- Generative AI is not just a buzzword,
but a technological leap enabling machines
to generate content autonomously.
For you, understanding generative AI is not just about
staying relevant, it's about becoming a driving force
in the future of business analysis.
Generative AI models are trained on vast data sets
containing examples of the type of content
they're expected to generate.
For example, many text-based models are trained
on written language, from articles and books to blog posts.
However, it doesn't just regurgitate information,
it creates, innovates, adapts, and learns.
Imagine AI as your personal virtual apprentice.
It's not great at making coffee runs, at least not yet,
but it's great at creating things
and reducing your workload, if you know how to use it.
During training, the model learns patterns, structures,
and relationships in the input data.
It develops an understanding of grammar, context,
and the nuances of the content it's trained on.
This process involves adjusting millions or even billions
of parameters to capture the complexity of the data.
Once trained, the generative model can create new content,
the output, by predicting what comes next
in a data sequence.
This means predicting the next word
or completing a sentence for text generation.
You've seen an example of this on your mobile phone
when typing a text message or an email,
and the phone suggests the next word.
This is like that at an exponential scale.
The model learns to mimic the style
and structure of the input data you give it.
This means just like with your text messaging app,
the more you work with it, feeding data,
the better it gets at mimicking your style.
Generative AI is not merely a technological novelty.
It's a transformative force in business analysis.
In your role, you're no stranger to the ever evolving
landscape of technology.