Here we will be knowing about terms related with Ai and Branches of Ai by simple Discussion on AI vs Machine Learning vs Deep Learning. These are the term which have confused a lot of people and if you too are one among them, let me resolve it for you.
Well Artificial intelligence is a broader umbrella under which machine learning and deep learning come you can also see in the diagram that even deep learning is a subset of machine learning so you can say that all three of them the AI the machine learning and deep learning are just the subset of each other. So let's move on and understand how exactly the differ from each other.
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| AI Vs ML Vs Deep Learning |
History of Ai:
The term AI was first coined within the year 1956 by John McCarthy. The concept is pretty old, but it has gained its popularity recently. But why well, the reason is earlier we had very bit of knowledge the info we had wasn't enough to predict the accurate result, but now there is a tremendous increase in the amount of data statistics suggest that by 2020 the accumulated volume of data will increase from 4.4 zettabyte stew roughly around 44 zettabytes or 44 trillion GBs of data along with such enormous amount of data. Now, we have more advanced algorithm and high-end computing power and storage that can deal with such large amount of data as a result. It is expected that 70% of Enterprise will Implement ai over the next 12 months which is up from 40 percent in 2016 and 51 percent in 2017.
What is Ai?
for understanding what does AI well, it's nothing but a technique that enables the machine to act like humans by replicating the behavior and nature with AI it is possible for machine to learn from the experience. The machines are just the responses based on new input there by performing human-like tasks. Artificial intelligence can be trained to accomplish specific tasks by processing large amount of data and recognizing pattern in them. You can consider that building an artificial intelligence is like Building a Church, the first church took generations to finish.
So most of the workers were working in it never saw the final outcome those working on it took pride in their craft building bricks and chiseling stone that was going to be placed into the great structure. So as AI researchers, we should think of ourselves as humble brick makers whose job is to study how to build components example Parts is planners or learning algorithm or accept anything that someday someone and somewhere will integrate into the intelligent systems.
some of the examples of artificial intelligence from our day-to-day life
our Apple series just playing computer.
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| Tesla Self driving Car |
Tesla self-driving car and many more these examples are based on deep learning and natural language processing.
Well, this was about what is AI and how it gains its hype.
Why machine learning was introduced?
Let's discuss about machine learning and see what it is and white pros of an introduced.
Well Machine learning came into existence in the late 80s and the early 90s, but what were the issues with the people which made the machine learning come into existence? Let us discuss them one by one in the field of Statistics.
- The problem was how to efficiently train large complex model in the field of computer science and artificial intelligence.
- The problem was how to train more robust version of AI system while in the case of Neuroscience problem faced by the researchers was how to design operation model of the brain.
So these are some of the issues which had the largest influence and led to the existence of the machine learning.
The Machine learning shifted its focus from the symbolic approaches. It had inherited from the AI and move towards the methods and model. It had borrowed from statistics and probability Theory.
What is Machine learning?
Let's see what exactly is Machine learning.
Well Machine learning is a subset of AI. which The computer to act and make datad decisions to carry out a certain task. These programs are algorithms are designed in a way that they can learn and improve over time when exposed to new data.
Let's see an example of machine learning. Let's say you want to create a system which tells the expected weight of a person based on its side.
The first thing you do is you collect the data.
Let's see there is how your data looks like now each point on the graph represent one data point to start with we can draw a simple line to predict the weight based on the height.
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| Eg- Machin Learning |
For example, a simple line W equal X minus hundred where W is waiting kgs and edges hide and centimeter this line can help us to make the prediction. Our main goal is to reduce the difference between the estimated value and the actual value. So as to realize it we attempt to draw a line that matches through of these different points and minimize the error. (Fig.^)
So our main goal is to minimize the error and make them as small as possible decreasing the error or the difference between In the actual value and estimated value, increases the performance of the model further on the more data points.
We collect the better.
Our model will become we can also improve our model by adding more variables and creating different production lines for them. Once the line is created. So from the next time if we feed a new data, for example height of a person to the model, it would easily predict the data for you and it will tell you what has predicted weight could be.
What is Deep learning?
Let's know about deep learning. Now what is deep learning? You can consider deep learning model as a rocket engine. and its fuel is its huge amount of data that we feed to these algorithms the concept of deep learning is not new, but recently it's hype as increase and deep learning is getting more attention.
Deep Learning is a particular kind of machine learning that is inspired by the functionality of our brain cells called neurons which led to the concept of artificial neural network.
It simply takes the data connection between all the artificial neurons and adjust them according to the data pattern more neurons are added at the size of the data is large it automatically features learning at multiple levels of abstraction. Thereby allowing a system to learn complex function mapping without depending on any specific algorithm.
You know, what no one actually knows what happens inside a Neural Network and why it works so well, so currently you can call it as a black box. Let us discuss some of the example of deep learning and understand it in a better way.
Let me start with a simple example and explain you how things happen at a conceptual level. Let us attempt to understand how you recognize a square from other shapes.
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| Eg:- Deep Learning |
The first thing you do is you check whether there are four lines associated with a figure or not simple concept, right? If yes, we further check if they are connected and closed again a few years. We finally check whether it is perpendicular and all its sides are equal, correct, if Fulfills. Yes, it is a square (Fig.^).
Well, it is nothing but a nested hierarchy of Concepts what we did here we took a complex task of identifying a square and this case and broken into simpler tasks.
Now this Deep learning also does the same thing but at a larger scale, let's take an example of machine which recognizes the animal the task of the machine is to recognize whether the given image is of a cat or a dog.
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| Demo of ML vs DL |
What if we were asked to resolve the same issue using the concept of machine learning what we would do first. We would Define the features like check whether the animal has whiskers aren't a check if the animal has pointed ears or not or whether its tail is straight or curved in short. We will Define the countenance and let the system identify which features are more important in classifying a specific animal.
now when it involves deep learning it takes this to one step ahead deep learning automatically finds out the feature which are most important for classification compare into Machine learning where we Had to manually give out that features.
By now I guess you have understood that AI is a bigger picture and machine learning and deep learning or it's apart.
let's move on and focus our discussion on machine learning and deep learning
Machine learning vs Deep learning:
the easiest way to understand the difference between the Machine learning and Deep learning is to know that. deep learning is machine learning more specifically. It is the next evolution of machine learning.
Let's take few important parameter and compare machine learning with deep learning.
Data dependencies:
So starting with Data dependencies, the most important difference between deep learning and machine learning is its performance as the volume of the data gets increased from the below graph. You can see that when the size of the data is small deep learning algorithm doesn't perform that well, but why well, this is because deep learning algorithm needs a large amount of data to understand it perfectly on the other hand the machine learning algorithm can easily work with smaller data set fine.
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| Machine learning Vs Deep Learning |
Hardware Dependencies:
Next comes the hardware dependencies deep learning. Are heavily dependent on high-end machines while the machine learning algorithm can work on low and machines as well. This is because the requirement of deep learning algorithm include gpus which is an integral part of its working the Deep learning algorithm requires gpus as they do a large amount of matrix multiplication operations, and these operations can only be efficiently optimized using a GPU as it is built for this purpose.
Only.
Feature Engineering:
Feature engineering is a process of putting the domain knowledge to reduce the complexity of the data and make patterns more visible to learning algorithms.
This process is difficult and expensive in terms of time and expertise in case of machine learning.
Most of the features are needed to be identified by an expert and then hand coded as per the domain and the data type. For example, the features can be a pixel value shapes texture position orientationor anything fine the Performance of most of the machine learning algorithm depends on how accurately the features are identified and extracted whereas in case of deep learning algorithms it try to learn high level features from the data.
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| Working of Feature Engineering in pixel value shape. |
This is a very distinctive part of deep learning which makes it way ahead of traditional Machine learning deep learning reduces the task of developing new feature extractor for every problem like in the case of CN n algorithm it first try to learn the low-level features of the image such as edges and lines and then it proceeds to the parts of faces of people and then finally to the high-level representation of the face.
Problem Solving Approach:
So let's move on ahead and see the next parameter. So our next parameter is problem solving approach when we are solving a problem using traditional machine learning algorithm. It is generally recommended that we first break down the problem into different sub parts solve them individually and then finally combine them to get the desired result. This is how the machine learning algorithm handles the L'm on the other hand the Deep learning algorithm solves the problem from end to end.
Let's take an example to understand this suppose. You have a task of multiple object detection. And your task is to identify.What is the object and where it is present in the image. So, let's see and compare. How will you tackle this issue using the concept of machine learning and deep learning.
starting with Machine learning in a typical machine learning approach. You would first divide the problem into two step first object detection and then object recognization.
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| Multiple Object Detection Task |
First of all, you would use a Bounding box detection algorithm like grab cut for example to scan through the image and find out all the possible objects. Now, once the objects are recognized you would use object recognization algorithm like svm with hog to recognize relevant objects. Now, finally, when you combine the result you would be able to identify. What is the object and where it is present in the image on the other hand.
In Deep learning approach you would do Process from end to end for example in a euro net which is a type of deep learning algorithm. You would pass an image and it would give out the location along with the name of the object.
execution Time:
Our fifth comparison parameter its Execution time. Usually a deep learning algorithm takes a long time to train this is because there's so many parameter in a deep learning algorithm that makes the training longer than usual the training might even last for two weeks or more than that. If you are training completely from the scratch, whereas in the case of machine learning it relatively takes much less time to train ranging from a few weeks to few Arts.
Now, the execution time is completely reversed when it comes to the testing of data during testing the Deep learning algorithm takes much less time to run. Whereas if you compare it with a KNN algorithm, which is a type of machine learning algorithm the test time increases as the size of the data increase.
Interpretability:
Interpretability as a factor for comparison of machine learning and Running this fact is the main reason why deep learning is still thought ten times before anyone uses it in the industry.
Let's take an example suppose. We use deep learning to give automated scoring two essays the performance it gives and scoring is quite excellent and is near to the human performance, but there's an issue with it. It does not reveal white has given that score indeed mathematically. It is possible to find out that which node of a deep neural network were activated but we don't know what the neurons are supposed to model and what these layers of neuron we're doing collectively.
So if able to interpret the result on the other hand machine learning algorithm, like decision tree gives us a crisp rule for void chose and watered chose. So it is particularly easy to interpret the reasoning behind therefore the algorithms like decision tree and linear or logistic regression are primarily used in industry for interpretability. Before we end this session.
Summary:
- Machine learning uses algorithm to parse the data learn from the data and make informed decision based on what it has learned fine.
- Deep learning structures algorithms in layers to create artificial neural network that can learn and make Intelligent Decisions on their own.
- Deep learning is a subfield of machine learning while both fall under the broad category of artificial intelligence deep learning is usually what's behind the most human-like artificial intelligence.
Well, this was all for This discussion in case you have any doubt feel free to add your query to the comment section. Thank you. Please be kind enough to like it and you can comment any of your doubts and queries and we will reply them.
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