4. Essential Terminology
● Artificial Intelligence
● Neural Networks
● Deep Learning
● Natural Language Processing(NLP)
● Generator
● Transformer
● Large Language Model (LLM)
● Generative pre-trained transformer
(GPT)
5. Artificial Intelligence
● Artificial intelligence is the
simulation of human
intelligence processes by
machines, especially computer
systems.
● Specific applications of AI
include expert systems, natural
language processing, speech
recognition and machine vision.
6. Neural Networks
● A neural network is a method in artificial
intelligence that teaches computers to
process data in a way that is inspired by
the human brain.
● It is a type of machine learning process
that uses interconnected nodes or
neurons in a layered structure that
resembles the human brain.
● Creates an adaptive system that
computers use to learn from their
mistakes and improve continuously.
● Example - Summarizing documents or
recognizing faces, with greater accuracy.
7. Deep Learning
● Deep learning teaches computers to
process data in a way that is inspired
by the human brain.
● Deep learning models can recognize
complex patterns in pictures, text,
sounds, and other data to produce
accurate insights and predictions.
● Can be used to automate tasks that
typically require human intelligence,
such as describing images or
transcribing a sound file into text.
8. Natural Language Processing (NLP)
● NLP is a field of artificial
intelligence that focuses on how
computers can understand,
interpret, and generate human
language.
● It’s the technology behind things
like voice-activated virtual
assistants, language translation
apps, and chatbots.
9. Generator
● A generator is an AI-based
software tool that creates new
content from a request or input.
● It will learn from any supplied
training data, then create new
information that mimics those
patterns and characteristics.
● ChatGPT by OpenAI is a
well-known example of a
text-based generator.
10. Transformer
● Transformers are a type of deep
learning programs, and are especially
useful for processing language.
● They’re really good at understanding
the context of words in a sentence
because they create their outputs
based on sequential data (like an
ongoing conversation), not just
individual data points (like a sentence
without context).
● The name “transformer” comes from
the way they can transform input data
(like a sentence) into output data (like a
translation of the sentence).
11. Large Learning Models (LLM)
● Large language models (LLM)
are very large deep learning
models that are pre-trained on
vast amounts of data.
● Consists of encoder and a
decoder with self-attention
capabilities.
● The encoder and decoder
extract meanings from a
sequence of text and
understand the relationships
between words and phrases in
it.
12. Generative Pre-Trained Transformer (GPT)
● GPT is a neural network family
that is trained to generate
content.
● GPT models are pre-trained on a
large amount of text data, which
lets them generate clear and
relevant text based on user
prompts or queries.
● Example- Create Images, Write
Code, Analyse Data, Generate
Learning Materials etc.
13. What is Generative AI?
● Generative AI enables users to
quickly generate new content
based on a variety of inputs.
● Inputs and outputs to these
models can include text,
images, sounds, animation, 3D
models, or other types of data.
● Uses statistical models to
predict what an expected
response might be - and this
generates new content.
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20. CRM with Generative AI
● CRM is a technology that keeps
customer records in one place to
serve as the single source of truth for
every department, which helps
companies manage current and
potential customer relationships.
● Generative AI can make CRM even
more powerful — think personalized
emails pre-written for sales teams,
ecommerce product descriptions
written based on images alone,
marketing campaign landing pages,
contextual customer service ticket
replies, and more.
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35. Ready for a challenge… Open this link
https://tinyurl.com/genAIDehradun