Embracing change and resetting expectations Microsoft Unlocked

This Microsoft article discusses adjusting expectations about AI and change.

unlocked.microsoft.com/ai-anthology/terence-tao
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About Embracing change and resetting expectations Microsoft Unlocked

This Microsoft Unlocked article by Terence Tao, Professor of Mathematics at University of California, Los Angeles, published June 12, 2023, discusses recalibrating expectations for information technology with generative AI tools such as GPT-4.

It describes using GPT-4 with vaguely phrased prompts and raw text from papers, drafts and web pages, and conversational uses such as sounding board, translator or teacher. It also notes that current models can mimic expert responses but often produce incorrect content that requires line-by-line checking.

Key features

  • Explains prior expectations for hardware and software improvement
  • Describes using GPT-4 with vaguely phrased prompts and raw text
  • Gives example of generating questions from math preprint PDF pages
  • Describes conversational uses such as translator, teacher, sounding board
  • Discusses need for line-by-line checking of LLM-generated mathematics
  • Raises questions about journals and graduate education adaptation
Best for: Readers interested in perspectives on generative AI use in mathematics research and education.
Good for
  • Describes direct use of GPT-4 for papers, presentations, and drafts
  • States accuracy limits and need for verification of model output
Watch out for
  • Reflects 2023-level tools as of its June 12, 2023 publication date

Embracing change and resetting expectations Microsoft Unlocked at a glance

Type
Publication
Platforms
Web
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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Embracing change and resetting expectations Microsoft Unlocked FAQ

Who wrote the article?
Terence Tao, Professor of Mathematics at University of California, Los Angeles.
What use of GPT-4 does the author describe for reading papers?
Feeding the first few PDF pages of a math preprint to generate questions an expert might ask, to prepare presentations or begin reading a complex paper.
What problem does the author note with LLM-generated mathematics?
It can persuasively mimic correct expert responses but often contains nonsense on close inspection, so only line-by-line reading can determine if there is substance.
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