Today, we’re excited to announce AI Labyrinth, a new mitigation approach that uses AI-generated content to slow down, confuse, and waste the resources of AI Crawlers and other bots that don’t respect “no crawl” directives. When you opt in, Cloudflare will automatically deploy an AI-generated set of linked pages when we detect inappropriate bot activity, without the need for customers to create any custom rules.
…While Cloudflare has several tools for identifying and blocking unauthorized AI crawling, we have found that blocking malicious bots can alert the attacker that you are on to them, leading to a shift in approach, and a never-ending arms race. So, we wanted to create a new way to thwart these unwanted bots, without letting them know they’ve been thwarted.
To do this, we decided to use a new offensive tool in the bot creator’s toolset that we haven’t really seen used defensively: AI-generated content. — Read More
Daily Archives: April 3, 2025
Vision-Speech Models: Teaching Speech Models to Converse about Images
The recent successes of Vision-Language models raise the question of how to equivalently imbue a pretrained speech model with vision understanding, an important milestone towards building a multimodal speech model able to freely converse about images. Building such a conversational Vision-Speech model brings its unique challenges: (i) paired image-speech datasets are much scarcer than their image-text counterparts, (ii) ensuring real-time latency at inference is crucial thus bringing compute and memory constraints, and (iii) the model should preserve prosodic features (e.g., speaker tone) which cannot be inferred from text alone. In this work, we introduce MoshiVis, augmenting a recent dialogue speech LLM, Moshi, with visual inputs through lightweight adaptation modules. An additional dynamic gating mechanism enables the model to more easily switch between the visual inputs and unrelated conversation topics. To reduce training costs, we design a simple one-stage, parameter-efficient fine-tuning pipeline in which we leverage a mixture of image-text (i.e., “speechless”) and image-speech samples. We evaluate the model on downstream visual understanding tasks with both audio and text prompts, and report qualitative samples of interactions with MoshiVis. Our inference code will be made available, as well as the image-speech data used for audio evaluation. — Read More
Rick's Cafe AI 