How to Stay Updated on AI Research

Many ask me how I stay updated with all the AI news and announcements. Here are my strategies: 1. Poscasts I Listen to Weekly - All In Podcast: podcast not solely focused on AI; it covers a wide range of topics including economics, technology, politics, social issues, and poker. The hosts—Chamath, Jason, Sacks, and Friedberg—are successful tech veterans. - The AI Podcast is produced by Nvidia. Each episode focuses on one person, one interview, and one story. I like it because it emphasizes AI's impact on our world. - Latent Space: the most technical, from AI Engineers to AI Engineers. It covers in-depth new AI technology with a big focus on Open Source. I also watch all the videos on YouTube from Matthew Berman. He conducts technical deep dives. 2. Social Media Accounts I Follow On X: - Bindu Reddy: CEO of Abacus AI, using Gen AI to build Applied AI and LLM agents and systems at scale, ex-AWS / Google. Her tweets are technical, provocative, and fun. - Jerry Liu, CEO and Founder of LlamaIndex, will provide many updates on their project and examples of famous use cases. - Andrew Ng: Stanford professor and AI luminary, co-founder of Google Brain and Coursera, shares insights on AI research and applications through his active X presence. - Yann LeCun: French computer scientist known for his pioneering work in deep learning and convolutional neural networks, and he is the founding Director of Facebook AI Research. - Rowan Cheung: founder of The Rundown AI newsletter, shares the latest developments in artificial intelligence. - Jim Fan: Researcher at Nvidia, he explains advanced AI innovations and is now focused on Models for Humanoid Robots. On Linkedin: - Philipp Schmid: Technical Lead at HuggingFace, publishes a lot about new Open Source releases and innovations. - Allie Miller: we worked together at IBM; she previously worked as Head of Business Development for Startups at Amazon. She shares a lot of updates and tips on how to apply AI for Business. - Aishwarya Srinivasan: great friend and ex-colleague of IBM. She worked at Google and is now an AI Advisor at Microsoft AI. She posts a lot of educational content. - Bojan Tunguz: senior Systems Software Engineer at Nvidia. He posts insightful content about AI (and xgboost!). 3. Newsletters I Am Subscribed To - The Algorithm: by MIT Technology Review, great to explore and clarify AI breakthroughs weekly and discuss unexpected impacts. - The Rundown AI: My favorite one to get the latest news in AI every day. - The Augmented Advantage: I enjoyed Tobias’ newsletter because it explained the practical application of AI in business. - Alpha Signal: too many papers, too little time to read them all. Get a weekly summary of the top innovations from the researcher community.

Many students ask how to keep up with AI progress. So here’s the approach that works for me, broken into three levels: 1. Short-term trends (~1 hour/day): I follow real-time discussions on X, Reddit, and LinkedIn, focusing on a curated list of accounts. This keeps me updated on the latest news, debates, and announcements. 2. Mid-term trends (~5 hours/week): I read publications from ML conferences like NeurIPS, ICLR, and ICML, journals like Nature and Science, blogs from AI labs in universities or companies, newsletters (like The Batch from DeepLearning.AI), and podcasts from the investment community. These provide insights into emerging tech and the next wave of AI developments. 3. Long-term trends (~5 hours/month): I have in-depth conversations with experts and thought leaders in my network. These discussions help me connect the dots and understand the broader direction of AI. This approach is structured and helps me make informed decisions at work, such as determining what to build and what not to build, given the roadmaps of model and infrastructure providers. If you're able to share your learning strategy or some of your favorite sources, please do!

I often get asked how I keep up with the latest generative AI research, especially considering how quickly the field is evolving. 😅 First off, I'm not the best at it, there's still so much I feel I need to catch up on, but if you're curious, here are all the sources I rely on. ⛳ Gen AI news and high impact work 👉 AI News: It aggregates AI updates from popular Discord channels, Twitter and Reddit. The user experience isn't the best, but it's my go-to place for the latest in generative AI—and it's free! ⛳ New Research Papers: 👉 Hugging Face's daily papers: They publish a daily list of top papers. There's been a flood of them lately, since authors can now upload their papers to the dashboard, which I'd say has affected the quality somewhat. Still, I quickly scan through them for anything interesting. 👉 LinkedIn: I follow quite some people now, too hard to tag everyone, but I consume most of my content from Elvis S., Pascal Biese and Raphaël MANSUY. The thing I love about their posts is they cut straight to the chase without lot of extra material. I also like creators who provide longer content and share their opinions—it adds perspective to the research. But as a content creator, I prefer reading straightforward information and forming my own opinions without biasing myself beforehand. ⛳ AI Metrics and Experiences 👉 Reddit: For model metrics, implementation details, and other scoop that's not typically covered by mainstream platforms, I enjoy browsing through r/LocalLLaMA and r/singularity mostly. I read other blogs/articles too, but that's more ad hoc, the sources above are part of my daily routine, and they already keep me quite busy :) Adding all the links below! I’d also love to know if there are other high-quality resources I might be missing out on. Please leave a comment if you have any suggestions!

Folks interested in AI / AI PM, I recommend watching this recent session by the awesome Aishwarya Naresh Reganti talking about Gen AI Trends. ANR is a "Top Voice" that I follow regularly, leverage her awesome GitHub repository, consume her Instagram shorts like candy and looking forward to her upcoming Maven Course on AI Engineering. https://lnkd.in/g4DiZXBU Aishwarya highlights the growing importance of prompt engineering, particularly goal engineering, where AI agents break down complex tasks into smaller steps and self-prompt to achieve higher-order goals. This trend reduces the need for users to have extensive prompt engineering skills. In the model layer, she discusses the rise of small language models (SLMs) that achieve impressive performance with less computational power, often through knowledge distillation from larger models. Multimodal foundation models are also gaining traction, with research focusing on integrating text, images, videos, and audio seamlessly. Aishwarya emphasizes Retrieval Augmented Generation (RAG) as a successful application of LLMs in the enterprise. She notes ongoing research to improve RAG's efficiency and accuracy, including better retrieval methods and noise handling. AI agents are discussed in detail, with a focus on their potential and current limitations in real-world deployments. Finally, Aishwarya provides advice for staying updated on AI research, recommending focusing on reliable sources like Hugging Face and prioritizing papers relevant to one's specific interests. She also touches upon the evolving concept of "trust scores" for AI models and the importance of actionable evaluation metrics. Key Takeaways: Goal Engineering: AI agents are learning to break down complex tasks into smaller steps, reducing the need for users to have extensive prompt engineering skills. Small Language Models (SLMs): SLMs are achieving impressive performance with less computational power, often by learning from larger models. Multimodal Foundation Models: These models are integrating text, images, videos, and audio seamlessly. Retrieval Augmented Generation (RAG): RAG is a key application of LLMs in the enterprise, with ongoing research to improve its efficiency and accuracy. AI Agents: AI agents have great potential but face limitations in real-world deployments due to challenges like novelty and evolution. Staying Updated: Focus on reliable sources like Hugging Face and prioritize papers relevant to your interests. 🤔 Trust Scores: The concept of "trust scores" for AI models is evolving, emphasizing the importance of actionable evaluation metrics. 📏 Context Length: Models can now handle much larger amounts of input text, enabling more complex tasks. 💰 Cost: The cost of using AI models is decreasing, making fine-tuning more accessible. 📚 Modularity: The trend is moving towards using multiple smaller AI models working together instead of one large model.

Want to learn how to make the most effective use of AI, and stay up to date on the major developments without spending a lot of time doing it? Here are six experts you should be following... 🔸 Kris Ograbek - AI Engineer specializing in Large Language Models (LLMs). He characterizes his content as "AI Engineering for Beginners", which is spot on. Despite his deep technical expertise, Kris' content is highly accessible to analysts/business users, and he is very generous about answering questions on his posts. His Medium site is also an AI learning goldmine, and I just started his free course on the Open AI API. A "must follow" if you're interested in better understanding how LLMs work, and how to use them more effectively. Medium: https://lnkd.in/eaubwnwB Free API Course: https://lnkd.in/eWgtf9Rt 🔸 Ethan Mollick - Professor at Wharton, specializing in AI, entrepreneurship, and innovation. His book, "Co-Intelligence - Living and Working with AI" is an incredibly insightful and highly practical guide to understanding the role AI is likely to play from the societal to the individual level, and how we can best partner with this new "alien" coworker. His multiple posts on Linkedin daily are a combination of key news and research, and updates on his own creative experiments in exploring the "jagged frontier" of what AI can do. I also recommend subscribing to his weekly SubStack newsletter at: https://lnkd.in/eF5Fvkbt 🔸 Chad Lauffer and Tianyu Xu - two experts in generative AI. Chad is a long-time Art Director and Graphic Designer, doing amazing things daily with AI images. Tianyu is my primary source for keeping up to date on AI-generated video technology and techniques. His Linkedin feed offers some of the most detailed and useful AI "how to" guides I have found to date. 🔸 The Neuron - this is a short, well-organized and engaging daily summary of the last 24 hour's top AI news and developments.  It's typically the first thing I read in the morning, and it only takes a few minutes to go through. You can read it online, or have it sent to your email.  They also do a good podcast, and maintain a useful list of the best AI tools in various categories: https://www.theneuron.ai/  🔸 AI Explained - IMO by far the best AI content on YouTube. Well-researched, substantive information that draws from the primary research papers and their interviews with key AI players. Avoids the breathless hype of most other AI YouTube content.  https://lnkd.in/g_GXdEea  The host of this site also has a paid Patreon site (link below) that provides a lot of great additional information and interviews, but I think for most people the free YouTube content will be sufficient.        https://lnkd.in/eTuQw-Px I hope you find this helpful. Would love to hear if you have any other "must follow" AI resources. #ai #llm #generativeai   

People are always asking for recommendations for other great content to read, but few people find that I maintain a full list of recommendations with my blog Interconnects AI (link to page below). Here's the list in no structured order: 1. Helen Toner (@hlntnr), Rising Tide: One of the great independent AI thinkers around geopolitics & policy for AI.  2. Joseph Gonzalez (@profjoeyg) + Vikram Sreekanti, The AI Frontier: Easily digestible nuggets of LLM insight from two builders in the field.  3. Finbarr Timbers (@finbarrtimbers), Artificial Fintelligence: Insider perspectives and round‑ups of crucial ML research, not just every paper across the desk.  4. Melanie Mitchell (@MelMitchell1), AI: A Guide for Thinking Humans: The place I trust to make sense of AI evaluations and how people should use the models.  5. Sebastian Raschka, PhD (@rasbt), Ahead of AI: The original newsletter summarizing the most important AI research—an excellent educational resource.  6. Cameron R. Wolfe, Ph.D. (@cwolferesearch), Deep (Learning) Focus: A thorough resource for mastering concepts behind modern AI systems.  7. Swyx (@swyx), Latent Space Podcast (@latentspacepod): The AI‑engineer newsletter/podcast that keeps you up‑to‑date and entertained.  8. Arvind Narayanan (@random_walker) & Sayash Kapoor(@sayashk), AI Snake Oil: They simply don’t miss when it comes to the big questions in AI policy.  9. Alberto Romero García (@Alber_RomGar), The Algorithmic Bridge: Balanced coverage of AI happenings—optimistic yet never ahead of its skis.  10. Dwarkesh Patel (@dwarkesh_sp), Dwarkesh Podcast (@dwarkeshpodcast): Deeply researched interviews that bring fresh energy and huge names.  11. Jasmine Sun (@jasminewsun), @jasmine: A fresh anthropology‑of‑tech lens on society’s biggest topics.  12. Jordan Schneider (@jordanschnyc), ChinaTalk: Deep, original analysis at the intersection of technology, China & US policy.  13. Azeem Azhar (@azeem), Exponential View (@ExponentialView): The place to start when you want to understand fast‑moving tech trends. And a few I don't actually recommend on my blog platform, but regularly read (and they should be self explanatory). 14. Dylan Patel(@dylan522p), SemiAnalysis (@SemiAnalysis_) 15. Ben Thompson (@benthompson), Stratechery (@stratechery) 16. Jack Clark (@jackclarkSF), Import AI