Trying something new to keep you ahead of the curve: The 5 AI stories of the week - a weekly curation of the most important AI news you need to know. Do you like it?
🎯 Perplexity drops their FIRST open-weight model on Hugging Face: A decensored DeepSeek-R1 with full reasoning capabilities. Tested on 1000+ examples for unbiased responses.
Will we soon all have our own personalized AI news agents? And what does it mean for journalism?
Just built a simple prototype based on the Hugging Face course. It lets you get customized news updates on any topic.
Not perfect yet, but you can see where things could go: we'll all be able to build personalized AI agents that curate & analyze news for each of us. And users who could decide to build custom news products for their needs, such as truly personalized newsletters or podcasts.
The implications for both readers & news organizations are significant. To name a few: - Will news articles remain the best format for informing people? - What monetization model will work for news organizations? - How do you create an effective conversion funnel?
⭐️ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.
You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.
Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.
166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.
Why this matters: - Teams can pick efficient models that still get the job done - Developers can optimize for energy use from day one - Organizations can finally predict their AI environmental impact
If you're building with AI at any scale, definitely worth checking out.
🔥 Video AI is taking over! Out of 17 papers dropped on Hugging Face today, 6 are video-focused - from Sliding Tile Attention to On-device Sora. The race for next-gen video tech is heating up! 🎬🚀
📢 SmolLM2 paper released! Learn how the 🤗 team built one of the best small language models: from data choices to training insights. Check out our findings and share your thoughts! 🤏💡
Small but mighty: 82M parameters, runs locally, speaks multiple languages. The best part? It's Apache 2.0 licensed! This could unlock so many possibilities ✨
🚀 The open source community is unstoppable: 4M total downloads for DeepSeek models on Hugging Face, with 3.2M coming from the +600 models created by the community.
Yes, DeepSeek R1's release is impressive. But the real story is what happened in just 7 days after:
- Original release: 8 models, 540K downloads. Just the beginning...
- The community turned those open-weight models into +550 NEW models on Hugging Face. Total downloads? 2.5M—nearly 5X the originals.
The reason? DeepSeek models are open-weight, letting anyone build on top of them. Interesting to note that the community focused on quantized versions for better efficiency & accessibility. They want models that use less memory, run faster, and are more energy-efficient.
When you empower builders, innovation explodes. For everyone. 🚀
The most popular community model? @bartowski's DeepSeek-R1-Distill-Qwen-32B-GGUF version — 1M downloads alone.
Reminder: Don’t. Use. ChatGPT. As. A. Calculator. Seriously. 🤖
Loved listening to @sasha on Hard Fork—it really made me think.
A few takeaways that hit home: - Individual culpability only gets you so far. The real priority: demanding accountability and transparency from companies. - Evaluate if generative AI is the right tool for certain tasks (like search) before using it.
@meg, one of the best researchers in AI ethics, makes a critical point about autonomy: fully autonomous systems carry unknowable risks because they operate on computer logic rather than human logic.
The solution? Build systems that support & assist rather than override human decisions.
I highly recommend reading the blog post written by Meg, @evijit@sasha and @giadap. They define different levels of agent autonomy & provide a values-based analysis of risks, benefits, and uses of AI agents to help you make better decisions.
🔥 The AI Agent hype is real! This blog post deep dives into everything you need to know before deploying them: from key definitions to practical recommendations. A must-read for anyone building the future of autonomous systems.
📊 Key insight: A clear table breaking down the 5 levels of AI agents - from simple processors to fully autonomous systems. Essential framework for understanding where your agent stands on the autonomy spectrum
⚖️ Deep analysis of 15 core values reveals critical trade-offs: accuracy, privacy, safety, equity & more. The same features that make agents powerful can make them risky. Understanding these trade-offs is crucial for responsible deployment
🎯 6 key recommendations for the road ahead: - Create rigorous evaluation protocols - Study societal effects - Understand ripple effects - Improve transparency - Open source can make a positive difference - Monitor base model evolution
🔍 From instruction-following to creative storytelling, dive into 2024's most impactful AI datasets! These gems are shaping everything from scientific research to video understanding.
Did a fun experiment: What are the main themes emerging from the 100+ Nieman Journalism Lab predictions for 2025?
I used natural language processing to cluster and map them — really helps spot patterns that weren't obvious when reading predictions one by one. So what will shape journalism next year? A lot of AI and US politics (surprise!), but there's also this horizontal axis that spans from industry strategies to deep reflections on how to talk to the public.
Click any dot to explore the original prediction. What themes surprise/interest you the most?
