Jeff Dean & Noam Shazeer – 25 years at Google: from PageRank to AGI

How Google Scientists Made Computers Talk Like Humans

• How did Jeff and Noam's early computer projects lead to today's talking AI?
  
  Jeff and Noam were like builders who made tiny toy blocks before anyone had big castles!

  1. Jeff's college project in 1990 taught computers to learn patterns, but computers were too slow back then
  2. Noam built a smart spelling checker in 2001 that could understand "scrumbled uggs Bundict" meant "scrambled eggs Benedict"
  3. In 2007, they created a program that could look at 2 trillion words to understand language
  4. They had a feeling that if computers could learn patterns from lots of words, they could become really smart

  These early toys were the great-grandparents of today's AI that can talk with you!
• How did Google's goal to organize information lead to smart AI?
  
  Google started with a simple idea: help people find information. This perfectly matched with making smart AI!

  1. Finding information meant understanding what people are asking for
  2. Google's search engine needed to understand words and their meanings, just like AI needs to
  3. Translating languages was one of Google's early AI projects that helped organize world information
  4. As Jeff said: "Organizing the world's information" is a really big job that needs smart computers

  It's like Google was building a giant library, and needed a super-smart librarian (AI) to help everyone find exactly what they need!
• How did computer power and clever ideas work together to make AI better?
  
  Imagine trying to build a rocket with bicycle parts - that's what early AI was like!

  1. 20 years ago, computers just got faster on their own every 18 months, so scientists just waited for better computers
  2. Around 10 years ago, computers stopped getting faster as easily, so scientists had to get more clever
  3. Google built special chips called TPUs that were super good at the math AI needs
  4. Scientists changed their AI design to work better with these special chips

  It's like a dance between the computer parts and the smart ideas! When one gets better, the other one changes too. This teamwork made today's amazing AI possible!

How AI Brains Are Changing: From One Big Brain to Many Small Parts

• Why is making AI with smaller parts better than one big brain?
  
  Imagine if your toy had different puzzle pieces instead of being one big block:

  1. Special talents: Different parts can be good at different things - like one part for math and another for drawing pictures
  2. Easy fixing: If something breaks, you only need to fix that one piece, not rebuild the whole toy
  3. Team work: Different people can work on different parts at the same time

  But it's tricky because all the pieces need to talk to each other nicely, like friends playing together without fighting!
• How could building AI in parts help more people work together?
  
  Think about building a giant LEGO castle with your friends:

  1. Everyone gets a job: One friend builds towers, another makes doors, and someone else works on the knights
  2. Work happens faster: Instead of waiting for one person to finish, everyone works at the same time
  3. Special experts: Your friend who loves dragons can make amazing dragon parts without needing to know about drawbridges
  4. Mix and match: You can swap parts in and out to try different castle designs

  This means AI could get smarter faster because lots of different teams can help build it together!
• What happens when AI can choose how much brain power to use for different jobs?
  
  Imagine if your brain could decide how hard to think:

  1. Save energy: For easy questions like "what's 2+2?" the AI uses just a tiny bit of power
  2. Think harder when needed: For tough problems like "design a rocket ship," it uses lots more brain power
  3. Less waiting: Easy answers come back super fast instead of taking the same time as hard questions
  4. More helpful: The AI can solve really big, complicated problems by using extra thinking power when it needs to

  It's like having a friend who sometimes thinks quickly and sometimes thinks really deeply, depending on what you ask them!

How Computer Thinking Time Makes AI Smarter

• Why does letting AI think longer make it solve problems better?
  

  Imagine if you could make your toy robot smarter just by letting it think longer about a problem! That's what "inference-time compute" means.

  1. Different thinking time for different problems - Just like you think quickly about "1+1" but take longer for "56×24", AI can use more thinking time for harder questions.
  2. Better answers when needed - For important questions, we can give AI extra thinking time to get better answers.
  3. Save energy on easy stuff - For simple questions, AI can think quickly and save energy.

  It's like giving AI a "think harder" button we can press when we need the very best answers!

• What problems happen when AI needs entire buildings of computers?
  

  When AI needs HUGE computer buildings to work, we face some tricky problems:

  1. Power problems - These big AI systems need as much electricity as a small city! We need to find clean ways to power them.
  2. Heat troubles - All those computers get super hot, like hundreds of ovens running at once. We need special cooling systems.
  3. Computer talking - Computers in different buildings need to share information quickly, like playing telephone across miles.
  4. Breaking down work - We need to split big AI jobs across many computers, like many friends each doing part of a giant puzzle.

  Solving these problems is like building the world's biggest teamwork project!

• How will AI save money by getting smarter about using its brain power?
  

  When AI learns to use its "thinking power" more wisely, lots of money things change:

  1. Smart spending - AI will learn to use more brain power on hard problems and less on easy ones, like you spending more time on hard homework.
  2. Cheaper helpers - AI helpers might cost much less than human helpers. Talking to AI might be 100 times cheaper than buying a book!
  3. Special brain parts - AI might grow special "brain parts" that only wake up for certain problems, like having a math expert who only shows up for math questions.
  4. Growing as needed - Instead of building one giant AI, we could have smaller ones that work together, adding more help only when needed.

  It's like having a super-smart friend who knows exactly when to think hard and when to relax!

How Can AI Learn More Like Humans Do?

• How can we help AI learn with less information like humans do?
  

  AI today needs to see way more examples than humans to learn things! To help AI learn more like we do:

  1. Different learning goals: Instead of just guessing the next word, AI could try solving bigger puzzles
  2. Learning from visuals: Humans learn tons from seeing things, not just reading
  3. Thinking deeply: AI could spend more time thinking about what it learned, like how you might think about a story after reading it
  4. Self-improvement: AI could practice by itself, like how you can get better at drawing just by trying, without needing a teacher
• What would happen if AI could try things and learn from what happens?
  

  Imagine if AI could do things and see what happens - just like how you learn by trying!

  1. Learning by doing: A baby learns about gravity by dropping toys. AI could learn much faster if it could try things out
  2. Real-world understanding: AI would better understand cause and effect ("if I push this, that happens")
  3. Getting smarter by playing: Like how you learn games by playing them, AI could "play" to improve itself
  4. Solving new problems: AI could figure out things nobody taught it, just by experimenting

  This would make AI much smarter because it wouldn't just learn from what it's told - it would learn from what it does!

• How could AI get smarter just by thinking to itself?
  

  Did you know AI could get smarter just by thinking to itself? Here's how:

  1. Practice makes perfect: Just like you can practice math problems in your head, AI can practice solving problems it already knows about
  2. Thinking games: AI could play against itself, like playing chess against yourself helps you improve
  3. Making connections: AI could find new patterns in what it already knows - like how you might suddenly realize dinosaurs and birds are related!
  4. Imagination workouts: AI could make up new examples to learn from, like how you might imagine different ways to build with blocks

  This is super cool because it means AI wouldn't always need new information from humans to keep getting smarter!

Making AI Smarter Safely: When Computers Learn to Improve Themselves

• What should we do if computers start making themselves smarter really fast?
  

  Think of AI like teaching a super-smart dog new tricks, but the dog can teach itself too!

  1. Keep humans in charge - Always have people checking what the AI is learning
  2. Take small steps - Don't let AI make huge changes to itself all at once
  3. Balance speed and safety - Being careful is more important than being fast
  4. Have good rules - Make sure AI follows guidelines that keep everyone safe

  Just like you wouldn't let a car drive itself without testing it first, we need to make sure self-improving AI stays helpful and doesn't make mistakes!

• What safety rules do we need when computers get super-smart at certain jobs?
  

  Imagine if you had a robot helper that became better than humans at some things. Here's what we'd need to keep it safe:

  1. Off buttons - Ways to turn AI off if it starts doing something wrong
  2. Truth checkers - Tools that make sure AI isn't making things up
  3. Explain itself - The AI should be able to tell us why it makes decisions
  4. Guard rails - Limits that stop AI from doing harmful things
  5. Test, test, test - Try the AI in safe places before letting it do important jobs

  It's like teaching a super-smart elephant to be gentle - we need to make sure it knows how to use its strength safely!