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#continuallearning — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #continuallearning, aggregated by home.social.

  1. Demonstrating "Catastrophic Forgetting" in neural networks with 1-D function approximation #AI #ContinualLearning

  2. Demonstrating "Catastrophic Forgetting" in neural networks with 1-D function approximation #AI #ContinualLearning

  3. Demonstrating "Catastrophic Forgetting" in neural networks with 1-D function approximation #AI #ContinualLearning

  4. Demonstrating "Catastrophic Forgetting" in neural networks with 1-D function approximation #AI #ContinualLearning

  5. Demonstrating "Catastrophic Forgetting" in neural networks with 1-D function approximation #AI #ContinualLearning

  6. Liên tục học (Continual Learning) trong AI 2026 thực sự nghĩa là gì? Cập nhật trọng số mô hình theo thời gian thật hay chỉ là hệ thống bộ nhớ ngoài + huấn luyện định kỳ? Mô hình như Opus 5.0 có được vá liên tục? Hay kiến trúc chỉ là retrieval + làm mịn offline? Phân biệt giữa "học thực sự" và "nhớ + cập nhật phần mềm" là then chốt cho tiến tới AGI. #ContinualLearning #AI #MachineLearning #TríTuệNhânTạo #HọcMáy #AGI

    reddit.com/r/singularity/comme

  7. Dự đoán: Bước đầu tiên của học tập liên tục sẽ thông qua tự nhắc nhở siêu cấp với RLMs. Các kỹ thuật prompt như CoT từng cải thiện suy luận; nay RLMs có thể mở rộng học trong ngữ cảnh thành dài hạn, liên tục. Prime Intellect đang nghiên cứu hướng này – có thể là bước đột phá tiếp theo sau CoT. #ContinualLearning #RLMs #AI #MachineLearning #Học_liên_tục #Trí_tuệ_nhân_tạo #AI_cao_cấp

    reddit.com/r/singularity/comme

  8. New research shows Nested Learning’s Continuum Memory System lets AI bridge short‑term cues and long‑term understanding, building richer world models without catastrophic forgetting. A leap for continual learning and open‑source AI. Dive in! #NestedLearning #ContinuumMemory #ContinualLearning #WorldModels

    🔗 aidailypost.com/news/nested-le

  9. Ease your way into Problem Solving ~ Beginning with these: 3 Simple Strategies ~

    ✅ Stay Positive: Focus on what you can control.
    ✅ Break It Down: Into small, manageable parts & tackle one at a time.
    ✅ Seek Support: Ask for help or advice.

    Every problem has a solution with time & belief that you can do it. 💪
    👉 Join Here buff.ly/3Toypds
    #ProblemSolving #StayingPositive #SelfBelief #ContinualLearning

  10. As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. #ArtificialIntelligence #ContinualLearning #AGI

    AI Leaders Eye New Breakthroug...

  11. As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. #ArtificialIntelligence #ContinualLearning #AGI

    AI Leaders Eye New Breakthroug...

  12. As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. #ArtificialIntelligence #ContinualLearning #AGI

    AI Leaders Eye New Breakthroug...

  13. As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. #ArtificialIntelligence #ContinualLearning #AGI

    AI Leaders Eye New Breakthroug...

  14. As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. #ArtificialIntelligence #ContinualLearning #AGI

    AI Leaders Eye New Breakthroug...

  15. AI Leaders Eye New Breakthrough to Build More Powerful Models

    As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. The debate highlights a structural limit of current AI systems and why new architectures may matter more than incremental gains.
    #ArtificialIntelligence #ContinualLearning #AGI
    bloomberg.com/news/newsletters

  16. AI Leaders Eye New Breakthrough to Build More Powerful Models

    As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. The debate highlights a structural limit of current AI systems and why new architectures may matter more than incremental gains.
    #ArtificialIntelligence #ContinualLearning #AGI
    bloomberg.com/news/newsletters

  17. AI Leaders Eye New Breakthrough to Build More Powerful Models

    As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. The debate highlights a structural limit of current AI systems and why new architectures may matter more than incremental gains.
    #ArtificialIntelligence #ContinualLearning #AGI
    bloomberg.com/news/newsletters

  18. AI Leaders Eye New Breakthrough to Build More Powerful Models

    As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. The debate highlights a structural limit of current AI systems and why new architectures may matter more than incremental gains.
    #ArtificialIntelligence #ContinualLearning #AGI
    bloomberg.com/news/newsletters

  19. AI Leaders Eye New Breakthrough to Build More Powerful Models

    As leading labs hit diminishing returns from scale and fine-tuning, attention is shifting toward continual learning—models that keep learning after deployment rather than relying on static training cut-offs. The debate highlights a structural limit of current AI systems and why new architectures may matter more than incremental gains.
    #ArtificialIntelligence #ContinualLearning #AGI
    bloomberg.com/news/newsletters

  20. Discover ~ How to Fuel your Mind to Embrace change without self-doubt or fear! Every day brings something new. Some changes are small, others life-altering. But resisting change often leads to vulnerability & hardship.

    The decision to change is only a thought away. Learn How🌱This article is exclusive to Free Community Members. Join or Login here 👉 buff.ly/KLtyaSz
    #Decisiontochange #EmpoweryourLife #PersonalGrowth #ContinualLearning

  21. Continual learning feels key for truly adaptive AI. Interesting take on memory layers + avoiding catastrophic forgetting. Worth a read if you think beyond static models.

    🔗 jessylin.com/2025/10/20/contin

    #AI #ContinualLearning #LLM #MachineLearning #AdaptiveAI

  22. Continual learning feels key for truly adaptive AI. Interesting take on memory layers + avoiding catastrophic forgetting. Worth a read if you think beyond static models.

    🔗 jessylin.com/2025/10/20/contin

    #AI #ContinualLearning #LLM #MachineLearning #AdaptiveAI

  23. Continual learning feels key for truly adaptive AI. Interesting take on memory layers + avoiding catastrophic forgetting. Worth a read if you think beyond static models.

    🔗 jessylin.com/2025/10/20/contin

    #AI #ContinualLearning #LLM #MachineLearning #AdaptiveAI

  24. Continual learning feels key for truly adaptive AI. Interesting take on memory layers + avoiding catastrophic forgetting. Worth a read if you think beyond static models.

    🔗 jessylin.com/2025/10/20/contin

    #AI #ContinualLearning #LLM #MachineLearning #AdaptiveAI

  25. Continual learning feels key for truly adaptive AI. Interesting take on memory layers + avoiding catastrophic forgetting. Worth a read if you think beyond static models.

    🔗 jessylin.com/2025/10/20/contin

    #AI #ContinualLearning #LLM #MachineLearning #AdaptiveAI

  26. #AndrejKarpathy believes #AGI is still a decade away, citing the need for advancements in #continuallearning, #multimodality, and #computeruse. He argues that while the problems are solvable, they remain challenging. Karpathy also reflects on the history of AI, highlighting the impact of #deeplearning, #reinforcementlearning, and #largelanguagemodels on the field. dwarkesh.com/p/andrej-karpathy #tech #media #news

  27. #AndrejKarpathy believes #AGI is still a decade away, citing the need for advancements in #continuallearning, #multimodality, and #computeruse. He argues that while the problems are solvable, they remain challenging. Karpathy also reflects on the history of AI, highlighting the impact of #deeplearning, #reinforcementlearning, and #largelanguagemodels on the field. dwarkesh.com/p/andrej-karpathy #tech #media #news

  28. #AndrejKarpathy believes #AGI is still a decade away, citing the need for advancements in #continuallearning, #multimodality, and #computeruse. He argues that while the problems are solvable, they remain challenging. Karpathy also reflects on the history of AI, highlighting the impact of #deeplearning, #reinforcementlearning, and #largelanguagemodels on the field. dwarkesh.com/p/andrej-karpathy #tech #media #news

  29. #AndrejKarpathy believes #AGI is still a decade away, citing the need for advancements in #continuallearning, #multimodality, and #computeruse. He argues that while the problems are solvable, they remain challenging. Karpathy also reflects on the history of AI, highlighting the impact of #deeplearning, #reinforcementlearning, and #largelanguagemodels on the field. dwarkesh.com/p/andrej-karpathy #tech #media #news

  30. #AndrejKarpathy believes #AGI is still a decade away, citing the need for advancements in #continuallearning, #multimodality, and #computeruse. He argues that while the problems are solvable, they remain challenging. Karpathy also reflects on the history of AI, highlighting the impact of #deeplearning, #reinforcementlearning, and #largelanguagemodels on the field. dwarkesh.com/p/andrej-karpathy #tech #media #news

  31. 𝗧𝘄𝗲𝗲𝘁 𝟭𝟰/𝟭𝟰
    Co myślisz o tej koncepcji?
    Czy AI uczące się przez doświadczenie to przyszłość, czy może wolisz kontrolę przez "przeprogramowywanie"?
    Jak widzisz praktyczne zastosowania takiego podejścia w swojej branży?
    #AI #MachineLearning #ContinualLearning #ArtificialIntelligence

  32. As the Spirit Series evolves, they intend to harness the profound power of story and challenge as forms of immersive learning. They've expanded into video-assisted storytelling and teacher training. #ContinualLearning #DramaInEducation

  33. As the Spirit Series evolves, they intend to harness the profound power of story and challenge as forms of immersive learning. They've expanded into video-assisted storytelling and teacher training.

  34. As they present their idea for producing symbolic AI domains using #LLMs, they present an interesting summary of why "Good Old-Fashioned #AI" is still totally relevant today - despite being aware of its limitations.

    youtube.com/watch?v=_TrKARhF5c

    I, too, rather recommend traditional #AI in my solutions, instead of blindly yielding to the current trends.

    Plus it paves the way for #continuallearning to improve the skills with time.

  35. As they present their idea for producing symbolic AI domains using #LLMs, they present an interesting summary of why "Good Old-Fashioned #AI" is still totally relevant today - despite being aware of its limitations.

    youtube.com/watch?v=_TrKARhF5c

    I, too, rather recommend traditional #AI in my solutions, instead of blindly yielding to the current trends.

    Plus it paves the way for #continuallearning to improve the skills with time.

  36. As they present their idea for producing symbolic AI domains using #LLMs, they present an interesting summary of why "Good Old-Fashioned #AI" is still totally relevant today - despite being aware of its limitations.

    youtube.com/watch?v=_TrKARhF5c

    I, too, rather recommend traditional #AI in my solutions, instead of blindly yielding to the current trends.

    Plus it paves the way for #continuallearning to improve the skills with time.