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.
The basic definition of machine learning is:
Algorithms that analyze data, learn from it and make informed decisions based on the learned insights.
Machine learning leads to a variety of automated tasks. It affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for cheap trades. Machine learning requires complex math and a lot of coding to finally get the desired functions and results.
Machine learning algorithms need to be trained on large amounts of data.
The more data you provide for your algorithm, the better it gets.
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.
These classic algorithms include algorithms such as the so-called Naive Bayes Classifier and the Support Vector Machines. Both are often used in the classification of data.
In addition to the classification, there are also cluster analysis algorithms such as the well-known K-Means and the tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tSNE.
Now let’s focus on the essential thing that is at stake here. On deep learning.
Deep Learning is a very young field of artificial intelligence based on artificial neural networks.
Again, deep learning can be seen as a part of machine learning because deep learning algorithms also need data to learn how to solve problems. Therefore, the terms of machine learning and deep learning are often treated as the same. However, these systems have different capabilities.
Deep Learning uses a multi-layered structure of algorithms called the neural network:
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 of Deep Learning over machine learning is the needlessness of the so-called Feature Extraction.
Long before deep learning was used, traditional machine learning methods were popular, such as Decision Trees, SVM, Naïve Bayes Classifier and Logistic Regression. These algorithms are also called “flat algorithms”.
Flat means here that these algorithms can not normally be applied directly to the raw data (such as .csv, images, text, etc.). We require a preprocessing step called Feature Extraction.
The result of Feature Extraction is an abstract representation of the given raw data that can now be used by these classic machine learning algorithms to perform a task. For example, the classification of the data into several categories or classes.
Feature Extraction is usually pretty complicated and requires detailed knowledge of the problem domain. This step must be adapted, tested and refined over several iterations for optimal results.
On the other side are the artificial neural networks. These do not require the step of feature extraction. The layers are able to learn an implicit representation of the raw data directly on their own.
Here, a more and more abstract and compressed representation of the raw data is produced over several layers of an artificial neural network. This compressed representation of the input data is then used to produce the result. The result can be, for example, the classification of the input data into different classes.
In other words, we can also say that the feature extraction step is already a part of the process that takes place in an artificial neural network. During the training process, this step is also optimized by the neural network to obtain the best possible abstract representation of the input data. This means that the models of deep learning thus require little to no manual effort to perform and optimize the feature extraction process.
For example, if you want to use a machine learning model to determine whether a particular image shows a car or not, we humans first need to identify the unique features of a car (shape, size, windows, wheels, etc.), extract these features and give them to the algorithm as input data. This way, the machine learning algorithm would perform a classification of the image. That is, in machine learning, a programmer must intervene directly in the classification process.
In the case of a deep learning model, the feature extraction step is completely unnecessary. The model would recognize these unique characteristics of a car and make correct predictions- completely without the help of a human.
In fact, this applies to every other task you’ll ever do with neural networks.
They just give the raw data to the neural network, the rest is done by the model.
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 scale better with a larger amount of data
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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Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.
If you are familiar with the basics of machine learning and deep learning, it is excellent news!
However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?
There is nothing to worry about. This article will explain the differences in easy to understand language.
Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.
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This cheat sheet helps you to choose the proper estimate for the task that is the hardest portion of the work. With modern computer technology, today’s machine learning isn’t like machine learning from the past.
The notion that computer may learn without being trained to do certain tasks came from pattern recognition researchers interested in artificial intelligence sought to explore if computers could learn from the information.
The iterative component of machine education is crucial because they may adjust autonomously when models are exposed to fresh data. From past calculations, they learn to create dependable, repeatable judgments and results. It’s not a new science, but a new one.
The usage of programming and even equipment is automation for computerized commands. AI, again, is the robots’ ability to reproduce human habits and thinking and get more clever all the time. It is important, while a misleadingly sharp computer may learn and modify its job as it receives new information, it cannot completely replace people. Everything is equal, it’s a resource, not a risk.
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So you find yourself saying “Well, it’s time we step on to digital transformation for our organization. Let’s look at the technologies we can implement.”
When you complete saying that sentence, the first thing that comes to your mind is Artificial Intelligence(AI) systems. You think of intelligence machines that can execute tasks on their human and make insightful decisions — like Sophie or Watson.
“So artificial intelligence(AI) is what we need.”, you say to yourself
Yes and No. Yes, in the sense that AI machines are useful for digital transformation.
No in the sense that you will integrate Artificial Intelligence with the help of algorithms which build the foundation for these systems. So you are not integrating AI but the algorithms that make AI machines work.
Here’s a simple explanation — The process that you want to improve through digital transformation will be optimized through AI machines.
These machines will be developed using a subset of AI — Machine Learning Algorithms. To go further deep — your organization can also implement Deep Learning — a subset of Machine Learning.
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Artificial Intelligence has powerfully penetrated the way we live. It doesn’t only change the way we work but also reshaped how we used to live. Speaking of AI, it is one of the most interesting technologies that we’ve ever encountered.
Without a doubt, AI is contributing a lot in boosting business and IT productivity. Therefore, in this blog, I will highlight important insights on how AI is reshaping IT. Before digging deeper into details, let’s start with some basics on AI and how it works.
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Artificial Intelligence (AI) made headlines recently when people started reporting that Alexa was laughing unexpectedly. Those news reports led to the standard jokes about computers taking up the planet.
The AI Career Landscape
AI is returning more traction lately due to recent innovations that have made headlines, Alexa’s unexpected laughing notwithstanding. But AI has been a sound career choice for a short time now due to the growing adoption of the technology across industries and therefore the need for trained professionals to try to to the roles created by this growth.
AI and Machine Learning Explained
If you’re new to the sector, you would possibly be wondering, just what’s AI then? AI is how we make intelligent machines. It’s software that learns almost like how humans learn, mimicking human learning so it can take over a number of our jobs for us and do other jobs better and faster than we humans ever could. Machine learning may be a subset of AI, so sometimes when we’re describing AI, we’re describing machine learning join online machine learning course, which is that the process by which learn Artificial Intelligence course now!
The Three Main Stages of AI
AI is rapidly evolving, which is one reason why a career in AI offers such a lot potential. As technology evolves, learning improves. Van Loon described the three stages of AI and machine learning development as follow:
Stage one is machine learning - Machine learning consists of intelligent systems using algorithms to find out from experience.
Stage two is machine intelligence - Which is where our current AI technology resides now. during this stage, machines learn from experience supported false algorithms. it’s a more evolved sort of machine learning, with improved cognitive abilities.
Stage three is machine consciousness - this is often when systems can do self-learning from experience with none external data. Siri is an example of machine consciousness.
Subsets of Machine Learning
Natural Language Processing (NLP)
How to start in AI?
If you’re intrigued by this career field and wondering the way to start , Van Loon described the training paths for 3 differing types of professionals; those new the sector , programmers, and people already working in data science. He also points out that various industries require different skill sets, but all working in AI should have excellent communication skills before addressing the maths and computing skills needed.
Specific Jobs in AI
The Future of AI
As the demand for AI and machine learning has increased, organizations require professionals with in-and-out knowledge of those growing technologies and hands-on experience.If you would like to be one among those professionals, get certified, because the earlier you get your training started, the earlier you’ll be working during this exciting and rapidly changing field.CETPA provides Graduate program will assist you substitute the gang and grow your career in thriving fields like AI , Machine Learning, and Deep Learning.
If you’re curious about becoming an AI expert then we’ve just the proper guide for you. the synthetic Intelligence Career Guide will offer you insights into the foremost trending technologies, the highest companies that are hiring, the talents required to jumpstart your career within the thriving field of AI, and offers you a customized roadmap to becoming a successful AI expert.
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