Hacker News
- Minerva: Solving Quantitative Reasoning Problems with Language Models https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html 36 comments
- On ChatGPT and the future of research mathematics https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html 61 comments math
- Minerva: Solving Quantitative Reasoning Problems with Language Models https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html 5 comments math
Linking pages
- Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds? https://garymarcus.substack.com/p/did-googleai-just-snooker-one-of 290 comments
- AI scientists are studying the “emergent” abilities of large language models – TechTalks https://bdtechtalks.com/2022/08/22/llm-emergent-abilities/ 10 comments
- AI Forecasting: One Year In https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ 8 comments
- Google Research, 2022 & beyond: Language, vision and generative models – Google AI Blog https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html 5 comments
- Google AI Developed a Language Model to Solve Quantitative Reasoning Problems https://www.infoq.com/news/2022/07/google-ai-minerva/ 2 comments
- Google AI Introduces Minerva: A Natural Language Processing (NLP) Model That Solves Mathematical Questions - MarkTechPost https://www.marktechpost.com/2022/07/04/google-ai-introduces-minerva-a-natural-language-processing-nlp-model-that-solves-mathematical-questions/ 1 comment
- Artificial Intelligence (AI) Research Innovations in 2022 from Google, NVIDIA, Salesforce, Meta, Apple, Amazon, and AI2 - MarkTechPost https://www.marktechpost.com/2023/01/10/artificial-intelligence-ai-research-innovations-in-2022-from-google-nvidia-salesforce-meta-apple-amazon-and-ai2/ 0 comments
- Google is Leading the AGI race. But can it win? https://sergey.substack.com/p/google-is-leading-the-agi-race-but 0 comments
- 6 important AI near-future breakthroughs, or why the AI hype peak is likely to be ahead of us https://www.codium.ai/blog/6-important-ai-near-future-breakthroughs-or-why-the-ai-hype-peak-is-likely-to-be-ahead-of-us/ 0 comments
- LLaMA: LLMs for Everyone! - by Cameron R. Wolfe https://cameronrwolfe.substack.com/p/llama-llms-for-everyone 0 comments
- Timeline of AI and language models – Dr Alan D. Thompson – Life Architect https://lifearchitect.ai/timeline/ 0 comments
Linked pages
- MIT OpenCourseWare | Free Online Course Materials https://ocw.mit.edu/ 522 comments
- arXiv.org e-Print archive https://arxiv.org/ 312 comments
- Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance – Google AI Blog https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html 279 comments
- MathJax | Beautiful math in all browsers. https://www.mathjax.org/ 221 comments
- [2005.14165] Language Models are Few-Shot Learners https://arxiv.org/abs/2005.14165 201 comments
- Welcome! | The Coq Proof Assistant https://coq.inria.fr/ 125 comments
- Lean http://leanprover.github.io 46 comments
- [1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/abs/1810.04805 25 comments
- LaTeX - A document preparation system https://www.latex-project.org/ 7 comments
- [2112.00114] Show Your Work: Scratchpads for Intermediate Computation with Language Models https://arxiv.org/abs/2112.00114 3 comments
- Minerva Explorer https://minerva-demo.github.io/ 1 comment
- Language Models Perform Reasoning via Chain of Thought – Google AI Blog https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html 1 comment
- [2206.14858] Solving Quantitative Reasoning Problems with Language Models https://arxiv.org/abs/2206.14858 0 comments
- Updates and Lessons from AI Forecasting https://bounded-regret.ghost.io/ai-forecasting/ 0 comments
- [2103.03874] Measuring Mathematical Problem Solving With the MATH Dataset https://arxiv.org/abs/2103.03874 0 comments
- Isabelle https://isabelle.in.tum.de/ 0 comments
- [2112.11446] Scaling Language Models: Methods, Analysis & Insights from Training Gopher https://arxiv.org/abs/2112.11446 0 comments
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