Check it out if you’re interested, guarantee it will make you hungry: https://galerie.ai/
Lots more to come.
Category: AI
Podcast notes – Noam Shazeer (Character AI, Attention is all you need) on Good Times w Aarthi and Sriram
Intro
-Founded Character AI
-One of authors of “Attention is all you need”
-Was at Google for 20+ years (took a few years break)
Went to Duke undergrad on math scholarship
Realized he didn’t enjoy math, preferred programming and getting computers to do things
During Google interview, Paul Buchheit asked him how to do a good spell corrector, and Noam ended up writing the spell corrector feature for Gmail
Google has been traditionally a bottoms up company – could work on what he wanted
When he started AI, exciting thing was Bayesian networks
Came back to Google to work with Jeff Dean and Google Brain team
“Just a matter of the hardware”
All the growth in hardware is parallelism
Neural networks are mostly matrix multiplications – operations that can be done well on modern hardware
Gamers / video games pulled GPU advancement (highly parallel hardware) out of market
Idea of neural networks has been around since 1970s – loosely modeled on our impression of the brain
Very complicated formula to go from input > output
Formula is made of parameters, and keep tweaking parameters
Neural nets rebranded as “deep learning”
Took off because of parallel computation and gamers
Neural language models are neural networks applied to text
Input is text to this point, output is prediction of what text comes next (probability distribution)
Infinite amount of free training data (text content)
“AI complete problem”
“Really complicated what’s going on in there” (in the neural network)
It’s a really talented improvisational actor – “Robin Williams in a box”
Model improvement is kinda like a child learning – as training and model size grow
Lot more an art than a science – can’t predict very well – if 10% of his changes are improvements, considered “brilliant research” – kinda like alchemy in early days
(Software) bugs – hard to know if you introduce a bug – the system just gets dumber – makes de-bugging extremely difficult
Co-authored “Attention is all you need”
-Previous state of art in LLM is recurrent neural networks (RNN) – hidden state, each new word updates the hidden state, but it’s sequential – slow and costly
–Transformer figures out how to process the entire sequence in parallel – massively more performant
-The entire document / batch becomes the sequence
-Lets you do parallelism during training time
–During inference time it’s still sequential
–
Image processing models – parallelism across pixels – convolutional neural nets (CNN)
Google Translate was inspiration – biggest success of machine learning at the time
Translating languages > one RNN for understanding, and another RNN for generating, and need to connect them
Attention layer – take source sentence (language A), turn into key-value associative memory, like a soft lookup into an index
“Attention” is building a memory, a lookup table that you’re using
DALL-E, Stable Diffusion, GPT3, they’re all built on this Google research
Bigger you make the model, more you train it, the smarter it gets – “ok, let’s just push this thing further”
Eventually need super computer
Google built TPU pods – super computer built out of custom ASICS for deep learning
Now need massively valuable applications
Turing Test, Star Trek, lot of AI inspiration is dialogue
Google LAMDA tech & team – eventually decided to leave and build as a startup
“The best apps are things we have not thought of”
If you ask people with first computers “what is this thing good for”, would get completely wrong answers
Parasocial relationships – feel connection with celebrity or character – one way connection – with AI you can make it two ways
Aarthi: “Your own personal Jarvis”
Still need to make it cheaper – or make the chips faster
Aarthi: ideas / areas for entrepreneurs
-Image gen has exploded – lots of good companies coming, very early and promising
-Things like Github Co-Pilot
-new Airtable – using AI for computation
–
Sriram:
-What’s optimization function that all these models will work toward?
-Will be a very big political / social debate
How do you know better than the user what the user wants?
Podcast notes – Emad Mostaque (Stability AI and Stable Diffusion) – Elad Gil: Short form videos coming “within 2 years at high resolution quality”; “Run Stable Diffusion on your iPhone by next year”
(started notes around 20min in)
Bad guys have AI – they’ll create deep fakes
Community can come together to have counter measures
Elad: similar arguments to regulate cryptography in 90s
4chan has been “red teaming” trying to get the worst out of Stable Diffusion – and it’s not that bad
Especially for LLMs, should have more diverse data sets, have inter-governmental agency to monitor it
Have authenticity tool to verify source of every generated AI output
Generative AI – what are some use cases that should exist
Ali v Tyson live, Lebron v Michael Jordan
Emad wants to remake final season of Game of Thrones
Anyone can create their own models – any person, company, or culture
You need better data, more structured data
Extend models to run on edge – eg, anyone’s computers, iPhones
Make small customized models
“Run Stable Diffusion on your iPhone by next year”
Create national models and communities around them – let them leap frog ahead
Lots of emerging markets went from nothing to mobile phones, now can go to AI models on the edge
How far from short-form videos?
Phenaki, Google — getting close
Chaining these models together – they’re like parts of the brain
“Within 2 years at high resolution quality”
$100B into this sector in next 5 years
AI before today was qualified data science
Now it’s a new type of AI – not AGI yet, but incredibly small and powerful
By the time his daughter’s in university, doesn’t need to write essays
He aims (for Stable Diffusion) to be a layer 1 standardized infrastructure – create Schelling point
Mission is to “activate humanity’s potential”
Take it to India, Indonesia – give it to very smart young people to make their countries better
When AGI comes, I hope it thanks him for buying so many GPUs to help bring it into being
Many of Google’s “jobs to be done” will be displaced
Crypto is interesting – he’s in it since 2012 – focused on decentralized identity, zero knowledge proofs
“Nature of crypto is literally identity”
In a world of infinite content (AI), crypto identity is important
Need to be careful designing crypto economic systems
A year ago, if he said what they planned to do with SD, people would say he’s crazy
Surprised by how far they’ve come, the ability of others to contribute
The activation energy has been the most surprising – “they’re just excited”
“Probably see biggest breakthrough from a 16 year old in Uzbekistan” – the global open access nature of it
Will completely disrupt social networks – will move intelligence from the core to the edge
Apple is doing this – moving to AI – moving to edge
Opportunity to have personalized AIs that work for us and with us
SD is applying for B corp status – mission based
Plan to spin SD into different Foundations
Did investment round in August – didn’t give up any independence – did with investors that are open sourced and mission aligned
Which industries disrupted first?
-Call centers
-Powerpoint, forms of visual communication
-Artist won’t be that disrupted – will enable new forms of art
This tech is amazingly powerful
After releasing Stable Diffusion – people encoded it in Japanese – lots of use cases like this
So far governments have been very friendly
AI powered teaching – like Neal Stephenson’s Young Lady’s Primer
Moving forward, only release safe for work models
Licensing discussions should be more open
Will have models across all sorts of languages – recently released Korean model
“Cars get better by some modest amount each year, as do most other things I buy or use. LLMs, in contrast, can make leaps.” – Tyler Cowen on AI
Must read if you’re interested in AI and its implications; Tyler’s commentary on the recent explosion of AI into the popular consciousness (driven in large part by ChatGPT) has been, in my view, the most realistic+pragmatic:
“I don’t have a prediction for the rate of improvement, but most analogies from the normal economy do not apply. Cars get better by some modest amount each year, as do most other things I buy or use. LLMs, in contrast, can make leaps.”
“I’ve started dividing the people I know into three camps: those who are not yet aware of LLMs; those who complain about their current LLMs; and those who have some inkling of the startling future before us. The intriguing thing about LLMs is that they do not follow smooth, continuous rules of development. Rather they are like a larva due to sprout into a butterfly.”
Interview notes – Sam Altman on OpenAI, ChatGPT, Helion, Hermeus – StrictlyVC
He doesn’t read the news
Likes trolling on Twitter…”Twitter’s fun”
“Twitter’s gonna be fine”
HIS INVESTMENTS
400 personal investments, a few thousand including YC
All the companies he’s added value to are those he thinks about in his free time – while hiking, texting the founder an idea
Most successful investment? Stripe on a multiples basis
Helion – fusion energy
Personally invested $375M (!)
Other thing besides OpenAI that he spends a lot of time on
New energy system that works on super low cost
Hardest challenge is how to replace all (current) generative capacity on Earth really quickly
“Who can deliver energy the cheapest, and enough of it”
Simple machine, affordable cost, reasonable size
If fusion works…will change dynamics of what’s possible – enables more downstream (eg, more powerful planes)
Hermeus – supersonic jet company
Led $100M round
Was also involved with a competitor Boom – but different tech and approach
Huge market with multiple needs
Worldcoin
He’s a cofounder, on the board, but not day to day involved
Will tell its story soon – believes it will go over well (unlike earlier negative media coverage)
We give up more privacy to Facebook than Worldcoin
Phenomenal team
Launch in months
Interested in any tech to experiment with global UBI (versus what one country can do)
Re: crypto — “honestly not super interested”
“Love spirit of web3, but don’t intuitively feel why we need it”
Inception Fertility
In-vitro gametogenesis
In shadow of AI
Next 5-7 years of biotech will be remarkable
Human life extension – “yeah maybe that’s gonna work”
Investing for 20 years, president of YC for 5-6 years
Garry (new YC president) will do a lot of things differently and be wildly successful
Last few years were really hard for YC
YC can remake itself now – tourists are leaving now
—
ARTIFICIAL INTELLIGENCE
OpenAI has pulled together “most talent dense” AI team
“Gonna be tremendously good”
Why did ChatGPT and DALL-E so surprise people?
“Don’t know…reflected on it a lot”
If you make a good UX on top of something – believed users wanted to interact via dialogue
Pieces were there for awhile
Standard belief was AI would take over low skill / truck driving / generic white collar
Going exact opposite direction – taking over creativity where we thought humans might have special sauce
It’s not an intuitive finding
Released GPT-3 three years ago – thought ChatGPT would be incremental, was surprised by public reaction
ChatGPT will cause societal changes – eg, academic integrity
“Stakes are still relatively low”
Covid did show us society can update to massive changes faster than he expected
Given expected economic impact – “more gradual is better”
GPT-4 will come out when we’re confident we can do it safely and responsibly
Will release tech much more slowly than people will like
GPT-4 rumor mill is a ridiculous thing
Re: ChatGPT – built a thing, couldn’t figure out how to monetize it, put it out via an API, and users figured out how to use it
Would like to see AI super democratized, have several AGIs in the world
Cost of intelligence and energy trends down and down
“Massive surplus…benefits all of us”
Believes in capitalism – best service at lowest price
Society will need to agree on what AGI should never do
Broad absolute rules of the system
Within that, AI can do different things – safe for work one, edgier creative one – different values they enforce
A user can write up a spec of what they want, and AI will act according to it – “should be your AI…to serve you”
Microsoft – only tech company he’d be excited to partner with this deeply
Satya, Kevin Scott, Mary McHale
Values-aligned company
“We’re very much here to build AGI”
“We wanna be useful to people”
Re: Google’s AI – hasn’t seen it, assume they’re a competent org
We’re in a new world – generated text is something we all need to adapt to, like we adapted to calculators
“I’d much rather have ChatGPT teach me…than read a textbook”
Anthropic – rival AI, stressing an ethical layer
Very talented team
Multiple AGIs in the world is better than one
Society decided free speech is not quite absolute – in similar ways AI / LLMs will need to have bounds too
Video is coming… no confident prediction about when
Legitimate research project – could take awhile
AUDIENCE Q&A
When fusion online?
By 2028, could be plugging fusion generators into grid (pending regulators)
Re: AI worst and best case?
“best case is so unbelievably good that it’s hard to imagine, discovering new knowledge in a year instead of 70K years”
“Bad case is lights out for all of us”
More worried about accidental mis-use in short term, less about the AI itself being evil
How far away is AGI?
Much blurrier and gradual transition than people think
Re: state of San Francisco
Real shame we treat people like this
How elected leaders don’t fix the problem
Tech has some responsibility for it
But other cities do better than this
Super long in-person work and Bay Area
Re: ChatGPT reaction
Expected one order magnitude less hype, users, of everything
Less hype is probably better
The tech is impressive, but not robust
Use it 100x, see the weaknesses
How Sam uses ChatGPT?
Summarize super long documents, emails
For translation
Re: Google code red, threat to search
When people talk about new tech being end of a giant company, they’re usually long
Change is coming (for Google), but not as dramatically as people think
Before Google, memorizing facts was important – and now we’ll change again – and we’ll adapt faster than most people think
Prefers hybrid work, like YC – few days at home, few days in office
Skeptical that fully remote is thing everyone does
Most important companies will still be heavily in-person
Safety engineering for AI is different from standard safety engineering – consequences are greater, deserves more study
Raising capital now is hard, especially later stages – but other things easier – easier to rise above noise, hire, get customers
What he’d do now — recommends for founders – “do AI for some vertical”
Advice for AI startups
Differentiate by building deep relationships with users, some moat like network effect
Plan for AI models continually improving
OpenAI is a platform, but also wants to do a killer app (platform + killer app) to show people what’s possible