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🏷️ Should AI-Generated media be transparently labeled?

Contents:

  • 🏷️ Should AI-Generated media be transparently labeled?

  • 🧰 AI Tools of the Week

  • 🥷 Did Meta Just Unlock the Future of AI-Powered Image Editing?

  • 💰 How to Make Money using AI

  • 💪🏽 Miscellaneous and AI updates

  • ⚙️ Are you looking for a role in an elite company?

  • 🎓 NYU released an interesting study on LLMs.


🏷️ Should AI-Generated media be transparently labeled?

AI leaders have created the first transparent deepfake—a convincingly real synthetic video that is labeled as synthetically generated.

Revel.ai produced the video featuring AI author Nina Schick, and it is labeled using Truepic's technology, which is based on a standard from the Content Authenticity Initiative. The labeling aims to show how images and videos were produced.


Truepic CEO Jeffrey McGregor said that labeling synthetic content can help educate consumers, but ideally, all content should be transparently labeled. Truepic assists organizations in creating secure pipelines to prove the legitimacy of their images and video, and it is currently working with over 170 organizations, including a pilot with Microsoft to authenticate images from Ukraine. McGregor warned that people should be suspicious of any video without provenance, as the era of deepfakes is already here.

The ability to create transparent deep fakes that are clearly labeled as such—is important to avoid misinformation and deception, for ethical concerns and public trust, and to incentivize good actors. We believe it should become an online rule.

Yaro on Web3 and its Global Impact is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribed


🧰 AI Tools of the week

  • SmartWriter - Create highly personalized cold emails or Linkedin messages.

  • Socialbu - Generate ready-to-post content for your social media with text prompts.

  • NovusWriter - Create written and visual content with text prompts.

  • Automata - Repurpose blogs and videos as LinkedIn posts, and Twitter threads.

  • Decktopus - Helps you create captivating product launch copy.

  • Fact GPT - Generate fresh and relevant user-sourced content with citations.

  • Personal AI - Create your own intelligent personal AI that can generate new ideas, recall key concepts, and write original content.

  • Elephas - Writing Assistant from proposals and cover letters to blogs and creating social media posts.

  • Glasp - Newsletter writing tool by training your personalized AI models.

Check our 100+ photo AI tools here.


 

🥷 Did Meta just Unlock the Future of AI-Powered Image Editing?



The Segment Anything project by Meta introduces the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B) to democratize image segmentation.

SAM is a general, promptable segmentation model that can identify and generate masks for any object in images or videos, even those it hasn't seen before, without additional training. The SA-1B dataset is the largest segmentation dataset ever created. The project aims to reduce the need for expertise and custom data annotation in segmentation and envisions broad future applications, including multimodal AI systems, AR/VR, creative editing, and scientific studies.

Advanced image segmentation technology is set to revolutionize the way we interact with visual media, unlocking limitless possibilities across diverse domains.

Users will have the power to actively engage with images by selecting and manipulating specific objects—creating interactive experiences in marketing, education, and entertainment. Augmented Reality (AR) will enhance shopping experiences by enabling users to virtually try on clothing, visualize furniture placement, and even transport themselves to dream destinations.

The technology also holds the promise of greater accessibility, with visual aids such as image annotations enhancing the experiences of visually impaired individuals. In healthcare, medical professionals will benefit from a precise analysis of medical images, aiding in surgical planning and diagnostics.

As we embark on this extraordinary journey, the future of image segmentation promises to be bright and boundless, redefining visual interactions for practical, creative, and social impact.


⚙️ Are you looking for a role in an elite company?


We have teamed up with pallet to offer you the perfect platform to introduce yourselves and show your talents and expertise to world-class employers.


💰 How to Make Money using AI

A video explaining how to create a trading bot using ChatGPT. This developer put $2k behind his trading bot, check how it was built, how it works, and the results.


Psst: trading is highly risky and you should do your own research


🎓 NYU released an interesting study on LLMs.

we summarized it so you can get the gist of it, but feel free the entire paper here:

  1. Language models like LLMs are powerful AI systems that use patterns from text data to generate human-like responses. They can handle tasks like language translation, answering questions, and summarizing text.

  2. LLMs don't have thoughts or beliefs. They generate text by predicting the most likely next word based on their training data. They don't "understand" the text they generate, but they can produce sensible responses.

  3. LLMs have limitations. They might generate text that sounds plausible but is false or nonsensical. They may struggle with math problems or tasks that require reasoning, but clever prompting can improve their performance.

  4. While LLMs are excellent at imitating humans, they don't have human-like motivations or desires. Their behavior depends on training data, prompts, and techniques used to control them.

  5. Experts find it challenging to interpret LLMs' inner workings. Current techniques can't fully explain how LLMs use knowledge and reasoning to produce output. Understanding LLMs is a complex and ongoing research area.

  6. LLMs can potentially outperform humans on certain tasks. They have access to vast amounts of data and receive additional training to produce helpful responses. They will go beyond humans capabilities and execute in ways we cant even imagine.

  7. LLMs' values aren't fixed. Developers can control and adapt the values expressed in LLMs' output. Techniques like constitutional AI allow developers to guide models toward desired values, reduce biases, and follow norms.

  8. Interacting with LLMs can be tricky. How instructions are given matters, and slight changes in phrasing might affect their responses. LLMs' successes or failures should be interpreted cautiously.

Overall, LLMs are advanced AI tools with impressive language capabilities, but they come with challenges related to understanding, limitations, values, and behavior. Researchers are working to address these challenges to ensure responsible AI use.


💪🏽 Miscellaneous and AI updates:

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