5/ The team is already targeting GJB2 and TMC1, two more common deafness genes, with promising animal results. We are watching gene therapy move from rare-disease fix to a platform that could one day treat most forms of genetic hearing loss.
RESEARCH
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Gene Therapy Breakthrough: Single Injection Restores Hearing in Deaf Patients
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1/ Insane: A single injection into the inner ear reversed deafness in all ten patients. Some started hearing again within weeks. Gene therapy just crossed a threshold we thought was still years away. Lets dig into this breakthrough and how it works
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Gemma 4 directs SAM 3 and RF-DETR for local video analysis
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Gemma 4 watches raw video. Understands the scene. Then prompts SAM 3 to segment and RF-DETR to track.
— Maziyar PANAHI (@MaziyarPanahi) 4 avril 2026
One AI directing two others. Fighter jets. Crowds. Aerial defense footage.
All three models running locally on a MacBook. No cloud.
What scene should I point this at next? pic.twitter.com/vNVgVloAGBGemma 4 watches raw video. Understands the scene. Then prompts SAM 3 to segment and RF-DETR to track. One AI directing two others. Fighter jets. Crowds. Aerial defense footage. All three models running locally on a MacBook. No cloud. What scene should I point this at next?
→ View original post on X — @huggingface, 2026-04-04 14:44 UTC
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Synthetic Data Reduces Defect Detection Training from Months to Hours
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Training time collapsed from months to hours using generative synthetic data instead of collecting real defect samples.
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University remains valuable despite AI advancement
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You’re showing why it still makes sense to go to Uni. Really amazing initiative.
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Grok-4.20-Beta 1 Dominates Medical AI Rankings with Multi-Agent Architecture
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🚨 Grok-4.20-Beta 1 just took the #1 spot in Medicine & Healthcare on Arena — and it’s not even close. With style control enabled, Grok isn’t just accurate — it’s adaptable, aligning responses to clinical context and communication needs. Even more impressive? 👉 The multi-agent version ranked #3 That means xAI now holds 2 of the top 3 positions in medical AI. Let that sink in. 🧠 Why this matters (beyond rankings) Medicine is one of the hardest domains for AI to excel in: – Zero tolerance for hallucinations – High-stakes, life-or-death decision support – Complex, context-heavy reasoning – Need for both precision and clarity And yet — Grok is not just performing well in benchmarks… 👉 It’s already being used in real-world, critical medical scenarios, helping guide decisions where timing and accuracy matter most. ⚙️ Technical Insight What stands out here is the combination of: – Style-controlled generation → tailoring outputs for clinicians vs patients – Multi-agent orchestration → distributed reasoning across specialized agents – High factual grounding → critical for clinical reliability This signals a shift from “general-purpose LLMs” → domain-optimized AI systems with structured reasoning layers 🏗️ Architecture Takeaways We’re seeing a clear pattern emerge in next-gen AI systems: 1. Single-model excellence is no longer enough → Multi-agent systems are becoming the new frontier 2. Control > Raw Intelligence → Style control, guardrails, and contextual tuning are essential in healthcare 3. Real-world validation beats benchmark hype → Impact in live medical scenarios is the true benchmark 🌍 Bigger Picture Grok isn’t just chasing leaderboard positions. It’s being positioned as an AI that can actually help humanity in its most critical moments. And in medicine — that’s the ultimate test. This milestone isn’t just about dominance… It’s about trust. 🔗 Follow my communities and personal initiatives: – Amazing AI, Data, Quantum Computing & Emerging Technologies — drdebashisdutta.com/ – Research & Innovation – Quantum, AI & Advanced Systems — researchedge.org/
→ View original post on X — @debashis_dutta, 2026-04-04 13:34 UTC
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Media Generation Still Far From Truly Solved Creative AI
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We’re still so far away. Solved media gen would be imagination realised, it needs to be predictable and rule following and prompt adhering, but it also needs to be malleable and novel, with the capacity to surprise and invent, and go somewhere new. Current state of the art media
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The Well: Open-Source Library of Physics Simulations for AI
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Imagine trying to teach someone how to swim just by letting them read books about water. That is how we have been training AI on physics, using text descriptions. To really learn, you need to get in the water. "The Well" is that water. Polymathic AI has released a massive 15TB open-source library of physics simulations. It allows AI models to experience physical phenomena directly. Instead of reading about a supernova, the model processes the actual data of the explosion. Instead of reading about aerodynamics, it analyzes the fluid flow. This moves us from [Generative AI] (making things up) to [Scientific AI] (discovering truth). A huge step forward for open science. GitHub Repo: github.com/PolymathicAI/the_well/ [Translated from EN to English]
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Research Workflow Standardization for AI Teams
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Same here! We use skills and md setup with the same docs we give to juniors. The research workflow standardization has been a game changer for us at Towards AI.
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AI Hallucinations in Medical Contexts: Reliability Concerns
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Hallucinations in medical contexts is a bit alarming TBH. Jagged intelligence at its finest, brilliant at some things and completely unreliable at others.