Jeff Dean & Noam Shazeer — 25 years at Google: from PageRank to AGI
This week I welcome on the show two of the most important technologists ever, in any field.
Jeff Dean is Google's Chief Scientist, and through 25 years at the company, has worked on basically the most transformative systems in modern computing: from MapReduce, BigTable, Tensorflow, AlphaChip, to Gemini.
Noam Shazeer invented or co-invented all the main architectures and techniques that are used for modern LLMs: from the Transformer itself, to Mixture of Experts, to Mesh Tensorflow, to Gemini and many other things.
We talk about their 25 years at Google, going from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and maybe soon to ASI.
My favorite part was Jeff's vision for Pathways, Google’s grand plan for a mutually-reinforcing loop of hardware and algorithmic design and for going past autoregression. That culminates in us imagining *all* of Google-the-company, going through one huge MoE model.
And Noam just bites every bullet: 100x world GDP soon; let’s get a million automated researchers running in the Google datacenter; living to see the year 3000.Watch on Youtube; listen on Apple Podcasts or Spotify.
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Timestamps
00:00:00 - Intro
00:02:44 - Joining Google in 1999
00:05:36 - Future of Moore's Law
00:10:21 - Future TPUs
00:13:13 - Jeff’s undergrad thesis: parallel backprop
00:15:10 - LLMs in 2007
00:23:07 - “Holy s**t” moments
00:29:46 - AI fulfills Google’s original mission
00:34:19 - Doing Search in-context
00:38:32 - The internal coding model
00:39:49 - What will 2027 models do?
00:46:00 - A new architecture every day?
00:49:21 - Automated chip design and intelligence explosion
00:57:31 - Future of inference scaling
01:03:56 - Already doing multi-datacenter runs
01:22:33 - Debugging at scale
01:26:05 - Fast takeoff and superalignment
01:34:40 - A million evil Jeff Deans
01:38:16 - Fun times at Google
01:41:50 - World compute demand in 2030
01:48:21 - Getting back to modularity
01:59:13 - Keeping a giga-MoE in-memory
02:04:09 - All of Google in one model
02:12:43 - What’s missing from distillation
02:18:03 - Open research, pros and cons
02:24:54 - Going the distance
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20VC: Spending $2M to Train a Single AI Model: What Matters More; Model Size or Data Size | Hallucinations: Feature or Bug | Will Everyone Have an AI Friend in the Future & Raising $150M from a16z with Noam Shazeer, Co-Founder & CEO @ Character.ai
Noam Shazeer is the co-founder and CEO of Character.AI, a full-stack AI computing platform that gives people access to their own flexible superintelligence. A renowned computer scientist and researcher, Shazeer is one of the foremost experts in artificial intelligence (AI) and natural language processing (NLP). He is a key author for the Transformer, a revolutionary deep learning model enabling language understanding, machine translation, and text generation that has become the foundation of many NLP models. A former member of the Google Brain team, Shazeer led the development of spelling corrector capabilities within Gmail, the algorithm at the heart of AdSense.
In Today's Episode with Noam Shazeer We Discuss:
1. Entry into the World of AI and NLP:
How did Noam first make his way into the world of AI and come to work on spell corrector with Google?
What are 1-2 of his biggest takeaways from spending 20 years at Google?
What does Noam know now that he wishes he had known when he started Character?
2. Model Size or Data Size:
What is more important, the size of the data or the size of the model?
Does Noam agree that "we will not use models in a year that we have today?" What is the lifespan of a model?
Does Noam agree that the companies that win are those that are able to switch between models with the most ease?
With the majority of data being able to be downloaded from the internet, is there real value in data anymore?
3. The Biggest Barriers:
What is the single biggest barrier to Character today?
What are the most challenging elements of model training? Why did they need to spend $2M to train an early model?
What are the most difficult elements of releasing a horizontal product with so many different use cases?
Where does the value accrue in the race for AI dominance; startups or incumbents?
4. AI's Role on Society:
Why does Noam believe that AI can create greater not worse human connections?
Why is Noam not concerned by the speed of adoption of AI tools?
What does Noam know about AI's impact on society that the world does not see?
Your AI Friends Have Awoken, With Noam Shazeer
Noam Shazeer played a key role in developing key foundations of modern AI - including co-inventing Transformers at Google, as well as pioneering AI chat pre-chatGPT. These are the foundations supporting today’s AI revolution. On this episode of No Priors, Noam discusses his work as an AI researcher, engineer, inventor, and now CEO.
Noam Shazeer is currently the CEO and Co-founder of Character AI, a service that allows users to design and interact with their own personal bots that take on the personalities of well-known individuals or archetypes. You could have a socratic conversation with Socrates. You could pretend you’re being interviewed by Oprah. Or you could work through a life decision with a therapist bot. Character recently raised $150M from A16Z, Elad Gil, and others. Noam talks about his early AI adventures at Google, why he started Character, and what he sees on the horizon of AI development.
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Show Links:
Noam Shazeer - Google Scholar
Noam Shazeer - Chief Executive Officer - Character.AI | LinkedIn
Character.AI
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Character_ai
Show Notes:
[1:50] - Noam’s early AI projects at Google
[7:13] - Noam’s focus on language models and AI applications
[11:13] - Character’s co-founder Daniel de Freitas Adiwardana work on Google’s Lambda
[13:53] - The origin story of Character.AI
[18:47] - How AI can express emotions
[26:51] - What Noam looks for in new hires