Reference above: "To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning" https://
arxiv.org/abs/2409.12183
MACHINE LEARNING
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Chain-of-Thought Reasoning Effectiveness in Math and Symbolic Tasks
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Dataset Selection: An Underrated Skill in Modern AI Research
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An underrated but occasionally make-or-break skill in AI research (that didn’t really exist ten years ago) is the ability to find a dataset that actually exercises a new method you are working on. Back in the day when the bottleneck in AI was learning, many methods were
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Biggest AI Impact Predictions for 2025
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What do you think will be the biggest impact on #AI in #2025?
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AI Engineer Bot: Build RAG Chats, Agents, and ML Models
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AI Building AI Tell the bot, or in this case, our AI engineer, what to build, and it can: •Create a RAG chat
•Build an AI agent
•Operate on your code base and generate PRs
•Build structured data ML models -
OLMo 2 introduces reordered norm and QK-norm innovations
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Yo! @allen_ai just dropped OLMo 2 Tech report, some interesting things I found Architecture
> Reordered norm: Normalizing outputs of attention and feedforward layers within transformer blocks instead of inputs
> QK-norm: Normalizing key and query projections with RMSNorm -
Eight Dimensions of Data Quality for AI Machine Learning
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Understanding the eight dimensions of data quality https://
bit.ly/4gIQFaC
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Autonomous AI Agents And The Dawn Of Singularity
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Autonomous AI Agents And The Dawn Of Singularity
#AI #AIio #BigData #ML #NLU #Futureofwork @oluskayacan @richardkimphd @MariaFariello @johnpearcenews5 @ResurgentAV @rohanmuralee @globaliqx http://
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Simple Framework for AI Agents by Abacus AI
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Check out this simple framework for #AI Agents from @AbacusAI at https://
abacus.ai/ai_agents • Connect to any data source or vector store
• Uses code execution engine
• Orchestrate with other ML models
• LLM-agnostic for flexibility
• Designed for chat or task-based operations -
LoRA Tutorial and Additional Tips from Readers
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Glad you found it useful, and thanks for the kind words! I think you probably already read that, but there is a LoRA from-scratch tutorial in the last Appendix. There was a reader how also had some useful additional tips regarding LoRA:
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RAG Search Approaches: Keyword vs Embedding Methods
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Here’s a quick rundown of the most popular (RAG) search approaches out there, plus tips on when they shine: • Keyword Search (e.g., BM25, BM42): Finds direct matches—great for straightforward data or well-defined keywords. • Embedding Search: Captures deeper semantics,