These neuroscience adjacent ideas are exotic armchair philosophy when the LLM simulation path will work really well and so much faster and it's not even remotely a contest.
RESEARCH
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Brain Upload via LLM Simulators: Near-term Tractable Alternative
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Yes it's the tractable form of brain upload. There's a ton of scifi on brain uploads that requires way too exotic tech (scanning and simulating brains etc), when we're about to get a lossy and approximate version of that *a lot* sooner via LLM simulators. You can easily imagine a
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Top 9 Algorithms Powering Modern World Technology
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Top 9 #Algorithms that Power the Modern World
by @Python_Dv #AI #DataStructures #ArtificialIntelligence -
PhD Defense on Reasoning Machines and Next Professional Chapter
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In 2023, I paused my PhD to join @OpenAI to build the world’s first reasoning machine — OpenAI o1. Earlier this year, I defended my PhD thesis “Building a Reasoning Machine” advised by @Yoshua_Bengio at @Mila_Quebec 🎓 🎉 Much has changed since Yoshua and I first discussed reasoning in 2022, but the main themes aged well: – Adding structures to computation unlocks strong reasoning capabilities; – Data & sample efficiency will become the bottleneck to useful intelligence; – Retaining Bayesian uncertainty is key to reliable and safe AI systems. You can read the introduction of my thesis here: edwardjhu.com/thesis/ My next professional chapter (TBA) will be on bridging frontier intelligence with real economic impact, a theme dear to my heart after working closely with @drwconvexity and @suna_said in the last year 🚀
→ View original post on X — @ceobillionaire, 2026-04-10 15:08 UTC
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New research framework proposes training tools instead of retraining AI agents
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Stop retraining your AI agents. Train their tools instead. Most AI agents look great in demos. Then they break in production. A new paper from Stanford and Harvard explains why. It introduces a framework that changes how we think about building agents. The core finding: when
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Understanding Composite AI: The Future of Intelligence Systems
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What Is Composite AI?
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @lexfridman @sama @kaifulee @ID_AA_Carmack @karpathy @2morrowknight @ylecun -
Meta to Release Muse Spark API Soon
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Meta is planning to release Muse Spark on the APIs soon. Would be curious also to play with Meta’s 9B model if it will ever come out. Soon
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BLIP-2: Connecting Vision and Language Models Efficiently
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BLIP-2: Bridging Vision and Language Without Full Retraining
— Satya Mallick (@LearnOpenCV) 10 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore BLIP-2, a powerful vision–language model that connects pretrained image encoders with large language models without requiring expensive… pic.twitter.com/EnefAwliTzBLIP-2: Bridging Vision and Language Without Full Retraining In this episode of Artificial Intelligence: Papers and Concepts, we explore BLIP-2, a powerful vision–language model that connects pretrained image encoders with large language models without requiring expensive
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Exponential Quantum Advantage in Processing Massive Classical Data
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Exponential quantum advantage in processing massive classical data Zhao et al. : https://
arxiv.org/abs/2604.07639 #ArtificialIntelligence -
OpenAI’s AI Reaching Research Intern Level by September 2026
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OpenAI's Chief Scientist says AI is getting close to being as good as a human research intern.
— Jacob Effron (@jacobeffron) 10 avril 2026
This past September, @sama and @merettm predicted fully autonomous AI researchers by 2028.
Jakub's update: "I think we're not very far from models that can work autonomously for a… https://t.co/jsmSU6cSNH pic.twitter.com/gzzyonGEF8OpenAI's Chief Scientist says AI is getting close to being as good as a human research intern. This past September, @sama and @merettm predicted fully autonomous AI researchers by 2028. Jakub's update: "I think we're not very far from models that can work autonomously for a couple days… and produce much higher quality artifacts on their own." Jacob Effron (@jacobeffron) At @OpenAI, Chief Scientist @merettm helps lead the research roadmap to AGI including a research intern-level AI system by September 2026 and a fully automated AI researcher by March 2028. I sat down with Jakub to check on those timelines and ask him all of my top-of-mind AI questions including: ▪️ How OpenAI thinks about extending RL beyond code and math ▪️ The current state of alignment research as more powerful models loom ▪️ The future of continual learning ▪️ How startups should think about building their own models/harnesses And he also shared some great stories around OpenAI’s pioneering work on math. YouTube: piped.video/vK1qEF3a3WM Spotify: bit.ly/4sjUyrN Apple: bit.ly/41jAdrN 0:00 Intro 1:53 Research Intern Capability Timelines 4:59 Math Breakthroughs 7:59 RL Beyond Verifiable Tasks 12:32 RL vs In-Context 19:01 Allocating Compute Internally 28:18 AI for Science 31:40 Pattern Matching 33:23 Solving the Hardest Math Problems 37:40 Chain of Thought Monitoring 44:33 Generalization and Value Alignment in Models 47:57 Inside OpenAI 51:55 Quickfire — https://nitter.net/jacobeffron/status/2042234897134162077#m
→ View original post on X — @ceobillionaire, 2026-04-10 14:10 UTC