Michael Jackson’s Post

All AI engineers in my network. This video https://lnkd.in/exRyjSnS from Welch lab clears one of the fundamentals in AI. The summary is that every neural network goes from a bias variance tradeoff phase to a double descent phase after large steps without violating any statistical rules. The insight is that neural networks through long training evolves from behaving like a 1. high bias model to 2. high variance model to 3. low variance model. 2 nagging questions for you to figure out - 1. Why after phase shift into double descent phase model choses low variance curves and not higher variance curves ? 2. Looks like highly parameterised neural network changes its character from high bias to high variance to low variance - cant we arrive at correct "character" of models in sample efficient way ?

You don’t need a PhD to learn or be a pro in using AI. Here are 10 AI skills everyone should master before 2026 to stay competitive 👇

Understanding the Intricacies of AI and Machine Learning: Exploring Their Differences, Applications, and the Rise of Generative AI - https://lnkd.in/g7HGWGzq

Altman: "In many ways, GPT-5 is already smarter than many humans, and soon 30-40% of economic tasks will be performed by AI."

Altman: "In many ways, GPT-5 is already smarter than many humans, and soon 30-40% of economic tasks will be performed by AI."

 Somebody asked my opinion on AI: (Current) AI is designed to answer all questions. But the finding or generating an answer is not the source of human progress. Human progress evolved from learning.

Our new development in AI: Anchor-Span-based Named Entity Recognition. Token + span classification for knowledge extraction from (short) text with multiple levels of entity nesting. (AI, NNs, CNNs, knowledge engineering). https://lnkd.in/ggquu2X3

The Artificial Intelligence tag on Medium continues to be a goldmine for thought leadership and innovation—especially for those exploring human-like interactions with AI. In this insightful article, the author introduces the Model-Context Protocol

Axiom Founder, Carina Hong and Reflection AI’s Research Lead, Irwan Bello, discuss the strengths and weaknesses of current generative AI models: “Currently we see large language models crossing many astonishing thresholds.” “...when it's just outputting text tokens, I'd say like in most domains it's kind of like, you know, top tier PhD level performance.” “We find large language models, uh, getting a bit confused when it comes to piecing these codes together... This capability also known as hierarchical reasoning.” Read the full AI Agenda event recap: https://thein.fo/486x57f

🔥 Pearadox Daily Picks — 5 Breakthroughs in AI You’ll Want to Know About Every day, we scan the latest AI papers so you don’t have to — and break them down in plain English you can actually use. Here’s what’s new 👇 ⸻ 1️⃣ Computers Remember Story Threads! When you tell a story, can AI pick up where you left off and stay true to its meaning? This study shows that large language models like GPT-4o can indeed “remember” the key themes and threads — called semantic isotopies — across story continuations. It’s a step toward more coherent AI writing that keeps a consistent sense of meaning from start to finish. 🔗 https://lnkd.in/dYCPFUXV ⸻ 2️⃣ AI Still Struggles to Count Shapes Accurately Turns out, even today’s smartest vision-language models — the ones that “see” and “read” — fail at a basic task: counting. Researchers built a minimalist test with simple colored shapes and found that when multiple object types appear together, AI often miscounts badly. The finding exposes a big gap in how these systems truly understand what they see. 🔗 https://lnkd.in/dXRERPuT ⸻ 3️⃣ Making AI Predictions More Trustworthy AI predictions can be powerful — but how much can we trust them? This new framework breaks down uncertainty step by step, showing exactly where prediction errors come from in multi-stage models. The result: AI systems that explain their confidence and improve reliability in areas like finance, supply chains, and forecasting. 🔗 https://lnkd.in/dUtb9_ai ⸻ 4️⃣ Smarter Game Strategy: A New Computer Method Beats Old Ones AI systems that play games like poker or Go have long relied on regret matching to learn optimal strategies — but they’re not perfect. This new method, IREG-PRM+, bridges theory and practice with faster learning and better convergence. It’s a major upgrade in how AI learns to make smart, competitive decisions. 🔗 https://lnkd.in/dB6kRYkZ ⸻ 5️⃣ Making Blurry Faces Clear Again Old photos or low-res images often blur faces beyond recognition. CodeFormer++ changes that. This advanced AI method restores faces while keeping their true identity intact — balancing clarity and realism better than before. It’s a leap forward for photo restoration and digital forensics. 🔗 https://lnkd.in/dmeYW-ye ⸻ 💡 Pearadox Daily turns complex AI research into quick insights you can read over coffee. Follow for tomorrow’s top 5 breakthroughs. 🔗 pearadox.app #AI #MachineLearning #Research #AIDaily #GenerativeAI #Pearadox