Speaker 1: How does all this talk about, you know, relationships and AI, Like,
Speaker 1: could you see yourself developing a relationship with an AI?
Speaker 2: I'd say yes, as a reliable tool that enhances my life,
Speaker 2: makes my life better.
Speaker 3: I'm Emily Chang, and this is the circuit. We're inside
Speaker 3: a nondescript building in the heart of San Francisco where
Speaker 3: one of the world's buzziest startups is making our AI
Speaker 3: powered future feel more real than ever before. It's giving
Speaker 3: me very West World Spa vibes. It's almost like suspended
Speaker 3: in space and time a little bit. They're behind two
Speaker 3: monster hits, chat Gypt and Dollie, and somehow the biggest
Speaker 3: tech giants to market, kicking off this competitive race that's
Speaker 3: forced them all to show us what they've got. Is
Speaker 3: it magic? Is it just algorithms? Is it gonna save
Speaker 3: us or destroy us? To help us separate AI height
Speaker 3: from reality, I sat down with LinkedIn co founder and
Speaker 3: Facebook investor Reid Hoffman, who was an early backer and
Speaker 3: board member of Open Ai. He also used Chatchi to
Speaker 3: write a novel. But first, here's my conversation with Miramrati,
Speaker 3: chief technology officer from inside open Ai.
Speaker 4: Well, thank you so much. For doing us. It's really
Speaker 4: great to have you, and you've been very busy.
Speaker 1: I want you to take us back a little bit
Speaker 1: when you were making the decision about releasing chat Gpt
Speaker 1: into the wild. I'm sure there was like a go
Speaker 1: or no go moment. Take me back to that day.
Speaker 2: You know, we had chat gipt for a while, and
Speaker 2: we had been exploring it internally and with a few
Speaker 2: trusted users, and we realized that we sort of hit
Speaker 2: a point where we could really benefit from having more
Speaker 2: feedback and having more people try to break it and
Speaker 2: try to figure out how best to use it. Let's
Speaker 2: make sure that we've got some guardrails in place and
Speaker 2: start rolling it out incrementally so we can get feedback
Speaker 2: from how people are using it, what are the risks,
Speaker 2: what are the limitations, and learn more about this technology
Speaker 2: that we have created and started bringing it in the
Speaker 2: public consciousness.
Speaker 4: So you wanted people to break it or try.
Speaker 2: Yes, we definitely wanted people to try to break it
Speaker 2: and find the fragilities in the system. We had reached
Speaker 2: the point where we had done a lot of that
Speaker 2: internally and.
Speaker 5: With a small group of people.
Speaker 2: External experts as well, and we wanted more external researchers
Speaker 2: to play with it.
Speaker 3: It became the fastest growing tech product in history. Did
Speaker 3: that surprise you, I mean, what was your reaction to
Speaker 3: the world's reaction.
Speaker 5: Yeah, it was a huge surprise for us. We were surprised.
Speaker 2: By how much it captured the imaginations of the general
Speaker 2: public and how much people just loved spending time talking
Speaker 2: to this AI system and interacting with it.
Speaker 3: I want to take a step back a little bit,
Speaker 3: you know, because a lot of people still don't really
Speaker 3: understand how it works. Chatgbts trained on you know, tons
Speaker 3: and tons of data and text.
Speaker 1: It can now mimic a human. It can write, It
Speaker 1: can code at the most.
Speaker 4: Basic level and in the most succinct way that you
Speaker 4: can How does it work? How does this all happen?
Speaker 5: So?
Speaker 2: TUGBD is a neural network that has been trained on
Speaker 2: a huge amount of data on a massive supercomputer, and
Speaker 2: the goal during this training process was to predict the
Speaker 2: next word in a sentence, and we found out that
Speaker 2: by doing this we also got the ability to understand
Speaker 2: the world in text more like humans do. The goal
Speaker 2: here is to have these systems have more robust concepts
Speaker 2: of reality, similar to how we think of the world.
Speaker 2: We don't just think and reason in text. We also
Speaker 2: obviously have the world in images, visual world around us.
Speaker 2: That's been the goal over time, which is why we've
Speaker 2: been adding more and more modalities. And it turns out
Speaker 2: that as you trained larger and larger models and more
Speaker 2: and more data, the capabilities of these models also increase.
Speaker 2: They become more powerful, more helpful, and as you invest
Speaker 2: more on alignment and safety, they become more reliable and
Speaker 2: safe over time.
Speaker 3: So I'd love to hear a little bit more about
Speaker 3: your personal story. I know you grew up in Albania.
Speaker 3: What was the road like from Albania to Silicon Valley.
Speaker 2: I grew up in Albania, and when I was growing
Speaker 2: up in Albania, it was a pretty tumultuous place politically
Speaker 2: and economically, so I always knew that I wanted to
Speaker 2: study abroad. I always loved learning, and in this pursuit
Speaker 2: of knowledge, took me to Canada on a scholarship, and
Speaker 2: from there I came to the US and I've stayed
Speaker 2: in the US ever since.
Speaker 1: You've worked on aerospace, you worked at Tesla, you worked
Speaker 1: on virtual reality. How did you become CTO of Open Ai.
Speaker 2: It's been My training has been in mechanical engineering. I've
Speaker 2: always loved maths and physics and these were my favorite
Speaker 2: subjects as a kid. So my training took me from
Speaker 2: aerospace engineering to automotive engineering and then in applications in
Speaker 2: virtual reality and augmented reality.
Speaker 5: But there was always this.
Speaker 2: You know, deep technological advancement in pursuit of some problem
Speaker 2: that makes our lives a little better. And five years
Speaker 2: ago that brought me to Opening Eye because I thought
Speaker 2: that there is no other more important problem that I
Speaker 2: could be working on than artificial general intelligence. And I
Speaker 2: joined Opening Eye to help with leading research teams, and
Speaker 2: from there I went on to build a product team.
Speaker 2: And you know, after having done a few of the
Speaker 2: roles in the company and having built a lot of
Speaker 2: the technical teams, I'm now leading all of the technical
Speaker 2: teams done.
Speaker 1: So as CTO, how do you set the pace of
Speaker 1: open AIS technology development? How do you balance speed versus
Speaker 1: responsibility versus safety?
Speaker 4: Like, how do you where are your priorities?
Speaker 2: Essentially, so, I think today we are dealing with unprecedented
Speaker 2: advancement in technology, and I think the most important thing
Speaker 2: we can do is to manage its advancement and do
Speaker 2: some in a way that's going to benefit people, maximize
Speaker 2: the number of amazing applications that AI can bring, and
Speaker 2: really fuel this energy that people have about interacting with
Speaker 2: AI and making great use of AI, also giving people
Speaker 2: the tools to do so in a reliable and safe way.
Speaker 2: So Atpenyi, our safety teams and research teams collaborate very closely,
Speaker 2: and safety teams are integrated in many of our research domains.
Speaker 5: But we also.
Speaker 2: Provide more room for long term research for safety and
Speaker 2: policy research as well. It's important to work both on
Speaker 2: kind of the near term present issues that we see clearly,
Speaker 2: but also have make a lot of room for exploratory
Speaker 2: and frontiers research when it comes to safety and policy.
Speaker 1: So CHATGBT could revolutionize so many things, and obviously AI
Speaker 1: more broadly, What are the things you're most excited about?
Speaker 4: Like, what's the amazing What.
Speaker 2: I'm most excited about is how it will transform education
Speaker 2: and our ability to learn because you can really see
Speaker 2: that advancing society in a way. You know, there are
Speaker 2: many even the most advanced societies that they are quite
Speaker 2: limited when it comes to education. There is this formula
Speaker 2: on how people are supposed to learn and we all
Speaker 2: learn very differently. We have different interests, and so I
Speaker 2: think by using technologies like tragibiity the underlying models, we
Speaker 2: can really build custom virtual tutors or virtual teachers that
Speaker 2: can help us learn about the things that we are
Speaker 2: really interested in, can really push our creativity, and by
Speaker 2: pushing human knowledge and human creativity, I think we can
Speaker 2: really transform the fabric of society.
Speaker 5: What about the.
Speaker 4: Scary stuff, like what are you most concerned about?
Speaker 2: You know, whenever you have a technology that is so
Speaker 2: powerful and so general, there's always the other side of it,
Speaker 2: and there are always that we have to worry about.
Speaker 2: And we've been very vocal about this since the beginning
Speaker 2: of open AYE and very active in studying the limitations
Speaker 2: that come with the technology. Right now, one of the
Speaker 2: things that I'm most worried about is the ability of
Speaker 2: models like jubid for to make up things. We refer
Speaker 2: to this as hallucinations, so they will convincingly make up things,
Speaker 2: and it requires you know, being aware and being.
Speaker 5: And just really knowing.
Speaker 2: That you cannot fully blindly rely on what the technology
Speaker 2: is providing as an output.
Speaker 5: But on the other hand.
Speaker 2: It also makes it glaringly obvious that this is a
Speaker 2: tool with which you're collaborating. People can misuse it in
Speaker 2: various ways. They can spread misinformation, it can be misused
Speaker 2: in high stakes scenario. So from GBD three point five
Speaker 2: to GPT four we work very hard to reduce hallucinations
Speaker 2: or increase the.
Speaker 5: Factual output of the models.
Speaker 2: And we worked on GBT four for over six months
Speaker 2: just to make it more aligned, safer, more helpful, more accurate,
Speaker 2: more reliable, and held back the release of the model
Speaker 2: so that we could focus on these aspects of it.
Speaker 2: But it's far from perfect, and we're continuing to work
Speaker 2: on it and get the feedback from the daily use
Speaker 2: and make the model better and more reliable.
Speaker 3: I want to talk about this term hallucination because it's
Speaker 3: a very human term. Why use such a human term
Speaker 3: for basically an AI that's just making mistakes.
Speaker 2: A lot of these general capabilities are actually quite human like.
Speaker 2: Sometimes and we don't know the answer to something, we
Speaker 2: will just make up an answer. We will rarely say
Speaker 2: I don't know a lot of human hallucination in a conversation,
Speaker 2: and sometimes we don't do it on purpose. So we're
Speaker 2: constantly borrowing from the way that we learn the way
Speaker 2: we see the.
Speaker 5: World to.
Speaker 2: Have a more intuitive understanding of the systems.
Speaker 3: Should we be worried about AI though that feels more
Speaker 3: and more human like? Should AI have to identify itself
Speaker 3: as artificial when it's interacting with us?
Speaker 2: I think it's a different kind of intelligence. It is
Speaker 2: important to distinguish output that's been provided by a machine
Speaker 2: versus another humans. So you have that understanding, but we
Speaker 2: are moving towards a world we are collaborating with these
Speaker 2: machines more and more, and so output will be hybrid
Speaker 2: from a machine and a human and so they're almost like,
Speaker 2: you know, amplifying tools that are pushing the ability that
Speaker 2: we already have, whether that's reasoning or creativity, and these
Speaker 2: machines are helping us push the bounds of that even further.
Speaker 2: So it's going to be difficult to you know, distinguish
Speaker 2: the output once you have this collaborative engagement between the
Speaker 2: human and the missione the.
Speaker 4: Air of confidence.
Speaker 1: Obviously that CHATGPT sometimes delivers an answer with is it
Speaker 1: can take you off your toes a little bit, right,
Speaker 1: why not just sometimes say I don't know, or program
Speaker 1: that into che GIPT.
Speaker 2: So it turns out that when you're building such a
Speaker 2: general technology. Like with large language models, the goal is
Speaker 2: to predict the next word in a sentence. The goal
Speaker 2: is not to predict the next word reliably or safely.
Speaker 2: Just from this simple goal, we got the ability to
Speaker 2: understand language quite well. We got a lot of creativity,
Speaker 2: ability to even code. And it turns out when you
Speaker 2: have such general capabilities, it's very difficult to handle some
Speaker 2: of the limitations such as what is correct. Also, the
Speaker 2: model doesn't really know much about the user in terms
Speaker 2: of their context and their preferences. But it's still the
Speaker 2: early days, and that's why we're pushing out these systems slowly,
Speaker 2: in a controlled way, but in a way that allows
Speaker 2: us to get some feedback from how people are using them,
Speaker 2: so we can use that, implement it and make them
Speaker 2: better and more reliable. One thing that we did recently
Speaker 2: with child jipt is we rolled out this ability to
Speaker 2: browse the Internet so that it can become a bit
Speaker 2: more reliable on questions that have factual nature. And this
Speaker 2: is now offered as a plugin on child jipd plus service.
Speaker 2: But it's still the early days and this feature is
Speaker 2: only in alpha.
Speaker 3: Some of these texts and some of the data is
Speaker 3: highsed some of it may be correct. Isn't this going
Speaker 3: to accelerate the misinformation problem? I mean, we haven't been
Speaker 3: able to crack it on social media for like a
Speaker 3: couple of decades.
Speaker 5: Misinformation is a really complex heart problem. But you know,
Speaker 5: these systems become smarter.
Speaker 2: It's actually also easier to guide them because you can
Speaker 2: give direction in just natural language and say I don't
Speaker 2: want you to do xting. Then the system, by being
Speaker 2: more intelligent, more capable, has the ability to actually.
Speaker 5: Follow that particular instruction.
Speaker 2: Obviously, with more powerful models, you're also expanding the profile
Speaker 2: of risks, and so you have more risks that you
Speaker 2: need to understand.
Speaker 5: And deal with.
Speaker 2: There are several things that we are exploring. For example,
Speaker 2: one of the things that we've been researching and exploring
Speaker 2: is water marketing. The output where you are able to
Speaker 2: you distinguish what is AI generated output versus human generated output.
Speaker 5: There are ways to deal with it.
Speaker 2: Also from a policy standpoint, I think it's a complex issue.
Speaker 2: There needs to be addressed from research policy perspective. But
Speaker 2: on the other hand, also you know, society needs to
Speaker 2: adapt to these challenges and the capabilities that these models
Speaker 2: are bringing just like we adapted, you know, to using
Speaker 2: calculators and other technologies.
Speaker 4: There's sort of like underlying anxiety. I feel like when
Speaker 4: you talk to.
Speaker 3: Most people about AI, you know that's cool, but it's
Speaker 3: also scary.
Speaker 4: And I've heard.
Speaker 1: AI experts talk about the potential for the good future
Speaker 1: versus the.
Speaker 3: Bad future, and the bad future gets kind of scary.
Speaker 3: You know.
Speaker 4: There's talk about this lead leading to human extinction. Are
Speaker 4: those people wrong?
Speaker 2: You know, there's certainly a risk that when we have
Speaker 2: these AI systems that are able to set to then goals,
Speaker 2: they decide that their goals are not aligned with ours
Speaker 2: and they do not benefit from having us around and
Speaker 2: could lead to human extinction.
Speaker 5: I don't think this risk.
Speaker 2: Has gone up or down from the things that have
Speaker 2: been happening in the past few months. I think it's
Speaker 2: certainly been quite hyped and there is a lot of
Speaker 2: anxiety around it. Well, this risk is important, and we
Speaker 2: need to work on frontier research to figure out how
Speaker 2: to deal with super intelligent AI alignments. We are dealing
Speaker 2: with a lot of risks today that are very real,
Speaker 2: very present, very high probability that they impact us, and
Speaker 2: I think if we cannot figure out how to handle
Speaker 2: and deal with these risks while the stakes are low.
Speaker 2: Then you know, we wouldn't have much hope to deal
Speaker 2: with it when things are more complex. So my view
Speaker 2: is a bit more pragmatic that one, where you know,
Speaker 2: we really need to figure out how to deal with
Speaker 2: the present risks that the systems pose, and coordinate among
Speaker 2: developers and work with regulators, legislators and governments various countries
Speaker 2: to come up with reasonable policies and regulation.
Speaker 4: Around the are elon.
Speaker 1: Steve Wosniak, a bunch of other you know experts have
Speaker 1: called for six months pause on AI development. Do you
Speaker 1: have any intention of slowing down or what's your response
Speaker 1: to that letter?
Speaker 2: So the letter from FLI makes a lot of good
Speaker 2: points about the risks that the technology poses, and we've
Speaker 2: been talking about some of them.
Speaker 5: Opening Eye is being.
Speaker 2: Very vocal about these risks for many, many years, and
Speaker 2: we've been doing active research on them. One of them
Speaker 2: is acceleration. I think that's a significant risk that we
Speaker 2: as a society need to grapple with. The Private companies
Speaker 2: and governments need to work together to figure out the
Speaker 2: risks that acceleration brings building safe AI systems that are
Speaker 2: in general is very complex. It's incredibly hard, and I
Speaker 2: don't think that it can be reduced to a parameter
Speaker 2: set by a letter. The question then becomes, you know
Speaker 2: who is abiding to this letter? Is it all different
Speaker 2: countries in the world? How is that happening? I think
Speaker 2: the issuing reality is far more complex, and it requires
Speaker 2: coordination from private companies, from governments, and figuring out how
Speaker 2: do you deal with these advancements in technology versus blocking advancement.
Speaker 1: There have been parallels drawn to the Manhattan Project, which
Speaker 1: you know, they gathered the.
Speaker 3: Best scientific minds to develop nuclear weapons, and Robert Oppenheimer,
Speaker 3: who led that project, said when he saw the first detonate,
Speaker 3: a line from Hindu scripture ran through his head. Now
Speaker 3: I am become death, the destroyer of worlds. I realize
Speaker 3: the sound dramatic, but if we're talking about the risk
Speaker 3: for human extinction, you know, not being totally out of
Speaker 3: the question. Like in your development of AI, have you
Speaker 3: had a moment like that where you're just like, Wow, this.
Speaker 4: Is this is big.
Speaker 2: I think a lot of us at Open AI joined
Speaker 2: because we thought that this would be the most important
Speaker 2: technology that humanity would ever creates.
Speaker 5: I suddenly think.
Speaker 2: That now with that comes a lot of responsibility. Of course,
Speaker 2: I think AI is going to be amazing.
Speaker 5: It already is.
Speaker 2: It has this incredible potential to extend our creativity and
Speaker 2: human knowledge and make our lives better in so many vectors.
Speaker 2: But of course the risks, on the other hand, are
Speaker 2: also pretty significant, and this is why we're here.
Speaker 3: I just rewatched the movie her which has this very
Speaker 3: vivid depiction of life with AI in ten years.
Speaker 4: How will our lives be different? How will daily life
Speaker 4: be different?
Speaker 2: I haven't watched a movie herd a lot aboutime. I
Speaker 2: hope ten years is a long time, but I hope
Speaker 2: that in the next few years we will have a
Speaker 2: future where we use AI systems as tools to amplify
Speaker 2: a lot of our own abilities. And I hope that
Speaker 2: we have systems that help bring customized education to as
Speaker 2: many people out there as possible, and I hope that
Speaker 2: they You know, we can build tools, diagnostics, tools or
Speaker 2: ways to understand diseases and the problems in healthcare much
Speaker 2: much earlier and figure out how to deal with them
Speaker 2: at scale. And you know, we're dealing with massive problems
Speaker 2: in climate change, figuring out new solutions figuring out ways
Speaker 2: in which we can help reduce the risks that climate
Speaker 2: change processes.
Speaker 1: Could you put what you're developing here inside robots and
Speaker 1: could they combat loneliness?
Speaker 2: I think bringing these systems into the physical world is
Speaker 2: a pretty significant step.
Speaker 5: Feels like we're a bit far from that.
Speaker 2: But also, you know, just having a chatbot that you
Speaker 2: can ask for advice suddenly, not in high stake scenarios
Speaker 2: right now, seems like that would be helpful for a
Speaker 2: lot of people.
Speaker 3: That's quite profound that we could someday have relationships with computers.
Speaker 5: In a way we already do.
Speaker 2: Right We're spending so much time on our computers, We're
Speaker 2: always on our phones. We're almost like enslaved to this
Speaker 2: interaction that we have with the keyboards and with the
Speaker 2: touch screen.
Speaker 3: I think a lot about my kids and them having
Speaker 3: relationships with AI someday and this thing that has much
Speaker 3: more time to spend with them than I do. How
Speaker 3: do you think about what the limits should be and
Speaker 3: what the possibility should be when you're thinking about a child.
Speaker 2: I think we should be very careful in general with
Speaker 2: putting very powerful systems in front of more vulnerable populations,
Speaker 2: people under thirteen cannot access it, and even under eighteen
Speaker 2: requires parents of supervision. So there are certainly checks and
Speaker 2: balances in place because it's still early and we still
Speaker 2: don't understand all the ways in which this could affect people.
Speaker 3: There's also some business interests here, and by releasing chatgbt
Speaker 3: open ays kind of turbo charge this competitive frenzy. Do
Speaker 3: you think you can bet Google at its own game?
Speaker 3: Do you think you can take significant market share and search?
Speaker 2: You know, we didn't set out to dominate search when
Speaker 2: we build child jepet. In fact, it actually started as
Speaker 2: a project around understanding and dealing with truthfulness of large
Speaker 2: language models, and then it evolved. But I think what
Speaker 2: child gipt offers is a different way to understand information
Speaker 2: and a different way to interact with the same tool.
Speaker 2: And you could be, you know, searching, but you're searching
Speaker 2: in a much more intuitive way versus keyword based. That
Speaker 2: is definitely an outcome that we saw afterwards, and we
Speaker 2: built an interface that would allow people to interact with
Speaker 2: it much more smoothly, and as we can see, it
Speaker 2: is pushing other people to build more assistant like products
Speaker 2: companies and small companies. I think the whole world is
Speaker 2: sort of now moving in this direction.
Speaker 5: I think our focus will.
Speaker 2: Remain on building these general technologies and figuring out how
Speaker 2: we can bring them to the public in ways that
Speaker 2: are useful.
Speaker 3: So there's this report that these workers in Kenya we're
Speaker 3: getting paid two dollars an hour to do the work
Speaker 3: on the back end to make answers less toxic. And
Speaker 3: my understanding is this work it can be difficult, right,
Speaker 3: because you're reading texts that might be disturbing and trying
Speaker 3: to clean them up, right, Like, what's your response to that?
Speaker 2: So we need to use contractors sometimes to scale. You know,
Speaker 2: in this particular case, we chose the particular contractor because
Speaker 2: of their known safety standards, and since then we've stopped
Speaker 2: working with them. But as you said, this is difficult
Speaker 2: to work, and we recognize that, and we have mental
Speaker 2: health standards and wellness standards that we share with contractors
Speaker 2: when we engage them.
Speaker 3: All of the data that you're using, and this has
Speaker 3: been talked about a lot, like all of the data
Speaker 3: that you're training.
Speaker 1: This AI on, it's coming from writers, it's coming from artists,
Speaker 1: it's coming from.
Speaker 4: Other people have created who've created things.
Speaker 1: How do you think about giving value back to those
Speaker 1: people when these.
Speaker 3: Are also people who are worried about their jobs going away.
Speaker 2: These models are trained on a lot of public information,
Speaker 2: a lot of data on the Internet, and also licensed data,
Speaker 2: and the output that is generated by the models is
Speaker 2: original our users.
Speaker 5: They have all their rights to that output.
Speaker 2: I know Microsoft has been doing some research on this
Speaker 2: on how do you make sure that you recognize the
Speaker 2: value that people are bringing with their data, And there
Speaker 2: is some research that has been done in this direction
Speaker 2: with the data dignity projects that some folks at Microsoft
Speaker 2: have been working on, and there is some research of
Speaker 2: figuring out the economics of this and how to do
Speaker 2: that at scale. I don't know exactly how to work
Speaker 2: in practice that you can sort of account for information
Speaker 2: created by everyone on the Internet, but there is probably
Speaker 2: some other way where, you know, people contributing specific kind
Speaker 2: of data can sort of have a share of the
Speaker 2: gains produced by this model. I'm not sure exactly how
Speaker 2: that would work, but I think there is some research
Speaker 2: on the economics of this, and I think it's definitely
Speaker 2: worth exploring further. As far as the question of jobs goes,
Speaker 2: I think there are definitely going to be jobs that
Speaker 2: will be lost and jobs that will be changed. I
Speaker 2: think there will be a lot of jobs that will
Speaker 2: be created as well. We don't know exactly what they are,
Speaker 2: and probably some of them we can't even imagine. Like
Speaker 2: prompt engineer is a job today. That's not something that
Speaker 2: we could have predicted totallyory.
Speaker 4: So what does responsible innovation look like to you?
Speaker 1: You know, like, would you support, for example, a federal
Speaker 1: agency like the FDA that that's technology, like it that's drugs.
Speaker 2: You know, having some sort of trusted authority that can
Speaker 2: audit these systems based on some agreed upon principles would
Speaker 2: be very helpful. And having some standards around predicting capabilities
Speaker 2: and auditing these systems once they're trend could be helpful.
Speaker 3: Do open AI employees still vote on AGI and when
Speaker 3: it will happen?
Speaker 5: I actually don't know. I believe that what they did.
Speaker 2: I think we kind of do it, but I don't
Speaker 2: know last time we did.
Speaker 1: What is your prediction about AGI now and how far
Speaker 1: away it really is? This is when computers can learn
Speaker 1: and reason and rationalize just as good as us, if
Speaker 1: not better.
Speaker 2: I think we're making a ton of progress on technology
Speaker 2: and it is really helping us in so many ways,
Speaker 2: But we're still quite far away from being a point
Speaker 2: where you know, these systems can make decisions autonomously and
Speaker 2: discover new knowledge that we couldn't have predicted previously.
Speaker 4: So is that decades away?
Speaker 5: I'm not sure.
Speaker 4: Is it sooner than you thought when you started this work.
Speaker 5: I don't know if it's sooner.
Speaker 2: I think I have more certainty around the advent of
Speaker 2: having powerful systems in our future that we'll be able
Speaker 2: to make decisions autonomously and discover new knowledge.
Speaker 3: Should we even be driving towards agi? And do humans
Speaker 3: really want it?
Speaker 1: Do we want computers to be smarter than us ultimately,
Speaker 1: even though we don't know what that really looks like
Speaker 1: or means.
Speaker 2: I think that through the course of history, pushing human
Speaker 2: knowledge has pushed our societies in so many different ways.
Speaker 2: It's been key to advancing our society, and I think
Speaker 2: it would be a mistake to hold technological innovation or
Speaker 2: our ability to pursue human knowledge further.
Speaker 5: And I'm not even sure.
Speaker 2: That that's possible in the first place, but theoretically if
Speaker 2: it were, I think it would be a mistake. A
Speaker 2: lot of our inspiration and advancements in society come from
Speaker 2: pushing human knowledge. Now that doesn't mean that we should
Speaker 2: do so in careless and reckless ways. I think there
Speaker 2: are ways to guide this development and manage this development
Speaker 2: versus bring it to a screeching hold because of our
Speaker 2: potential fears.
Speaker 4: So the train has left the station and we should
Speaker 4: stay on it.
Speaker 5: That's one way to put it for now.
Speaker 4: I'm sure Chatchpt would say it much more eloquent.
Speaker 3: Beyond open AI, there's an artificial intelligence gold rush happening
Speaker 3: in Silicon Valley. Venture capitalists are pouring money into Anything
Speaker 3: AI startups, hoping to find the next big thing. Now
Speaker 3: here's my conversation with Reid Hoffman, who knows a thing
Speaker 3: or two about striking gold.
Speaker 4: Thank you so much for doing this.
Speaker 3: I'm so grateful to have you, and obviously you've helped
Speaker 3: us make sense of platform shifts over I mean, gosh,
Speaker 3: twelve years.
Speaker 6: We've been talking, maybe longer. That's awesome.
Speaker 5: A long time.
Speaker 6: More than a decade.
Speaker 3: Generative AI has had two big hits so far, Dolly
Speaker 3: and Chatchypt.
Speaker 7: Both from open AI. Why do you think.
Speaker 3: Chatchypt exploded more than Instagram, even.
Speaker 7: More than TikTok.
Speaker 6: Well, there's a couple of reasons. One is it's a
Speaker 6: little bit like the movie industry, and each year has
Speaker 6: a new biggest box office. The world's more connected, there's
Speaker 6: more people, there's more curiosity of what's going on, so
Speaker 6: you have your new biggest hit. So there's always that
Speaker 6: as a backdrop. This will be the year as I
Speaker 6: kind of put on fireside chatbots where one or more
Speaker 6: of the person of the Year lists will be a
Speaker 6: chatbot or an AI or open AI or something like
Speaker 6: this as a way of doing that. Because it's a
Speaker 6: magical experience to say, suddenly I can have a conversation
Speaker 6: with this thing, like I'm talking to another person and
Speaker 6: it not being another person. Right. That's like that has
Speaker 6: not happened in history till November sometime last year, Right,
Speaker 6: And so that's why I think it exploded.
Speaker 3: You have been on the ground floor of some of
Speaker 3: the biggest tech platform shifts in history, the.
Speaker 7: Beginnings of the internet mobile.
Speaker 3: Do you think AI is going to be even bigger?
Speaker 6: I think so at minimum for the following reason, which
Speaker 6: is it builds on the Internet, mobile, cloud data. All
Speaker 6: of these things come together to make AI work, and
Speaker 6: so that causes it to be the crescendo, the addition
Speaker 6: to all of us.
Speaker 7: So, hey, it's gonna be bigger than all those things. Yeah,
Speaker 7: and that's kind of a big deal.
Speaker 6: Yes, absolutely. But now part of it's because just like
Speaker 6: we saw a chat GBT, we have billions of people
Speaker 6: connecting in the world. They can all reach it very
Speaker 6: quickly too, so all of a sudden you start interacting
Speaker 6: with it, and then you begin to think, well, what
Speaker 6: could happen with AI here? I mean, one of the
Speaker 6: problems with the current discourse is that it's too much
Speaker 6: of the fear based versus hope based. Imagine a tutor
Speaker 6: on every smartphone for every child in the world who
Speaker 6: has access to a smartphone. Imagines a doctor on every
Speaker 6: smartphone where many communities don't have any access to doctors.
Speaker 6: That's line of sight from what we see with current
Speaker 6: AI models today.
Speaker 7: You coined this term blitzcaling. Does AI blitzcale?
Speaker 6: Well, it certainly seems like it today, doesn't it. The
Speaker 6: speed at which we will integrate it into our lives
Speaker 6: will be faster than only integrated the iPhone into our lives.
Speaker 6: There's going to be a co pilot for every profession
Speaker 6: and if you think about that, that's huge. Well, that
Speaker 6: changes industries, that changes products.
Speaker 3: And not professional activities, because it's going to write my
Speaker 3: kids papers, right, it's high school papers.
Speaker 6: Yes, although the hope is that in the interaction with it,
Speaker 6: they'll learn to create much more interesting papers.
Speaker 7: You and Ela must go way back.
Speaker 3: He co founded open ai with Sam Waltman, the CEO
Speaker 3: of open Ai.
Speaker 7: What did Elon say that got you interested? So early?
Speaker 6: Elon came and said, look, this AI thing is coming.
Speaker 6: You know, I always trust people from my network were
Speaker 6: smart to say, go look at this. Go look. I'm
Speaker 6: always curious. Once I started digging into it, I realized
Speaker 6: that this pattern that we're going to see the next
Speaker 6: generation of amazing capabilities coming from these kind of you know, computers,
Speaker 6: computational devices, and that that's something that could shape a
Speaker 6: much better society that we'd all be in. And that's
Speaker 6: the reason I do technology. One of the things I
Speaker 6: had been arguing with Elon at the time about was
Speaker 6: that Elon was constantly using the word robocalypse, which you know,
Speaker 6: we as human beings, tend to be more easily and
Speaker 6: quickly motivated by fear than my hope, So you're using
Speaker 6: the term robocalypse, and everyone imagines the terminator and all
Speaker 6: the else.
Speaker 7: It sounds pretty scary.
Speaker 6: It sounds very Scaredocalypse doesn't sound like something we want. Yeah,
Speaker 6: stop saying that, because because actually, in fact, the chance
Speaker 6: that I could see anything like a robocalypse happening is
Speaker 6: so deminimous relative to everything else.
Speaker 7: How remote is the chance of the robocalypse in your mind?
Speaker 6: Let me put this away. I'm more worried about what
Speaker 6: technology does in the hands of humans that I am
Speaker 6: about a robocalypse. And what we've seen through the scaling
Speaker 6: of these large language models is that the larger you get,
Speaker 6: the easier it is to train them to be aligned
Speaker 6: to human interests. That's good, doesn't mean it's perfect, doesn't
Speaker 6: mean we shouldn't be attentive. But that's exactly the kind
Speaker 6: of thing where you can build to a really good
Speaker 6: future and be motivated by hope and optimism versus fear.
Speaker 7: So just on Elon for a second.
Speaker 4: You did come together on open AI, and how did
Speaker 4: that happen?
Speaker 6: I think it started with Elon and Sam having a
Speaker 6: bunch of conversations, and then since I know both of
Speaker 6: them quite well, I got called in something should be
Speaker 6: the counterweight to all of the natural work that's going
Speaker 6: to happen within commercial realms, right within companies, you know, buildings,
Speaker 6: which is by the way, as you know, a huge
Speaker 6: fan that companies can build really good things. An I
Speaker 6: int company and a different thing. But it's good to
Speaker 6: have the counterweight too. And as part of having that counterweight,
Speaker 6: what how do you bring in considerations like well, what
Speaker 6: are we going to do for a bunch of people
Speaker 6: who are not as well off economically or anything else,
Speaker 6: and how do we make sure they're included? How do
Speaker 6: we make sure that one company doesn't dominate the industry,
Speaker 6: but the tools are provided across the industry so innovation
Speaker 6: can benefit from startups and all the rest. It was like, great,
Speaker 6: and let's do this thing open Ai.
Speaker 3: Sam Altman has said he thinks this is going to
Speaker 3: usher in this new era of economic prosperity. It's obviously
Speaker 3: going to change a lot of jobs, going to eliminate
Speaker 3: a lot of jobs. Is it going to create enough
Speaker 3: jobs to balance all that out?
Speaker 6: So you can't one hundred percent say absolutely yes, because
Speaker 6: it's part of the uncertain part of human nature and
Speaker 6: human progress. But the same question is confronted us multiple times.
Speaker 6: It's confronted us in the move from agriculture to industry.
Speaker 6: It's confronted us in computerization of things like you know,
Speaker 6: And again fear first is like, oh my god, it's
Speaker 6: going to employee change. And a lot of work is people
Speaker 6: to people interaction, and people interaction can be education, it
Speaker 6: can be medicine, it can be legal, it could be communications.
Speaker 6: I think that all of that there's infinite demand for
Speaker 6: that work. Entertainment media there's infinite demand for that, and
Speaker 6: so those can open up new realms of jobs and
Speaker 6: all the rest. Am I ultimately very optimistic that it
Speaker 6: will create a lot more jobs than it will consume.
Speaker 6: The answer is yes, but it doesn't mean it won't
Speaker 6: consume jobs, and it doesn't mean that we have to
Speaker 6: not navigate the transition, the revolution of moving from agriculture
Speaker 6: and industry. We had a lot of suffering in the
Speaker 6: cities as we've moved to manufacturing and all the rest,
Speaker 6: and you say, okay, let's try to minimize these transitions.
Speaker 3: I did ask chat GPT what questions I should ask you.
Speaker 3: I thought it's questions were pretty boring. Yes, your answers
Speaker 3: were pretty boring too, So we're not getting replaced anytime soon.
Speaker 7: Yes, but clearly this is really struck a nerve, this.
Speaker 3: Baning thing, Bing's chatbot saying telling folks it's in love
Speaker 3: with them.
Speaker 7: Yes, there are people out there who aren't going to
Speaker 7: fall for it. Yes, should we be worried about that?
Speaker 6: So that's a deminimous worry. I think that's specific one.
Speaker 6: And the reason is, Okay, so everyone's encountered a crazy
Speaker 6: person who's drunk off their ass at a cocktail party
Speaker 6: who says really odd things, or at least every adult has,
Speaker 6: and you know that's not like the world didn't end right.
Speaker 6: And so the real issues I think are things like
Speaker 6: if we put in a whole bunch of computational systems,
Speaker 6: that we are on our trajectory to improving areas of
Speaker 6: racial bias or discrimination. Now, I think AI can be
Speaker 6: a very positive tool in that because we can improve it,
Speaker 6: we can learn it, we can fix it. We can
Speaker 6: probably fix it better than we can fix for example,
Speaker 6: systems of judges issuing paroles probably easier to do iteratively
Speaker 6: by studying it and getting it better through an AI system,
Speaker 6: which will function in partnership, not in replacement, but as
Speaker 6: a way of kind of improving those things. So those
Speaker 6: are the things that really matter. We have to we
Speaker 6: do have to pay attention areas or harmful. For example,
Speaker 6: someone's depressed. The thing about self harm, you want all
Speaker 6: channels by which they can get into self harm to
Speaker 6: be limited. That isn't just chatbots, that could be communities
Speaker 6: and human beings, that could be search engines. You have
Speaker 6: to pay attention to all the dimensions of it. And
Speaker 6: by the way, you can never get it perfect.
Speaker 3: So I agree that computers don't have feelings. These chatbots
Speaker 3: are just predicting the next word in a string.
Speaker 6: Right.
Speaker 7: What does worry me as a mom.
Speaker 4: Is my kids.
Speaker 3: So what if my kid is spending more time talking
Speaker 3: to a chatbot than me, or developing relationships with these chatbots,
Speaker 3: or making decisions based on what a chatbot has told
Speaker 3: them or nudged them to do, Like, why shouldn't I
Speaker 3: be terrified of that?
Speaker 6: Well, I think the question is is what kind of
Speaker 6: relationship and what are they nudging them to do? So
Speaker 6: for example, say you had your kid and the kid
Speaker 6: was interacting with the chatbot that was causing them to
Speaker 6: reflect on who they were and their feelings a little
Speaker 6: bit better and help them discover themselves and you're like,
Speaker 6: well that seems to be an okay relationship, maybe better
Speaker 6: than their friends at school even in some ways, and
Speaker 6: help them kind of be a to follow the path
Speaker 6: they want to be doing. Or say, for example, it
Speaker 6: was like, well, here's why actually, in fact, doing your
Speaker 6: homework is actually useful to you and here you know,
Speaker 6: let's let's help do that. You said, well, that's okay.
Speaker 6: So it's not the it's not the fact that there's
Speaker 6: an interaction there that bothers you. It's like, is the
Speaker 6: interaction going to be in a positive direction? Is going
Speaker 6: to be broadly there?
Speaker 7: How are we overestimating AI right now?
Speaker 6: Many ways that we're overestimating it, it still doesn't really
Speaker 6: do something that I would say is original to an expert. So,
Speaker 6: for example, one of the questions I asked was how
Speaker 6: would Read Hoffman make money by investing in artificial intelligence?
Speaker 6: And the answer gave me was a very smart, very
Speaker 6: well written answer that would have been written by a
Speaker 6: professor at a business school who didn't understand venture capital. Right,
Speaker 6: So it seems smart would study large markets, would realize
Speaker 6: what products would be substitute in the large markets would
Speaker 6: find teams to go do that and invest in them.
Speaker 6: And this is all written very credible and completely wrong.
Speaker 6: And part of that's because the newest edge of the
Speaker 6: information is still beyond these systems. Now. It's great when
Speaker 6: I said something like what would read Hoffman say on
Speaker 6: a German documentary about settlers of Katan, right, and it
Speaker 6: gave a very good answer.
Speaker 3: Billions of dollars are going into AI. My inbox is
Speaker 3: filled with AI pitches. Last year it was crypto and
Speaker 3: Web three. Before that it was self driving cars. Now
Speaker 3: everyone's on the AI train. Yes, how do we know
Speaker 3: this isn't just the next bubble?
Speaker 6: Well, I think neither Web three or autonomous vehicles actually
Speaker 6: think we're bubbles. I do think that the generative AI
Speaker 6: is the thing that has the broadest touch of everything.
Speaker 5: Now.
Speaker 6: Obviously, as venture capitalists, part of what we do is
Speaker 6: we try to figure that out in advance, you know,
Speaker 6: years before the people seeing coming. But I think that
Speaker 6: there will be massive new companies built.
Speaker 3: Feces have played a role, and you know, you could
Speaker 3: say in the hype cycles, how much is FOMO driving decisions?
Speaker 5: Right now?
Speaker 6: FOM will always drives some decisions, as you know, because
Speaker 6: people who are not with it suddenly try to jump
Speaker 6: on the train, and some byblay. Sometimes it works. And
Speaker 6: it is true that when you study the sequence of technology,
Speaker 6: what happens is there's a wave. There's an Internet wave,
Speaker 6: there's a mobile wave, there's a cloud wave. There's these waves,
Speaker 6: and that transforms the industries and that you need to
Speaker 6: be on that wave. So whether you're an early adopter
Speaker 6: or late adopter, everyone goes and tries to get on
Speaker 6: the wave.
Speaker 7: There's another concern, and I wonder if you share it.
Speaker 3: It does seem in some ways.
Speaker 7: Like a lot of AI is.
Speaker 3: Being developed by an elite group of companies, and people
Speaker 3: look in.
Speaker 6: Some ideal universe. You'd say, for a technology that would
Speaker 6: impact billions of people, somehow billions of people should directly
Speaker 6: be involved in creating it. But that's not how any
Speaker 6: technology anywhere anywhere in history gets built. It's a small
Speaker 6: number of people. How do you offset that and how
Speaker 6: do you expand that? And I think the way that
Speaker 6: you do that is try to have broader conversations, try
Speaker 6: to be more inclusive about what the concerns are, what's
Speaker 6: going on, what their intents are. That's the thing that
Speaker 6: I try to help push to.
Speaker 3: So do you see an aim mafia form?
Speaker 6: Hopefully not, especially in the exact term of mafia. I
Speaker 6: definitely think that there is because you're firm the PayPal mafia.
Speaker 6: I think that there's a network of folks who have
Speaker 6: been deeply involved over the last few years and is broadening.
Speaker 6: That will have a lot of influence on how the
Speaker 6: technology happens.
Speaker 3: Do you think AI will shake up the big tech
Speaker 3: hierarchy significantly? It seems like the big tech giants, all
Speaker 3: of them are on their toes.
Speaker 6: Well. What it certainly does is it creates a wave
Speaker 6: of disruption. For example, with these large language models in search,
Speaker 6: what do you want? Do you want ten blue links
Speaker 6: or do you want an answer? In a lot of
Speaker 6: search cases, you want an answer and a generated answer
Speaker 6: that's like a Mickey mini Wikipedia page is awesome. That's
Speaker 6: a shift. When you're working in a document, do you
Speaker 6: want to just be able to pull out a template
Speaker 6: that says here's what a memo template is, or would
Speaker 6: you like to say, give me a first draft of
Speaker 6: a memo on how artificial intelligence can improve government services?
Speaker 6: And it drafts something and then you go okay, and
Speaker 6: startups work much more nimbly than large companies. So I
Speaker 6: think we'll see a profusion of startups doing interesting things.
Speaker 3: This Can the next Google or Facebook really emerge if
Speaker 3: Google and Facebook or Meta and Apple and Amazon are
Speaker 3: running the playbook and Microsoft?
Speaker 6: Yes, as I tend to think we have five large
Speaker 6: tech companies heading to ten, not five heading to two
Speaker 6: or three, and it's competition, and that competition creates space
Speaker 6: for startups and all the rest. So do I think
Speaker 6: there will be another one to three companies that will
Speaker 6: be the size of the five big tech giants emerging
Speaker 6: possibly from AI? Absolutely? Yes, right now, does that mean
Speaker 6: that one of them is going to collapse? No, not necessarily,
Speaker 6: and it doesn't need to. The more that we have,
Speaker 6: the better.
Speaker 7: So what are the next big five?
Speaker 6: Well, that's what we're trying to invest in.
Speaker 3: You're on the board of Microsoft, obviously, you know Microsoft is.
Speaker 7: Making a big AI push. How do you see the
Speaker 7: balance of power between Microsoft and Google.
Speaker 6: I think it unequivocally has a shot. But one of
Speaker 6: the things that I think that Satis said very well
Speaker 6: is at minimum with what you're seeing happening with you know,
Speaker 6: bing Chad and everything else. And what means is is
Speaker 6: all of a sudden, Microsoft's back in the game. It's here,
Speaker 6: it's doing stuff, it's inventing, it's creating things. What is
Speaker 6: pretty amazing to have had a seat watching how Sati
Speaker 6: and his team are kind of bringing a tech company
Speaker 6: back to where, you know, a few decades ago it
Speaker 6: was one of the leading tech companies and then everyone's
Speaker 6: not paying attention to anymore, back to being a leading
Speaker 6: tech company, to doing search.
Speaker 3: Did you bring Satia and Sam or have any role
Speaker 3: in bringing Satia and Sam closer together? Because Microsoft obviously
Speaker 3: has ten billion dollars.
Speaker 4: Now in open Ai.
Speaker 6: Both of them are close to me and know me
Speaker 6: and trust me well, so I think I have helped
Speaker 6: facilitate understanding and communication. And I would not want to
Speaker 6: take anything away from how brilliant each of them is
Speaker 6: and how much the thing they have architected is because
Speaker 6: they're amazing.
Speaker 3: The AI graveyard is filled with algorithms that got into trouble.
Speaker 3: How can we trust open ai or Microsoft or Google
Speaker 3: or anyone to do the right thing.
Speaker 6: Well, there's a whole field of aix, AI, safety, etc.
Speaker 6: There's people in all of these companies, a lot of
Speaker 6: them employed with asking questions and making that work, so
Speaker 6: we need to be more transparent. Well, everyone agrees that
Speaker 6: we should be protective of children. Everyone agrees that we
Speaker 6: should try to make sure self harm isn't there. Everyone
Speaker 6: agrees that we should try to not have this lock
Speaker 6: in economic classes or other kinds of things, and should
Speaker 6: be more broadly provisioned. But on the other hand, of course,
Speaker 6: a problem exactly as you're alluding to, is people say, well,
Speaker 6: the AI should say that, or shouldn't say that, or
Speaker 6: they I should allow people to say that, or shouldn't
Speaker 6: allow people to say that, And you're like, well, we
Speaker 6: can't even really agree on that ourselves, so we don't
Speaker 6: want that to be litigated by other people. We want
Speaker 6: that to be a social decision.
Speaker 3: It's a minefield of ethics and fairness and governance issues.
Speaker 7: Is the answer regulation and how can regulation possibly.
Speaker 4: Even keep up?
Speaker 6: When people think regulation, they think you must come and
Speaker 6: seek approval before you do something, And that's the reason
Speaker 6: why most of these regulated industries have all massively slowed
Speaker 6: down on their innovation. So to start regulating now, I
Speaker 6: think would be broadly dangerous and destructive and kind of
Speaker 6: how do we create and own the industries of the future.
Speaker 6: But that doesn't mean do nothing. Say, for example, you're
Speaker 6: working with AI companies, we'd like to hear what your
Speaker 6: top concerns are. Here are some of ours. We'd like
Speaker 6: to have you figure out how to tell us about
Speaker 6: how you're addressing our concerns and how you're making improvements
Speaker 6: on it month by month, year by year. Maybe you
Speaker 6: could have a dashboard. Maybe you could be telling us
Speaker 6: about here's how you're measuring how racial bias might creep
Speaker 6: into your systems from the data that you're training on.
Speaker 6: And if, by the way, you're not doing that well enough,
Speaker 6: then we'll talk about the next phase of regulation. But
Speaker 6: started as a dialogue and positioning the concerns and kind
Speaker 6: of what improvements we want to see and what we'd
Speaker 6: like to see, and start that way.
Speaker 3: Elon left open AI years ago and pointed out that
Speaker 3: it's not as open as it used to be. He
Speaker 3: said he wanted it to be a non profit counterweight
Speaker 3: to Google. Now it's a closed source, maximum profit company
Speaker 3: effectively controlled by Microsoft.
Speaker 6: Does he have a point, Well, he's wrong on a
Speaker 6: number of levels there. So one is it's run by
Speaker 6: a fiber one C three. It is a nonprofit, but
Speaker 6: it does have a for profit part. It has a
Speaker 6: for profit but the for profit part as is structurally
Speaker 6: controlled in every way that really matters by the nonprofit.
Speaker 6: It's employees run to it's board. Governs too, are all
Speaker 6: in on pre profit mission. The commercial system, which is
Speaker 6: all carefully done, is to bring in capital to support
Speaker 6: the nonprofit mission. Now get to the question of for example,
Speaker 6: Open so Dolly, when it was ready for four months
Speaker 6: before it was released, why did the delay for four
Speaker 6: months and delayed for four months because it was doing
Speaker 6: safety training. It said, well, we don't want to have
Speaker 6: this being used to create child sexual material. We don't
Speaker 6: want to have this being used for assaulting individuals or
Speaker 6: doing deep fakes. We don't want it to have being
Speaker 6: like revenge pornography or that kind of stuff. So we're
Speaker 6: not going to open source it. We're going to release
Speaker 6: it through an API so we can be seeing what
Speaker 6: the results are and making sure it doesn't do any
Speaker 6: of these harms. So it's open because it has open
Speaker 6: access to the APIs, but it's not open because it's
Speaker 6: open source.
Speaker 3: You've resigned from the board of open AI because of
Speaker 3: the appearance of a conflict of interest.
Speaker 7: There are folks out there who are angry actually about.
Speaker 3: Open AIS branching out from nonprofit to for profit.
Speaker 7: Is there a bit of a bait and switch there?
Speaker 6: The first thing is to make a difference in the
Speaker 6: AI technologies and how to be a counterweight to all
Speaker 6: of the commercial things. To do that, open AI needs
Speaker 6: a lot of capital. The cleverness that Sam and everyone
Speaker 6: else figured out is they could say, look, we can
Speaker 6: do a market commercial deal where we say we'll give
Speaker 6: you commercial licenses to parts of our technology in various ways,
Speaker 6: and then we can continue our mission of beneficial AI
Speaker 6: because we're not primarily motivated commercial. We're primarily and we're
Speaker 6: primarily motivated by how do we make this great for society,
Speaker 6: great for humanity?
Speaker 3: So you don't think this nonprofit to for profit thing
Speaker 3: was a bait and switch.
Speaker 6: No, not at all. And I think that the question
Speaker 6: about ok it was all done, I think very transparently.
Speaker 6: And I think that the question about it is is
Speaker 6: making sure that open AI can provide all of the
Speaker 6: broad based kind of AI technology across multiple industries and
Speaker 6: not be contained within one company.
Speaker 7: It can't be all AI and rainbows.
Speaker 3: There must be stuff that's keeping you up at night,
Speaker 3: Like what keeps you up at night?
Speaker 6: Do I pay attention to what are the unintended consequences,
Speaker 6: how it might cement layers of power? Like, for example,
Speaker 6: do I pay attention to the fact that it could
Speaker 6: flood our media ecosystems with misinformation? Yes, I absolutely pay
Speaker 6: attention to that. Of course, our media ecosystems are already
Speaker 6: flooded with misinformation. It comes from Russians hacking our political stuff,
Speaker 6: or Nigerians or you know, Philippine farms or weird conspiracy theories.
Speaker 6: But what really keeps you up at night is in
Speaker 6: our fears? Do we miss the things that could be
Speaker 6: really valuable? Right? That's that's part of the reason why
Speaker 6: I come out so clearly. And it's not because like
Speaker 6: if you literally said, like, any money that I'm gonna
Speaker 6: make from investing these days already kind of heads to
Speaker 6: my foundation and all the rest. That's what I do.
Speaker 6: It's not because I have any economic interests here. It's because,
Speaker 6: like I think about first, you say, okay, so who
Speaker 6: will the first AI tutors be. We'll probably be for
Speaker 6: upper middle class families because of economic things, Well, can
Speaker 6: we get them to everybody in developed countries? And then well,
Speaker 6: what about the kids in you know, Nigeria, or what
Speaker 6: about the kids in Indonesia, or what about the kids
Speaker 6: in you know, all throughout India. Well can we do
Speaker 6: that too? That that's the kind of thing, and how
Speaker 6: quickly do we get there? Because I think, you know,
Speaker 6: we had this old expression from the eighties. No, no,
Speaker 6: it was nineties. I think the digital divide right, Well,
Speaker 6: look we all have a digital divide issue. That kind
Speaker 6: of thing definitely keeps me up. Now again, I don't
Speaker 6: mean to be pollyannish about this, and I and I
Speaker 6: put a lot of energy into making sure we're asking
Speaker 6: the right what are you in only like alignment questions
Speaker 6: or safety questions and so forth. But like when I
Speaker 6: read a weird bing chat, I'm mostly just laugh.
Speaker 3: Agi, when computers will be smarter than humans?
Speaker 4: How far out is that?
Speaker 6: So this is one of the kinds of things that
Speaker 6: human beings are very bad at making judgments on. What
Speaker 6: I mean is like, Agi, is there a percentage that
Speaker 6: we will get a computer smarter than humans in our lifetime?
Speaker 6: And the answer is yes. And the question is, well,
Speaker 6: is it a large percentage or a small percentage, and
Speaker 6: what counts as a large percentage of small percentage? You know,
Speaker 6: I think that percentage is small, and who knows, maybe
Speaker 6: it'll happen. Then it comes back to, well, what kind
Speaker 6: of superintelligence? So if you're worried about things being hostile,
Speaker 6: Terminix said, well, that's very concerning. But if you're like, oh,
Speaker 6: well we could create a superintelligence that is a Buddhist
Speaker 6: and thinks that sentient life is very good and goes, oh,
Speaker 6: how do I work in collaboration with you? Well that
Speaker 6: could be really good. Right. So the whole thing is,
Speaker 6: I think it's never good to be driven by your fear.
Speaker 6: I think it's much better to be driven by your
Speaker 6: curiosity but being very diligent and work very hard at
Speaker 6: trying to make the right things happen.
Speaker 3: So does this mean you think super intelligence is quite
Speaker 3: a ways out?
Speaker 6: I would say that it's more likely outside of our
Speaker 6: lifetimes than in our lifetimes.
Speaker 7: Okay, I appreciate a definitive picture.
Speaker 3: Thank you, Yes, thanks so much for listening to this
Speaker 3: episode of the Circuit. I'm Emily Chang. You can follow
Speaker 3: me on Twitter and Instagram at Emily Chang TV. You
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Speaker 3: I'm your host and executive producer. Our senior producer is
Speaker 3: Lauren Ellis. Our associate producer is Lizzie Phillip. Our editor
Speaker 3: is Sebastian Escobar. Thanks so much for listening.