Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning
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, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.
But still, there is a big misconception among many people about the meaning of these terms.
In the worst case, one may think that these terms describe the same thing — which is simply false.
A large number of companies claim nowadays to incorporate some kind of “ _Artificial Intelligence_” (AI) in their applications or services.
But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.
On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.
An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:
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
Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.
Machine Learning incorporates “ _classical_” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.
The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.
For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.
When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?
At this point, you may ask: How do we minimize the error?
One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.
Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.
In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.
Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.
Some known methods of classification and prediction are the Naive Bayes Classifier and the Support Vector Machines. In addition to the classification, there are also clustering algorithms such as the well-known K-Means and tree-based clustering. To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and tSNE are used.
Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks. It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.
Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.
All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.
We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.
The first advantage is the needlessness of Feature Extraction. What do I mean by this?
Well if you want to use a Machine Learning model to determine whether a given picture shows a car or not, we as humans, must first program the unique features of a car (shape, size, windows, wheels etc.) into the algorithm. This way the algorithm would know what to look after in the given pictures.
In the case of a Deep Learning model, is step is completely unnecessary. The model would recognize all the unique characteristics of a car by itself and make correct predictions.
In fact, the needlessness of feature extraction applies to any other task for a deep learning model. You simply give the neural network the raw data, the rest is done by the model. While for a machine learning model, you would need to perform additional steps, such as the already mentioned extraction of the features of the given data.
The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:
“ The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms. “
Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.
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