Imagine performing a sweep around an object with your smartphone and getting a realistic, fully editable 3D model that you can view from any angle. This is fast becoming reality, thanks to advances in AI.
Researchers at Simon Fraser University (SFU) in Canada have unveiled new AI technology for doing exactly this. Soon, rather than merely taking 2D photos, everyday consumers will be able to take 3D captures of real-life objects and edit their shapes and appearance as they wish, just as easily as they would with regular 2D photos today.
In a new paper appearing on the arXiv preprint server and presented at the 2023 Conference on Neural Information Processing Systems (NeurIPS) in New Orleans, Louisiana, researchers demonstrated a new technique called Proximity Attention Point Rendering (PAPR) that can turn a set of 2D photos of an object into a cloud of 3D points that represents the object's shape and appearance.
A team of computer scientists and engineers at Apple has developed an LLM model that the company claims can interpret both images and data. The group has posted a paper to the arXiv preprint server describing their new MM1 family of multimodal models and test results.
Over the past year, LLMs have received a lot of press for their advanced AI capabilities. One company notably absent from the conversation is Apple. In this new effort, the research team makes it clear that the company is not interested in simply adding an LLM developed by another company (currently they are negotiating with Google to add Gemini AI tech to Apple devices); instead, they have been working to develop a next-generation LLM, one that can interpret both images and text data.
Multimodal AI works by integrating and processing different types of data inputs, such as visual, auditory and textual information. This integration allows the AI to have a more comprehensive understanding of complex data, leading to more accurate and context-aware interpretations than single-mode AI systems.
Apple's research team claims they have made major advancements in using multimodal AI with their MM1 models, which integrate text and image data to improve capabilities in image captioning, visual question answering and query learning. Their MM1 is part of what they describe as a family of multimodal models, each of which include as many as 30 billion parameters.
