- How smart can a machine really be?
Could it ever replace the incredibly complex
and organic machine between our ears?
And if it did, what would that mean for us?
Could we quit our day job,
pack things up, and move to the nearest beach?
Well, maybe not yet, but artificial intelligence
is reshaping how marketers are going about their business.
If you haven't already, check out our video that dives
into how AI has changed the world for people everywhere.
In this video, let's dig a little bit deeper
and see why computers are advancing so quickly
and how that's affecting us and marketers in particular.
I wanna talk about two programs,
machine learning and deep learning.
Both of these are playing a huge role
in closing the gap between the human brain and the computer.
How does that affect marketers like you and me?
Let's break this down and look at what these terms mean,
how they relate to each other,
and what they're actually used for.
In a nutshell, machine learning is an algorithm
or a statistical model that enables a machine
to improve its performance through experience.
Now in traditional programming,
a coder explicitly writes the rules
for the algorithm to follow.
But machine learning can absorb information
without being explicitly programmed.
It's a way of training algorithms using data,
allowing the machine to learn how to make decisions
with constant human intervention.
Machine learning leverages structured
and labeled data to make predictions and create outcomes.
It's the process of allowing machines
to carry out tasks independently.
Over time, it builds up knowledge
about that task or object over time.
However, machine learning does require an initial
human input to distinguish features between objects.
Before you unleash your machine learning program
on the world, you need to make sure
it understands the parameters of that world.
Take the classic example of cats and dogs.
As a human, which I assume you are,
you can look at a dog next
to a cat and clearly see which is which.
For a computer, it's not so simple.
Both have whiskers, four legs, and a tail.
Initially, a coder needs to train the machine
on the fundamental differences
between the two animals, the different features,
eye shape, posture, running style, size,
and all the other small details that add
up to make a dog a dog and a cat a cat.
Over time, the machine will learn the difference
to a reliable degree and from there,
it can be allowed to run solo.
(soft music)
Off it goes into machinedom.
Now imagine that applied to, say,
detecting spam in your inbox.
You probably know a fraudulent email
when you see one, all caps headline,
lots of exclamation marks,
an account you've never interacted with before.
Google has trained a machine learning algorithm
to recognize emails with these features
and send them to your junk folder.
It's not 100% perfect but it catches
a lot of nonsense from cluttering your inbox.
So if you're a marketer who writes a lot of emails,
you better make sure those headlines are strong
and your content is on point.
Machine learning is raising the bar on all of us.
Okay, so what's deep learning then?
Just as machine learning is a subset of AI,
deep learning is a subset of machine learning.
AI is a machine mimicking human behavior.
Machine learning is a way to train AI through algorithms.
Deep learning is a type of machine learning
that mimics the human brain.
Let's talk about neurons for a minute.
Your brain is full of them, about 86 billion.
They make up up your nervous system.
Each neuron sends and receives chemical signals
and electrical impulses that transmit information.
Deep learning uses an artificial neural network
to pick out distinct features
between objects without human intervention
by using the same process as the neurons in your brain.
To do this, you'll need a huge amount
of data to train the machine.
So while deep learning is highly accurate,
it takes a long time to process
due to the high volume of data.
If you have less than ideal hardware
and lots of labeled data,
machine learning is more appropriate
as it's less complex and less labor-intensive,
but both are ways to train models
and classify data depending on the processing power
and data you have available.
So how does deep learning actually work?
Deep learning programs are built
on at least three layers of neurons.
The first layer of neurons is called the input layer.
This captures a piece of information about the subject.
This is then passed down
through the subsequent layers called channel neurons
into the final output layer.
Each neuron has a value attached.
For example, a dog's paw will trigger a reaction
while a cat's will not.
If an activation threshold is reached that matches the value
of the neuron, the appropriate neuron
in the output layer activates.
This process repeats and adjusts
to create a trained network,
and because the algorithm only needs unstructured data,
texts and images, it doesn't need human input
to characterize objects.
By observing repeat patterns,
deep learning can cluster inputs
and discover hidden patterns
and groupings without human intervention.
This is known as unsupervised learning.
Take a chat bot, for example.
By analyzing thousands of customer interactions,
deep learning can figure out the common keywords
and responses that lead to a resolution.
Over time, the chatbot program will learn how
to not only give useful information, but also how to respond
in a way that feels relatively natural and almost human.
And the great thing about deep learning
is that the more interactions it has,
the more effective it gets.
So if you're a marketer whose company uses chatbots,
deep learning provides an amazing look
into your customers' attitudes towards your business.
These are the kinds of insights
that can form super targeted messaging,
better UX, and smoother customer journeys.
AI is the grandfather of smart computing.
Machine learning is the son
and deep learning is the grandson.
Both are valuable ways
of training programs to ease human administration
and improve internal and external facing experiences.
Machine learning is leading
to more accurate illness diagnosis,
wine quality predictions, and stock price predictions,
while deep learning is creating self-driving cars,
faster customer support, and even safer surgical procedures.
So are computers going to replace your brain?
Well, not today, but they're already doing
a pretty incredible job of doing human-based tasks
as well as if not better than people today,
and they're only getting smarter.
Ultimately, both models are here
to help us do amazing things
and make the things we already do a whole lot better.
If you wanna keep learning
about the weird world of AI and computing,
subscribe to the channel so you don't miss a thing.
Now if you'll excuse me, I'm gonna go deep learn
how to make some cookies.
You know what I'm saying?
Your boy's a little bit hungry.
I'll see you next time.
- I can't find this client info.
- Have you heard of HubSpot?
HubSpot is a CRM platform
so it shares its data across every application.
Every team can stay aligned,
no out-of-sync spreadsheets or dueling databases.
HubSpot, grow better.
(upbeat music)