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  • OpenAI’s AI Reaching Research Intern Level by September 2026

    By

    @ceobillionaire

    –

    OpenAI'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

  • Exponential quantum advantage for processing massive classical data
    Exponential quantum advantage for processing massive classical data

    By

    @ceobillionaire

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    Exponential quantum advantage in processing massive classical data Zhao et al.: arxiv.org/abs/2604.07639 #ArtificialIntelligence [Translated from EN to English]

    → View original post on X — @ceobillionaire, 2026-04-10 14:09 UTC

  • GarmentPile++: Teaching Robots Chaotic Clothing Manipulation

    By

    @jiqizhixin

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    How do we teach robots to handle a chaotic pile of clothes? Peking University, The University of Hong Kong, and Northwestern University present GarmentPile++. This system intelligently combines vision-language AI with robotic dexterity. It identifies individual garments in a

    → View original post on X — @jiqizhixin

  • AI reveals sex-based immune cell aging differences across populations

    By

    @erictopol

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    The way our immune cells age differs substantially by sex, from single-cell analysis of ~1,000 people. Implications for propensity for autoimmune diseases (women), vulnerability to cancer (men), immunosenescence and inflammaging @NatureAging

    → View original post on X — @erictopol

  • AI Detects High-Risk Cancer Cell Clusters for Metastasis Prevention

    By

    @erictopol

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    These cell clusters are up to 50X more potential to spread cancer compared with single circulating tumor cells. A very useful target for preventing metastasis, which is the predominant way cancer kills.

    → View original post on X — @erictopol

  • Neural Computers – New arxiv Research with Video Demonstration

    By

    @ceobillionaire

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    Neural Computers arxiv.org/abs/2604.06425 [Translated from EN to English]

    → View original post on X — @ceobillionaire, 2026-04-10 13:50 UTC

  • AI Detects Heart Failure Risk 5 Years Before Symptoms
    AI Detects Heart Failure Risk 5 Years Before Symptoms

    By

    @erictopol

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    AI to detect risk of heart failure 5 years ahead of symptoms via CT scan epicardial fat tissue https://
    jacc.org/doi/10.1016/j.
    jacc.2026.02.5116
    … @JACCJournals

    → View original post on X — @erictopol

  • MIT Lottery Ticket Hypothesis: Tiny Networks Match Full Model Performance
    MIT Lottery Ticket Hypothesis: Tiny Networks Match Full Model Performance

    By

    @debashis_dutta

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    What if 90% of your neural network is doing… nothing? MIT’s Lottery Ticket Hypothesis showed something radical:
    Inside every large model, there exists a tiny “winning ticket” — a subnetwork that can match full-model performance. Same accuracy. Fraction of the size. But

    → View original post on X — @debashis_dutta

  • Galaxy Project: Open-Source Platform for Biomedical Research and Data Science
    Galaxy Project: Open-Source Platform for Biomedical Research and Data Science

    By

    @gp_pulipaka

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    Galaxy Project The Galaxy Project is a cutting-edge, open-source, web-based platform that revolutionizes the mathematical computation of biomedical research by making it accessible, reproducible, and transparent to the mathematical deep learning and machine learning community. It was initially developed at Ivy League Pennsylvania State University (Penn State) and the University of Texas at Austin, making it a U.S.-originated initiative from America. However, over the years, it has grown into an internationally adopted platform with contributors and contributors from all over the world. Galaxy Project is the most high performance computing tool that empowers mathematical and machine learning community to conduct complex analyses revolutionizing data science. The Galaxy Project creates a light way by fostering collaboration and facilitating the sharing and repetition of scientific workflows for breakthroughs in research and enhancing scientific findings' credibility. It has the most intense mathematical tools you might have ever come across! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode References Afgan, E., Baker, D., Batut, B., van den Beek, M., Bouvier, D., Čech, M., Chilton, J., Clements, D., Coraor, N., Grüning, B. A., Guerler, A., Hillman-Jackson, J., Hiltemann, S., Jalili, V., Rasche, H., Soranzo, N., Goecks, J., Taylor, J., Nekrutenko, A., & Blankenberg, D. (2024). The Galaxy platform for accessible, reproducible, and collaborative biomedical analyses: 2024 update. Nucleic Acids Research, 52(W1), W83–W88. doi.org/10.1093/nar/gkac247. Retrieved March 14, 2025. Galaxy Community Hub. (2010, January 1). Galaxy Publication Library. Galaxy Project. Retrieved March 14, 2025, from galaxyproject.org/publicatio… Galaxy Community Hub. (2010, January 1). Governance. Galaxy Project. Retrieved March 14, 2025, from galaxyproject.org/community/… Galaxy Community Hub. (2024, June 10). The 2024 Galaxy Community Update Paper is Here!. Galaxy Project. Retrieved March 14, 2025, from galaxyproject.org/news/2024-… Galaxy Training Network. (2024). Galaxy Training: A powerful framework for teaching!. PLOS Computational Biology, 20(1), e1008537. doi.org/10.1371/journal.pcbi…. Retrieved March 14, 2025.

    → View original post on X — @gp_pulipaka, 2026-04-10 13:26 UTC

  • The Matrix Calculus You Need For Deep Learning
    The Matrix Calculus You Need For Deep Learning

    By

    @gp_pulipaka

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    The Matrix Calculus You Need For Deep Learning! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode geni.us/Matrix-Calculus

    → View original post on X — @gp_pulipaka, 2026-04-10 13:26 UTC