Technology
Artificial intelligence
Artificial Intelligence & Machine Learning
Also known as AI, Machine learning
These are systems that learn from piles of examples instead of following rules a human wrote — so they can recognize faces, write text, and make judgement calls, but nobody can fully explain each choice. That raises a deep question philosophers already ask about knowledge versus belief: does the machine actually know, or just guess well? Because these systems now decide loans and bail, they run straight into what 'fair' means in law, into race and ethnicity when the data carries old bias, and into media questions of representation and power — who gets shown, who gets blamed, and who answers when it's wrong.
Key people
- Geoffrey HintonBritish-Canadian computer scientist and psychologist
- John McCarthyAmerican computer scientist and cognitive scientist (1927-2011)
- Marvin MinskyAmerican cognitive scientist (1927-2016)
- John HopfieldAmerican scientist (born 1933)
Timeline
- 1950Gaming Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.
- 1960Many of the workshop attendees became the leaders of AI research in the 1960s.
- 1970This issue was actively discussed in the 1970s and 1980s.
- 1985By 1985, the market for AI had reached over a billion dollars.
- 2002Beginning around 2002, they founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.
Read
- Artificial IntelligenceStuart J. Russell · 1994Book
- Artificial Intelligence and Soft ComputingLeszek Rutkowski · 2012Book
- The Fifth GenerationEdward A. Feigenbaum · 1983Book
- Life 3.0Max Tegmark · 2017Book
Watch
- AI, Machine Learning, Deep Learning and Generative AI ExplainedIBM TechnologyVideo
- What is Artificial Intelligence? | ChatGPT | The Dr Binocs Show | Peekaboo KidzPeekaboo KidzVideo
- What is Artificial Intelligence? | Artificial Intelligence In 5 Minutes | AI Explained | SimplilearnSimplilearnVideo
Listen
- The Artificial Intelligence ShowPaul Roetzer and Mike KaputPodcast
- AI LITERACY - A Podcast about Artificial IntelligenceAI LITERACYPodcast
- The AI Daily Brief: Artificial Intelligence News and AnalysisNathaniel WhittemorePodcast
- Artificial Intelligence MasterclassAI MasterclassPodcast
By the numbers
- 73.6Internet users (%) — global, 2025 (World Bank)
- 111.5Mobile subs / 100 — global, 2025 (World Bank)
Debates
- Should AI development be regulated globally?One view: Regulation is vital to prevent misuse, ensure safety, and address ethical concerns before AI becomes too powerful. · Another: Over-regulation could stifle innovation and slow progress, potentially hindering beneficial advancements.Open question
- Will AI lead to widespread job displacement?One view: AI will automate many routine tasks, leading to significant job losses across various industries. · Another: AI will create new jobs and enhance human capabilities, shifting the workforce rather than eliminating it entirely.Open question
- Can AI truly be conscious or sentient?One view: Advanced AI could develop self-awareness through complex learning and interaction, evolving beyond mere programming. · Another: AI operates based on algorithms and data, lacking biological components and subjective experience required for consciousness.Open question
Glossary
- Machine LearningA subset of AI where systems learn from data to identify patterns and make predictions without explicit programming.
- Neural NetworkA computing system inspired by the human brain, designed to recognize patterns in data.
- AlgorithmA set of rules or instructions followed by a computer to solve a problem or perform a task.
- Deep LearningA type of machine learning that uses multi-layered neural networks to learn from vast amounts of data.
- Natural Language Processing (NLP)A field of AI that enables computers to understand, interpret, and generate human language.
- Computer VisionA field of AI that trains computers to 'see' and interpret visual information from images and videos.
Careers
Roles this can lead toward
Student research
Published policy papers by One Young India delegates — every delegate leaves published under their own name.
- Leveraging Artificial Intelligence to Prevent Armed Conflict through Predictive Trade PoliciesDaivik Suri
- The Plasticity Paradox: Technology’s Impact on The Adolescent BrainNeeti Singh
- The Future of Cybersecurity: Is Artificial Intelligence a Friend or Foe?Pradhyumna Prakash
- Transforming Education Through Artificial Intelligence: Personalization, Accessibility, and Efficiency in the 21st Century ClassroomSrinja Mallik
- Rethinking Education in the Digital Age: Bridging the Gap Between Technology and AccessGovind Mehra
- Artificial Intelligence in Businesses Decision MakingAanya Kapur
Threads 9
Where this connects to other fields — and why it's worth knowing.
- Epistemology Philosophy
An AI will tell you it's 99% sure of an answer that's completely made up. Engineers now test whether a machine's confidence actually matches how often it's right, and they call it 'calibration.' It's a high-tech version of an ancient puzzle: the difference between truly knowing something and just strongly believing it.
- Social Media & Society Sociology
Social feeds show you whatever keeps you scrolling, and nothing grips people like outrage. Studies find that every angry, moral word you add to a post makes it spread about a fifth further. So without anyone deciding to, the algorithm keeps breeding a angrier society just by chasing your attention.
- Theories of Justice Law
When AI predicts things like who might reoffend, people demanded it be 'fair.' But mathematicians proved something wild: you literally cannot satisfy every reasonable definition of fair at the same time. So building 'fair' AI forces a hard choice about which kind of unfairness you're willing to keep.
- How Beliefs About Learning Shape Teaching Education
Teachers have argued forever: do you drill facts into kids, or let them figure things out themselves? Decades later, people building AI hit the exact same fork, calling it 'supervised' versus 'reinforcement' learning. Two totally separate fields ended up fighting the same fight about how any mind should learn.
- Race & Ethnicity Sociology
You can tell an AI to ignore someone's race entirely, and it'll still find it anyway, through their zip code, their name, even what they buy. Those clues quietly stand in for race. So a program built to be 'colorblind' can end up rebuilding the same old discrimination, just wearing a data disguise.
- Media Ethics & Representation Media
Some face-recognition systems badly misread darker-skinned faces, because those faces were barely in the photos the machine learned from. It's the same problem as a TV show where whole groups never appear on screen. Who gets left out of the training data gets left out of how the machine sees the world, hard-coding old blind spots into new technology.
- Endangered and Dying Languages Literature
People hoped AI translators would rescue dying languages. But an AI only learns a language by reading mountains of text in it, and rare languages barely have any. So the tongues that most need saving are the ones AI quietly ignores, pushing them even closer to vanishing.
- Utilitarianism vs Kantian Ethics Philosophy
A self-driving car about to crash may have to 'choose' who gets hurt, and a programmer has to write that choice into code before the car ever hits the road. So the old classroom debate about right and wrong, save the most people or never cross certain lines, suddenly becomes an actual engineering spec someone has to type out.
- The Caste System Sociology
An AI deciding who gets a loan or bail learns from old records — records shaped by caste. So it quietly repeats that bias while looking neutral and "data-driven." Caste gets outlawed on paper but survives inside the algorithm.