AI vsML vs DL

Kajal Agrawal

4 months ago

So, in this article, you will read one of the basic questions and ask what is the difference between Artificial Intelligence, Machine Learning, and Deep learning?
There is a great quote about one of the pioneers of Artificial Intelligence.
“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.
AI Vs ML Vs DL | insideaiml
AI Vs ML Vs DL | insideaiml
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 intelligence?

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 humans.
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.
Garter hype cycle | insideaiml
Garter hype cycle | insideaiml 
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 
1.    Artificial Narrow Intelligence
2.    Artificial General Intelligence
3.    Artificial super intelligence

Artificial Narrow Intelligence

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 Intelligence

Artificial General Intelligence

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 human life.

Artificial Super Intelligence

Artificial Super Intelligence is that stage of Artificial intelligence when the capability of a computer will surpass human beings.
Artificial superintelligence is currently, considered as a hypothetical situation similar to the one described in films and science fiction books.
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 Artificial intelligence.

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 types.
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.
  • Classification: If the output is a category like "yes" or "no".if the data is divide into a category then this is a classification problem 
  • Regression: To predict a continuous outcome variable (y) based on the value of one or multiple input variables (x).

Unsupervised Learning

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 tyрes:
  • Сlustering
  • Аssосiаtiоn
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.

Reinforcement learning

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
  • Self-driving саrs
  • Аirсrаft
  • соntrоl аnd rоbоt mоtiоn соntrоl etс.
Аs оf nоw I think yоu understаnd whаt is mасhine leаrning аnd its tyрes. Let’s mоve fоrwаrd
аnd try tо understаnd whаt is deeр leаrning.

What is Deep Learning (DL)?

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 decision-making nodes.
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 situational awareness.
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 brain works.
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
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative 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.
I hope you enjoyed this article.
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