Tag: openAI

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

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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 – Skeptical take on the AI revolution – Gary Marcus on Ezra Klein: “They’re just autocomplete…it’s mysticism to think otherwise”

Guest: Gary Marcus, NYU professor emeritus

Derived from things humans said, but doesn’t always know connections between the things – which can lead to it saying whacky things

Transforms everything into an “embedding space”

It’s pastiche – imitating styles, cutting and pasting, template aspect
Both brilliance and errors come from this

Humans have some internal model – of physical world, of a relationship
Understanding is also about indirect meanings

AI doesn’t have these internal representations

Sam Altman – “humans are energy flowing through a neural network”

Human neural networks have much more structure than AI neural nets

Neural nets like ChatGPT have 2 problems:
-not reliable
-not truthful
Making them bigger doesn’t solve those problems – because they don’t have models of “reliability” or “truthfulness”
“They’re just auto-complete”
“It’s mysticism to think otherwise”

No conception of truth – all fundamentally bullshitting
It’s a very serious problem

Can use GPT3 to make up stories about covid, spread bullshit and misinformation

No existing tech to protect us from this potential tidal wave of misinformation

People used ChatGPT to make up fake coding answers to put on StackOverflow
StackOverflow had to ban ChatGPT

“Talking about having submachine guns of misinformation”

Ezra – ChatGPT is really good at mimicking styles, but core doesn’t have truth value or embedded meaning

Silicon Valley always good at surveillance capitalism
Now you can use AI to write targeted propaganda all day long

Ezra – this AI will be good for people and companies who don’t care about truthfulness. eg, Google doesn’t care about “truth” but about clicks

One of main AI use cases is SEO copy to drive clicks and engagement

In some aspects, as models get bigger they get better (eg, at generating synonyms); but at truthfulness, wasn’t as much progress

Loves AI and have thought about it his whole life – just wants it to work better, be on better path

Biology has enormous complexity – too much for humans – AI could really help us
Also climate change
Could empower individuals like DALL-E does for artists
“Reason according to human values”
Right now we have mediocre AI – risk of being net negative – polarization of society, growth of misinformation

Parable of drunk looking for keys at night around a street light
The street light in AI is deep learning

Human mind does many things: pattern recognition, use analogies, plan things, interpret language
We’ve only made progress on a tiny part
We need other tools (not just deep learning)

Fight between neural nets and symbols (symbolic systems / symbolic learning)
Find ways to bridge these 2 traditions

Ezra:
Deep learning – give all this data, figure it out itself, but bad at abstraction
Symbolic systems – great at abstraction (eg, teach it multiplication from first principles), abstract procedures that are guaranteed to work

Most symbol manipulation is hard wired
Kids learn to count using generalized rules

AI hasn’t matched humans in capacity to acquire new rules – some paradigm shift needed
We’re worshipping the false god of “more data”
Believes will be genuine new innovation in next decade

Stopped ~50 minutes in

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

The Venn of blockchain and AI

I’ve been thinking about the relationship between blockchains and AI lately. Both are emerging foundational technologies and I think it’s no accident they are both coming of age at the same time.

Multiple writers have already expressed this view:

AIs can be used to generate “deep fakes” while cryptographic techniques can be used to reliably authenticate things against such fakery. Flipping it around, crypto is a target-rich environment for scammers and hackers, and machine learning can be used to audit crypto code for vulnerabilities. I am convinced there is something deeper going on here. This reeks of real yin-yangery that extends to the roots of computing somehow

From Venkatesh Rao: https://studio.ribbonfarm.com/p/the-dawn-of-mediocre-computing

I think AI and Web3 are two sides of the same coin. As machines increasingly do the work that humans used to do, we will need tools to manage our identity and our humanity. Web3 is producing those tools and some of us are already using them to write, tweet/cast, make and collect art, and do a host of other things that machines can also do. Web3 will be the human place to do these things when machines start corrupting the traditional places we do/did these things.

From Fred Wilson: https://avc.com/2022/12/sign-everything/

In both writers’ examples, blockchain helps solve some of the problems that AI creates, and vice-versa. I’m reminded of Kevin Kelly who said, Each new technology creates more problems than it solves.

Blockchains and AI have a sort of weird and emergent technological symbiosis and I’m here for it.

So the brain flatulence below is just my way to think aloud, using the writing process to work through the question(s).

*Note: when I say “blockchain”, I include what Fred Wilson calls web3 and Venkatesh calls crypto; there are just a few canonical applications that we’re all familiar with (namely, bitcoin and ethereum); and when I say “AI”, I am thinking about the most popular machine learning models like GPT3 and Stable Diffusion

*Note also: I am just a humble user of these new and powerful AI tools, and can barely understand the abstract of a typical machine learning research paper; so part of the reason why I’m writing this is to find out where I’m wrong a la Cunningham’s law

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A blockchain is a tool for individual sovereignty; while an AI is a tool for individual creativity

A blockchain operates at maximum transparency; while an AI operates largely as a black box

A blockchain clearly shows the chain of ownership and history; while an AI… (does something like the opposite in the way it aggregates and melds and mutates as much data as possible?)

A blockchain is “trustless”, in the sense that what you see on-chain is the agreed upon “truth” of all its users; while an AI is (?), in the sense that what it generates is more or less unique to the specific prompt / question / user (and even this can change as the model is updated, or new data is added]

An AI is much easier to use than a blockchain

An AI can create vast quantities of content, very cheaply; while a (truly “decentralized”) blockchain is limited by scalability and cost

An AI is centralized (to a specific company, or model, or data set) in the sense that decision making rests with a team or company; while a blockchain is decentralized and decision making is distributed

A surprising user experience – as in, an unexpected but delightful output – is typically net positive for a user of AI, while seeing something happen on a blockchain that you don’t expect would generally be pretty bad (yes, of course there are airdrops)

Blockchains are a competitive threat to industries with a high degree of centralization (such as fiat currency issuance, and payment networks); AI is a competitive threat to many individual online workers (such as language translators, and freelance writers, and basic QA/QC employees)

Both blockchains and AI have multiple open source products that can be forked by developers

Both blockchains and AI are platforms upon which many other products and services can be built

Both blockchains and AI are technologies that exploded into the popular consciousness in the last 10 years

Both “blockchain” and “AI” are very broad suitcase words, in part because they are both the product of many technologies combined in innovative ways: for blockchain that is everything from cryptography to smart contract programming to PoW mining to distributed consensus mechanisms; for AI that’s, uh…well everything listed here and more, I suppose

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I’ll end here for now, but let me know what I got wrong, what I’m missing, and what questions or ideas this might inspire

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Addendum #1: I asked ChatGPT

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Addendum #2: This NYT article notes that SBF (“Sam Bankscam Fraud”) donated at least $500M to organizations researching AI alignment and AI safety. Not exactly the kind of symbiosis I want to explore, but worth noting.

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Addendum #3:

Blockchains can only give precise answers, while AI can give approximate answers or even fabricate answers

Blockchains are censorship resistant, while AI is centralized (most are created by small doxxed teams) and have implemented restrictions on usage (most have rules against, for example, CSAM or nudity)