- [Host] Before we dive into specific AI tools like ChatGPT
and Google Bard, it's worth taking a step back
and understanding the broader AI landscape.
Artificial intelligence is a very broad term that relates
to machines and computer systems
that mimic functions associated with human intelligence,
like decision-making, image recognition,
self-driving cars, and more.
Within the field of artificial intelligence
lies machine learning.
These are going to be the models that act as the brains
of artificial intelligence systems.
They help computers learn with minimal human instruction
and get more accurate when exposed to more data.
In that more data piece is critical in explaining why
we've seen an explosion in AI
and machine learning in the past few decades.
With the proliferation of the internet,
mobile devices, internet of things,
we're now generating more data than we ever have,
and that's enabled machine learning
to become more accurate than it ever could have been prior
to some of these inventions.
And within machine learning lies deep learning.
This is a very complex family of algorithms that's designed
to mimic the human brain
and learn almost exclusively without human intervention.
Large language models like ChatGPT and Google Bard
fall within this category.
And speaking of the human brain, there's still one area
where these models are not able to approach
what humans are able to do.
And that brings up the conversation
between weak versus strong AI.
Currently, we're in this state of weak AI, which is defined
as AI that can only perform specific tasks.
So for example, ChatGPT could tell you how
to drive a car if you asked it,
but if you asked it to drive your car
for you, it couldn't do that,
even though it surfaced all of this knowledge
that you'd need to drive a car.
Strong AI or artificial general intelligence would be able
to do that at some point in the future.
This is often what's depicted in science fiction
and often what is very scary to many folks about this field,
these systems would be able to learn
and perform any task that a human could do.
So if you could ask ChatGPT,
can you go buy me groceries today?
And it was able to solve all of the problems that it needed
to without your intervention, that would be much closer
to strong AI than we are today.
As of now, we haven't seen any strong AI systems
implemented, but this is likely on the horizon
and possibly within our lifetimes.
And just in case you were wondering, both machine learning
and deep learning have their roots and statistics.
A lot of the algorithms rely on statistical concepts,
but there's one major philosophical
difference between these fields.
Statistics is very much concerned with being able
to confidently say, yes, there is a relationship
between variable A and B, and that relationship is X.
Machine learning and deep learning don't care
about those relationships.
They just care about the accuracy of output.
And because they don't care about these relationships
and they certainly don't care about humans understanding
these relationships, they're able to be much more complex
than the same statistical algorithms
that they might be based off of.
This has led them to be called black box models
because we know what goes in and we know what comes out,
but we have no idea what goes on inside these algorithms.
They're often picking up millions or billions of nuances
and data that we really can't understand or detect.
But now let's take a look at generative AI specifically.