- [Instructor] Generative AI is one of the biggest advances
in artificial intelligence in decades.
So let's talk about what this is.
Now, generative AI, as I mentioned,
is really an important milestone in AI development.
Probably one of the biggest impacts on the space
that's happened since the 1980s or so.
But this has become a major trend
by Google, Microsoft, and Amazon,
and a variety of other firms,
all in response to a new software
that was developed by a company called Open AI.
In a nutshell, generative AI creates new media,
meaning stories, poems, dialogue, analysis,
or even pictures, graphics,
and in theory movies based on existing work.
Now, how does this work?
Well, all of these generative AI tools
work based on what's called a large language model or LLM.
Now, the AI isn't really imagining things
or thinking about things like humans do.
It's basically just taking data
and then putting it together in new ways.
So in a large language model, we take large amounts of data
and then input it into the software.
That data is then categorized by the software
based on various characteristics.
For example, we might categorize,
say text data based on the types of words
that are being used
and some sort of preexisting dictionary
that tells the software something
about the sentiment implied there.
Based on this existing data then,
the software figures out or in essence,
records what are appropriate types of responses
to different kinds of questions or queries.
So you could think about text data
from say social media posts, Facebook, Twitter, or X now,
maybe Reddit, Quora, a variety of different kinds
of text data coming in.
The AI software examines this
and then determines what are appropriate responses
based on a particular type of question.
So the software is then going to interact with users
through the questions the users ask,
and it's going to respond to those users
based on its dataset of existing responses
that it's seen out there.
It's combining words in new ways,
but it's all based on existing responses
that have been done by humans.
Now, as I noted, there's really a variety
of different approaches
that are being taken in generative AI.
So we started with text models.
The most famous example of this right now is ChatGPT,
which is developed by Open AI,
but we have Microsoft's Bing AI tool, and Copilot,
which is coming to Microsoft office pretty soon.
We have Google's Bard, we have Amazon's Anthropic,
which was just announced.
But then we have image models also,
things like Firefly from Adobe
or Dally from Open AI, Mid journey,
all of these are creating images, or graphics, or pictures
based on what users are asking for.
That is a text description of a picture
and then existing images that the model has synthesized.
But then we have a variety of other types
of generative AI tools that are coming out.
One of my favorite examples is Salesforce's Einstein GPT.
This is designed to help make salespeople more productive
when they're dealing with prospective customers.
We have video AI tools.
ChatGPT announced that their system
is now able to speak and listen to users.
So rather than having to type your responses into ChatGPT
to ask question, you can just talk to the system.
And the goal is to make ChatGPT
able to either respond in text
or respond in a verbal way to people.
So you can imagine this being used as an example
for say translations from one language to another.
But there's many, many opportunities
coming from generative AI as a result.
But in a nutshell, that's what generative AI does,
and that's what this course is really all about.
It's about how we can use generative AI in finance.