- At the heart of generative AI are foundational models.
These are large, pre-trained deep learning models
capable of tackling a wide range of tasks.
These models are trained on massive data sets
to learn general patterns
and structures, allowing them to be fine tuned
for specific applications with minimal additional training.
A key category of foundational models is large language
models or LLMs, which specialize in processing
and generating human language.
LLMs are trained on extensive bodies of text
to understand the nuances of language such as grammar,
context, and semantics.
For example, models like GPT-4 can perform tasks ranging
from drafting emails
and summarizing reports to generating creative writing
or answering complex questions.
LLMs use advanced deep learning techniques to produce text
that often feels human-like making them
indispensable in fields like customer support,
content creation and coding.
Generative AI builds upon the principles
of natural language processing or NLP,
a field dedicated to enabling computers to understand
and interact with human language.
NLP techniques allow generative AI models to parse,
interpret, and generate meaningful text.
While traditional NLP tasks focused on narrower objectives
such as translating text
or identifying sentiment,
modern generative AI expands the scope
to conclude creating entirely new and original content.