For more info: tri-ai.org #TRIAI #Cohort10 #GoogleDeepMind #LanguageModeling

Submission Link wireilla.com/submission/inde… #ProgrammingLanguages #SoftwareEngineering #CompilerDesign #LanguageTheory #CodeOptimization #SoftwareDevelopment #ProgrammingParadigms #TypeSystems #SyntaxSemantics #DomainSpecificLanguages #LanguageModeling #CodeAnalysis #StaticAnalysis

💬 Speech tip: model simple phrases kids can easily repeat and learn from. #LanguageModeling #SpeechTips #EarlyLanguage

📣 Deal of the Day 📣 Apr 2 Save 45% TODAY ONLY! The RLHF Book & selected titles: hubs.la/Q049rNTW0 The authoritative guide for Reinforcement learning from human feedback, alignment, and post-training LLMs. @natolambert #RLHF #posttraining, #RLVR #SFT #finetuning #languagemodeling #LLMs In this guide, AI expert Nathan Lambert gives a true industry insider's perspective on modern RLHF training pipelines and their trade-offs. Using hands-on experiments and mini implementations, Nathan clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool.

Expert Threshold Routing for Autoregressive Language Modeling with Dynamic Computation Allocation and Load Balancing Preprint: This paper proposes Expert Threshold routing for l… arxiv.org/abs/2603.11535 #AI #LanguageModeling #MachineLearning #Preprint #Arxiv #ScienceNews

📊 Text Analysis: Computational Language Modeling for the Social Sciences 📊 Join this workshop for an introduction to computational #LanguageModeling for social science data and problems. For more info: myumi.ch/A1gep #SumProg26 #ICPSR #AcademicGrowth #ResearchSkills

💥PARENT TIPS from Speech Therapist, EMILY. 💥Use modeling language. 💥Use a visual. #ParentTips #SpeechTherapy #SpeechTherapist #LanguageModeling #VisualLearning #EarlyIntervention #SpeechLanguagePathology #MetroEHS

Recursive Networks don’t just process language — they mirror the hierarchical way humans structure it. By aligning models with linguistic theory, we unlock better performance and deep interpretability. This benefits advanced language tasks across domains. Problem: Ambiguous phrasing and hierarchical dependencies lead to unpredictable model outputs in traditional sequential language models. Solution: RecNNs form representations by merging child subtrees into parent nodes, emulating the natural grammar of language and yielding robust contextual embeddings. 🔗 medium.com/betaflow/recursiv… If you are interested in gaining deep insights into AI and how it can be applied to solving your technical and business problems, follow me. If you want to learn more about how to apply AI to grow your business as a Tech Entrepreneur join my X community (AI for Entrepreneurs) x.com/i/communities/19918901… #AI #NLP #RecNN #TechEntrepreneur #ThinkDeep #LanguageModeling #BusinessAI #Entrepreneurship

Understanding how language meaning compounds is essential for smarter AI systems. Recursive models offer a natural solution. Today’s post explores how recursive structure boosts language modeling. 📌 Link: medium.com/betaflow/recursiv… Problem: Sequential language models treat words in isolation, losing hierarchical syntax/semantics. Solution: Recursive Neural Networks Model language as trees, retaining syntactic and semantic hierarchy — leading to deeper comprehension. If you want to apply cutting-edge AI to your business, follow me. Build AI advantage — Join: x.com/i/communities/19918901… #ML #AI #BusinessAI #LanguageModeling #Transformers #RecursiveLearning #TechGrowth

Language modeling is a foundational challenge in NLP teaching machines to understand and generate human language. Recursive Neural Networks (RecNNs) offer an intuitive framework for capturing hierarchical structure in language. This method helps us rethink how models interpret syntax and meaning. Problem: Standard sequence models (e.g., RNNs, LSTMs) struggle with complex nested dependencies in language — like understanding that “the cat that chased the mouse ran fast” has hierarchical meaning beyond linear order. Solution: Recursive Neural Networks build representations over tree structures (i.e., syntactic parse trees), allowing the model to combine word meanings recursively from leaves to root, capturing long-range and hierarchical dependencies effectively. 🔗 medium.com/betaflow/recursiv… If you are interested in gaining deep insights into AI and how it can be applied to solving your technical and business problems, follow me. If you want to learn more about how to apply AI to grow your business as a Tech Entrepreneur join my X community (AI for Entrepreneurs) x.com/i/communities/19918901… #AI #NLP #RecursiveNeuralNetworks #MachineLearning #DeepLearning #AIforBusiness #TechEntrepreneur #LanguageModeling #Innovation

Neural networks are reshaping how machines understand language — but not all architectures scale equally. Recursive models offer a unique hierarchical view of text. Understanding these structures can unlock more powerful language models today. Read more: medium.com/betaflow/recursiv… Problem: Traditional sequential models fail to capture long-range dependencies in language, making context understanding shallow. Solution: Recursive Neural Networks build hierarchical representations that mirror linguistic structure — improving context awareness and compositional reasoning. If you’re interested in deep AI insights & real problem-solving, follow me. Want to learn how to apply AI to grow your business? Join my X community: x.com/i/communities/19918901… #AI #DeepLearning #LanguageModeling #NeuralNetworks #RNN #MachineLearning #TechLeadership #Entrepreneurship

📢 📰 Infini-attention: a novel approach to infinite context in language models with 114x less memory! What does this mean for the future of AI? 🗞 🔔 #AI #LanguageModeling #InfiniAttention #FutureOfAI Reference: [towardsdatascience.com/llms-…

📣 Deal of the Day 📣 Jan 2 SAVE 45% on The RLHF Book & selected titles: hubs.la/Q03Zm_cd0 The authoritative guide for #Reinforcement learning from human feedback, alignment, and post-training #LLMs. #RLHF #posttraining, #RLVR #SFT #finetuning #languagemodeling In The RLHF Book, AI expert Nathan Lambert gives a true industry insider's perspective on modern RLHF training pipelines, and their trade-offs. Using hands-on experiments and mini-implementations, Nathan clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool.

📣 Deal of the Day 📣 Dec 17 New MEAP! SAVE HALF on The RLHF Book and more: hubs.la/Q03Ytmfb0 The authoritative guide for Reinforcement learning from human feedback, alignment, and post-training LLMs. #RLHF #posttraining #RLVR #SFT #finetuning #languagemodeling This book explores the ideas, established techniques and best practices of RLHF you can use to understand what it takes to align your AI models. Using hands-on experiments and mini-implementations, the author @natolambert clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool. The Countdown to 2026 is here! hubs.la/Q03YtgWz0 #Countdownto2026

PlantBiMoE: A Bidirectional Foundation Model with SparseMoE for Plant Genomes 1. PlantBiMoE introduces a novel approach to plant genome language modeling by integrating bidirectional Mamba and SparseMoE frameworks. This combination not only enhances the model's ability to capture bidirectional dependencies in DNA sequences but also significantly reduces computational costs through sparse parameter activation. 2. The model achieves state-of-the-art performance on the Modified Plants Genome Benchmark (MPGB), outperforming previous models like AgroNT and PDLLMs in tasks such as chromatin accessibility prediction and histone modification analysis. This demonstrates its superior generalization across diverse plant species and genomic tasks. 3. PlantBiMoE is pre-trained on a comprehensive dataset of 42 plant species, covering a wide range of categories from model plants to algae. This extensive pre-training ensures the model's robustness and adaptability to various genomic contexts. 4. The bidirectional Mamba module in PlantBiMoE allows for efficient encoding of both forward and reverse DNA strands, addressing the limitations of unidirectional models. This is crucial for accurately modeling the complex regulatory mechanisms in plant genomes. 5. SparseMoE enables the model to dynamically activate only a subset of experts for each input token, enhancing specialization and reducing interference between unrelated inputs. This leads to improved generalization and efficiency. 6. PlantBiMoE's architecture includes a theoretical context window of 32,768 base pairs, allowing it to capture long-range dependencies in genomic sequences. This is a significant improvement over previous models and essential for tasks involving cis-regulatory elements. 7. The model's lightweight design, with only 116 million parameters, makes it highly practical for deployment in typical research settings, unlike larger models that require extensive computational resources. 8. The introduction of the Modified Plants Genome Benchmark (MPGB) provides a unified and enhanced evaluation framework for plant genome language models, consolidating 31 datasets across 11 tasks. This benchmark will facilitate future model comparisons and advancements. 📜Paper: arxiv.org/abs/2512.07113v1 #PlantGenomics #LanguageModeling #SparseMoE #BidirectionalMamba #ComputationalBiology

stanford-cs336.github.io/spr… - 這堂課是史丹佛大學 CS336，教你從頭打造語言模型。 - 課程內容涵蓋資料收集、模型建構、訓練和評估。 - 先備知識：Python 程式能力、深度學習和系統優化經驗、微積分、線性代數、機率統計、機器學習基礎。 - 課程作業著重實作，包括 Transformer 模型、系統優化、模型擴展、資料處理和對齊。 - 鼓勵在 CPU 上除錯，再用 GPU 訓練。 - 課程有 Honor Code，禁止直接用 AI 寫作業，鼓勵小組討論。 - 課程提供雲端 GPU 資源建議。 - 課程進度表列出各週主題和作業截止日期。 #LanguageModeling #StanfordCS336 #DeepLearning

Ready to push diffusion language modelling forward? ⭐️ Star the repo, fork the code, and help build the future of text generation. 💬 Tell us what algorithms / datasets you’d like to see next in UNI-D². 🤝 Happy to help you get onboarded — DMs / issues welcome. 🔗 GitHub: github.com/nkalyanv99/UNI-D2 📚 Docs: nkalyanv99.github.io/UNI-D2/ #DiscreteDiffusion #LanguageModeling #NLP #GenerativeAI #DLM #OpenSource

From DTC Laboratory, Jadavpur University #Deeplearning #Machinelearning #CLM #LLM #languagemodeling #qsar #Cheminformatics #Features #text #Paradigm #Wiley #WIREs

Ai for good #ai #TrM #AImodel Tiny #recursive model #model #new #ChatGPT #grok #grokipedia #LanguageLearning #language #aivideo #languagemodeling #languageandlearning #meta #Tinyrecursivemodel #samsung NOt #Gemini #DeepSeek x.com/i/broadcasts/1lDxLBeRQ… youtube.com/watch?v=AOem-S2x…

This new language model chDzDT is really good at understanding Algerian Arabic, even though it has tricky spelling and grammar rules. It learns by looking at individual characters instead of whole words, which helps it handle all the different ways words can change in this dialect. This means chDzDT can be used for cool things like figuring out what part of speech a word is or how positive or negative a sentence is. #NLP #ArabicDialect #LanguageModeling arxiv.org/abs/2509.01772 #ArtificialIntelligence