- [Instructor] You may have heard about Gen AI.
Let's talk about it.
Gen AI, or generative AI, was based on older forms of AI
which were trained on labeled or structured data.
This is similar to what you'd find in rows and columns
on your Excel sheet.
These older forms of AI were highly specialized.
They lacked the ability to create something new
based on what they learned,
so they would learn based on patterns,
say, math, where 1 plus 1 was equal to 2,
but you cannot ask it to compose a new musical song,
as that required more than a binary output;
it requires some level of intuitiveness.
Then came the contemporary shift towards multi-faceted AI
that can self-learn
from unlabeled or unstructured data, e.g. big data,
and then generate or create its own content.
These are called foundational models.
This generative ability is called generative AI, or Gen AI.
Here are a couple use cases for Gen AI.
For audio synthesis,
Gen AI can produce new audio tracks or modify existing ones,
which is valuable in music production and voice synthesis.
In health care, we can use Gen AI
to create synthetic medical images
that can help train diagnostic models
without risking patient privacy.
For marketing, companies also use AI-generated content,
like blog posts, ads, social media updates
to better engage customers.
Now, of course, we can't forget the Hollywood writers strike
where the use of AI and how much to use it
without taking the creativity aspect was a major issue.
With regards to entertainment,
video game developers can also use Gen AI
to create immersive environments
by dynamically creating landscapes,
characters, and narratives.
This definitely makes world building and player experience
more beautiful to the eye and fun.
Some examples of Gen AI include open AI's ChatGPT.
Large language models
are a subset of FMs, or foundational models,
and these LLMs can extract meaning from text
and understand the meaning of said text.
ChatGPT falls under this category.
ChatGPT can create human-like text based on prompts,
making it useful for tasks like content creation,
customer support, and language translation.
DALL·E for image generation
can generate new images from text descriptions,
and that's great for design, marketing, and entertainment.
Microsoft Copilot can help with task automation,
creating summaries from all those long Teams calls,
brainstorming ideas, and also image generation.
Even WhatsApp uses Meta AI for image generation.
We also have GitHub Copilot for code generation,
and there's so many other examples out there.
We can't talk about AI
without talking about the limitations of Gen AI.
With regards to cost, training and running new Gen AI models
need a lot of computational power.
Now, using the cloud can help reduce costs,
but it does not eliminate the costs.
With respect to creativity, the success of any given task
depends on the quality of training data
and the quality of prompts provided.
However, whatever is generated could be repetitive,
so don't worry:
AI can't take over human creativity completely.
Talking about security, it is easy to misuse Gen AI,
as we see a lot of deep fakes
in video and audio, songs, paintings
that are hard to distinguish
from the original creator's work.
To combat this, we can limit access to proprietary data,
and due to the complex nature of our data,
we should also have, you know,
clear accountability and transparency
on how the AI made its decision,
but talking about transparency,
the complexity of the AI also makes it hard
to communicate how the AI arrived at its decision.
Improving this portion can make people more comfortable
and accepting of the technology.
There are more calls for the intentional management
of the powerful, versatile tools that we have,
including Gen AI,
whose only limitation and dependency is humankind.
This is not to scare you and to say, you know,
robots are going to take over or something.
No, but as with any tool that we use,
we should hope for proper use,
but also create safe boundaries
so we can respect the use of this tool.