Complete Guide: Artificial Intelligence Vs Machine Learning Vs Deep Learning

Artificial Intelligence Vs Machine Learning Vs Deep Learning

Artificial intelligence is a modern invention, but it was dreamed of long before, as human ambitions soared high with the development of civilization. In Greek mythology, there is a mention of a huge bronze sculpture, Talos, who guarded the island of Crete. The intelligence in artificial intelligence stemmed from the human brain. It is one of the inventions that altered our lives forever. It bought us closer to not just the future but also the past.

In simple words, any humanlike activity performed by a machine, whose learning capability is enhanced with the aid of programming is artificial intelligence. In 2021, AI is everywhere, from our home to workplace and even inside our body, as devices like cardiac pacemakers can be monitored with AI.

However, for people who are not learning, researching, and developing the functionality of this technology, terms like machine learning and deep learning can be confusing. Here’s what you need to know.

AI – It is a science, developed to create intelligent programs that enable creative and efficient solutions sans the involvement of a human mind. It is like creating a mirror image of human perception that can be programmed to be more efficient.

ML – A subset of AI, machine learning provides the system with the ability to learn and improve from experience. A great example of this is how a human child picks on language and behavior from what he/she experience from a very young age. This is what ML mimics.

DL – A category of machine learning, deep/neural learning enables analysis of experiences and programs. The function and structure are quite similar to the neural system of the human brain.

This explanation must have cleared your doubts regarding the difference between deep learning, machine learning, and artificial intelligence. So, let’s dive into the three faces of artificial intelligence. Three types of AI are categorized as per their specific application, namely ANI, AGI, and ASI. Take a look at the sections mentioned below for a better understanding.

ANI (Artificial Narrow Intelligence)

This is the earliest form of artificial intelligence that is widely deployed in the fields like business, healthcare, and science. AI software system of this type is a powerful analytic tool that can efficiently detect abnormalities and patterns, thus immensely helping in immaculate planning of operations and risk management.

AGI (Artificial General Intelligence)

Decision-making is a key feature of this AI variant. This sophisticated form of artificial intelligence allows highly-analyzed and on-point selection of actions. Modern-day chatbots and virtual assistants deploy this technology. Another interesting fact about AGI is that it can mimic some human emotions verbally in response to stimuli.

ASI (Artificial Super Intelligence)

If you are reading about AI then you do tend to wonder whether it has superseded human intelligence or not. Yes, it is smarter, more creative, and brilliant in terms of decision-making and social skills that are primarily human traits. However, to our disappointment, this isn’t the goal of AI. The focus of inventors working in this field currently are as mentioned below:

  1. Machine Learning
  2. Machine Reasoning
  3. Robotics

Machine Learning

The first step to gain a better understanding of AI is to learn the relationship between machine learning and artificial intelligence. This is a subset that deals with developing the ability to learn without programming in machines. A simple explanation of machine learning can be found in the human experience. When we visit a place for the very first time or get our first job as an adult, we tend to be more vigilant to the things that are going on around us. We gain experience from the environment that we are in. Machine learning captures this capability to make AI more human-like and more efficient.

Components of Machine Learning

Algorithms

To solve problems, the AI can deploy different sets of algorithms. Machine learning algorithms for data science help boost both speed and accuracy. AI today is not manually coded. Its operation is more independent and problem-based. The system develops an understanding of the problem, analyzes different sets of algorithms, and then implements the one that offers the highest efficiency and accuracy levels.

Datasets

Data science, artificial intelligence, and machine learning are interconnected. The ML systems are trained with a collection of datasets and samples of text, numbers, and images. In machine learning, building datasets that can offer accurate results within a short span of time are ideal and hard to develop.

Features

Crucial pieces of data that work as a key to diverse solutions are known as features. For instance, there is a diamond necklace you have your eyes on in an auction. Now, before you bid, it’s necessary to be aware of the exact market value. If the price is not available, we can calculate the carat and size of diamonds to come to a conclusion. Features in ML help artificial intelligence in a deeper analysis based on accurate sets of data. They help the system focus on what’s important and relevant.

Deep Learning

Neural networks in AI systems operate just like our brain. The algorithms involved in this subset of machine learning are capable of operating with huge sets of unstructured and structured data. The aim of deep learning is to help the machine in taking decisions that are analyzed with different datasets along with result predictions for each dataset. The difference between deep learning and machine learning lies in the data structure. In deep learning, numerous layers of artificial networks are deployed, while ML algorithms use highly structured data.

Why Is It Essential?

With the explosion of data in the digital era, deep learning has evolved drastically. Big data accumulated from various resources including search engines, social media, and online entertainment is more accessible than ever. However, it is so unstructured and vast that extracting relevant information with just human resources can take up a lot of time. This is where deep learning turns out to be incredibly useful. It helps analyze Big Data in a non-linear and efficient pattern that results in information that is precise and practical.

Application of Deep Learning

Speech recognition systems in virtual assistants like Alexa and Google Assistant are based on deep learning. The sound waves captured by the device are turned into sequences that can be recognized by the AI. These sequences are further processed to activate the production of results that the user can understand. Deep learning has bought AI closer to human intelligence. However, the algorithms involved in it are not perfect. This increases the need to understand and implement more approaches in machine learning.             

ML Algorithms

No algorithm can perform equally well at different tasks. This calls for more variants of ML algorithms that can yield the desired results. Here are the major ML algorithms grouped as per their learning style:

Supervised: This is when you introduce the system to different sets of data and how they should be analyzed and processed. Post this validation set is used to make sure that the learned functions are correct. If there are discrepancies at this stage, the programmer can spot and eliminate them. To achieve the desired level of accuracy, the training process continues until all the errors are removed.

Unsupervised: Herein the system can search for patterns independently, without deploying any sort of training to recognize and segregate information. Organizations operating in diverse fields use it to obtain insightful data analytics. In this type of ML algorithm highly accurate results can be achieved. It can pick anomalies that the human brain can miss while working with a huge amount of data.

Reinforcement: The reactions to our actions help us gain experience. For example, a child doesn’t know that he shouldn’t take stuff without permission. We have to teach him/her that it’s a bad habit. Positive behavior reinforcement is similarly used in AI systems to enable deep learning. It is all about letting the system understand with experience and develop a knowledge base independently.

Semi-Supervised: This ML algorithm trains the system with a mix of unlabeled and labeled samples. There is a desired result prediction but the system has to sieve through various patterns in order to achieve the required data structure. It allows the system to make an accurate prediction with very little supervision.

Now that you have understood the difference between deep learning, machine learning, and artificial intelligence, you can understand how crucial it is to our present and future. If you run a business then this information can be life-changing, as you will be able to efficiently plan the prospects of AI in your operations or products.

Whether you wish to hire machine learning experts in India or design an AI-based product, understanding what this technology brings to the table is essential. However, to get better insights, it is suggested to read journals and articles by renowned AI experts. The more you are clear on the relationship between machine learning and artificial intelligence, as well as deep learning, the better will be the implementation and results.

AI is one of the greatest inventions of our times. It has helped us understand the brilliance of our minds at a deeper level. Now, it is all set to open new avenues for the development of the medicine, science, and business worlds. So, without any doubt, it can be immensely beneficial for your organization, no matter what your operational vertical is.