10 AI Detection tools

 

AI could be a big boost to the NatCs

Two articles that were well worth reading in the Guardian have had me thinking. The first of them is by Nesrine Malik, and I strongly
Read the full article…

New AI research lets you click and drag images to manipulate them in second

AI tools to manipulate images continues to grow. The latest example is only a research paper for now, but a very impressive one, letting users simply drag elements of a picture to change their appearance. This doesn’t sound too exciting on the face of it, but take a look at the examples below to get an idea of what this system can do. Not only can you change the dimensions of a car or manipulate a smile into a frown with a simple click and drag, but you can rotate a picture’s subject as if it were a 3D model — changing the direction someone is facing, for example. One demo even shows the user adjusting the reflections on a lake and height of a mountain range with a few clicks…”

The Brainyacts

A light-hearted easy read about the usefulness of generative ai and the future of knowledge work.

Ten tools you can use while more and more of us are using AI, more of us are also thinking about how to tell the difference between AI and human-generated output. Here are 10 tools you can use to learn what has been generated with AI and what hasn’t.”

• AI Text Classifier
• Content at Scale AI Detector
• Copyleaks
• Crossplag
• GLTR AI Detector
• GPTZero
• Hugging Face
• Originality AI
• Sapling.ai
• Writer AI Content Detector

BLOOMChat: a New Open Multilingual Chat LLM

1. “SambaNova, in collaboration with Together, is excited to present BLOOMChat, a 176 billion parameter multilingual chat large language model (LLM). BLOOMChat is available for research and commercial use cases under a modified version of Apache 2.0, which includes RAIL’s use-based restrictions passed down from BLOOM.
2. BLOOMChat is a new, open, multilingual chat LLM that:
   1. Is trained on SambaNova RDUs (Reconfigurable Dataflow Units)
   2. Achieves a win-rate of 45.25% compared to GPT-4‘s 54.75% across 6 languages in a human preference study.
   3. Is preferred 66% of the time compared to mainstream open-source chat LLMs across 6 languages in a human preference study.
   4. Shows strong performance on WMT translation tasks by leading the results among BLOOM variants and mainstream open-source chat models.
3. Inspired by prior work that instruction tuning in one language can benefit performance in another language in multilingual models, we instruction-tuned BLOOM (176B) with English-focused assistant-style conversation datasets including OIG from OpenChatKit , Dolly 2.0, and OASST1datasets. Despite only fine-tuning in English, we observed substantial increases in chat quality in non-English languages.
4. While this is still an early study, we hope BLOOMChat serves as a valuable resource for the open-source community and as a stepping stone towards further advancements in the field.
5. BLOOMChat is available now [Chat with me!] for a limited time for live chatting on HuggingFace (model hosting and frontend UI provided by Together)…”