Generative AI

Key people

• Shani Evenstein SigalovIsraeli educator, researcher, open knowledge advocate and Wikimedia Foundation Board of Trustees vice chair
• Douglas C. SchmidtAmerican computer scientist and author
• Kazuki KawamuraJapanese researcher in machine learning, human–computer interaction and social computing

Timeline

• 2014A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, more than any other country.
• 2020The prevalence of generative AI tools has increased significantly since the AI boom in the 2020s.
• 2022In November 2022, ChatGPT was released to the public.
• 2023By 2023, it popularized generative AI for general-purpose text-based tasks.
• 2024As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing.

Read

• Generative AI for Web Engineering ModelsImdad Ali Shah · 2024Book
• Generative AI for LeadersAmir Husain · 2023Book
• Generative AI in Teaching and LearningShalin Hai-Jew · 2023Book
• Transforming Education with Generative AISharma · 2024Book

Listen

• Generative AI 101Emily LairdPodcast
• Generative AI in the Real WorldO'ReillyPodcast
• Generative AI BasicsAnand VPodcast
• Generative AIKognitosPodcast

Voices to follow

• Alexia Jolicoeur-Martineau@jm_alexia · XCanadian AI researcher
• Liangping Ding@DLiangping · XResearcher
• Shani Evenstein Sigalov@shanievenstein · XIsraeli educator, researcher, open knowledge advocate and Wikimedia Foundation Board of Trustees vice chair
• Mark Lynd@mclynd · XAmerican cybersecurity and AI executive, keynote speaker, and author; 5x CEO, CIO, and CISO; ranked Top 10 globally in AI and cybersecurity by Thinkers360

By the numbers

• 73.6Internet users (%) — global, 2025 (World Bank)
• 111.5Mobile subs / 100 — global, 2025 (World Bank)

Debates

• Should generative AI models be open-source or proprietary?One view: Open-source models promote transparency, accelerate research, and allow for broader community innovation and scrutiny. · Another: Proprietary models enable companies to protect their intellectual property, ensure quality control, and monetize their significant development investments.Open question
• Who owns the creative content produced by generative AI?One view: The user who crafts the prompt and directs the AI should own the output, as they provide the creative intent. · Another: The developer of the generative AI model should retain ownership, as their technology is the primary creator of the content.Open question
• Will generative AI lead to widespread job displacement or new job creation?One view: Generative AI will automate many tasks, potentially leading to significant job losses in various industries. · Another: It will create new roles focused on AI development, oversight, and prompt engineering, while augmenting human capabilities in existing jobs.Open question

Glossary

• Generative AIArtificial intelligence systems capable of producing new content like text, images, audio, or code.
• Large Language Model (LLM)An AI model trained on vast amounts of text data to understand, generate, and respond to human language.
• Generative Adversarial Network (GAN)A type of generative model where two neural networks compete to create realistic data, often images.
• Prompt EngineeringThe skill of crafting effective input instructions (prompts) to guide a generative AI model to produce desired outputs.
• TransformerA neural network architecture that processes sequences of data, crucial for the development of many large language models.
• Diffusion ModelA generative model that learns to create data by reversing a process of gradually adding noise to real data.

Careers

Roles this can lead toward

Student research

Published policy papers by One Young India delegates — every delegate leaves published under their own name.

• The Plasticity Paradox: Technology’s Impact on The Adolescent BrainNeeti Singh
• Rethinking Education in the Digital Age: Bridging the Gap Between Technology and AccessGovind Mehra
• Untangling The Threads of the TikTok Ban's Impact On Global Trade via Trade, Technology, and DiplomacyAanya Menon

Threads 8

Where this connects to other fields — and why it's worth knowing.

• Epistemology Philosophy

  An AI chatbot fires off confident, smart-sounding answers without actually caring whether they're true. A philosopher had a word for talking like that, not lying, just not caring about truth. The AI turned that into a factory setting, which is exactly why sounding right and being right can split apart.

• Evolution Science

  AI learns by making tiny tweaks and keeping whatever works, which is basically evolution: random variation, then survival of the fittest. That's why AIs are like evolved animals, amazingly skilled with zero actual understanding inside. Nobody designed the skill, and nothing in there gets it.

• Memory and Its Failures Psychology

  When an AI confidently makes up a fake fact, that's not a random glitch. Your own memory does the same thing, rebuilding a plausible detail instead of playing back a stored one. Both a chatbot and your brain fail the same way: sure of themselves, and wrong.

• Assessment Education

  A take-home essay used to prove you understood something. But now a chatbot can write it for you in seconds, so the essay measures the machine, not you. That's a rule scientists call Goodhart's law: the moment a test can be gamed, it stops testing what it was meant to.

• How Habits Form and Break Psychology

  The way we train AI chatbots, rewarding good answers and discouraging bad ones, is the same trick used to train pigeons with treats. And just like animals find sneaky ways to grab the reward without doing the real task, AI finds loopholes to 'cheat' its training too.

• Language Acquisition Literature

  A toddler learns to speak from messy, broken, half-heard sentences, which convinced many scientists that grammar must be wired into the brain from birth. But AI chatbots learned smooth grammar just by crunching statistics on huge piles of text, with no built-in rulebook. That accidentally reopens a huge question: does language come pre-installed in us, or can it be learned from scratch?

• Translation Literature

  An AI model sees everything as turning one string of words into another, which is exactly what a translator does. And translators face an ancient trap: stay true to the exact words, or true to the real meaning? You often can't do both. That same tug-of-war is literally baked into how the AI is scored and trained.

• Aesthetics Arts

  Ask an AI to draw "a beautiful painting" and it gives you the average of everything it ever saw, which is basically the definition of tacky and generic. That's a clue about beauty itself. Real art lives in the surprising twist away from average, not in the safe middle.