AI is revolutionizing logistics—cutting costs by 15%, slashing inventory by 35%, and boosting service levels by 65%! Discover how to structure your team for success with AI and ML: https://
okt.to/IKQ7ux @mvollmer1 @alvinfoo @blueyonder @nicochan33 @betamoroney
MACHINE LEARNING
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AI Revolutionizing Logistics: Cost Reduction and Team Structuring
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Transformers Learn Positional Information Without Explicit Encodings
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Transformer Language Models without Positional Encodings Still Learn Positional Information Haviv et al.: https://
arxiv.org/abs/2203.16634 #ArtificialIntelligence #DeepLearning #MachineLearning -
Gemma LLM Speech Recognition and Disordered Speech at ICASSP2025
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Today at 3:30 PM, Subhashini Venugopalan will be at the #ICASSP2025 Google booth to discuss how to enable the open source Gemma LLM to recognize speech and to act as an ASR model. She'll also discuss how Gemma can be trained using RL to understand disordered speech.
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Llama-4-Maverick tops smolagents leaderboard, beats DeepSeek-R1
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Llama-4-Maverick is CRAZY GOOD to power agents It's now the top open model on smolagents LLM leaderboard, beating the much larger DeepSeek-R1!
Congrats @ThomasScialom and team! -
AI Scientist-v2: Agentic Tree Search for Automated Scientific Discovery
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Excited to release our technical report: “The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search” https://
pub.sakana.ai/ai-scientist-v
2/paper/
… The AI Scientist-v2 incorporates an “Agentic Tree Search” approach into the workflow, enabling deeper and more -
MedSAM2: AI Model for 3D Medical Image Segmentation
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Harvard and University of Toronto researchers dropped an AI for 3D medical image & video segmentation
— Rowan Cheung (@rowancheung) 8 avril 2025
Built atop Segment Anything Model 2.1, MedSAM2 generalizes across organs, modalities, and pathologies with an 85%+ reduction in annotation costspic.twitter.com/ZW66mTldfjHarvard and University of Toronto researchers dropped an AI for 3D medical image & video segmentation Built atop Segment Anything Model 2.1, MedSAM2 generalizes across organs, modalities, and pathologies with an 85%+ reduction in annotation costs
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Actual LLM Agents Coming: Latest Developments
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Actual LLM agents are coming | Vintage Data https://
buff.ly/xcDK3c4 #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Stanford releases WorldScore benchmark for world model evaluation
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Check out our newly released WorldScore, by our @StanfordSVL team on benchmarking world models!!❤️🔥🌎🏆 https://t.co/39K7IHq4lC
— Fei-Fei Li (@drfeifei) 8 avril 2025Check out our newly released WorldScore, by our @StanfordSVL team on benchmarking world models!!
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Scaling Laws of Synthetic Data for Language Models
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Scaling Laws of Synthetic Data for Language Models Qin et al.: https://
arxiv.org/abs/2503.19551
v2
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APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation
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APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay Prabhakar et al.: https://
arxiv.org/abs/2504.03601 #ArtificialIntelligence #DeepLearning #MachineLearning