You must have noticed, right? Generative AI seems
to be absolutely everywhere now. Oh, yeah. Creating
images, writing text. It feels, I don't know,
almost like magic sometimes. It does have that
feel. But then you remember, AI isn't exactly
new. It's been working away in the background
for years, hasn't it? For a long time, yes. Often,
quite invisibly. So it got me thinking, what
is the actual difference? between this flashy
generative stuff and the traditional AI we've
had for ages. You probably wondered that too.
Absolutely. The buzz around generative AI is
huge and, well, for good reason. Yeah, to really
get what makes it tick, you need to understand
what came first, the traditional AI. Okay. And
that's exactly what we're diving into today.
We're using some insights from a really clear
article over on hackscience .education. It's
called AI, Generative, and Traditional. Right.
So the mission today is pretty straightforward.
We want to get clear on what these two types
of AI are, how they differ, and crucially, why
you should care about the difference. So let's
start with the baseline, this traditional AI,
the workhorse, you called it. What's its story?
Well, like the article points out, it's been
powering a lot of things we use every single
day, often without us noticing. Like what? Think
about search engine results or when you're shopping
online and get those product recommendations.
Yeah. Or your music playlist, how they seem to
know what you like. Even predictive text on your
phone. That's often traditional AI doing its
job. OK, so it's the quiet helper tech, making
things smoother. But how does it actually work?
The article mentioned classical data science
and a structured process. Sounds a bit formal.
It is quite methodical. It usually starts with
gathering data. Then you have to prepare that
data, clean it up. Then there's a step called
feature engineering, which in traditional AI
often means humans have to figure out and tell
the AI which bits of the data are most important
for the task. OK, so humans guide it quite a
bit there. Exactly. Then the model gets trained
on that data. And finally, you check how well
it performs, validate it. The main goal here
is usually prediction or classification. Meaning
it uses the data it learned from to answer questions
like, what category does this belong to? Or what's
likely to happen next based on past patterns?
Got it. Spotting patterns to make guesses or
sort things. That makes sense. The article also
said it operates within predefined boundaries.
What's that about? That's key. Traditional AI
works based on the rules and instructions programmed
into it. It learns from a specific data set for
a specific job. And its abilities are pretty
much limited to what it was taught. The results
are usually deterministic, same input, same output,
and predictable. It's like a tool built for one
specific purpose. So the example in the article.
A traditional customer service chatbot. How does
that fit? Right. That kind of bot probably has
a set list of questions it understands and prepared
answers it gives out. OK. It matches keywords,
finds the right script. It can be good for common
questions within its zone. But if you ask something
weird. Exactly. If you ask something outside
its programming, it'll likely just say, sorry,
I don't understand, or give a default response.
It can't really adapt or learn on the fly beyond
its rules. It's like a very complex flowchart
in a way. Right, a sophisticated flowchart. That's
a good way to put it. And what about complexity?
Resources. The article hinted traditional AI
is maybe simpler, less demanding. Generally,
yes. Compared to the big generative models, traditional
AI models are often less complex. They don't
need quite as much computing power. And like
the article says, they're usually trained on
smaller data sets, and importantly, labeled data
sets, data where someone has already marked the
right answer. And they don't really have ways
to adapt to new stuff unless you completely retrain
them with new labeled data. OK, solid picture
of traditional AI then. Focused, rule following,
good at predicting within its limits. Now, generative
AI. The exciting newcomer. What's the fundamental
shift? Well, the article calls it a unique and
fascinating advancement. And I think that's fair.
The absolute core difference is what it does.
Instead of just analyzing or predicting from
existing data, it can actually create new data,
new content, stuff that wasn't explicitly in
its training data. Whoa. OK. Creation. That's
a big leap from just prediction. Huge. It's not
just about identifying something, it's about
producing something novel. This ability to generate,
though, it comes with uncertainty. Sometimes
it creates amazing things, sometimes less so.
That needs checking, which isn't really a factor
for deterministic traditional AI. Making new
stuff versus recognizing old stuff, how does
it manage that? The article said something about
replicating patterns, but also imagining new
ones. That sounds, well... It does, doesn't it?
And that's the exciting part. Basically, it learns
the deep patterns, the structures, the relationships
within truly massive amounts of data. And then
it uses that learned understanding to generate
new examples that follow those same kinds of
patterns but are still original. It's crafting
scenarios, maybe even synthesizing knowledge
in a way. It's an evolution of deep learning,
as the article notes. Deep learning. Where the
AI figures out the important features itself
from all that data, unlike the manual feature
engineering in traditional AI. And the article
contrasted the deterministic, rule -based approach
with a probabilistic approach. What does that
mean in practice? Go ahead, think of it like
this. Traditional AI is like flipping a light
switch. You flip it, the light comes on, same
action, same result. Deterministic. Gotcha. Generative
AI is more like rolling dice. You roll them,
you know you'll get numbers between 1 and 6,
but you don't know exactly which numbers until
they land. It's based on probabilities. OK. It
looks at the input, considers the patterns it
knows, and then generates what it calculates
as the most likely next thing, word, pixel, note,
whatever. But there's an element of chance of
variability. So it's not rigidly fixed. Exactly.
The outcome isn't predetermined in the same way.
That probability aspect is what allows for the
novelty, the variation, the outputs that weren't
explicitly programmed line by line. So that's
the magic the article mentioned. Generating original
content. Stuff we used to think only humans could
do. It's a big mental shift. It really is. But
crucially, the article also said generative AI
doesn't just kick classical data science to the
curb, they work together. How? Yeah, that's super
important. Generative AI isn't replacing everything.
Classical data science is still vital. For one,
you need those techniques to manage and prepare
the enormous data sets these models learn from.
And maybe even more critically, you need traditional
methods to evaluate the output of generative
AI. Is it accurate? Is it biased? Is it safe?
Is it useful? We need ways to measure that. So
they complement each other. Definitely. It's
a partnership. OK, the article helpfully summarized
the key differences to make it clear. Let's run
through those first. Creation versus prediction.
More examples. Sure. Traditional AI, identifying
if an email is spam. That's classification, a
type of prediction. OK. Or forecasting stock
market trends based on past data. Prediction
again. Right. Generative AI, writing a poem.
composing music, generating an image from a text
prompt, like a cat riding a bicycle on the moon,
or writing software code, designing a new molecule.
It's producing something new. Got it. Understanding
versus making. Next difference. Approach and
outcome. Rule -based deterministic versus probabilistic
varied. You mentioned the outputs can vary with
generative AI. Can you control that? Yes, often
you can. The article mentions temperature. It's
like a creativity knob on many models. A creativity
knob. Sort of. Lower temperature means that AI
sticks to the most likely common patterns, more
predictable, maybe safer outputs. OK. Higher
temperature lets it take more chances, explore
less probable paths, can lead to more creative,
surprising results, but maybe also more nonsensical
ones. Interesting. So you can dial the randomness
up or down? Pretty much. That non -deterministic
nature is core to generative AI. Next. Training
data and method. Sounds like a massive difference
here. Oh, absolutely huge. Traditional AI often
uses smaller, carefully curated, labeled data
sets. Maybe thousands or tens of thousands of
examples. Right, labeled. Generative AI, especially
the big models like LLMs, train on vast amounts
of data. Think internet scale text and images.
Billions, trillions of data points. And the methods
are different too. Things like self -supervision,
where the AI learns from unlabeled data by, say,
predicting missing words and sentences. It takes
way more computer power, way more time. Which
leads directly to the next point, model complexity
and resources. Yeah. Generative AI needs more,
muscle. Way more. Traditional models can often
run on regular computers, even your phone sometimes.
These big generative models, they have billions,
sometimes trillions of parameters, the internal
variables they learn. They almost always need
powerful cloud computing setups, accessed via
APIs usually. That means higher costs too. Makes
sense. Okay, adaptation. Traditional AI sounds
pretty fixed after training. It mostly is. To
adapt, you usually have to retrain it with new
labeled data. which is a whole process. But generative
AI. It's more flexible. The article mentions
prompt engineering. Just changing your instructions
can change the output dramatically. Right, the
prompt. Then there's ARGI retrieval augmented
generation. That lets the AI pull in current
information from outside sources to answer questions.
Oh, clever. And fine -tuning, where you can tweak
a pre -trained model for a specific task without
starting from scratch. So yeah, more ways to
adapt its behavior after the main training. And
the last difference listed, modality of interaction,
how we talk to them. Yeah. Traditional AI often
uses structured input, filling forms, clicking
buttons, specific data formats, generative AI,
particularly LLMs. You interact using natural
language, just typing or speaking what you want,
prompts. Much more conversational. Exactly. It's
more expressive. And that's why, as the article
says, prompt engineering itself is becoming a
whole field, learning how to ask the right way
to get the best results. Okay, these distinctions
really help paint the picture. And you mentioned
LLMs. Large language models are central to the
generative AI buzz. Tell us more about them.
Right. LLMs are a specific, very powerful type
of generative AI. They're trained on truly immense
amounts of text data. Who's text? Primarily text,
which allows them to understand and generate
human language incredibly well. What's amazing
is their versatility. How so? Because their general
purpose. Give one the right prompt, and it can
answer questions, summarize articles, translate
between languages, write code. all from one model.
Often, yes, without specific retraining for each
task. The article names the big ones you hear
about OpenAI's GPT models, Anthropics Claude,
Google's Polym, and Gemini, foundational models.
Incredible flexibility. But the article also
warned it's not a silver bullet. There are catches,
right? Considerations. Definitely. It raises
important issues, financial costs, legal questions
like copyright, technical challenges, and significant
ethical concerns. risks of generating incorrect
or biased information, sometimes called hallucinations,
potential for misuse, creating deep fakes, or
spreading disinformation. Yeah, serious stuff.
So the article stresses that organizations need
a clear strategy. They need goals, good governance,
responsible AI policies. You need to think about
costs, reliability, especially if you're depending
on an API. Plan for problems, basically. Exactly.
And think about how to use your own unique data.
Manage the costs. It's not just plug and play.
magic. Right. Requires careful thought. But the
article finished on a key point. These two types
of AI, traditional and generative, they're not
enemies. Not at all. That's a crucial takeaway.
They are not mutually exclusive. Often they work
best together. How would that look? Think of
a sophisticated chatbot. It might use a generative
LLM for the fluid natural conversation part,
but rely on traditional models behind the scenes
to maybe detect customer frustration from the
text or route the query to the right department
based on classification. Combining strengths.
Precisely. And remember what we said about evaluation.
You absolutely need classical data science methods
to check the outputs of generative AI to make
sure it's accurate and responsible. So the future
isn't one or the other. No, the future is almost
certainly a blend, using the right tool for the
right job. Traditional AI for reliable prediction
and classification where rules work well. Generative
AI for creating, brainstorming, tackling new
kinds of problems that need that flexibility.
That makes perfect sense, a synergy. Exactly.
And that's really the core message today, I think.
Traditional AI predicts and classifies within
known rules. Generative AI creates novel things
using probabilities learned from vast data. Understanding
that difference feels really important now. It's
becoming essential. As AI gets woven deeper into
everything, knowing what kind of AI is involved
helps you understand its power and also its limits.
Absolutely. So something for you, the listener,
to think about as you go about your day. Consider
this. How might? this blend, this increasing
integration of both traditional and generative
AI actually reshape things, not just tech, but
maybe creativity, problem solving, even how we
think about intelligence itself. When you encounter
AI, a recommendation, a summary, an image, try
and ask yourself, is this mostly predicting something
based on rules and past data, or is it generating
something fundamentally new? It's a fascinating
lens to view the world through right now.