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The Lightning Onset of AI—What Suddenly Changed? | Ars Frontiers

Our panel discussion 'The Lightning Onset of AI—What Suddenly Changed?' from the Ars Frontiers 2023 Livestream

Released on 5/23/2023

Transcript

00:00
[upbeat music]
00:09
Oh, hi. I didn't see you there.
00:11
Welcome to my office.
00:12
I'm Benj Edwards, a large language model,
00:15
otherwise known as Ars Technica's AI
00:17
ans machine learning reporter.
00:19
And you're watching the lightning onset of AI.
00:21
What has suddenly changed?
00:23
While AI has been around since the 1950s,
00:26
recent advancements in generative AI systems
00:28
have enabled impressive creative feats,
00:30
such as writing, chatting, and creating images.
00:33
We've quickly gone from a world
00:34
where very few have heard of things like ChatGPT,
00:38
'cause it only came out six months ago,
00:40
to a time where the entire tech industry
00:44
is racing to integrate them into their products.
00:46
So, what we're gonna do today
00:48
is explore what exactly has happened in the past few years
00:51
to enable this apparent leap in technology.
00:55
And to help us answer those questions,
00:56
we've invited two very distinguished guests to our panel.
00:59
But before I introduce them,
01:00
I wanna give a quick reminder to send any questions
01:03
for them to the comments on YouTube.
01:06
Okay. So, onto the panelists.
01:09
First, Paige Bailey is the lead product manager
01:11
for generative models at Google DeepMind.
01:15
She's in charge of the PaLM 2 model
01:17
and the new Gemini large language model under training.
01:20
Previously, she spent over a year at Microsoft
01:23
as director of machine learning and MLOps for GitHub,
01:25
working on GitHub code spaces and Copilot.
01:28
Welcome, Paige.
01:31
Thank you.
01:32
I'm really excited to be here
01:33
and can't wait to hear the audience's questions
01:35
and to learn more about what folks
01:37
are excited about for generative AI.
01:40
That's awesome.
01:41
And next, Haiyan Zhang,
01:43
the general manager of Gaming AI at Xbox.
01:47
She has over 20 years of experience in software engineering,
01:49
hardware R&D, and service design.
01:52
She's also the former host
01:53
of the BBC TV tech series, Big Life Fix.
01:56
Welcome, Haiyan.
01:57
Hey, Benj. Great to be here.
01:59
Great to be chatting with you and Paige as well.
02:01
Awesome.
02:03
It's great to have both of you here.
02:04
Now, I'll just get to the questions.
02:07
Since we're gonna be discussing AI and it's a nebulous term,
02:11
I wanna take just a minute,
02:12
maybe in a sentence, could you tell me what AI means to you?
02:17
Like what's the definition of AI?
02:18
Paige, do you wanna do it first?
02:21
Sure, I'd be happy to.
02:22
So, in general,
02:24
I know that there's been a lot of hype
02:26
around like what is AI,
02:28
how is AI different than machine learning,
02:31
is it an evolution of machine learning?
02:33
And really, I like to think of AI
02:35
as helping derive patterns from data
02:39
and use it to predict insights.
02:42
So, you can think of AI as just a way
02:46
to take the wealth of knowledge,
02:49
the wealth of data that's been collected over time,
02:52
and to be able to do really interesting things
02:55
like we've seen with models like PaLM 2
02:58
and with with GPT-4 to create text,
03:04
to generate code samples to explain code,
03:07
all those sorts of things.
03:09
So, really, it's not anything more
03:14
than just deriving insights from data
03:16
and using it to make predictions
03:18
and to make even more useful information.
03:23
Haiyan, what do you think
03:24
about AI? Oh, wow.
03:25
[Benj laughing] Yeah.
03:26
Building on Paige's description, I'd say,
03:28
so I focus on video games
03:29
and we think of video games
03:31
as the ultimate expression of human creativity.
03:34
It's an art form for people to experience.
03:36
And we see the transformation of AI
03:39
from using these algorithms, these models to analyze data,
03:45
to look for patterns in data, to classify data,
03:48
now evolving to having creative capabilities
03:52
both in language and image and code.
03:56
And we see these creative generative capabilities
03:59
having ultimately the really amazing tools
04:03
to bring out human creativity.
04:06
Yeah, that's awesome.
04:07
So, let's get to the title of the panel is what has changed?
04:12
So, what has changed that's led to this new era of AI?
04:15
Is it all just hype
04:16
or just based on the visibility of ChatGPT?
04:19
Or have there been some major tech breakthroughs
04:21
that gave us this new wave?
04:23
Maybe, Haiyan, you could start.
04:27
Yeah, I mean for us in Xbox, in Microsoft gaming,
04:31
we've been shipping AI for over a decade initially with
04:37
Bayesian inference algorithms
04:39
like matchmaking and skills ranking for players.
04:43
And now, in the last few years,
04:44
we've seen breakthroughs in the model architecture
04:48
for transformer models,
04:49
as well as the recursive auto encoder models
04:53
and also the availability of large sets of data
04:57
to then train these models
04:59
and couple that with thirdly,
05:01
the availability of hardware such as GPUs, NPUs,
05:06
to be able to really take the models, to take the data,
05:11
and to be able to train them in new capabilities of compute.
05:16
Yeah, wow.
05:16
Paige, what do you think?
05:19
Absolutely.
05:19
I think at Google,
05:20
we've been thinking about AI for a long time.
05:23
We've been a machine learning first company since inception.
05:26
And so, many of the great kind of capabilities
05:29
and breakthroughs that we've seen
05:30
in the generative model space,
05:32
the transformer architecture,
05:34
things like instruction tuning and RLHF,
05:37
reinforcement learning from human feedback,
05:39
were actually, pioneered many years ago.
05:42
So, I think DeepMind's first RLHF paper
05:45
and blog post was around 2017.
05:47
But really now, we've come to this place
05:50
where we have really compelling hardware like TPUs.
05:54
We have massive amounts of data
05:57
that we can use to train and to learn from.
05:59
And then, we also have this vibrant community,
06:03
I think now of open source tinkerers
06:06
that are open sourcing models, models like LLaMA,
06:11
fine-tuning them with very high quality instruction tuning
06:15
and RLHF data sets.
06:17
And really, most compelling to me
06:19
is they're able to eek out the same performance
06:22
that you might see from a much, much larger model
06:25
just by virtue of having a smaller model
06:27
fine-tuned on a very high quality dataset.
06:31
So, I think, this is what I've been waiting my entire career
06:36
to see honestly is we have
06:39
this kind of exponential increase
06:40
in the capabilities of models,
06:42
but even more importantly, making them efficient,
06:45
making them small and concise,
06:47
and helping unlock their capabilities
06:50
for not just the people
06:51
who can afford the most expensive GPUs,
06:54
but also anyone who's interested
06:57
in having these productivity and creativity helpers tools.
07:01
Yeah, that's amazing.
07:02
As you said that, I was gonna ask you about
07:05
if you think that the collaborations and open source,
07:08
open source machine learning platforms
07:11
and even sharing code and research,
07:14
has that been really important in accelerating things in AI?
07:18
Well, I think it certainly has in the sense
07:21
that many of the frameworks that we use
07:25
for training open source models
07:27
like PyTorch and Jaxon, TensorFlow,
07:31
they're all open source,
07:33
people sharing models and making them available.
07:39
Things like Hugging Face,
07:41
these things would've never been possible
07:43
without the great help
07:45
and the contributions of the open source community.
07:48
And really sharing best practices as well
07:51
has inspired teams to take new research directions,
07:55
which can unlock many of these capabilities.
07:58
So, I certainly do think
08:00
that this machine learning community is only in existence,
08:05
because people are sharing their ideas,
08:08
their insights, and their code.
08:10
And I hope that's something that will continue
08:13
as we move more into the world of generative models.
08:16
Yeah, that's amazing.
08:18
So, does Google have any plans
08:21
to do any kind of open source models
08:23
that you could discuss?
08:26
So, I do know that we've open sourced many of our models
08:29
and if you go to github.com/googleresearch/googleresearch,
08:35
you can see many of the papers
08:38
and sort of models and code that we've released.
08:41
I also encourage you to check out Hugging Face.
08:44
We partner quite closely with them.
08:46
And so, as often as possible,
08:48
whenever we release a paper at an academic conference,
08:50
we try to have some kind of demo publicly available
08:54
either through a website
08:55
or through sharing our insights on Hugging Face.
08:59
But I also,
09:02
I don't want to give away anything
09:05
that might be coming down the pipe.
09:07
So- Yeah.
09:09
Make sure to check out Google Research on GitHub.
09:12
That makes sense.
09:13
Now, Haiyan, from your point of view,
09:19
have the advancements in hardware improved
09:22
and contributed to these AI breakthroughs?
09:26
She mentioned TPUs,
09:27
which I think is mostly a Google kind of thing,
09:29
but there's a lot of work on the GPU side of things.
09:32
So, what do you think about that hardware's role?
09:36
I think Paige makes such a great point
09:39
in that when people play video games,
09:44
it is either through a console in their house
09:48
or through their PC or through cloud streaming.
09:52
And in all of these experiences,
09:54
we want these to be consistent, to be best in class,
09:57
both in terms of the rendered quality of the games,
10:00
the AI that powers the games.
10:03
And so, we need to be looking at, hey,
10:05
how do we make sure that the AI in the future
10:08
as it runs in these games, performs consistently
10:11
across lots of different kinds of hardware?
10:15
Mm. Yeah.
10:16
How long do you think we can stick a generative AI model
10:18
in a game console?
10:20
Like [mumbles]
10:21
I'm very excited about that.
10:22
But I do think it will be a combination of working
10:27
for the AI to be inferencing in the cloud
10:30
and working in collaboration with local inference
10:33
for us to bring to life the best player experiences.
10:37
Wow. Right.
10:38
I also encourage you to that point there,
10:42
as a result of the LLaMA models being open sourced,
10:46
there were some people in the ML community
10:49
that were able to bring it down
10:51
to 1 billion parameters in size
10:54
and actually put it on a mobile device.
10:56
So, it's really just mind-boggling
11:00
to think of how cool it is
11:02
that you can have your own personal large language model
11:05
on your mobile device such that you can ask it questions.
11:09
The data stays on device,
11:10
so there's no need to ping an API.
11:14
And they're getting better and better each day.
11:18
I would love to have a hyper personalized
11:21
large language model running on a mobile device
11:23
or running on my own game console that can
11:29
perhaps make a boss
11:30
that is particularly gnarly for me to beat,
11:35
but that might be easier for somebody else to beat.
11:38
Oh, Paige- Right.
11:39
Oh, sorry.
11:41
Yeah, go on, Benj.
11:42
I was just gonna say, Paige,
11:43
if it runs locally on your device, does that cut Google out?
11:47
Where does that leave room for PaLM like in the cloud,
11:51
if everybody's running
11:52
their own open source model on a phone?
11:54
So, that's a great question.
11:56
I do think that there are probably,
11:59
there's probably space for a variety of options.
12:02
Like of course, there are great APIs
12:06
from many different companies.
12:07
So, from Google, from OpenAI,
12:11
from Hugging Face and Cohere,
12:14
and several others also have paid service API options.
12:18
But it might be that some companies
12:20
want additional data privacy
12:22
or they want to be able to have custom fine-tuned models.
12:26
And I think there should be options available
12:29
for all of these things to coexist meaningfully.
12:34
And as we've seen, I think generative models
12:38
have captured the attention
12:39
and the hearts of most people that use them.
12:43
And I don't think there's going to be any shortage
12:46
of use cases for generative models long-term.
12:50
So, making sure that there are a spectrum of ways
12:53
that people can leverage their capabilities
12:58
is going to be quite important.
13:01
Haiyan, did you have any thoughts about that?
13:03
Well, I was gonna say,
13:05
I think I'm super excited by all of the new AI technologies,
13:13
new generative models that are being developed.
13:16
I think some of the work that I do is really focused on,
13:19
hey, we've got all this crazy amazing new tech,
13:22
how do we turn them into tools
13:24
for game creators to bring their imagination to life?
13:29
And I think that translation layer between
13:33
the raw technology and the raw APIs and the tools of people
13:37
who are not necessarily wanting to drill into the APIs
13:41
and the tools themselves,
13:42
but are more like, hey, I wanna bring this vision,
13:45
this artistic vision of mind to life,
13:46
how do we make the tools available
13:48
to everybody to be able to do that?
13:50
And that translation, that layer needs a lot of work.
13:53
So, we see a lot of crazy demos,
13:57
really great samples out there.
14:00
When we look at them,
14:01
I think some of the work that the generative AI models
14:06
are doing can work in one instance and not another instance.
14:10
We call that kind of, it's a brittle demo.
14:13
It's like a great demo for a one-off,
14:15
but if you are gonna roll out a game
14:17
to 100 million, 200 million players,
14:19
we need that AI to work every single time on every endpoint
14:24
that the player is accessing that experience on.
14:27
And so, I feel like that is kind of the work
14:29
that I'm really passionate about.
14:31
Yeah, that's amazing.
14:33
So, since we're talking about the lightning onset of AI,
14:38
I'd be remiss if I didn't talk
14:39
about some of the risks maybe.
14:41
I know you're both socially conscious people.
14:43
How do you feel about any social risks
14:45
from AI systems like misinformation
14:48
or making factual mistakes
14:49
or deep fakes or anything like that?
14:52
Is that something that's on your mind?
14:54
So,
14:57
at Google, we care very deeply
15:00
about making sure that the models
15:01
that we produce are responsible
15:03
and that behave as ethically as possible.
15:06
And we actually incorporate our responsible AI team
15:09
from day zero whenever we train models
15:12
from curating our data,
15:15
making sure that the right pre-training mix is created,
15:18
to also making sure that we're asking the right questions
15:22
through the model development process
15:23
and also through deployments and fine-tuning.
15:26
So, I do think that
15:30
there is significant risk for
15:35
models to be misused
15:37
in the hands of people that might not necessarily understand
15:43
or be mindful of the risk.
15:45
And that's also part of the reason why sometimes,
15:48
it helps to prefer APIs
15:53
as opposed to open source models.
15:57
As I mentioned before,
16:01
the APIs that we produce from Google
16:04
go through very, very rigorous responsible AI filtering
16:08
from T equals zero.
16:11
If you are adopting an open source model,
16:16
you and your team have to be mindful
16:18
of all of those constraints and risks yourself.
16:21
And to make sure that you're asking the questions
16:24
through the deployment process
16:25
to make sure that the models are used responsibly.
16:29
I do think though that there are great tools
16:32
and lots of wonderful research being done
16:34
to help us understand how to better use
16:36
and responsibly use models.
16:39
And that this field of work
16:42
is only going to increase importance over time.
16:46
So, that is something
16:48
that I am really honestly very delighted
16:52
and heartened that Google cares deeply about,
16:56
and something that we all have to be mindful about
17:00
as machine learning practitioners.
17:02
And, Haiyan, I know Microsoft also does a lot of work
17:05
in the AI ethics and responsible AI space,
17:08
Kate Crawford and her team.
17:10
So, I mean, Paige, I love we're having this conversation.
17:15
To me, I think there's a few different facets
17:16
of responsible AI and our work
17:20
to make sure everybody's included in that conversation.
17:23
So, firstly, I think generative AI
17:26
has this incredible potential to make our existing games
17:30
and features more inclusive and more accessible.
17:33
So, things like, hey,
17:34
how can we make games adaptable to your skill level
17:37
or your particular set of capabilities?
17:39
So, these are some of the areas we're looking at,
17:41
hey, how can we make games way more accessible
17:44
to everybody no matter what kind of controller they're on,
17:47
no matter what kind of abilities they have.
17:49
The second piece is,
17:51
when we think about incorporating AI into games
17:54
or into our gaming platform, how do we make sure that AI,
17:58
those new AI products and features are inclusive?
18:01
And that means working with communities.
18:03
So, we have great programs inside of Xbox,
18:07
like our Xbox Ambassadors
18:09
that we reach out and we talk about, hey,
18:11
how do we make sure that this is inclusive
18:13
of all different communities,
18:15
folks from different cultural backgrounds.
18:18
And then, I think the third piece, you are right,
18:20
is responsible AI at its core is something
18:23
that we care very deeply about as Microsoft
18:26
and also as Xbox,
18:27
some of the conversations we have between gaming
18:30
and the core responsible AI team
18:32
is that I think our needs for responsibility
18:36
are going to be different,
18:37
because for each industry,
18:38
you are going to have different keywords,
18:40
different kinds of filters, different scenarios.
18:42
Imagine the game,
18:43
all the morally ambiguous choices you are making
18:47
and potentially using AI as your support system.
18:50
And how do we make sure that the AI features
18:54
we roll out in video games still have that same,
18:58
those AI tools available
19:00
to make sure that they are transparent, inclusive, safe,
19:04
while at the same time, understanding that those scenarios
19:06
are, I'm taking over an alien planet,
19:09
[Paige and Benj laughing] I'm taking my spacecraft
19:11
to Mars and I'm gonna terraform.
19:13
How does the AI really understand these new moral scenarios
19:17
that we can experiment within games?
19:20
Yeah. That's awesome.
19:21
Yeah, that makes sense.
19:22
Let me, unfortunately, we don't have a lot of time,
19:24
so I have to transition to the audience questions now.
19:27
Those are great answers.
19:29
I see two really big questions right off hand.
19:33
This is like, [sighs] it's on everybody's mind.
19:36
So, Tim asks, How do we put fears of AGI to rest?
19:41
And, Paige, do you wanna go first?
19:45
So, I think that's a really interesting question
19:51
and AGI is
19:54
something that if you ask five different people
19:58
what their definition of AGI might be,
20:01
you'll probably get five different answers.
20:03
If you view AGI as creating a model
20:08
that is generally useful at a broad variety of tasks,
20:11
so capable of doing many things,
20:13
whether it's generating text,
20:17
generating code, understanding images,
20:20
generating videos,
20:22
I think that we can see
20:26
how that might be a productivity enhancement,
20:29
a creativity enhancement.
20:32
But again, that's only if we start incorporating
20:37
these responsible AI features and roadblocks
20:40
and ensure that we use models responsibly.
20:44
I think, from my perspective, AGI is still something
20:52
that's ill-defined
20:57
and if we're just talking about
21:00
creating a generally useful model,
21:02
we're close to being there,
21:05
But AGI, it's viewed in science fiction scenarios,
21:11
I think
21:14
is still
21:16
a long way off if ever
21:18
and
21:21
is something that we should be mindful about as an industry
21:24
and start building processes.
21:27
I know also there's been a lot of discussion,
21:31
especially recently around things like AI regulation
21:35
and also making sure that companies
21:38
who are building these AI systems are behaving responsibly,
21:43
especially as they're building the largest models.
21:46
And hopefully that's something that will be used
21:50
as a way to build muscle in this space soon.
21:54
Though, of course,
22:00
that's above my field of expertise in order to enable.
22:06
But I do think that as an industry,
22:10
we need to get a little bit more crisp
22:12
about what is the definition that we have of AGI,
22:15
and then also encourage lawmakers and policymakers
22:22
to adopt new standards
22:24
and things that might help
22:26
ensure that we're building AI systems responsibly.
22:30
Mm. Haiyan, what about AGI super intelligence people?
22:34
I think most people are afraid of losing their jobs
22:36
and some people are afraid
22:38
of it taking over the earth and destroying civilization.
22:41
What do you think, Haiyan, is that in the cards?
22:45
Well, I hope not, Benj. [Paige laughing]
22:47
Firstly, I wholeheartedly agree with Paige
22:50
and I encourage us to have this conversation
22:53
out in the public's sphere
22:55
about the impact and future ramifications,
23:01
future implications of AI on our society.
23:05
What do we want our society to be?
23:08
What do we wanna focus our talents and efforts on?
23:11
And how can we bring in AI
23:13
to support us in the things we wanna do as humans,
23:17
as people working together in a community?
23:19
And I love the direction we're going
23:22
with talking about AI regulation,
23:25
what kind of rules do we wanna
23:27
and safety balances do we wanna put in place?
23:33
And I think interestingly, you mentioned,
23:37
for many decades,
23:38
the earliest interaction that people have had with AI
23:42
has been through game playing.
23:45
The first AI algorithms were really chess computer programs.
23:51
We've got Garry Kasparov being defeated by Big Blue.
23:56
We've got AlphaGo playing against Lee Sedol.
24:01
And even though these were simple algorithms, simple models,
24:05
not so simple when it comes to the research,
24:07
but they did one thing really well.
24:10
But you can see when people play,
24:12
when they play in these experiences,
24:14
they can't help but project personality.
24:17
When you play Pac-Man, those ghosts are chasing you,
24:20
you are cursing those ghosts as if they were alive.
24:23
You are projecting humanness personality
24:27
onto these artificial beings,
24:29
which are just rules-based algorithms.
24:32
So, I think in a way, we can't help but project that,
24:36
hey, I see this AI tool doing these amazing things
24:40
and thinking that it has more general intelligence
24:45
and liveliness than I think, under the hood, it really has.
24:51
So, I do think we have a long way to go to AGI.
24:54
I think we are starting those conversations now.
24:58
And also, I love, Paige talked about science fiction.
25:02
How can we explore these scenarios
25:04
in science fiction in video games?
25:06
How can we explore AI and video games
25:09
to help better inform how we feel about that as a society?
25:13
Yeah. And that was awesome.
25:16
And I also want to just ease people's thoughts a little bit
25:23
in terms of having generative models take away jobs.
25:27
If anything, I think it's going to be creating more jobs
25:30
and empowering us all to be more productive
25:33
and creative at our current workplaces.
25:36
There's a lot that I do every day
25:38
that's a little bit mundane.
25:40
It feels repetitive.
25:41
Having something that could take ownership of that,
25:45
like having a little grad student
25:47
as part of my own tiny research lab would be amazing.
25:50
And then, also finding ways to unlock new careers.
25:56
Flights, whenever planes were first created,
26:00
they created jobs for pilots,
26:02
for flight attendants, for people to work at airports.
26:06
It is clear that that generative models
26:08
are going to be more of the same.
26:10
So, instead of taking away jobs,
26:14
it will just make us all more delighted
26:17
at the places that we currently work,
26:19
and then also unlock the potential for many new roles.
26:23
That's awesome. Yeah, that makes sense to me.
26:26
One more question, this is a prickly one
26:28
and we only have about two minutes left.
26:29
So, how do you feel...
26:32
Let's see who put this question.
26:35
NotNoel said,
26:36
Are you concerned about AI models
26:39
that are trained using public data without consent?
26:42
Like from creators,
26:44
like scraping stuff from the internet
26:45
that's feeding these big models.
26:47
How do you feel about 'em? Is it ethical?
26:50
Is it something you're working to change?
26:54
Try to answer quickly. [laughs]
26:58
So, I can give an example of something
27:01
that we did recently at Google as part of our Bard project.
27:07
We introduced the concept
27:09
of recitation checking within the tool.
27:12
So, first off, the models that we train
27:15
are on publicly available data, publicly available code.
27:19
So, nothing outside of the realm of anything
27:24
that you would see if you went on google.com
27:26
and did a search.
27:28
And then, for the
27:31
sort of questions that folks ask on Bard,
27:34
if there's any code that they generate
27:38
that's a portion of which might be in a GitHub repo
27:43
or in another place where public source code
27:46
is stored on the internet,
27:48
will actually have a URL back to the source,
27:52
such that it gives attribution back to the author,
27:55
and then also calls out what license was used
27:58
in addition to that source code.
28:00
So, you can see if it's Apache 2.0
28:02
or if it's something less permissive like GPL.
28:05
But more importantly,
28:07
it's lending this idea to author accreditation.
28:12
And one thing that made me particularly delighted
28:14
was being able to see new projects
28:19
that I might not have discovered otherwise
28:20
when I was asking the model to accomplish a task.
28:25
It can point me to a function
28:26
that's already been implemented
28:27
as opposed to me trying to hash it out myself.
28:31
So, I do think there are ways
28:33
to introduce credit attribution,
28:36
and then also to be mindful
28:39
and to only include data that the authors
28:41
have explicitly listed
28:42
under a permissive license for your pre-training mix.
28:46
Yeah.
28:47
Haiyan, we're out of time,
28:48
but I want you to have a chance to answer that real quick,
28:51
'cause it's only fair.
28:52
Oh, thanks, Benj.
28:53
Well, you can see Yeah.
28:54
with the Bing Chat product,
28:57
the idea of attribution and really making sure
29:01
that all the information being surfaced in that chat
29:04
is grounded in actual sources across the internet
29:07
is baked into the product from day one.
29:09
So, if you go to Bing Chat,
29:10
you ask it a question, it summarizes answers for you,
29:13
it gives you answers,
29:14
but it's always attributing back to the pages,
29:17
the sources that it came from.
29:18
And I think that is core
29:20
to how we think about product development
29:22
with generative AI,
29:23
with these variations of GPT models
29:26
that we do think about that inclusion
29:29
and making sure every creator,
29:30
every contributor is supported
29:34
and feels that their content is being respected.
29:39
Yeah. Thank you.
29:40
Well, that's all the time we have for today.
29:42
Thank you so much, Paige and Haiyan,
29:44
for taking the time to be here.
29:45
It's been awesome. Now, back to Ken and Lee.
29:51
That was fantastic. Thanks, Benj.
29:54
He'll be joining us shortly. Yeah.
29:55
That last question is actually one that is super important
29:59
Mm. and I think
30:01
as much as attribution is important,
30:03
I still worry about who's gonna be left
30:07
to incentivize to create the things that get attributed.
30:09
So, Benj.
30:11
Hey. Oh, I'm surprised
30:12
to see you there
30:13
over my left shoulder. [Lee laughing]
30:15
Lemme get in the center of this big old TV.
30:17
There you go. [Ken laughing]
30:18
Yeah.
30:19
So, listen-
30:20
I'll be in here.
30:21
Thanks. This was great.
30:22
But
30:24
there's a danger I think in anthropomorphizing AI.
30:29
You guys talked about AGI, which to me,
30:32
will always be like the Sierra game engine from the '80s,
30:34
but I know it's...
30:35
What does AGI I stand for these days?
30:37
The AGI that we're afraid of.
30:38
Artificial general intelligence.
30:40
There you go. Right.
30:42
And there is a danger to treat AI as if it were human
30:46
and has human type perceptions.
30:48
And I think that's probably one of the,
30:49
something that's baked into us as people.
30:51
Because when Ken and I are speaking,
30:53
we're keeping eye contact, I'm looking at you,
30:55
we're helping each other along with the conversation.
30:57
AIs don't approach conversation in the same way.
31:00
AIs don't really do conversation really.
31:03
This is uncharted weird territory.
31:07
Yeah, I think we have an adjustment period
31:09
where we're confronted with an entirely new type of thing
31:14
and we have to build the metaphors to understand it
31:18
and the structure and the knowledge.
31:20
And I'm sure when some other new technology came out like,
31:25
I don't know, a typewriter or something,
31:27
people probably looked at it and said,
31:28
what do you do with this thing?
31:29
What does it mean for everything?
31:31
And that period may have taken a few decades,
31:34
I don't know. That was in the 1800s.
31:36
But I think as far as AI is concerned,
31:39
we're just at the very beginning of generative AI.
31:43
And we may get so bored with it that,
31:46
and it becomes so mundane that we don't think,
31:49
oh, this is magic people talking to us from a computer.
31:52
It's a tool, a creativity tool
31:55
and a text and information processing machine.
31:57
So, who knows.
32:00
In a lot of ways, it feels like this is fulfilling,
32:02
at least at a surface level,
32:03
the promise that a lot of tech companies have been making
32:06
for decades and decades about how like one day,
32:08
you'll have an AI assistant in your pocket
32:10
and it will be able to do this and that and that.
32:12
In a lot of ways,
32:13
we got that with the assistance
32:14
in our smartphones and stuff.
32:15
But this is beyond that.
32:17
This is maybe a little closer to doing that, would you say?
32:20
Yeah.
32:23
It's been foretold in sci-fi for so long
32:26
that we'll have some kind of intelligent agents
32:28
or intelligent assistance.
32:31
And so, when we see this coming,
32:33
something that even looks a little bit like it,
32:35
it just rings a lot of alarm bells for people.
32:38
'Cause we live by popular culture.
32:40
It cements us all together.
32:42
So, it's like everybody says, is sci-fi becoming real?
32:47
In a way, yes,
32:48
but the actual dimensions of what's actually gonna happen,
32:54
no one can fully predict that at the moment.
32:56
We have good guesses,
32:58
but we just have to adjust as a society,
33:00
I think, to these new technologies.
33:03
Yeah.
33:03
What is your dream application for generative AI?
33:08
Oh, man, that's a good one actually.
33:10
I have all these cassette tapes.
33:13
I used to be a musician
33:14
around a music website a long time ago, 20 years ago.
33:18
And I used to record all these song ideas on tapes.
33:22
And boy, would I love
33:23
to run those cassette tape things through
33:26
and make it produce a fully produced song
33:30
with my voice and everything.
33:31
I'd love to hear that music fully realized
33:34
and basically have my own artificial band to make it happen,
33:38
because I would never have the time
33:39
to do that myself anymore.
33:41
It's possible. Yeah.
33:42
So, man, that would be cool.
33:43
That's a great-
33:44
Thank you, Benj.
33:45
Yeah, thanks.
33:46
Thanks.
33:47
All right, so we are approaching our final panel.
33:52
And today, this panel will be talking
33:55
about what happens when AI is capable of coding.
34:00
We know at ours that this is a big concern.
34:02
So many of you are developers
34:06
and we've heard lots of great stories.
34:07
We've heard people really afraid of stuff.
34:10
To take us there
34:11
is going to be our own Lee Hutchinson right here.
34:15
Take it away, Lee.
34:16
Okay.