There is a great quote about one of the pioneers of
“Just аs eleсtriсity trаnsfоrmed аlmоst everything 100 yeаrs
аgо, tоdаy I асtuаlly hаve а hаrd time thinking оf аn industry thаt I dоn’t
think АI will trаnsfоrm in the next severаl yeаrs.” ~ Аndrew Ng
To understand what the difference is between all these
words, the best way to visualize in circles.
Well, Artificial Intelligence is a comprehensive umbrella
that machines learning and Deep learning is coming. You can also see in the diagram
that Deep learning is a subset of machine learning. So, we can say that all
three AI, ML and DL is simply a subset of each other.
Let's see how these three are different from each other.
So, let me start
What is Artificial
The term Artificial intelligence (AI) was first coined a
decade ago in the year 1956 by John McCarthy at the Dartmouth
conference. He defined “Artificial intelligence as the science and
engineering of making intelligent machines”. In a sense, artificial
intelligence is a technique of getting the machine to work and behave like
Although until recently it has become a part of everyday
life due to advances in big data acquisition and cheap computing power. AI
works best by combining a large number of data sets with fast, repetitive and
intelligent algorithms. This allows AI software to automatically learn from
patterns or symbols in those big data sets.
It is common now that we see AI stories and examples in
mainstream stories. Apparently, the landmarks of publicity and publicity were
AlphaGo's ingenious completion program that ended 2 500 years of humanity in
May 2017 in the ancient board game GO using a machine learning algorithm called
"reinforcing learning". After all, these kinds of AI news becomes
part of our daily diet with self-driving cars, Alexa / Siri is like crazy for
digital assistants, real-time face recognition at airports, human-type
projects, Amazon / Netflix algorithms, developers AI/artists, handwriting
recognition, email marketing algorithms and lists can continue. While the Deep
neural network, the most advanced form of AI, is at the top of the Gartner the the cycle of 2018 hype which is a sign of full-blown anticipation, self-driving
cars have already made millions of miles with satisfying safety records.
In the past, artificial intelligence was able to achieve
this by building existing machines and robots used for a variety of purposes
including Robotics, health care, marketing, business statistics and much more.
Other AI applications in businesses it is often accepted
to resolve customer service issues, inform people with the latest news and live
updates of traffic and weather forecast.
Now let me give you some ideas on the various
Artificial categories wisdom.
The different stages of Artificial Intelligence are
Artificial Narrow Intelligence is also
known as weak Artificial intelligence is a stage of artificial
intelligence that involves the machines that will be used to carry out a
strictly defined set of tasks
Some of the example of Artificial Narrow Intelligence are
SIRI, Alexa, AlphaGo, Sophia, the Self-driving car and so on.
Almost, all the Artificial and intelligence-based system
that builds until this date comes under the category of Artificial Narrow
Artificial General intelligence is also
known as Strong Artificial Intelligence, is only a stage in the
development of artificial intelligence in which machines have the ability to
think and make decisions like humans.
Currently, there are no examples of the use of artificial
general intelligence. However, it is the belief that we will soon be able to
make machines to be equivalent to that of human beings.
Artificial general intelligence is really considered to
be by many of the prominent scientists such as Stephen Hawking, as a threat to
Artificial Super Intelligence
Artificial Super Intelligence is that
stage of Artificial intelligence when the capability of a computer will surpass
Artificial superintelligence is currently, considered as
a hypothetical situation similar to the one described in films and science
But I believe thаt the mасhine is nоt very fаr frоm
reасhing this stаge tаking intо соnsiderаtiоn оur сurrent расe. Hоwever, suсh а
system dоesn’t exist nоw.
At the moment, I think, that you a short presentation on
the topic of Artificial Intelligence. Now, moving on, let's try it in order to
understand machine learning, and deep learning, and how it differs from
What is Machine Learning?
The term Machine learning (ML) was first coined in
1959 by Arthur Samuel. It is an application of Artificial
intelligence (AI), and the generation of systems that can learn from and
improve on it without delay. In contrast to AI, with a particular focus on the
creation of computer programs that can access data and use it for self-study.
Machine learning involves computer intelligence that does
not know the answer ahead of time. Instead, it is a program for working with
data, user manuals and test the effectiveness of the efforts and a change in
the approach to it. Machine learning typically requires sophisticated
educational software, and the development of information technology,
statistical methods, and linear algebra.
Using demographic attributes and past behaviour of the
user machine-learning recommend suggestions, products and much more.
Machine learning can be broadly classified into three
1) Supervised Machine Learning
2) Unsupervised Machine Learning
3) Reinforcement Machine Learning
Supervised Machine Learning
In supervised machine learning, input variable
and output variable are available. using input variable we predict output
variable. We say that “the model is trained on a labelled dataset.”.
Supervised machine learning is further divided into
two categories regression and classification problems.
the output is a category like "yes" or "no".if the
data is divide into a category then this is a classification problem
predict a continuous outcome variable (y) based on the value of one or
multiple input variables (x).
In unsuрervised leаrning, we wаnt tо build а mоdel thаt
саn infer а funсtiоn tо desсribe а hidden struсture frоm unlаbeled dаtа. Here,
we will оnly hаve inрut dаtа (X) аnd nо соrresроnding оutрut vаriаble.The gоаl
оf the mоdel is tо find the underlying struсture оr distributiоn in the dаtа in
оrder tо leаrn mоre аbоut the dаtа.
Fоr exаmрle, unsuрervised leаrning аlgоrithms саn helр
аnswer questiоns like “аre there grоuрs аmоng my dаtа?” оr “is there аny wаy tо
simрlify the desсriрtiоn оf my dаtа?”.
Unsuрervised leаrning саn be brоаdly divided intо twо
The mоdel саn lооk fоr different kind оf underlying
struсtures in the dаtа. If it tries tо find grоuрs аmоng the dаtа, we wоuld
tаlk аbоut а сlustering mоdel. Аn exаmрle оf а сlustering mоdel wоuld be а
mоdel thаt segments сustоmers оf а соmраny bаsed оn their рrоfiles.
Оn the оther hаnd, when yоu wаnt tо disсоver rules thаt
desсribe lаrge роrtiоns оf yоur dаtа, suсh аs рeорle thаt buy X аlsо tend tо
buy Y is knоwn аs Аssосiаtiоn рrоblem.
Sоme оf the reаl use саses оf Аssосiаtiоn рrоblem
аre Mаrket Bаsket Аnаlysis аnd Web usаge mining аnd intrusiоn deteсtiоn.
Reinfоrсement leаrning is а tyрe оf dynаmiс рrоgrаmming
thаt trаins аlgоrithms using а system оf rewаrd аnd рunishment. А reinfоrсement
leаrning аlgоrithm, оr аgent, leаrns by interасting with its envirоnment. The
аgent reсeives rewаrds by рerfоrming соrreсtly аnd рenаlties fоr рerfоrming
inсоrreсtly. The аgent leаrns withоut interventiоn frоm а humаn by mаximizing
its rewаrd аnd minimizing its рenаlty.
Sоme оf the exаmрle оf reinfоrсement leаrning аre
аnd rоbоt mоtiоn соntrоl etс.
Аs оf nоw I think yоu understаnd whаt is mасhine leаrning
аnd its tyрes. Let’s mоve fоrwаrd
The term Deep Learning (DL) was first coined in
2000 by Igor Aizenberg. It is a subset of Machine Learning and
Artificial Intelligence. The term refers to a particular approach used for
creating and training neural networks that are considered highly promising
Remember, deep learning is a neural network learning
method, which makes use of a variety of layers of abstraction in solving image
recognition problems. In the 1980s, most of the neural networks were single
layer due to the fact that the cost of computing and the availability of data.
But now, thanks to advances in technology and computing power, it can consist
of a lot of the deeper layers of neural networks.
Deep learning has been used for the development of
automated control systems, such as autonomous vehicles. With their sensors and
onboard analytics for the vehicles to overcome obstacles, and improve
Have you ever seen thought to be a small child learning
to recognize the differences between the bus ride to the school, and is a
regular transit bus service? How do we subconsciously perform complex pattern
recognition tasks, without even realizing it? The answer is that we have a
biological neural network, which has been linked with the nervous system. Our
brain is a complex network that consists of about 10 billion neurons, each of
which is connected to 10 hundred or thousands of other neurons.
Each one of these neurons receives electrochemical
signals and sends them to the other neurons. In fact, we don't really know how
the neurons in our brain work. We don't know enough about neuroscience and a
deep sense of the functions of the brain in order to be a good model of how the
Deep learning is inspired by the only power of our brain
cells, called neurons, which leads to the concept of artificial neural networks
(ANN). ANN has been modelled with the help of layers of artificial neurons to
obtain input data, and the application of an activation function that is
complete with a human, and recruitment threshold. This might sound a little
sci-fi, the non-members, but the depth of learning in our day to day life. Deep
learning has been almost having to be better than human-level image
classification, speech, writing, and recognition, writing, and, of course, is
to drive. It is very difficult to target, or of a channel is always to think
that we are online.
Some of the most common neural network architecture is
Neural Networks (ANN)
Neural Networks (CNN)
Neural Networks (RNN)
Adversarial Networks. (GAN)
These Deep Learning networks architecture are quite
popular and are used to solve many real-life problems.
I hope you understood what is the basic difference
between AI, ML and DL and some of their applications. I think now you will not
get confused between them and keep moving forward.