When Ai make mistake???

Getting ready to research with AI? Let's talk DOs and DON'Ts:

DO...

✅ Use Claude, Gemini, or ChatGPT to summarize papers, extract insights, and save reading time

✅ Generate hypotheses, refine ideas, and draft outlines faster

✅ Explore data with tools like Code Interpreter, DeepSeek, or Groq

✅ Speed up survey design, persona creation, and user feedback analysis

DON'T...

🚫 Trust outputs blindly. AI makes confident mistakes

🚫 Skip validation, especially for citations, stats, and code

🚫 Torget transparency. Disclose AI use in papers and proposals

Prompting well + editing critically = research superpower in 2025.

Full guide 🔗 https://ow.ly/qB2P50WhwvJ

Comments

deep learning with python

https://fun4yo.xyz/book/deep-learning-with-python-2nd-edition/?fbclid=IwY2xjawLHU6FleHRuA2FlbQIxMQABHtO12jFUNNPHx48XkCb3WHWmDrb0dVfcH9w_zFO-B4E_hFol9D-YTDI3QLj2_aem_gOPrdojnczoxyy1i0a7ltw

Is this the end of LoRA as a fine-tuning approach? Singular Value fine-tuning is here! We have a new paper in town called the "Transformers Squared". It promises to adapt any LLM to any task without external intervention. The core idea of the paper is to use Singular Value Decomposition (SVD) to factorize the weight matrices of transformers. During training, we learn different singular values for different tasks. More specifically, we learn to scale the singular values for different tasks. Tasks can be as diverse as math reasoning or coding. The possibilities are endless. During inference, we do a two-pass inference. In the first pass, the LLM decides which "scale" to use for which task. In the second pass, the LLM adapts itself to be a specialist in the task and responds to our prompt. How cool is that? Paper Title: Transformer2: Self-adaptive LLMs Paper: https://sakana.ai/transformer-squared Blog: https://arxiv.org/abs/2501.06252 Video Explanation: https://youtu...

TECH AND SCIENCE CONTENT BELOG.

Search This Blog