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

Podcast notes – ChatGPT goes prime time! – Practical AI (Daniel and Chris)

Practical AI 206: ChatGPT goes prime time! – Listen on Changelog.com

Hosts: Daniel Whitenack (Data scientist), Chris Benson (Lockheed Martin)

All about ChatGPT

Chris – feels collaborative, like having a partner

Lots of structuring in the output – bulleted lists, paragraphs

Humans get things wrong / incomplete all the time – yet we’re holding AI to a higher standard

Shifting to more open access – maybe in response to open source AI products like Stable Diffusion

Chris – expect to see more “fast followers” to ChatGPT soon

TECHNICALS

GPT language models – it’s a “causal language model” not a “mass language model”
Trained to predict next word in sequence of words, but based on all previous words
Auto-regressive – predicts next thing based on all previous things, and so forth
That’s why the text develops as if a human were typing
Few shot learning – can change answer style based on your questions and prompts

Zero shot = input that a model has never seen before
Few shot = provide a small number of inputs / prompts to guide the model

ChatGPT – trained with “reinforcement learning from human feedback” (RLHF)
Human preference is key part of this

How does it scale?
Human feedback is expensive
3 steps:
1. Pre-train a language model (aka a “policy”) – not based on human feedback
2. Gather human preference data to train a reward model – outputs prediction of human preference
3. Fine tune (1) based on (2)

eg for ChatGPT,
(1) is GPT3.5 (for ChatGPT)
(2) outputs data based on (1), add human labels of preference, and train a “reward model”

GPT3 is 100B+ parameters
ChatGPT reward model is 6B parameters

For (2), goal is to reduce harm by adding human feedback into the loop

For (3), will penalize if it strays too far from (1), and score output according to (2)
Try only to make small iterative changes (adiabatic)

What’s next?
Open research questions – (2) architecture and process hasn’t been fully optimized, lot to explore there
Will be new language models coming (eg, GPT4, Microsoft, Google) – trying different (1) and (2)

Chris Albon tweet:

Sci-fi got it wrong.

We assumed AI would be super logical and humans would provide creativity.

But in reality it’s the opposite. Generative AI is good at getting an approximately correct output, but if you need precision and accuracy you need a human.

Galerie.ai – make generative art with multiple models

A little something we’re working on, you can try it here: http://galerie.ai/

It does two things

1. Returns results from a growing library of the best AI art

2. Lets you create AI art from multiple generative models (including Stable Diffusion 1.5, 2.0, 2.1, and MidJourney)

Let me know what you think!

We auto-suggest some of the trending prompts and popular art results on the home page, try it out!

For example here is ” photorealistic portrait, a young beautiful woman Goddess wearing Echo of Souls Skull Mask armor, skeletal armor bones made from 24k gold and silver metal intricate scroll-work engraving details on armor plating, skeletal armor, gemstones, opals, halo, aura, intricate details, symmetrical,”:

Podcast notes: Sam Altman (OpenAI) on AI – “One of genuine new tech platforms since mobile”

Interviewer: Reid Hoffman
Guest: Sam Altman

Not yet trillion dollar “take on Googles” startups yet – but will be a serious challenge to Google for first time

eg, Human level chatbot interface that actually works – new medical services, new education services

Idea of language interface where you say in natural language, dialogue, and computer just does it for you

Very powerful models will be one of genuine new tech platforms since mobile

How to create an enduring differentiated business
-small handful of large base models will win – skeptical of startups doing small models
middle layer will become really important – take large models, tune it, create model for medicine, or model for AI friend – will have data flywheel

Lots of AI experts think these models won’t generate net new knowledge for humanity – thinks they’ll be wrong and surprised

AI in science:
1. Science dedicated products eg Alpha Fold – will see a lot more, bio cos will do amazing things
2. Tools that make us more productive – improve net output of scientists and engineers – eg, CoPilot
3. AI that can be an AI scientist to self improve – automate our own jobs, go off and test new science and research – teaching AI to do that

What is Alignment Problem?
A powerful system that has goals in conflict with ours
How do we build AGI that does things in best interests of humanity
How to avoid accidental or intentional mis-use
AI could eventually help us do alignment research itself
Reid: will be able to tell agent “don’t be racist” and let it figure out

AI moonshots?
-language models will go much further than people think – so much algorithmic progress to come, even if we run out of compute or data
true multi modal models – every modality, fluidly move between them
-continuous learning models
These above 3 things will be huge victory

OpenAI – focus on next thing where we have high confidence, let 10% of company go and explore
Can’t plan for greatness, but sometimes breakthroughs will happen

AI will seep in everywhere
Marginal cost of intelligence and energy will rapidly trend towards zero – will touch almost everything

Metaverse will become like iPhone – a new container for software
AI will be new technological revolution – more about how metaverse will fit into AI then vice-versa

Low cost + fast cycle times is how you compete as a startup

In bio – simulators are bad, AI could help

What are best utopian sci-fi universes so far
-Star Trek is pretty good
-The Last Question is incredible short story
-Reid: Ian Banks – Culture series
-tried to write his own sci fi story, was a lotta fun

Having a lot of kids is great – wants to do it

Won’t be doing prompt engineering in 5 years
Will be text / voice in natural language to get computer to do what you want
eg, Be my therapist and make my life better; Teach me something I want to know

Reid: great visual thinker can get more out of DALL-E — will be an evolving set of human talents going that extra mile

How to define AGI
Equivalent of a median human that you can hire as a coworker – be a doctor, be a coder
Meta-skill of getting good at whatever you need

Super intelligence = smarter than all of humanity put together

Economic impacts will be huge in 20-30 years
Society may not tolerate that change – what is the new social contract
How to fairly distribute wealth
How to ensure access to AI systems (“commodity of the realm”)
Not worried about human fulfillment – we’ll always solve it
But concepts of wealth and access and governance will all change

Running largest UBI experiment in world – 5 year project

Tools for creatives — will be the great application for AI in short-term
Mostly not replacing, but enhancing their jobs

How do these LLMs differentiate from each other?
The middle layer is what will differentiate – the startups fine-tuning the base models, about the data flywheel, could include prompt engineering

Podcast notes – Evolution of NLP – Oren Etzioni – TWIML: “Deep learning is the ultimate prediction engine”

Oren Etzioni – founder of AI2

Late Microsoft cofounder Paul Allen wanted to create Allen Institute for AI – hired Oren to make it happen
Paul had vision of computer revolution, relentless focus on prize of understanding intelligence and the brain

AI2’s mission is “AI for the common good”

AI2’s incubator – 20+ companies in pre-seed stage
Natural part of university lifecycle – ideas that can then grow with right resources

Created Semantic Scholar – free search engine for scientific content
New tool – help make PDFs easier to read, auto-create TLDRs for science papers

Sky Light – computer vision to fight illegal fishing

Deep learning for climate modeling – why use neural network? “Deep learning is ultimate prediction engine”

“Common Sense” project – holy grail for AI – how to endow computers with common sense
Common sense ethics are very important
eg, the paper clip creator that takes over humanity to maximize paper clip production
“Alignment problem” is part of it
Are neural nets enough? Do you need to create symbolic knowledge?
Yujin Choi’s team, Mosaic – common sense repository – a collection of common sense statements about the universe
What about when people disagree? Can relativize answers, eg, “if you’re conservative, you would think X; if liberal, think Y”, etc

“Never trust an AI demo” – need to kick tires and ask right questions
eg, Siri / Alexa – slight changes create very different responses

“You shall know a word by the company it keeps” – underlying principle of NLP

Used to think encoding grammar rules was important
But today’s tech is good at approximating those rules

What is the nature of human level intelligence?
How do we collect and understand human knowledge?

Tech that gets you to space station is different from going to Mars, different from leaving Solar System, etc

Large language models (LLMs) are doing “hallucination”, not very robust (different wording leads to different answers)
Eg, who was US president in 1492? “Columbus”

Is it a game of whack of mole? Or is there some fundamental paradigm of human intelligence?

Some experts believe our current algorithms – back propagation, supervised learning, etc – are foundation for more sophisticated architecture that could get us there
Eg, neural nets are very simple brain models

Disagrees strongly with Elon Musk’s views on AI — doesn’t believe we’re “summoning the demon” — it’s hype, not rooted in data

Neural net tuning – like a billion dials on a stereo

Science is hampered if there are third rails you’re not allowed to study or question

Steadfast in support of open inquiry

Researchers are cautious about releasing language models to public – easy to generate controversial outputs

Surprised by progress of the technology – but again, never trust an AI demo
Think about what’s under hood, implications for society