Visual persuasion, which uses visual elements to influence cognition and behaviors, is crucial in fields such as advertising and political communication. With recent advancements in artificial intelligence, there is growing potential to develop persuasive systems that automatically generate persuasive images tailored to individuals. However, a significant bottleneck in this area is the lack of comprehensive datasets that connect the persuasiveness of images with the personal information about those who evaluated the images. To address this gap and facilitate technological advancements in personalized visual persuasion, we release the Personalized Visual Persuasion (PVP) dataset, comprising 28,454 persuasive images across 596 messages and 9 persuasion strategies. Importantly, the PVP dataset provides persuasiveness scores of images evaluated by 2,521 human annotators, along with their demographic and psychological characteristics (personality traits and values). We demonstrate the utility of our dataset by developing a persuasive image generator and an automated evaluator, and establish benchmark baselines. Our experiments reveal that incorporating psychological characteristics enhances the generation and evaluation of persuasive images, providing valuable insights for personalized visual persuasion.

The recent rollout of iOS 18.2 finally brings many of the promised Apple Intelligence features, like Genmoji and Image Playground. One such long-awaited tool is Visual Intelligence, a feature currently reserved for the iPhone 16 Pro and Pro Max that was first introduced at the company's September event. Visual Intelligence is Apple's answer to Google Lens. It leverages the camera system and AI to analyze images in real-time and provide useful information. This can help people learn more about the world around them and is particularly handy for shopping, looking up details about a restaurant or business, translating written text, summarizing text or having something read aloud.

Google Lens has been around for some time now as the search giant's de facto AI search for images and image-based text. Now, following rumors that suggested Lens for desktop platforms might be coming, searching Google via an image upload uses the Assistant-related feature too. That's based on recent reports following a roll-out on the company's search page. For clarity, that's searches found at images.google.com. The site is effectively Google's solution for reverse searching images.

In the past couple of years, large companies including Google, Facebook, Microsoft, and Amazon have been releasing libraries, frameworks, and services that enable other businesses to build machine learning (ML)models. What's great about these frameworks is that it's now cheaper and faster to run a machine learning experiment for your business. Building useful machine learning models often takes a lot of data -- thousands of examples -- as well as a lot of time to prep the data in a format that is appropriate for the system. The content needs to be carefully curated and high quality. This isn't always easy to come by.

This is a technical demo of the large-scale on-the-fly web search technologies which are under development in the Visual Geometry Group in Oxford, using data provided by BBC R&D comprising over five years of prime-time news broadcasts from six channels. This is intended as a live demo of technologies under development at Oxford, and not as a working tool for general use. An item of interest can be specified at run time by a text query, and a discriminative classifier for that item is then learnt on-the-fly using images downloaded from Google Image search. The programmes were all broadcast originally from 6PM onwards on BBC ONE, BBC TWO, BBC Three, BBC FOUR, BBC Parliament and BBC News 24 channels, and in total comprise 10,131 hours of footage from 17,401 different programmes, and in total around 5M keyframes. Oxford and the BBC reserve the right to modify or withdraw any data and/or programme material provided as part of this live demo.

Updated 15 March 2016: Today concludes the five'Go' matches played by AlphaGo, an AI system built by DeepMind and South Korean champion, Lee Sedol. AlphaGo managed to win the series of games 4-1. 'Go' is a strategy-led board game in which two players aim to gather and surround the most territory on the board. The game is said to require a certain level of intuition and be considerably more complex than Chess. The first three games were won by AlphaGo with Sedol winning the fourth round, but still unable to claim back a victory.