Simply : DL ⊆ ML ⊆ AI.

DL is an application for training data. DL is just a tiny contribution to ML, it is indeed highly advertised as corona-virus. Its iterative form was already in use for solving series approximations for numerical differential equations. It is just a leveraged item from numerical approximation or numerical analysis. Take it simple and easy.

If you have a time, you can read my paper from International journal of molecular and theoretical physics, 2017, on Research Gate.

Your message encompasses two questions:

A) My own answer to the title question:

ML ==> DL ==> AI

B) My own answer to the message body question:

DL is a combination of several layers of NN which was not attainable before because of resource constraint, it includes processing two-dimensional data like digital images, but it can also do tasks that process audio signals/financial time series/ or sequences of text (i.e., LSTM).

Simply : DL ⊆ ML ⊆ AI.

DL is an application for training data. DL is just a tiny contribution to ML, it is indeed highly advertised as corona-virus. Its iterative form was already in use for solving series approximations for numerical differential equations. It is just a leveraged item from numerical approximation or numerical analysis. Take it simple and easy.

I have my queries regarding your statement that DL is more to AI than ML. AI is about dealing with decision making (not necessarily based on Data) like humans. On the other side, ML is all about learning from data. As DL is also about learning from data, I would rather put it close to ML. :-)

I agree with the answer that say AI is about dealing with decision making like humans not necessary base on data and can solve complex problems that need large space and time more efficient than traditional static methods. but machine learning and deep learning methods need data to learn and to give a good solutions

In essence, the concept of AI is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving" as stated in Russell & Norvig, (2009). "Artificial Intelligence: A Modern Approach," (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. p.2,

Now, you can see that mimicking human cognitive by "learning" comes in the above formal definition (learning from what if not from data or previous experience as ML does?). Next, I would argue that even decision-making is based on data as it is the process of identifying and choosing among alternatives based on certain values, preferences and beliefs of the decision-maker (machines in this case). "identifying and choosing among alternatives based on certain values" doesn't this ring a bell? probabilities / classes / classification?

ML and DL are just small steps (building blocks) towards the so-called AI, yet I still believe that the use of the term AI might be overstated in the current era, we are not there yet.

Danilo Rastovic I will read your article as soon as possible, thanks a lot.

Muhammad Ali As usual, your answer is so precise and I totally agree with you!

Abbas Cheddad So, I agree with you that ML leads to DL than to AI. Thanks a lot for your answer!

I`m not familiar with DL, but it looks a very interesting topic.

Sarfaraz Masood Nada N. Saleem It seems that you both agree that DL is closed to ML than AI. However, DL belongs to AI based on several articles that I've read!

I think that DL is necessary in the case of learning when several parameters ( temporal or spatial, time delay, thresholding) , are included.

DL is preferable when you have huge amount of data (normally data that have high dimensions like, images, videos etc), where Machine Learning is good for dealing with small and low dimensional data(like tabular data), where AI is refers to learn intelligently from data and make rational decision based on that learning whether that learning come from ML or DL.

I think the term "Artifical Intelligence " is comperhensive and a machine learning is a subsets in it which means that " teaching the computers to mimic the human" or to perform the hardest tasks which can be done by human but may take a lot of time so , using the machine learning modules is the alternative solution and it is prefered when you don't have a big amount of data which is maybe in the form of "tabular data", regarding to NN, Neural networks are deep learning technologies which are used to solve very complex problems particularly when you have a big amount of data, since there were some issues in dealing with data which is in the form images and texts , Deep learning (CNN ,RNN) came to the image to deal with such issues and it was considered as a subset of machine learning because it is an extension to the ideas, rules, and thoughts which are already exists in machine learning, machine learning and deep learning are integrated to eachother and they are a subset of AI Mokhaled N. A. Al-Hamadani

I believe that both areas are intrinsically interconnected. AI is a wider area, then ML. And for deeper networks with greater problem-solving capacity are DL. The greatest advances in AI are due to the application of DL, mainly for the analysis of a large amount of data.

Dear Rizwan Ali Hashem Alnabhi David Augusto Ribeiro thanks a lot for your answers, I agree with you all because each one can describe the relationship between ML,DL and AI, based on his knowledge!

DL is one of ML methods. DL is just a deep neural network. The ML is one of AI (GA, FL, ML, ....... )

I think that AI is not necessary connected with human ( observing and self-observing) . Animals, vehicles, machines etc. have their own problems for solving and the goal is to solve these problems.

Well, I think that you can't say that that DL is strictly included in ML. In fact, DL belongs to NN, being an advanced technique. It can be used wherever an NN is used.

Dear Virgiliu Calmatuianu, the journal IEEE Transactions on Neural Networks and Learning Systems was earlier under the name IEEE Transactions on Neural Networks. What do you think about change of the name of this yournal.

@Virgiliu

In details: DL belongs to NN and NN belongs to ML

In short: DL (multi-layered NNs) belongs to ML

Artificial Inteligence is a general field apparted from many subfields . "Machine Learning" ML, means actually the ML models that have been trained with some procedure gaining knowledge from exisiting data (and not the learning procedure that it should). ML is a clear part of AI because AI is based on the descisions made by MLmodels. Neural Networks, NN is one family of ML models (there are many others not based in the same idea). Deep neural networks are simply NN s with many layers, that seem to be able to get trained better with larger datasets, and the procedure to train these is slightly different and is called DL procedure. Lastly when you write "Deep Learning" you are probably asking about DL models, so these models is a subset of ML models that is a clean subfield of AI.

Maliamanis Theodoros Virgiliu Calmatuianu Lina Saeed Chato Thanks a lot for your replies, I do agree with your explanation of the subject. The beauty of this topic is that everyone has its own definition based on his/her knowledge!

Dear Danilo Rastovic I think that the change of the name of the journal is unfortunate as NN are "devices" used for a kind/sort of learning and Learning Systems means either sets (not only one kind of device) of learning OR systems used for learning.

@Abbas Cheddad: I do not intend to go into details but NN are used not only in ML; as an example is used to refine data massifs in medicine.

Larry Hardesty wrote an article entitled: "Explained: Neural networks" in which he states that: "Deep learning is in fact a new name for an approach to artificial intelligence called neural networks." MIT News Office, April 14, 2017. The goals of artificial intelligence include learning, reasoning, and perception.

DL is an architecture of multi-layered artificial neural networks (ANN) and the latter is part of ML algorithms. In this thread's context, we are discussing machine learning, so when we mention NN we mean the artificial neural networks used in ML. Would the use of the term ANN instead of NN eradicate the confusion?

As Dr.Abbas Cheddad wrote, ANN is the correct term for NN. I want also to add that for example "Machine Learning" term includes both the ML arlgorithms (learning procedures and techniques) and the ML models (devices materialized or usually virtual) that use ML algorithms to get trained. The same goes to the DL. So it is not correct to say for example DL is included in ANNs. The first is a (tiny) collection of models and algorithms and the latter is only models.

I agree with Dr. Muhammad Ali

I am quoting the following statements from a book, which clarifies the concept of AI in a better way.

Bertrand Russel has stated that as soon as definite knowledge concerning any subject becomes possible, this subject ceases to be called philosophy, and becomes a separate science.

AI research lives in-between philosophy and science. Ideas transition from philosophical thought to applied science and truth. Within Computer Science, the AI field resides at the edge of knowledge and as such includes a distinct part which is more philosophical and another which is more rooted in science.

AI science incorporates many areas of research e.g. Neural Networks, Bayesian Networks, Evolutionary Algorithms, Correlation, Game Theory, Planning, Vision recognition, Decision making, Natural Language Processing, etc

Machine Learning is the engineering discipline which applies the science to a real world problem

Machine Learning in recent years has been the desire to replace rigid rule-based systems. A strong candidate has been emerging which is both adaptive and outcome based.

As stated by Jeff Bezos, CEO of Amazon, "Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern Machine Learning techniques now allow us to do the same for tasks where describing the precise rules is much harder"

The above statements clearly explains the relation between AI and ML.

DL is an extension of NN, which is a part of Soft computing and in turn a part of Machine Learning.

I think the relations among the three domains AI, ML and DL is clear from the above.

Maliamanis Theodoros Thanks a lot for your reply. I really appreciate it

Dr.Huda E. Khalid Thanks a lot for your reply. I do agree with Muhammad Ali too as he's explaining it very simple and straight to the point!

Dear Dr. B.K. Tripathy , thanks a lot for your excellent answer based on your great knowledge. I do really appreciate your reply and an honor to have you as a friend in research gate.

Deep learning is a specific subfield of machine learning : a new take on learning representations from data that puts an emphasis on learning successive layers of increasingly meaningful representations. the deep in deep learning isn't a reference to any kind of deeper understanding achieved by the approach; rather, it stands for this idea of successive layers of representations.How many layers contribute to a model of the data is called the depth of the model

Meriem Ketfi Thanks for your reply!

Prisilla Jayanthi

Machine learning

Mothanna Sh. Aziz Ihab A. Satam Thanks a lot for your reply!

Mokhaled N. A. Al-Hamadani i think DL and ML both belongs to AI but, DL and ML don't have so much similarity between each other. Some of ML algorithms applicable or can be improvised in DL.

Argha Ghosh I agree with you regarding that all of them belong to AI.

All are under the wide umbrella of AI as follows: DL ⊆ ML ⊆ AI

Dear Mohamed Elhadad , thanks a lot for your reply. I do agree with you!

You are very welcome @Mokhaled

AI is a superset of ML and ML is a superset of DL.

DL ⊆ ML ⊆ AI.

Farhad Soleimanian Gharehchopogh Vankadhara Rajyalakshmi , thanks a lot for your reply. I do agree with both of you.

Mokhaled N. A. Al-Hamadani , thank you sir

Deep Learning is Artificial Neural Network with multiple (more than one) hidden layers. ANN is just one among other Machine Learning algorithms. And Machine Learning is a subset of a wider domain of AI. Based on my knowedge, DL gained its own sort of domain because of its popularity, and its all because of the increased computational power of GPUs. Before that, it was only a thing in research papers, SVMs were more popular back then.

Dear Cuburt Balanon , thanks a lot for your reply. I do agree with you totally that DL has gained its own sort of domain nowadays.

Deep learning is just one of the machine learning algorithms. The advantage of DL over ML is that ML requires feature engineering while as DL automatically generates features from the data. Also ML algorithms tend to flatten their learning curve as the no of data samples increase. with DL the more data we have better our results are. DL algorithms derive their power from layered representation of neutrons with repeated linear and non linear transformations on the data.

as for AI, it is the umbrella that encompasses both ML and DL, with the ultimate objective of AI is to achieve human level intelligence in machines.

Again image processing is the application of DL. we can do image processing with ML algorithms as well. (used as feature extractors)

Dear Mokhaled N. A. Al-Hamadani,

I do agree with the comments of different researchers. No doubt both DL and ML is the subset of AI. Generally, ML network models finds the outcome of the inputted data by using a fixed number of hidden layers. And the efficiency of the model varies based on the structure of the model.

But coming to the concept of DL, the number of hidden layers are not fixed and more number of hidden layers can be fit in the network model and the structure selection is also automotive . which in turn, the DL models obtain better result comparatively ML models when the size of the data set is big.

Thanks

I think deep learning close to machine learning becase it is part of machine learn algorithms with training

AI is the broad term which tries to simulate the human intelligence. Intelligence is needed to for Reasoning, Planning. Learning etc. Machine learning is the subset of AI which involves learning something with experience and use the learned knowledge for unseen scenarios. Deep learning is the subset of Machine learning, It is basically advanced Machine learning which is basically depends on representational learning or feature learning.

Dear Mohammad Khalid Pandit , thanks a lot for your reply. I do agree with you totally that DL belongs to ML and the AI is the umbrella that encompasses both ML and DL.

Dear Radha MOHAN Pattanayak , thank you so much for your reply. I do agree with you that totally about what you have mentioned.

Dear Nada N. Saleem , Akash Awasthi , thanks a lot for your reply. As mentioned by the dear Dr. Muhammad Ali , that Simply: DL ⊆ ML ⊆ AI.

Thanks, but the main difference between ml nd DL is the handcrafting of feature which is manually done in Ml tasks but Dl does this feature engineering itself , no need of manual interaction , so it's called feature learning

The deep learning belongs to machine learning becase it is part of ML algorithms

Thanks, Artificial Intelligence as if it's still in the future, kids to the robotic surgeon that's performing our family member's next scheduled surgery, Deep Learning — It is the next generation of Machine Learning.

Regards

All the same strand of AI. That is IA> ML> DL. When closer to DL, more neural networks are required and the complexity tends to increase to deal with big data or data analysis.

ML network models finds the outcome of the inputted data by using a fixed number of hidden layers. And the efficiency of the model varies based on the structure of the model.

ML network models finds the outcome of the inputted data by using a fixed number of hidden layers. And the efficiency of the model varies based on the structure of the model.

I do agree with the comments of different researchers.

Deep learning is part or aspect of machine learning, which belongs to the larger group of AI

DL is a subset of ML and ML is a subset of AI. so all are in one family.

Yes deep learning is close to machine learning it is part of it and also of AI

In addition to the relation provided by Dr.Rehman, which almost completes the query raised, we need not stress that they are in one family. Basically, family means they have the same origin although the components are different. Here even that does not happen. As branches of knowledge they have the inclusion relation as mentioned.

In my opinion can consider such hierarchy: AI -> ML -> DL