How to Become an AI Researcher: Foundations, Pathways, and a Reproducible First Experiment
How to become an AI researcher: math and computing foundations, experimental design, reproducibility, research ethics, and a first reproducible experiment.
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How to become an AI researcher: math and computing foundations, experimental design, reproducibility, research ethics, and a first reproducible experiment.
How to use Claude for research planning and synthesis with a source-linked brief, a verification log, and a clear line between evidence and interpretation.
AI research tools matched to the task — live citations, PDF stacks, literature reviews — with the verification discipline that keeps invented sources out of your work.
NotebookLM answers only from the sources you upload, with citations you can check. Setup, the features worth learning first, three real workflows, and what not to upload.
Deep research replaces a one-paragraph reply with real investigation: the AI plans a strategy, follows leads, and returns a cited report. How it works and when it’s worth the wait.
How to summarize a PDF with AI you can actually trust: the tools that work, prompts that cite page numbers, and the verification habit that keeps the summary honest.
How to use Perplexity AI well: write full questions, use the right mode, and always open the citations before trusting a fact, plus honest notes on pricing and limits.
The best AI search engines compared across three groups, matched to whether you do research, everyday questions or familiar web search, with honest limits.