This image, and this one, and this one. And these,
they all have something in common. They were all made by
machines, generative artificial intelligence software, to be
exact. It's a somewhat new field taking the tech world by storm.
Generative AI can write blog posts, tweets, academic papers,
and news stories. They can also now produce works of art that
win prizes like this one, which won the Colorado State Fair art
contest. And soon some people think they could replace the
armies of visual effects artists needed to work on blockbuster
movies, and democratize that kind of visual firepower to
anyone with an Internet connection. Who is of course,
willing to pay for those tools. And even for non artists. These
tools could become AI powered personal assistants, creating
slides for presentations or writing blog posts and minutes
as opposed to hours. Silicon Valley venture capital firms are
pouring hundreds of millions into these companies stability
AI, open AI in mid journey. There's a lot of hype around
generative AI, is it the next big thing in tech, or another
hyped up fad that goes bust?
Generative AI is here today, and it works. Generative AI refers
to a type of AI model that automates the creation of
content
generate AI is new because generative AI, these programs
create images. And these images have never been seen before,
anywhere.
Most of AI in the last couple of decades has really been around
analyzing existing data. So in finding an anomaly in data,
detecting fraud, making a movie recommendation, generative AI is
very different. It allows you to create brand new content, that
content can be text, like a news article, or poetry or marketing
copying a website, it can be images, let's say a character in
a movie, or creating a new scene, it could be video, it
could even be audio like creating brand new music.
And you can give them any you know, phrase that no matter how
absurd, and it's going to make something that's pretty close to
you know what you want. If I wanted to say, I want to see a
black cat on the moon in the style of Pablo Picasso, it will
do that for you. Even though you know no one has ever asked to do
that before. These programs are based on a subset of machine
learning. That's gotten a lot of attention last 10 years, which
is called Deep Learning Deep Learning uses neural networks or
matrix equations, basically to, to ingest data and learn about
relationships in that data and give you at the end, what is
called a model with weights in it that allow it to do these
magic things.
companies of all shapes and sizes are involved in AI. I
mean, both Mehta and Google have generative AI algorithms. A lot
of people are using the models more like toys right now, a
website like Shutterstock, or Getty Images, may be able to
incorporate AI generated imagery into their product offerings. I
mean, Getty in particular has said they're not going to do it,
and they're going to ban it. But I think the theory I heard the
most when I was doing my reporting, is that there'll be a
layer of what people have called API companies where you have big
giants do a lot of the training of these giant models, and then
offer access to those models to individuals into smaller
companies through what's called an API or application
programming interface, where they basically make a call to
the open API server or the or the stable defusion server. And
then that becomes a part of the infrastructure. And so a lot of
VCs and a lot of people in the space. We're excited about, you
know this API model.
Unlike other hyped up technologies that have received
hundreds of millions of dollars from VC firms, like crypto or
web three, generative AI tools are appear now and available to
anyone who wants to use them. Some are free, some are paid
apps, but the tools are ready to use.
So my name is Sarah Drummond. I'm a designer and a filmmaker.
So I kind of crossed the boundaries between doing
designing for like user experience and services and
making films really, and I run a school called the School of Good
Services where we train people how to design great services.
Basically,
part of Drummond's job includes sketching out storyboard scenes
and different product designs and workflows. She began to
employ generative AI tools to do all of that for her.
I think that would have taken me like a few hours maybe like
depends on what what fidelity right but like, I mean, if I was
doing it really quickly, and I might have done wasn't a bit of
a rush. I normally use like little blob kind of drawings,
like they're really bad. They're like little delete, like little
fat men, you know, doing various service interactions. But this
thing took me like, I don't know, two minutes, three
minutes, downloaded the squares put it in and was like, Whoa,
this is SIG.
Gaurav Gupta is a principal at lightspeed capital, a prominent
VC firm who invested a big round with stability AI, he also says
he's already using generative AI tools in his day job.
You know, for example, I like to write blog posts. Open AI, for
example, has a model called GPT. Three, right. And that allows
you to effectively give it a give it a set of text, maybe the
beginning of a sentence or a paragraph will automatically
fill in the content that follows. And I oftentimes will
use that when I've hit writer's block, you know, to give me a
set of ideas of what I could write about next. It truly I
don't think of it is as doing the work for me. But I think of
it as a way to inspire you know, my own writing.
There's a long list of potential ethical concerns if generative
AI does indeed take off first disinformation and
misinformation, think deep fakes, but on steroids.
A classic example is taking the image in the voice of the
President having him say something he wouldn't have never
done. The consequences could be severe in the sense of video
creates misinformation, and other things. These technologies
can also be used to harass people, right, you could take an
image of a person that you may not like and have them do
something. And it can have a lot of negative consequences.
The second big issue revolves around copyright. Generative AI
needs massively huge datasets in order to create digital art and
other media. In some cases, the dataset is a giant swath of the
searchable internet. That means the software is using existing
copyrighted art from real life artists, which could file real
life lawsuits for infringing upon their work, especially if
people use generative AI software to create art in the
same exact style as their real life artists using big chunks of
the internet as datasets could also introduce bias into the
final products,
the content that we're basing AI from, is born out of that
system. So when we have things like text prompts, and we base
the text responses on maybe a community like Reddit, which we
know maybe not so much, no, but in the past has been
particularly a certain way of political leaning right, you're
going to more likely get responses that could potentially
be racist, homophobic, etc.
There's a fierce debate going on right now about how disruptive
this technology will ultimately be some investors who I talked
to, you know, see this as kind of the moment where the total
market for something cool expands greatly from from, you
know, just the people, dedicated scientists and nerds who can who
can make the machines to really anyone who can use and when this
happened before, with PCs, and smartphones, many, many fortunes
were made.
I think generative AI is very different. We've already seen
explosion of real world use cases where this can help
people's lives, where it can make people more productive. So
I think the use cases for generative AI are a lot more
tangible, and are here today, and we're already seeing the
impact.
I definitely do not think at this point in time it will
replace jobs. But I do think that as creative artists,
photographers, musicians, videographers, like we should
play with this stuff now so we can help define how it can help
us do our jobs better. And then it's up to us to shape what we
need it to do for us, rather than be shaped by AI