Comparison of AI vs. Machine Learning vs. Deep Learning

2 August, 2018

Comparison of AI vs. Machine Learning vs. Deep Learning

Most of the people are acquainted with the term –Artificial Intelligence and the concept offers a wide variety of applications in daily life. There are other similar terms which are prevalent nowadays and they are Machine Learning and Deep Learning. These terms are occasionally used interchangeably with the term -Artificial Intelligence. For many people, the difference between these terms is unclear. It is found that deep learning is a subset of machine learning, and actually, machine learning is found to be a subset of AI. Hence, it is said that AI is an umbrella term for a computer program which works to accomplish the task wisely. You can understand in a way that all machine learning is AI, but take a note that every AI is not machine learning. Let’s first look at what these three terminologies mean and then we will have a look at their comparison.

What is Artificial intelligence?

Artificial intelligence is a science fiction and it is a portion of our daily life. It is basically a computer system capable to accomplish tasks that usually need e human intelligence like speech recognition, visual perception, translation between languages, and decision-making. AI can be alternatively defined as a pile of if-then statements, or a complicated statistical model that maps raw sensory data to symbolic classes.

What is Machine Learning?

Machine learning is identified as a subset of AI. In simple terms, Machine learning is the practice of utilizing algorithms to parse data, acquire knowledge from it, and later make decision or forecast. As an alternative of hand-coding software routines by the help of a precise set of instructions to fulfill a specific task, it is known that the machine is “trained” with the help of huge amounts of data as well as algorithms that offers it the capability to learn ways to perform the task.

To understand this, for instance, symbolic logic like expert systems, rules engines, and knowledge graphs can be defined as AI and they are actually machine learning. The concept of Machine learning originated from the ideas of the early AI crowd, and the algorithmic incorporates decision tree learning, inductive logic programming, reinforcement learning, clustering, and Bayesian networks, etc.

What is Deep Learning?

Deep learning is essentially a subset of machine learning and it is a technique for implementing the technology of machine learning. In simple terms, Deep learning works to feed a computer system with a huge amount of data which can be utilized to jump to decisions regarding other data. It is known that this data is supplied with the help of neural networks. While discussing the term –Deep learning, people are consigning to deep artificial neural networks and rather less often to deep reinforcement learning. The Deep artificial neural networks are found to be a collection of algorithms that are capable to resolve problems like sound recognition, image recognition, recommender systems, etc. with great accuracy.

The application of Deep Learning is wide because it is used by Amazon and Netflix to decide what you wish to watch or purchase, by Google into its voice and image recognition algorithms, and by MIT’s researchers to foresee the future. In addition to that, it also has business applications. It can seamlessly accept a large amount of data like images and can identify some of its characteristics. The list of tasks performed by Deep learning is long including spam detection, fraud detection, text-based searches, image search, speech recognition, handwriting recognition, etc.

Deep learning was encouraged by the function and structure of the brain, like the interconnection of neurons. It employs Artificial Neural Networks (ANNs) which are basically algorithms that imitate the structure of the brain.

Comparison of AI vs. Machine Learning vs. Deep Learning:

The scope of Artificial intelligence is wider than that of machine learning; this is because the latter one makes use of computers to emulate the cognitive human functions. AI is hence defined as machines performing different tasks depending on algorithms in a smart way. Whereas Machine learning is found to be a subset of AI and its concept emphasizes on the potential of machines to not just obtain data sets but even to learn for themselves, and modify the algorithms as per the information which they are processing.

On the other hand, Deep learning explores a level deeper and is frequently regarded to be machine learning’s subset. As compared to Deep learning and AI, Machine learning is identified as a sub-discipline of AI and presently it is in high demand because it is capable to offer relevant tools that people require to incorporate change.

It is important to note that machine learning and AI are frequently used interchangeably, particularly in the domain of big data. However, both of them are not identical and their application varies. As compared to machine learning, AI is a wider concept and it is capable to deal with the usage of computers to imitate the cognitive functions of mankind. Whenever machines perform tasks with the use of algorithms in a smart way, it is AI.

As an alternative of hard-coding software routines by the help of instructions to complete a specific task, it is found that machine learning is an approach of training a particular algorithm, in order that it can know how it is done. In this regards, training comprises of providing big amounts of data to the specific algorithm and enabling the algorithm to amend itself and enhance.

For example, machine learning is applied to make noticeable advancements in computer vision.  May collect lots of pictures and then allow people to tag them. To understand this, for instance, people may tag pictures containing a cat in them vs. those that do not. After that, the algorithm is applied to build a model which can precisely tag a picture as comprising of a cat or not. When the accuracy level becomes sufficiently high, the machine gets the capability to learn what a cat appears like.

It is found that the “learning” part of machine learning means ML algorithms work on to optimize in some specified dimension. These algorithms attempt to reduce error or increase the possibility of their predictions getting true. It comes with three different names: (i) an error function, (ii) a loss function, and (iii) an objective function.

Imparting training to computers to think like mankind is accomplished partially with the help of neural networks. If you do not know about neural networks then they are defined as a sequence of algorithms which are modeled after the human brain. The brain can identify patterns and assist you to classify the information, and similar to that neural networks perform the same things for computers. The working of the human brain is such that it continually attempts to make sense of the information getting processed. In order to fulfill this, it marks and allocates items to categories. It is a human tendency that whenever we came across something new, we usually attempt to compare it to a recognized item to let us understand. Exactly in a similar manner, neural networks do these for computers.

Now discussing Deep learning, it reaches to another level deeper and this concept can be regarded as a subset of the above-discussed concept i.e., machine learning. There are many layers of Deep learning and therefore it is regarded as “deep neural networks”. A neural network consists of only a single layer of data, whereas a deep neural network comes with two or multiple layers. These layers are perceived as a chain of pertinent concepts or decision trees. As the layers are interrelated by a chain, you can perceive that the answer to one question directly to a group of deeper related questions.

As discussed above, Deep learning is a subset of machine learning and therefore it is one of several approaches to machine learning. The remaining approaches encompass inductive logic programming, decision tree learning, reinforcement learning, clustering, etc.

The networks of Deep learning networks must see huge quantities of items with a purpose to get it trained. Deep learning functions slightly uniquely as compared to AI and machine learning because instead of getting programmed using the edges that describe items, its systems learn from contact with lots of data points. The networks of Deep learning networks need not be programmed with the metric that list items; however, they are capable to identify edges after getting exposure to huge amounts of data.

Concluding Note:

The discussion about Artificial intelligence vs machine learning vs deep learning may cover many other aspects. It is continued in many topmost organizations that wish to assimilate these technologies for innovations. It must be understood that data is located at the heart of everything. Whether you use AI, Machine learning or Deep learning, one thing is certain. It is that -if flawed data is implemented, the information and understanding gained from it would be flawed.  It may be possible that algorithms can be flawed as the errors may happen occasionally but these three concepts need to be smartly used to reduce any probable flaws. If you are looking to hire developers for your next project, then contact us today.

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