This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.

1. ARTIFICIAL INTELLIGENCE VS
MACHINE LEARNING VS DEEP
LEARNING
This technology is no longer a matter of science fiction. Instead, we see artificial
intelligence in every part of our lives. Smart assistants are on our phones and
speakers, helping us find information and complete everyday tasks. At work, chatbots
are affiliated with the Customer Support Team, with estimates that they will be
responsible for 85% of customer service by next year.
There are also intelligent algorithms that can use a lot of data to make accurate
predictive behavior of people and clients. However, even though AI is more common
today than it was in the world today, it is still something that many people do not
fully understand.
There are so many different phrases with this disruptive technology that some words
are often combined. For example, in a particular circle, terms like AI SERVICES
artificial intelligence, machine learning, and deep learning can be used
interchangeably. However, although these concepts are all linked, they are not the
same thing.
As intelligence experts explain, different parts of AI are positioned as Russian nesting
dolls. The outer layer is artificial intelligence, which is the largest, all-encompassing
part of technology. There is a more refined concept of machine learning in it, and
there is a small subset of deep learning in it.
What is Artificial Intelligence?
Let’s start with the basics.
By next year (2020), 30% of companies worldwide believe that AI will somehow be
used in their digital processes. The question is - what is artificial intelligence, and
why is it necessary for the modern landscape?

2. The definition of artificial intelligence is not always easy. At a basic level, AI is part
of our research labs and part of decades of scientific study - computer scientists first
coined the term at the 1956 Dartmouth conference.
Since then, AI has been described as the future of human civilization. However, at its
core, it is another computer program. Artificial intelligence is any computer
algorithm that can work intelligently. In other words, it uses a complex statistical
model or if these statements are used to perform tasks. Artificial intelligence is
"smart" because it can follow very complex instructions without responding to a
single or basic trigger.
In recent years, AI has gained in popularity, thanks to the increase in available GPUs
that make parallel processing easier, cheaper and more accessible. However, not all
AI is the same. There are 3 basic sides to artificial intelligence, which are the basis
of much debate. The first option is Narrow AI, where an intelligent bot can do an
important job - like defeating a human in a board game. This is what Google
DeepMind product Alpha Go 2016 did.
The second option is Artificial General Intelligence or AGI, which can successfully
perform intellectual tasks, such as responding to queries at the customer service

3. station. There is also Super-Intelligent AI - which scientists are still working on.
Super intelligent AI is smarter than humans.
What is Machine Learning?
While Artificial Intelligence is the umbrella term for all computer programs that
follow complex instructions, machine learning is something that falls under that
umbrella. So, what is machine learning? Be machine learning? Simply put, this is a
subset of AI. With machine learning tools, it is possible to establish computer
algorithms that are searchable by data and apply heaps of knowledge and training to
a specific task.
For example, machine learning service can use millions of face images to identify
specific people or certain features on the face. Machine learning is now used in fields
such as translation, object recognition, and speech recognition. It is also possible to
teach machine learning tools on how to understand emotion and moods.
Machine learning allows a system to detect patterns in data that a human cannot take
on his own. Because these algorithms can process such vast information almost
instantly, they can make informed decisions about the data much faster than a human.
For machine learning algorithms to thrive, they need massive amounts of data. The
more information you have to browse through a program, the easier it is to make that
decision and answer the necessary questions. Machine learning tools also take
considerable time to train so that they are as accurate as possible. The original
machine learning definition came from the earliest minds of the AI group. Over the
years, the algorithmic contacts us

4. The algorithmic approaches used for this technology include everything from
inductive logic programming to reinforcement networks and Bayesian networks.

5. What is Deep Learning?
Now we come to complicated things - deep learning.
When you compare deep learning vs. machine learning, you will find that deep
learning is a refined subset of machine learning. Deep artificial neural networks use
complex algorithms in deep learning to allow high levels of accuracy when solving
important problems such as sound recognition, image recognition, recommendations
and more.
Deep learning algorithms use some basic techniques in machine learning, and we
use human decision making to tap into neural networks to solve complex real-world
problems. Although deep learning is more complex and precise than artificial
intelligence or machine learning, it is also very expensive. Scientists need huge data
sets to train neural networks because there are too many parameters to understand
any learning algorithm before making accurate learning choices.

6. The neural networks responsible for deep learning strategies know our
understanding of human biology and how the brain works. It allows machines to
make more relevant and relevant decisions by creating connections between
hundreds, thousands or even millions of different data sets.
How Artificial Intelligence Works:
So, now that you know these concepts, let's dive a little deeper and ask, "How does
artificial intelligence work?" Less than a decade after he dismantled the enigma of
the Nazi encryption machine, mathematician Alan Turing changed the world by
asking if machines could think. In 1950 a paper called "Computing Machinery and
Intelligence" was published and the Turing test was established.
Since Turing made his initial question, much of the artificial intelligence that has
been dismantled is designed to see if it can teach machines to think like a human.
The artificial intelligence we have today falls into the categories of narrow AI and
artificial general intelligence.
Narrow AI is a "weak" AI that works in a limited context. It is a simulation of human
intelligence that applies to a specific task or series of tasks. Narrow AI focuses on
completing a task well, such as finding pictures of dogs or playing games.
Artificial general intelligence is very complex. This is the kind of artificial
intelligence we see on television - the ability to do many different things with the
help of machine learning and deep learning.
We have yet to fully discover the next stage of artificial super-intelligence AI. If we
unlock this extra level of AI, we have created robots that can think for themselves,
without any input from humans. Since those robots can think and process data faster
than humans, we are creating something smarter than ourselves.
How machine learning works:
Machine learning is an underlying concept that reinforces most artificial
intelligence. How can we ensure that these bots can work themselves, using vast data
sets, without relying on constant human input? So, how does machine learning
work?
Machine learning uses two basic methods to deliver results. The first option is
supervised learning, which refers to training a model based on relevant input and
output data so that the model can predict future needs and learn on its own. On the

7. other hand, unsupervised learning allows the bot to search through information and
find hidden patterns or trends in the data.
Supervised machine learning relies on humans to create models that allow a machine
to be evaluated based on the presence of information. Supervised algorithms take
known data sets and use that information to respond to queries and demands.
Supervised machine learning also enables things like predictive analytics.
Unsupervised learning is a very sophisticated approach to machine learning, which
requires the bot to find its hidden themes and structures in the data. It may also allow
the bot to conclude from incomplete data sources and information we cannot
translate. Clustering is one of the most common methods used for unsupervised
machine learning. It enables machines to use exploratory data analysis to find
answers in the areas of commodity identification, market research, and genome
analysis.
If a phone company wants to optimize the places they are building their cell towers,
g. They can use machine learning (unsupervised) to determine how many towers
depend on different locations around one location. This allows the machine to use
clustering algorithms to create the right placement strategy for the business.
How Does Deep Learning Works?
Deep learning is a sophisticated subset of machine learning, so it uses a lot of similar
processes to the ones we mentioned above. Deep learning relies on very valuable
information.
If you are given a picture of a cat, you will be able to determine if the cat you saw
was a different color, or if the cat was lying on its side. You can identify the image
as you are aware of all the different factors that go into the shape and image of the
cat. Deep learning machines end up similarly. It brings together multiple data points
to identify information.
Deep learning is commonly used in autonomous vehicles because it allows cars to
know what is going on around them before doing anything. To do this, you need to
identify car bikes, vehicles, people, road signs, and more. Standard machine learning
algorithms cannot process this information at once.
Tools that are created using deep learning beyond the basics of machine learning to
find out how different types of information relate to one another in a vast neural
network. This is the difference between a machine's perception of looking at a

8. picture of a fox as it examines images from a certain part of the countryside in
response to a specific question, and the same machine is pointing ears, four legs and
a tail thinking "dog".
To develop deep learning algorithms, they need highly precise and immersive neural
networks, which provide vast amounts of information to bring the task into question.
These neural networks can take months or even years to train, and require much
investment from data scientists and the companies behind them.
AI vs. Machine Learning vs. Deep Learning: Applying these processes together
Machine learning is a subfield of AI that uses pre-loaded information to make
decisions. Deep learning is a form of artificial intelligence that goes much deeper
than that. This technique uses deep neural networks to retrieve and retrieve samples
from too much data.
Although artificial intelligence, machine learning, and deep learning are not the
same things, they are all part of the same family. Often, these components can work
together to help businesses solve complex problems in their environment.
For example, in a task that requires a machine to detect a cat's image, the artificial
intelligence requires the programmer to input all the code needed to automatically
associate a cat's image with what it already knows. Machine learning, on the other
hand, requires that the programmer be taught what kinds of factors to identify a cat.
It also includes a programmer who corrects machine analysis until the computer
becomes more precise in its work.
Finally, deep learning requires the task of identifying the cat as a host of different
layers. At one layer, the artificial intelligence algorithm divides the cat's task of
detecting the eye, while examining the shape of another layer. Connected layers or
neural network results.
In an intelligent contact center, on the other hand, artificial intelligence can use pre-
loaded information to find out where to send individual callers to get the best
answers to their questions. Machine learning can understand the caller's language
and make suggestions on how the agent can respond. Deep learning can analyze the
sentiments of the caller and formulate strategies for how to get a good return on
investment for the call.
Both machine learning and deep learning make AI much smarter and more
accessible.

9. AI, ML, and DL in the cloud:
Today, significant advances in the world of cloud technology make deep learning,
machine learning, and artificial intelligence more accessible and accessible. Cloud-
like AWS, Google Cloud, and AI service providers in Microsoft Azure provide
solutions in the computing, networking, memory, and bandwidth that are scalable
and easy to use.
At the same time, cloud-integrated technology platforms such as PASS, SASS, IAS,
and IPAS allow small and medium-sized companies to use everything from big data
storage to advanced analytics. Natural language processing techniques, computer
vision, and ML algorithms are all pre-loaded into the service, and the data center
performs the calculation remotely. This means that there is no need for specialized
training in data engineering and data science.
The cloud means that anyone can access the amazing global AI and continue to help
technology grow, evolve and transform.