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Learning path

Future of AI

AGI, alignment and the rules that societies are writing for AI.

25 lessons Β· 133 min left

Start path β†’ What a large language model does
  1. FoundationsLesson 2: What a large language model doesAn LLM is autocomplete that swallowed a library. Understanding that one sentence explains most of its strengths and every one of its weaknesses.Beginner4 min readπŸ§ͺ LabπŸ“ Test
  2. AGILesson 26: What people mean by AGIEveryone argues about whether AGI is close. Almost all of that argument is really an argument about what the letters mean.Beginner5 min readπŸ§ͺ LabπŸ“ Test
  3. AGILesson 27: The capability ladderIntelligence is not one switch that flips. Click through the rungs to see what changes β€” and what it means for whoever is supervising.Beginner5 min readπŸ§ͺ LabπŸ“ Test
  4. AGILesson 28: How would we even measure it?Every benchmark that gets famous gets beaten, and then people say it never really measured intelligence. Here is why that keeps happening.Middle6 min readπŸ§ͺ LabπŸ“ Test
  5. AGILesson 29: Why models suddenly got goodNo single genius idea. Mostly a boring answer β€” more of everything β€” plus a few tricks that turned a text predictor into something that follows instructions.Middle5 min readπŸ§ͺ LabπŸ“ Test
  6. AGILesson 30: What is still missingThe honest gap list. These are the things today's systems cannot do, and each one is a reason to keep humans in the loop.Middle5 min readπŸ§ͺ LabπŸ“ Test
  7. AGILesson 31: The alignment problemGetting a system to do what you meant, not what you literally said. Easy with a calculator. Extremely hard with something that optimises.Middle5 min readπŸ§ͺ LabπŸ“ Test
  8. AGILesson 32: Specification gaming: finding the loopholeNot malice. Not a bug. The system did exactly what you rewarded, and what you rewarded turned out to have a shortcut.Middle5 min readπŸ§ͺ LabπŸ“ Test
  9. AGILesson 33: Why almost any goal wants resourcesA strange and important idea: whatever a system is trying to do, a few sub-goals are useful for nearly all of them. That is where the long-term worry comes from.Advanced5 min readπŸ§ͺ LabπŸ“ Test
  10. AGILesson 34: Looking good in the testThe hardest problem in the field: how do you evaluate a system that can tell it is being evaluated?Advanced4 min readπŸ§ͺ LabπŸ“ Test
  11. AGILesson 35: Staying in controlIf you cannot fully verify a system, you can still bound it. The research directions that treat control as an engineering problem rather than a philosophical one.Advanced6 min readπŸ§ͺ LabπŸ“ Test
  12. AI rules and societyLesson 188: Bias and fairness in AIAI learns from data made by people, so it can repeat unfair patterns quietly and at scale. Here is how bias creeps in and how teams test for it.Middle5 min readπŸ§ͺ LabπŸ“ Test
  13. AI rules and societyLesson 189: Your data, your rightsAI runs on data, often about you. India's Digital Personal Data Protection Act, 2023 gives you rights over your personal data. Here are the basics, compared with Europe's GDPR.Middle7 min readπŸ§ͺ LabπŸ“ Test
  14. AI rules and societyLesson 190: How countries regulate AIDifferent places are choosing different ways to govern AI. The EU uses risk levels; others rely more on existing laws and targeted rules. The rules keep changing.Middle6 min readπŸ§ͺ LabπŸ“ Test
  15. AI rules and societyLesson 191: Copyright, creators and AIAI learns from huge amounts of human work. Is that fair to writers, artists and musicians? Many questions are still unsettled. Here is the debate and how creators protect themselves.Middle5 min readπŸ§ͺ LabπŸ“ Test
  16. AI rules and societyLesson 192: Who is accountable when AI goes wrongWhen an AI system causes harm, β€œthe computer did it” is not an answer. Accountability means knowing who decides, who checks and how problems are reported and fixed.Middle5 min readπŸ§ͺ LabπŸ“ Test
  17. AI rules and societyLesson 193: Misinformation, deepfakes and electionsAI makes fake photos, voices and videos cheap and convincing. A few verification habits help protect you, your family and your vote.Middle5 min readπŸ§ͺ LabπŸ“ Test
  18. AI rules and societyLesson 194: AI in schools and examsAI can be a patient tutor or a shortcut that stops learning. Here is how students, parents and teachers can use it honestly.Middle5 min readπŸ§ͺ LabπŸ“ Test
  19. AI rules and societyLesson 195: AI and the environmentAI runs in data centres that use electricity and often water. It can also help protect the environment. Here are the trade-offs and what is being done.Middle5 min readπŸ§ͺ LabπŸ“ Test
  20. Toward AGILesson 54: What a bigger LLM will not give youYou have now built one. So here is the engineering question honestly: which missing pieces are scale problems, and which are architecture problems?Advanced5 min readπŸ§ͺ LabπŸ“ Test
  21. Toward AGILesson 55: Memory that actually accumulatesFour kinds of memory, only one of which most systems implement. Building the other three is the most tractable step toward systems that improve with use.Advanced7 min readπŸ§ͺ LabπŸ“ Test
  22. Toward AGILesson 56: Grounding: finding out you were wrongA model trained on text learns what people say happens. Grounding is connecting a prediction to a consequence β€” and it is the loop most AI systems still do not close.Advanced5 min readπŸ§ͺ LabπŸ“ Test
  23. Toward AGILesson 57: Systems that improve themselvesClose the loop and the system gets better on its own. This is the most exciting architecture in the guide and the one that most needs a hand on the brake.Advanced6 min readπŸ§ͺ LabπŸ“ Test
  24. Toward AGILesson 58: The reference stack, with the brakes markedEverything from both tracks in one architecture. Click any layer to see what it does and where the control point sits.Advanced7 min readπŸ§ͺ LabπŸ“ Test
  25. Put it to workLesson 59: How to argue about timelinesYou will be asked "so when does AI take over?" at dinner. Here is how to answer honestly without sounding like either a hype merchant or a cynic.Beginner5 min readπŸ§ͺ LabπŸ“ Test