Hi guys.
Welcome to this session.
So in this session we want to talk about the key terminologies we use in the world of AI and the players
in generative AI currently.
So if you look at what is generative AI, it is going to be the AI systems that can create new content
around text, images, music, coding by learning patterns from large data sets.
Now, the key difference between traditional AI and generative AI is that generative AI can generate
original outputs instead of merely analyzing data.
Now, the popular tools which we are using currently in this particular field are ChatGPT, which is
for text generation, Midjourney for image generation, you have pseudo AI, which is for music creation
as well.
And the key applications which we have at this moment are text generation, image generation, coding
assistance, and enterprise solutions.
So for enterprise solutions, we are using Adobe Sensei.
Coding assistance is being done by GitHub Copilot.
So there are different key applications now in in the market which which provides these kind of services.
Now if you look at the core generative AI terminologies.
So LMS.
LMS are large language models.
So these are AI trained on enormous amount of data sets to understand and generate human like text.
Now these are powerful tools like these are built on like ChatGPT for Claude.
These are all LMS which we can use now.
At the background of it there is neural networks being used, which is a brain inspired systems for
learning and decision making, which we will get now.
There is also Gans, which is Generative Adversarial Networks, which is basically two neuro neural
networks that compete to create and refine the outputs.
It is used in artistic tools like stable diffusion, which we'll see in this course.
Also there is NLP, which is natural language processing.
It is a subfield of AI focused on enabling machines to understand, interpret and respond to human language.
Now it also enables AI to understand respond in human language, which can be done and it gives a better
user experience.
So there are in ChatGPT specifically, if you look at it, there are certain specific terminologies
which you'll get to see which is like prompting prompts, which is like inputs or instructions, which
we give to the AI guiding the AI's response.
Now there is temperature adjusting creativity, which is you will get in OpenAI playground or workbenches
or API usage, wherein there is a degree which is low and high, low being precise, which is at 0.2
and high can be imaginative, which is 0.8.
eight.
There is also a concept of tokens, units of data, basically in in the form of text, images and symbols.
Uh, different models have different token limits, like GPT four have thousands of tokens for input
and output.
Similarly, there is something we call as context window.
It is a memory unit for conversation history.
Older details are dropped off as the window fills and there is hallucinations, which is AI generated
responses that are factually incorrect or nonsensical.
Now, if you look at the tech giants who are basically driving the generative AI, currently there is
Microsoft, which is partnered with OpenAI, and they have tools like Azure AI, GitHub, copilot,
Co-Pilot, which is for coding.
Then there is Google or DeepMind, and their tools are Gemini Palm models.
They're integrated into Google Workspace, Google products.
Then there is Nvidia.
Nvidia focuses on manufacturing GPUs and AI infrastructure.
It powers the hardware behind AI innovations.
Other than this, there's Adobe.
Adobe has tools like Adobe Sensei, which enhances creativity for professionals.
Lastly, if you look at the specialized AI companies driving innovations, there is OpenAI specifically
focusing on GPT four Dall-E.
It's a versatile text and image generation platform.
Then there is anthropic, which has who has created Claude.
It prioritizes AI safety and ethical development.
Then there is stable AI, which has created stable diffusion, which is an open source image creation
platform.
And then there is hugging face, which is basically a collaborative platform for AI models and data
sets.
So these are all the developments, companies, platforms, terms, terminologies which you will get
to see a lot in the AI space.