Are there possibilities of machines learning to do things that humans currently do like in the factories, warehouses, offices, and homes? There was a time people use to discuss on this topic continues.

There is a lot of conversation on this till now. But its 2020, people benefit from artificial intelligence every day. You, me, everybody; through music recommender systems, Google Maps, Uber, Ola, and many more, and these are powered by AI.

Since, the technology is evolving each and every day — quickly or slowly — along with fears and excitement, you must have heard the terms such as artificial intelligence, machine learning, and deep learning, and surely these terms may leave you mystified.

There’s a lot of confusion between artificial intelligence, machine learning, and deep learning. You know one of the popular Google searches goes like this — “are artificial intelligence and machine learning are the same thing?” People are that much confused about this!!!!!!!

We tried by our side to help you to understand these terms and sort out the confusion around artificial intelligence deep learning. We tried to cover each essential topic in this article.

Hope our examples will help to clarify the actual use of artificial intelligence deep learning technology today.

AI, MI, and DI: The difference

Artificial intelligence, machine learning, and deep learning are actually three different things.

Artificial intelligence (AI)

Just like mathematics or biology, it’s a science. This artificial intelligence is the studied ways to build intelligent programs and machines that can creatively solve problems, that have been considered as a human privilege.

Machine learning

It’s a subset of artificial intelligence (AI). Machine learning provides systems the ability to automatically learn and improve from experience and acquire skills without human involvement. There are different algorithms in ML (e.g. neural networks) that helps to solve problems.

Deep learning or deep neural learning

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Deep learning or deep neural learning is the subset of machine learning where artificial neural networks, algorithms inspired by human brains, learn from large amounts of data. It uses neural networks to analyze different factors with a structure that is similar to the human neural system.

How do we learn from the experience? The answer to this is, the deep learning algorithm would perform a task repeatedly and each time tweaking it a little to improve the outcome.

We refer to ‘deep learning’. This is because the neural networks have various (deep) layers that enable learning. Any problem that requires “thought” to figure out is a problem that deep learning can learn to solve.

Artificial Intelligence: 3 Faces

In 1956, the term artificial intelligence was first used at a computer science conference in Dartmouth. AI explains an attempt to model how the human brain works and based on this knowledge, create more advanced computers.

The scientists expected that it should not take too much time to understand how the human mind works and digitalized. And why shouldn’t they expect, after all, the conference was conducted between some of the brightest minds of that time for an intensive 2-months brainstorming session.

The researchers had fun for sure during that summer in Dartmouth but unfortunately, the results were a bit devastating. Imitating the brain with the means of programming turned out to be intricate.

In spite of that, some results were achieved. As an instance, the researchers understood that the key factors for an intelligent machine are learning, natural language processing, and creativity.

  • Learning — to interact with changing and spontaneous environments
  • Natural language processing — for human interaction
  • Creativity — to liberate humanity from many of its troubles?

Even in the present time when artificial intelligence is all over the place, the computer is still far from modeling human intelligence to perfection.

Artificial Intelligence (AI) divided into 3 categories:

Narrow/Weak AI

What weak AI is? To make it understandable for you, it is pretty good to contrast it with strong AI. These are the two versions of AI that are trying to achieve different goals.

Strong AI seeks to create machines that have all the mental powers that humans have i.e., an artificial person, including phenomenal consciousness. On the other hand, weak AI seeks to build information i.e. processing machines that appear to have the full mental repertoire of human persons (Searle 1997).

When it comes to performing a particular task weak or narrow AI is pretty good but it will not pass for humans in any other field outside of its defined capacities.

Probably, you have heard of Deep Blue, the first computer that defeat human in chess and not just any human but Garry Kasparov (in 1997). You know ‘Deep Blue’ could generate and evaluate about 200 million chess positions per second.

At that time, some of them were not ready to call it AI in its full meaning, while others claimed it to be one of the earliest examples of weak AI.

‘AlphaGo’, another famous example of AI beating humans in games. This program won in one of the most complicated games ever invented, learning how to play it and not just calculating all the possible moves which are almost impossible.

These days, narrow artificial intelligence is widely used in science, healthcare, and business. For an instance, a company named DOMO in 2017 declared the launch of Mr. Roboto.

This AI software system contains powerful analytics tools and with recommendations and insights for business development, it provides business owners. It can also detect abnormalities and spot patterns that can be useful for risk management and resourceful planning.

For other industries, similar kinds of programs exist as well and large companies such as Google and Amazon invest money in their development.

General/strong AI

General or Strong AI is the point in the future when machines become human-like. That means they can learn and make their own decisions without any human input. They are not only competent in solving logical tasks but they also have emotions.

But the question that strikes is: how to build a living machine? You can program the machine so that it produces some emotional verbal reactions in response to stimuli.

Chatbot and virtual assistants are already quite good at maintaining a conversation. Additionally, the experiments on teaching robots to read human emotions are already in action. But reproducing emotional reactions does make the machines truly emotional? Do you think so?


This is usually the expected content of AI as the readers expect this to be mentioned in the article while reading. Superintelligence: machines that are way ahead of humans.

Smart, wise, creative, with excellent social skills; this is what superintelligence means. Its goal is to make humans live better and easier or maybe destroy them all.

And the disappointment comes here — the scientists of today’s time don’t even dream of creating autonomous emotional machines like the Bicentennial Man. Maybe except for this guy who has created a robocopy of himself.

Some of the tasks that data scientists are focusing on right now (that can help to create general and superintelligence) are:

  • Machine Reasoning

Machine Reasoning or MR systems have some information at their disposal, like a database or a library. They can formulate some valuable insights based on this information using deduction and induction techniques. It can include planning, search, data representation, and optimization for AI systems.

  • Robotics

This field of science concentrates on robots. The building, developing, and controlling robots from Roombas to intelligent androids.

  • Machine learning

This is the study of algorithms and computer models used by machines in order to perform a given task.

You can call them methods of creating Artificial Intelligence. It is possible to use just one of them or combine all of them into one system. Now, move on to the deeper sea of these.

How can machines learn?

Machine learning is a subset of the larger field of artificial intelligence (AI) that “focuses on teaching computers how to learn without the need to be programmed for a specific task”, note Sujit Pal and Antonio Gulli in Deep Learning with Keras.

In fact, “it is impossible to create algorithms that learn from and make predictions on data”, this is the key idea behind ML.


Special collections of samples are used to trained machine learning systems called datasets. The samples can include numbers, texts, images, or any other kind of data. Usually, it takes a lot of time and effort to create a good dataset.


Features are important pieces of data. They work as the key to the solution of the task. They demonstrate to the machine what to pay attention to and where to focus on. But the thing is how do you select the features?

For that, suppose, you want to predict the price of an apartment. It is quite difficult to predict by linear regression how much the place can cost based on the combination of its length and width.

Whereas it is much easier to find a correlation between the price and the area where the building is located.

Note: It works in the case of supervised learning when you have training data with labeled data, which contain the “right solutions”, and a validation set. The program learns how to get the “right” solution during the learning process.

And then, to avoid overfitting, the validation set is used to tune hyper parameters. However, features are learned with unlabelled input data in unsupervised learning. There is no need to tell the machine where to look at, it learns to notice patterns by itself. Read about 7 CREATIVE ANDROID AND IOS MOBILE APPLICATION IDEAS


Is it possible to solve the same task using different algorithms? Yes, obviously it is. But the accuracy or speed of getting the results can be different. It depends on the algorithm.

And in order to achieve better performance, sometimes you combine different algorithms, like in ensemble learning.

Any software that uses ML is more independent than manually encoded instructions when it comes to performing specific tasks. The system itself learns to recognize patterns and make valuable predictions.

If the quality of the dataset was high and the features too were chosen right, then an ML-powered system can become better at a given task than humans.

Deep learning

A class of machine learning algorithms that are inspired by the structure of a human brain is basically what “Deep learning” is. Its algorithms use complex multi-layered neural networks, where the level of abstraction increases slowly by non-linear transformations of input data.

The information is transferred from one layer to another over connecting channels in a neural network. Each of them has a value attached to it, that is why they are called weighted channels.

All neurons have a unique number called bias. This bias added to the weighted sum of inputs reaching the neuron and that reaching neuron is then applied to the activation function.

If the neurons get activated, then the result of the function determines. Each activated neuron passes the information to the following layers. This continues up to the second last layer. The last layer i.e. the output layer in an artificial neural network produces output for the program.

So as to train such neural networks, a data scientist needs massive amounts of training data. This is due to the fact that a huge number of parameters have to be considered for the solution to be accurate.

Nowadays, deep learning algorithms have become the hype, however, there is actually no well-defined threshold between deep and not-so-deep algorithms.

Some practical applications of DL include speech recognition systems such as Google Assistance and Amazon Alexa. The sound waves of the speaker represented as a spectrogram, i.e. a time snapshot of different frequencies.

A neural network that is capable of recalling sequence inputs (such as LSTM, short for long-short-term-memory) can recognize and process such sequences of spatial-temporal input signals. It learns to map the spectrogram feeds to words.

Deep learning comes really close to what people imagine when hearing the words “artificial intelligence”. The computer learns by itself; isn’t it awesome?! Well, the truth is DP algorithms are not flawless.

Despite this, programmers love DL because it is applicable to a variety of tasks. However, there are other approaches to ML. We discuss some of them below.

No free lunch and why there are so many Machine Learning algorithms

Before we start: There are a lot of ways to classify the algorithms, and it’s up to you what you want to choose and what is best for you.

In artificial intelligence science, there’s a theorem named, No Free Lunch. It says, there is no perfect algorithm that works equally well for all tasks: from natural speech recognition to surviving in the environment. That’s why there’s a need for a variety of tools.

Algorithms can be grouped by their similarities or learning style. We here give you a glimpse at the algorithms grouped based on their learning style.

The reason behind this: it is more intuitive for a first-timer. Classification of ML algorithms based on similarities:

Four groups of ML algorithms

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So, there are usually 4 groups of machine learning algorithms based on how they learn.

Supervised Learning

Supervised means that someone as a teacher helps the program throughout the training process. There is a training set with labeled data. For example, you want to teach the computer to put green, red, and blue gloves into different baskets.

First, you have to show the computer each of the items and tell what is what. Next, run the program on a validation set that checks whether the learned function was correct.

The program makes assertions and when finding that the conclusions are wrong, the programmer corrects it. Until the model achieves a desired level of accuracy on the training data, the training process continues.

Programmers frequently use this type of learning for classification and regression.

Algorithm examples:

Naive Bayes,

Support Vector Machine,

Decision Tree,

K-Nearest Neighbours

Logistic Regression,

Linear and Polynomial regressions.

Used for: spam filtering, computer vision, language detection, search, and classification.

Unsupervised Learning

In unsupervised learning, there is no need to provide any features to the program and allowing it to search for patterns independently. Try to understand like this, suppose you have a big basket of laundry that the system has to separate into different categories: socks, T-shirts, jeans.

This is what clustering is. And we frequently use unsupervised learning to divide data into groups by similarity.

For insightful data analytics, unsupervised learning is also good. Even the program can sometimes recognize patterns that would be missed by humans because of the inability to process large amounts of numerical data.

For example, UL can be used to find fraudulent transactions, discounts, and forecast sales or analyze preferences of customers based on their history. The programmers themselves do not know what are they trying to find but surely there are some patterns and the system can detect them.

Algorithm examples:

K-means clustering,



Singular Value Decomposition (SVD),

Principal Component Analysis (PCA),

Latent Dirichlet allocation (LDA),

Latent Semantic Analysis, FP-growth.

Used for: segmentation of data, anomaly detection, recommendation systems, risk management, fake image analysis.

Semi-supervised Learning

As the title is suggesting, semi-supervised learning means that the input data is a mixture of labeled and unlabelled samples.

The desired prediction outcome is in the mind of the programmer but the model should find patterns to structure the data and make predictions itself.

Reinforcement Learning

Reinforcement learning is very similar to humans learn i.e. through the trail. We humans don’t need constant supervision to learn effectively like in supervised learning. We learn very effectively by receiving positive or negative reinforcement signals in response to our actions. For example, only after feeling pain, a child learns not to touch a hot pan.

One of the most exciting parts of Reinforcement Learning is, it allows you to step away from training on static datasets. Instead, the system is able to learn in dynamic and noisy environments such as game worlds or the real world.

Games are very useful for reinforcement learning research. This is because they provide ideal data-rich environments. The score in games is ideal reward signals to train reward-motivated behaviors. For example, Mario.

Algorithm examples:

Used for: self-driving cars, games robots, resource management.

Summing up

Artificial intelligence has already many great applications that are changing the world in terms of technology. To create an AI system that is generally as intelligent as humans remain a dream but at least we are in this stage where ML allows the computer to outperform us in computations, pattern recognition as well as anomaly detection.

It is like that the more experience artificial intelligence algorithms get, the better they become. It should be an extraordinary few years as technology continues to mature. And hopefully, soon we will create an AI system that will be as intelligent as humans and we will live our dream. Web Development Company USA