The Difference Between the Most Popular Buzzwords in Tech Today. The world -- AI, Machine Learning and Deep
In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning.
Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning. Artificial Intelligence (also known as Machine Learning) and Deep Learning are all buzzwords that people use today. Many people have a misconception about what these terms mean.
In the worst cases, you may mistakenly believe that they refer to the exact same thing.
Numerous companies claim that they incorporate AI into their products or services.
Artificial intelligence can also be used to describe applications where a machine emulates cognitive functions that humans associate with other human minds.
"A program or rule that instructs an AI to behave in certain circumstances can be considered an AI. Artificial Intelligence cannot be more than a series of if-else statements."
An if/else statement refers to a simple rule programmed explicitly by a human. A robot moving on a roadway is an abstract example. Here's a possible programming rule that could be used to guide the robot.
Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial Intelligence
Machine Learning vs Deep Learning
We are now better able to distinguish between Machine Learning and Artificial Intelligence .
Machine Learning employs "classic" algorithms for different types of tasks like classification, regression and clustering. Machine Learning algorithms require data to train. The better your algorithm works, the more data you can provide.
The "training" portion of a Machine Learning model is meant to optimize along a specific dimension. Machine Learning models aim to minimize errors between their predictions, and the ground truth values.
We need to define an " error function ", also known as a loss function, or an objective function... since the model has an end goal. This objective could be used to classify data into different categories (e.g. The goal could be to predict the price of a stock in the near future or to capture cat and dog images.
"You can find out the essence of a machine-learning algorithm when someone says they're using it. Ask: What is the objective function?"
This is where you might ask: How can we minimize the error?
One approach would be to compare the prediction and the ground truth value, and then to adjust the parameters so that the error between them is smaller the next time. This process is repeated over and over again.
It has been repeated thousands upon thousands of times until the parameters used to predict the outcomes are so precise that the difference between predictions and ground truth labels is as small as possible.
Machine learning models can also be called optimization algorithms. You can tune them to reduce their error by guessing again and guessing again.
Machine Learning is old...
Machine learning is defined as:
Algorithms analyze data, extract information and make informed decisions using the learned insights.
Machine learning allows for a wide range of automated tasks. It is used in almost all industries, including IT security, weather forecasting, and stockbrokers seeking cheap trades. Machine learning involves complex math and a lot of coding in order to get the desired functions or results.
Large data sets are necessary to train machine-learning algorithms.
The algorithm will perform better if there is more data.
Machine Learning is a very old field. It incorporates methods, algorithms, and techniques that have been around for many years. Some of these have been around since at least the sixties.
These classic algorithms include algorithms like the Naive Bayes classifier and Support Vector Machines. Both are commonly used for data classification.
Cluster analysis algorithms are available, such as the K-Means or the tree-based. Machine learning uses principal component analysis, tSNE and other methods to reduce data size and gain insight into the data's true nature.
Deep Learning -- The next big thing
Let's get to the important thing. The importance of deep learning. Deep Learning is an emerging field of artificial intelligence that relies on artificial neural networks.
Deep Learning can be seen as a subfield in Machine Learning because Deep Learning algorithms need data to solve tasks. Deep learning and machine learning are often referred to as the same thing. But these systems can have different capabilities.
Deep Learning employs a multi-layered structure, called the neural net.
Deep Learning models are unable to tackle tasks that Machine Learning models couldn't solve because of the unique capabilities of artificial neural networks:
Deep Learning is the key to all of our recent intelligence breakthroughs. Deep Learning is what would prevent us from self-driving cars and chatbots as well as personal assistants like Siri, Alexa, or Siri. Google Translate app and Netflix would not know which TV series or movies we enjoy or don't like.
Deep Learning and artificial neural network are driving the new industrial revolution. This is the most accurate and efficient way to achieve true machine intelligence. Deep Learning offers two main advantages over Machine Intelligence.
Deep Learning is better than Machine Learning.
Feature extraction
Deep Learning is superior to machine learning because it doesn't require the use of feature extraction.
Before deep learning, machine learning methods like Decision Trees, SVM and Naive Bayes Classifier were well-known. These algorithms are also known as "flat algorithms".
Flat is the term used to describe how these algorithms are not usually applied to raw data (such.CSV or images, text, etc.). We will need to do feature extraction as a preprocessing step.
The result of Feature extraction is an abstract representation that contains the raw data. This can then be used by machine learning algorithms in order to accomplish a task. The classification of data into multiple classes or categories is one example.
Feature extraction can be quite complex and requires knowledge of the problem domain. This step needs to be adjusted, tested, and refined over multiple iterations in order to achieve optimal results.
Artificial neural networks are another option. These networks do not need feature extraction. The layers can automatically learn an implicit representation for the raw data.
Over several layers of artificial neural networks, an abstract representation of the raw data can be produced. The result is then created from the compressed representation of the input data. The result could be, for instance, the classification or sub-classification of the input data.
This means that feature extraction is already part of an artificial neural network process. The neural network optimizes this step during the training process to achieve the best possible abstraction of the input data. Deep learning models require very little manual effort to perform and optimize feature extraction.
To use a machine-learning model to determine if a specific image is a car, you will first need to identify its unique features (shape, size windows, etc. Extract these features from the image and provide them as input data to the algorithm. This would allow the machine learning algorithm to classify the image. In machine learning, this means that a programmer must be involved in the classification process.
A deep learning model eliminates the need for feature extraction. The model could recognize the unique characteristics of each car and make predictions without the assistance of anyone.
This is true for all tasks that you can do with a neural network. They simply provide the raw data for the neural network. The model takes care of the rest.
Big Data: The Era of Big Data
Deep Learning has another advantage, and this is why it's so popular. It's powered by massive amounts of data. There will be a lot of opportunities to develop deep learning technologies in the "Big Data Era". Andrew Ng, the chief scientist of China’s main search engine Baidu and one of the leaders of Google Brain Project, is to be quoted.
"The analogy for deep learning is that the rocket engine represents deep learning and the fuel is the enormous amounts of data we are able to feed to it. "
Deep Learning models tend to improve their accuracy as they receive more training data. Traditional machine-learning models such as SVM and Naive Bayes classifiers cease improving after a certain point.