Hi, I'm Ankit currently I enrolled for AWS Training and Certification.
In this article,
We will learn
what Artificial Intelligence is,
how it adds value to different businesses,
and how Amazon uses AI in its products.
We'll look at a use case
where AI plays an important role,
and we'll learn about some AWS services
that you can use to develop an AI ready application.
Simply stated, AI is intelligent behavior by machines,
that means any device that can perceive
its environment and take actions accordingly, has AI.
By using AI, a machine
can mimic cognitive human functions,
like learning and problem-solving.
A common example of using
Artificial Intelligence is giving
machines the ability to scan
and interpret their physical environment,
so that they can handle moving
around and even up and down the stairs.
To make the machines act and react like humans,
we need to provide them with
information from the real world,
in order to mimic human intelligence AI
relies on something called knowledge engineering.
Knowledge engineering is
the key component of AI research,
machines with AI are
expected to solve problems like humans would.
To do that, machines
need extensive knowledge of the real world.
In other words, they need to understand things
like the relationships between objects and situations,
the properties of an event,
cause and effect, and more.
This data is then processed
and fed to software programs that in
turn analyze the data and come
up with decisions for a particular problem,
the way humans do.
In short, the goal is to
transfer human expertise to a software program,
that can take in the same data and come
to the same conclusions as humans would.
This process of feeding data to
a software program and coming up
with human-like decisions is also
known as the modeling process.
The model, which is basically your software algorithm is
consistently refined until its decisions
are close to those a human would come up with.
If the decision for a particular problem is
inconsistent with what a human decision would be,
then we go back to
the model and debug it until we improve it.
As you might expect,
this is an iterative process.
AI presents us with
new possibilities and promotes growth in business,
all kinds of companies are using AI to innovate.
Companies are making significant investment to
improve their products based on user satisfaction,
feedback, trends and more,
and they are using AI to do it.
Here are a few examples of how AI is being used today,
detecting and deterring security threats and fraud,
resolving users technology issues
through automated call center or chatbot,
automating repeatable tasks such as payroll, data entry,
and audit, anticipating
users' actions and providing recommendations,
monitoring social media comments,
and tailoring advertising content as per search trends.
Once you start learning about AI,
you start seeing terms like
Machine Learning and Deep Learning.
Machine Learning also called
ML and Deep Learning also called DL,
are really subsets of AI.
You can create an AI system with
the help of ML and DL algorithms,
for example a software program
to predict user actions and suggest
recommendations or a system that understands
thoughts and sentences spoken by a human like Alexa.
Let's talk about these fields
and how they differ from each other.
Machine Learning is often deployed where
explicit programming is too rigid or impractical.
Unlike regular computer code,
Machine Learning uses data to generate
statistical code that will output the right result,
based on the pattern recognized
from previous examples of input.
Machine Learning starts with the data it
already has about a situation.
It processes data using algorithms to
recognize patterns of a behavior and outcomes,
it then interprets those patterns
to predict the future outcomes.
These predictions are used to make a decision
about the next step for the Machine Learning to take.
That decision produces results,
which are then evaluated and added into the pool of data,
the new data would influence the predictions
and subsequent decisions made going forward,
this is how Machine Learning learns over time.
Machine Learning can make predictions from huge datasets,
optimize utility functions, and extract hidden patterns
and structures from the datasets by classifying data.
This enables a software program to
learn and make predictions in the future.
Deep Learning takes Machine Learning a step further.
Rather than telling the machine
what features it needs to look for,
Deep Learning enables the machine to
define the features it needs to look
for itself based on the data it's being provided.
In this example,
traditional Machine Learning requires you to
tell the machine how to differentiate
between a rectangle and a circle.
Deep Learning on the other hand,
shows machines several examples of rectangles.
It analyzes those examples and
infers common features that define a rectangle.
At this point, it can identify on its
own whether it's looking at a rectangle.
In the same way our brains
process information using neurons,
Deep Learning processes information using
similar but artificial processing structures
known as artificial neural networks. 
It builds these structures from the data it analyzes,
and then infers features about
its subject matter based on the data.
Then it weighs those features
according to certainty and commonality,
and organizes them into layers of
hierarchies and relationships with each order.
To return to the circle and rectangle example,
if the Deep Learning machine looks at
its reference data on what a rectangle is,
it can infer that rectangles are built
from four sides at right angles.
Unlike Machine Learning,
the Deep Learning machine doesn't have to
be told to look for the number or angle or sides,
instead, it recognizes the sides as
a common feature of the reference data on its own.
It can then look at the big blue rectangle,
see that it has four sides at right angles,
and determine with strong certainty
that it's a rectangle.
It can also determine that
the purple square is probably a rectangle,
since it also has four sides at right angles,
even though its four sides appear to be equal and
it's not of a color
that is included in the reference data.
To help understand the differences between AI,
Machine Learning, and Deep Learning,
let's go through a very high-level example
of how these three
might be applied to common task of facial recognition.
In this example, an Artificial Intelligence wouldn't
necessarily know that it was looking at three people,
unless it has been thought what to
look for in order to spot people.
This requires a lot of trial and error on
the part of the developers creating the algorithm,
and it doesn't involve the machine having to
learn anything about what humans look like,
other than what the developers tell it to look for.
The machine may be provided with
the ability to identify head shapes or skin tones,
but without the ability to learn,
the machine could fail simply
because of the wide range of
diversity in what humans look like.
For instance, it might not
recognize a person because of a beard,
which could generate a false negative.
With Machine Learning however,
you can give the machine a rough framework
for what a person looks like and
the ability to iteratively process and
learn other human appearances through experience.
So here the machine can
recognize the figure in the middle,
since it's the closest to the figure example it already
knows with a similar facial shape and hair shape.
Once it confirms that
these new appearance is a person's face,
it becomes more confident in its ability to
recognize humans based on facial and hair shape,
but less confident in brown hair color.
With this new information,
it might now be able to recognize
person three as its confidence in facial shape
is high enough to overcome its lack of
knowledge in other areas
such as hair-shape and skin tone.
But because the machine was not prepared
to recognize facial hair ahead of time,
it still doesn't have
the ability to recognize person one.
That's why deep learning is
such a popular choice for facial recognition.
With deep learning, the machine is provided
lots of facial reference data upfront,
and unlike traditional Machine Learning or AI,
it isn't always told exactly what features to look for.
It uses it's
highly advanced data processing capabilities and
neural networks to derive
the important features it needs to look
for from the data itself.
Rather than the developers telling
the machine ahead of time how to recognize specific,
how to define things like facial hair,
the machine simply looks for the common features
that define all of the humans in this data,
and looks for those in the things that it sees. 
In other words, the machine defines
the essential features of
its subject rather than the developer.
That's what distinguishes deep learning
from the traditional machine learning.
Now that we understand what AI is,
let's talk about how to
establish an effective AI strategy.
You can establish an effective AI strategy in
your organization with the help
of fast computing environment,
data gathered from various sources such as social media,
browsing trends and more,
and advanced learning algorithms.
Let's start with the data.
More data means better analytics
and better analytics results in better products.
Better products means more users
and that in turn generates more data for you.
This in simple terms is the flywheel for data.
You can gather data from a number of
sources like clickstream and user activity,
then you can analyze it using tools like Hadoop,
and Spark, and Amazon Elastic search surveys.
Using the analysis, you
can feed the AI and machine learning
algorithms to form
pattern recognitions and generate predictions.
Then, you can use those predictions to make
your products better and drive more users do it.
By using a combination of programming models,
algorithms, data,
and hardware acceleration with
infrastructure such as GPUs,
you can develop a framework that helps with AI
enabled features like image understanding,
speech recognition,
natural language processing, and autonomy.
These combination of programming models, algorithms,
and data is usually what forms
the basis of machine learning
and deep learning frameworks,
and the underlying hardware infrastructure
supports the frameworks.
Today, AI is being used all across Amazon.
On amazon.com, users see
recommendations suggested by
Amazon's recommendation engine,
which improves their shopping experience.
We also use AI to spot trends in
the customer's experience so that we can
develop new products and enhance existing products.
In the fulfillment and
logistic departments, robots pick, pile,
sort, and move boxes
around so that they can be shipped to customers.
Our employees used to have to walk miles each day.
By using AI, we save time and
free up our staff to serve more customers faster.
Now AWS is making AI tools broadly available
so that businesses can
innovate and improve their products.
Amazon Web Services offers a range of services in AI by
leveraging Amazon's internal experience
with AI and machine learning.
These services are separated
here according to four layers,
AI services, AI platforms,
AI frameworks, and AI Infrastructure.
They organize from the least complex
to the most complex going from top to bottom.
Let's take a brief look into each of these layers.
Our AI services are each built to
handle specific common AI tasks.
These services enable developers to add
Intelligence to their applications through
an API called to pre-train services
rather than developing and training
their own deep learning models.
Amazon Recognition makes it easy to
add image analysis for your applications.
With recognition, you can
detect specific objects, scenes,
and faces like celebrities and
identify inappropriate content in images.
You can also search and compare faces.
Recognitions API enables you to quickly add
sophisticated deep learning-based visual search
and image classification to your applications.
Amazon Polly is a service that
turns texts into lifelike speech,
allowing you to create applications that talk and
build entirely new categories of speech enabled product.
Amazon Polly's text-to-speech service uses
advanced deep learning technologies to
synthesize speech that sounds like human voice.
Amazon Lex is a service for building
conversational interfaces into any application
using voice and text.
It provides automatic speech recognition for converting
speech-to-text and natural language understanding
to recognize the intent of the text.
That lets you build applications with highly engaging
user experiences and
life-like conversational interactions.
The AI platforms layer of the stack includes products and
frameworks that are designed to
support custom AI related tasks,
such as training and
Machine Learning model with your own data.
For customers who want to fully manage
platform for building models using their own data,
we have Amazon Machine Learning.
It's designed for developers and data scientists
who want to focus on building models.
The Platform removes
the undifferentiated overhead associated with
deploying and managing infrastructure
for training and hosting models.
It can analyze your data,
provide you with suggested transformations for the data,
train your model, and even help you
with evaluating your model for accuracy.
Amazon EMR is a flexible,
customizable, and manage big data processing platform.
It's a manage solution in that it can handle
things like scaling and high availability for you.
Amazon EMR does not require a deep understanding
of how to set up and administer Big Data Platforms,
you get a preconfigured cluster
ready to receive your analytics workload.
It is built for any Data Science Workload not just AI.
Apache Spark is an open-source,
distributed processing system commonly
used for Big Data workloads.
Apache Spark utilizes in-memory caching and optimize
execution for fast performance
and it supports general batch processing,
Streaming Analytics, Machine Learning,
graph database, and ad hoc queries.
It can be run and managed on Amazon EMR clusters.
The AI frameworks and
infrastructure layers are for
expert machine learning practitioners.
In other words, for the people who
are comfortable building deep learning models,
training them, doing predictions,
also known as inference,
and getting the data from
the models into production applications.
The underlying infrastructure consists
of Amazon EC2 P3 instances,
which are optimized for
machine learning and deep learning.
Amazon EC2 P3 instances provide
powerful NVIDIA GPUs to accelerate computations,
so that customers can train their models in
a fraction of the time required by traditional CPUs.
After training, Amazon EC2 C5
compute optimize and aim for general-purpose instances.
In addition to GPU based instances,
are well-suited for running
inferences with the training model.
AWS supports all the major deep-learning frameworks
and makes them easy to deploy without AWS,
deep-learning Amazon machine image,
which is available for Amazon Linux and Ubuntu,
so that you can create managed,
automatically scalable clusters of
GPUs for training and inference at any scale.
It comes pre-installed with technologies
like Apache MX net, tenser flow,
Cafe and Caffe2 and auto-populate
Machine Learning software such as
the Anaconda package for data science.
Now let's go through a few use cases.
Almost all industry domains are
now innovating with AWS AI.
For example, for fraud dot net uses
Amazon Machine Learning to
support its Machine Learning models.
AWS Lambda to run code
without provisioning and managing servers.
Fraud.net also uses Amazon Redshift for data analysis.
What are the benefits that they get from that setup?
Fraud.net lunches and trains Machine Learning models
in almost half the time it took on other platforms.
It reduces complexity and
makes sense of emerging Fraud patterns.
It saves customers about a million dollars each week.
To summarize, you can create an impact in
your business by automating repetitive and manual tasks,
engaging customers and optimizing
product quality using AI.
I hope you learned a little something and we'll
continue to explore all the courses.
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| I'm Dan Banger with AWS AI and thanks for watching. |
Full video link is here
Hi, I'm Dan Mbanga with AWS Training and Certification. Welcome to our introduction to Artif: Added to Selection. Press [CTRL
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