Difference between Machine Learning and Traditional Programming
Difference between Machine Learning and Traditional Programming
While some call AI and ML overhyped themes that are nothing more than if statements, or just programming stuff, I offer you to come face to face with all pieces of evidence to check this out. In this post, I will contrast these terms and also showcase the difference between the specialists who are involved in these two spheres: who are they? Software engineer, software developer, machine learning expert, data scientist…some people even use a programmer or coder, and some even go as far as a ninja, guru, or rock star! But, are they really the same? And if so, is there a line between Machine Learning and Traditional Programming?ML vs Programming: First, What’s Machine Learning?
It’s easy to say that AI and ML are nothing more than if statements. Or what is more, it is just simple statistics. What else do we hear about it? Is ML just a new word to describe math + algorithms? Sometimes such simplifications seem to be funny, but obviously, ML is more complicated.
But let’s have a look at a more appropriate explanation.
So, in simple words, Artificial Intelligence is an umbrella that contains other realms like image processing, cognitive science, neural networks and much more. Machine Learning is also a component located under this umbrella. Its core idea is that the computer does not just use a pre-written algorithm, but learns how to solve the problem itself. Or, to explain it in other words, there is an excellent definition by Arthur Samuel (who actually had coined the term of ML):
Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.
So, yes, ML teach a machine to solve various complex tasks that are difficult to be solved algorithmically. What are those tasks? Well, you probably have already stumble at them in practice. For example, it can be face recognition on your phone or voice understanding, driving a car (Google Self-Driving Car), diagnose diseases by symptoms (Watson), advise products, books (Amazon), movies (Netflix), music (Spotify), perform the functions of a personal assistant (Siri, Cortana)…this list can go on and on.
Okay, I hope it was clear enough and now it’s time to move on onto another important thing about ML.
Any working ML-technology can be conditionally attributed to one of three levels of accessibility. What does this mean? Well, the first level is when it is available exceptionally to major tech-giants like Google or IBM. The second level is when, let’s say, a student with a certain amount of knowledge can use it. And the last one, the third level of ML accessibility is when even a granny is able to cope with it.
What we have on the current stage of development is Machine learning at the junction of the second and third levels. Due to this, the rate of change of the world with the help of this technology grows with cosmic speed.
Last but the least thing about ML: most of the ML-tasks can be divided into learning with a teacher (supervised learning) and learning without a teacher (unsupervised learning). And if you imagine a programmer with a whip in one hand and a piece of sugar in the other, you are a little bit mistaken.
The name “teacher” means the idea of human intervention in data processing. When training with a teacher, which is supervised learning, we have the data, and we need to predict something on its basis. On the other hand, when teaching without a teacher, which is unsupervised learning, we again have the data, but here we need to find its properties.ML vs Programming: Okay, Then How it differs from Programming?
In traditional programming you hard code the behavior of the program. In machine learning, you leave a lot of that to the machine to learn from data.
Consequently, these terms are no way interchangeable: data engineer can’t replace the work of traditional programming and vice versa. Although every data engineer is obliged to use at least one coding language, traditional programming is only a small part of what he (or she?) does. On the other hand, we can’t say software developer is using ML-algorithms to launch a website.
ML just like AI is not a substitution, but supplementation for traditional programming approaches. For instance, ML can be used to build predictive algorithms for an online trading platform, while the platform’s UI, data visualization and other elements will be performed in a mainstream programming language such as Ruby, or Java.
So, here is the main thing: ML is used in the case when traditional programming strategy falls behind and it is not enough to fully implement a certain task.
What does this mean in practice? Here is a great explanation on the basis of a classical ML-problem of exchange rate forecasting and two different ways to do it:
Traditional programming approach
For any solution, the first task is the creation of the most suitable algorithm and writing the code. Thereafter, it is mandatory to set the input parameters and, in fact, if an implemented algorithm is ok it will produce the expected result.
How a software developer creates a solution
However, when we need to predict something, we need to use an algorithm with a variety of input parameters. In case of prediction of the exchange rate, it’s mandatory to add such details like yesterday’s rate; external and internal economic changes in the country that issues the currency and more.
Consequently, we handcraft a solution that is able to accept a set of parameters and, based on the input data, predict a new exchange rate.
But it’s extremely important to add one more thing or to be more clear one problem of such an approach. So what is it?
Well, it’s simple, we need to add a thousand and hundreds of parameters, whereas their limited set allows building a very basic and unscalable model. So yes, for any person is troublesome to work with such massive data arrays.
Then we have a slightly different Machine learning approach for this task, so what is it?
To solve the same problem using ML-methods, data engineers use a totally different procedure. Instead of developing an algorithm on its own, they need to collect an array of historical data that will be used for semi-automatic model building.
Following managing a satisfactory set of data, the data engineer loads it into already tailored ML-algorithms. The result is a model that can predict a new result, receiving new data as input.
How a data engineer develops a solution using machine learning
A distinctive feature of ML is there is no need to build a model. This complicated yet meaningful responsibility is executed by ML-algorithms. And ML expert will only add just a minor edit to this.
Another significant difference between ML and Programming is determined by the number of input parameters that the model is capable of processing. For an accurate prediction, you have to add thousands of parameters and do it with high accuracy, as every bit will affect the final result. A human being a priori cannot build an algorithm that will use all of those details in a reasonable way.
However, for ML, there are no such restrictions. As long as you have enough processor power and memory, you can use as many input parameters as you see fit. Undoubtedly, this fact makes ML be so powerful and widespread nowadays.Summing it up: ML expert, Data scientist, programmer, and software engineer…who is who?
According to Wiki,** Data Science*** is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.*
So far it does not sound very cool.
But then there is something interesting:
use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems.
And then even more interesting part:
In 2012, Harvard Business Review called it The Sexiest Job of the 21st Century.
So, Data Science is another extensive umbrella, just like Computer Science, only Data Science aimed at processing data and extracting useful information from them.
What about programming? Data scientists nowadays do it exceptionally in the interest of research. They are not only programmers, but they are also usually supposed to have an applied statistics or research background. Some also do software engineering, especially at companies serving data science/ML in their products. The most interesting thing is that Data Scientist is not obliged to be able to program well, but can be limited to tools like Matlab, SPSS, SAS, etc.What then is the position of Machine Learning Engineer?
The position of the Machine Learning Engineer is more “technical”. In other words, ML Engineer has more in common with classic Software Engineering than Data Scientist.
The standard tasks of ML Engineer are generally similar to Data Scientist. You also need to be able to work with data, experiment with different machine learning algorithms that will solve the problem, create prototypes and ready-made solutions.
Of the key differences, I would highlight:
And now let’s move back to the programming and have a closer look at what tasks are assigned to the programmer.
A programmer is actually someone like a data analyst or business systems developer. They don’t have to build systems themselves, they just write a loosely structured code against existing systems. So, yes, we can call data science a new wave of programming, but coding is only a small part of it. So, don’t be mistaken.
But if digging deeper, we will found out there are other terms like **Software Engineer **and Software Developer, and both of them are also not similar. For example, software engineers have to engineer things. They deal with production applications, distributed systems, concurrency, build systems, microservices. And just for the record, a software developer needs to understand all the cycles of software development, not just implementation (which sometimes won’t even need any programming or coding).
So, programming and machine learning…do you feel the difference now? I hope this post helped you to avoid confusion around those terms. Undoubtedly, all of them have something in common which is technology, but the number of their differences is much bigger. Thus, ml-engineer, software engineer and software developer are completely not interchangeable.
What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?
US and China are massively investing in Artificial Intelligence which create a promising career in the field. One of the first steps to a successful artificial Intelligence career is to learn the basics around the domain. Articles and Guides are your opening friends towards a successful AI Career. Read on to know more.
Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...
Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...What is Data Science?
Data Science is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of various mathematical and Machine Learning-based algorithms. Hence, Machine Learning is a key element of Data Science.
Alongside Machine Learning, as the name suggests, “data” itself is the fuel for Data Science. Without the availability of appropriate data, key insights cannot be extracted from it. Both the volume and accuracy of data matters in this field, since the algorithms are designed to “learn” with “experience”, which comes through the data provided. Data Science involves the use of various types of data, from multiple sources. Some of the types of data are image data, text data, video data, time-dependent data, time-independent data, audio data, etc.
Data Science requires knowledge of multiple disciplines. As shown in the figure, it is a combination of Mathematics and Statistics, Computer Science skills and Domain Specific Knowledge. Without a mastery of all these sub-domains, the grasp on Data Science will be incomplete.What is Machine Learning?
Machine Learning is a subset or a part of Artificial Intelligence. It primarily involves the scientific study of algorithmic, mathematical, and statistical models which performs a specific task by analyzing data, without any explicit step-by-step instructions, by relying on patterns and inference, which is drawn from the data. This also contributes to its alias, Pattern Recognition.
Its objective is to recognize patterns in a given data and draw inferences, which allows it to perform a similar task on similar but unseen data. These two separate sets of data are known as the “Training Set” and “Testing Set” respectively.
Machine Learning primarily finds its applications in solving complex problems, which, a normal procedure oriented program cannot solve, or in places where there are too many variables that need to be explicitly programmed, which is not feasible.
As shown in the figure, Machine Learning is primarily of three types, namely: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Artificial Intelligence is a vast field made up of multidisciplinary subjects, which aims to artificially create “intelligence” to machines, similar to that displayed by humans and animals. The term is used to describe machines that mimic cognitive functions such as learning and problem-solving.
Artificial Intelligence can be broadly classified into three parts: Analytical AI, Human-Inspired AI, and Humanized AI.
From the above introductions, it may seem that these fields are not related to each other. However, that is not the case. Each of these three fields is quite closely related to each other than it may seem.
If we look at Venn Diagrams, Artificial Intelligence, Machine Learning and Data Science are overlapping sets, with Machine Learning being a subset or a part of Artificial Intelligence, and Data Science having a significant chunk of it under Artificial Intelligence and Machine Learning.
Artificial Intelligence is a much broader field and it incorporates most of the other intelligence-related fields of study. Machine Learning, being a part of AI, deals with the algorithmic learning and inference based on data, and finally, Data Science is primarily based on statistics, probability theory, and has significant contribution of Machine Learning to it; of course, AI also being a part of it, since Machine Learning is indeed a subset of Artificial Intelligence.
Similarities: All of the three fields have one thing in common, Machine Learning. Each of these is heavily dependent on Machine Learning Algorithms.
In Data Science, the statistical algorithms that are used are limited to certain applications. In most cases, Data Scientists rely on Machine Learning techniques to extract inferences from data.
The current technological advancement in Artificial Intelligence is heavily based on Machine Learning. The part of AI without Machine Learning is like a car without an engine. However, without the “learning” part, Artificial Intelligence is basically Expert Systems, Search and Optimization algorithms.
Difference between the three
Even though they are significantly similar to each other, there are still a few key differences that are to be noted.
Since all the three domains are interrelated, they have some common applications and some unique to each of them. Most applications involve the use of Machine Learning in some form or the other. Even then, there are certain applications of each domain, which are unique. A few of them are listed below:
Since the fields are interrelated by a significant degree, the skill-set required to master each of these fields is nearly the same and overlapping. However, there are a few skill-sets that are uniquely associated with each of them. The same has been discussed further.
The Job Market for each of these fields is in very high demand. As a direct quote from Andrew Ng says, “AI is the new Electricity”. This is quite true as the extended field of Artificial Intelligence is at the verge of revolutionizing every industry in ways that could not be anticipated earlier.
Hence, the demand for jobs in the field of Data Science and Machine Learning is quite high. There are more job openings worldwide than the number of qualified Engineers who are eligible to fill that position. Hence, due to supply-demand constraints, the amount of compensation offered by companies for such roles exceeds any other domain.
The job scenario for each of the different domains are discussed further:
Data Science, Machine Learning and Artificial Intelligence are like the different branches of the same tree. They are highly overlapping and there is no clear boundary amongst them. They have common skill set requirements and common applications as well. They are just different names given to slightly different versions of AI.
Finally, it is worth mentioning that since there is high overlap in required skill-set, an optimally skilled Engineer is eligible to work in either of the three domains and switch domains without any major changes.
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 IntelligenceMachine Learning vs Deep Learning
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 — The next big Thing
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.Why is Deep Learning better than 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.