An α‑AGI Business is a fully autonomous enterprise that identifies latent “alpha” opportunities across all industries and converts them into compounding value by out‑learning, out‑thinking, out‑designing, out‑strategizing, and out‑executing all competition. #AGI #AGIALPHA
@ceobillionaire
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Model Collapse in Deep Learning: Research Insights
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Strong Model Collapse Dohmatob et al.: https://
arxiv.org/abs/2410.04840 #ArtificialIntelligence #DeepLearning #MachineLearning -
Google’s Prompt Engineering Whitepaper Guide
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"PROMPT ENGINEERING" Lee Boonstra, Google: https://
kaggle.com/whitepaper-pro
mpt-engineering
… #Prompt #ChatGPT #LLMs -
Matrix Calculus for Machine Learning and Beyond
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Matrix Calculus (for Machine Learning and Beyond) Paige Bright, Alan Edelman, Steven G. Johnson: https://
arxiv.org/abs/2501.14787 #ArtificialIntelligence #DeepLearning #MachineLearning -
Hourglass Diffusion Transformers Enable Scalable High-Resolution Image
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Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers Crowson et al.: https://
arxiv.org/abs/2401.11605 #ArtificialIntelligence #DeepLearning #MachineLearning -
The GrandMaster Arrives: AGI and ASI Development
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GM The GrandMaster has arrived. #AGIALPHA #ASI #ASIFirst
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Sampling Over Search: New Test-Time Alignment for Language Models
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Sample, Don't Search: Rethinking Test-Time Alignment for Language Models Gonçalo Faria, Noah A. Smith: https://
arxiv.org/abs/2504.03790 #ArtificialIntelligence #DeepLearning #MachineLearning -
DeepSeek-R1 Thoughtology: Understanding LLM Reasoning Mechanisms
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DeepSeek-R1 Thoughtology: Let's about LLM Reasoning Marjanović et al.: https://
arxiv.org/abs/2504.07128 #ArtificialIntelligence #Deeplearning #MachineLearning -
Scaling Laws for Native Multimodal Models
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Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models Shukor et al.: https://
arxiv.org/abs/2504.07951 #ArtificialIntelligence #DeepLearning #MachineLearning