Quick Summary: Compare the most trending technologies Artificial Intelligence, Machine Learning, and Deep Learning to understand what differentiates them and what relates. The article gives you an analytical overview of all these three technology terms —AI vs. ML vs. DL.
Even though Artificial Intelligence is not a new term, it was away from the limelight until recently. However, today AI is all around us, helping us by solving most of our day-to-day complex problems. Have you realized how you solve the most complex banking issue in just a second or minute of time? How do you find the nearbout gas station, shopping mall, or eatery with a simple voice command?
Similarly, there are plenty of things happening around us that are powered by AI, ML/DL. Google Assistant, Siri, self-driving cars, wearables, OR tools, etc. are some of the great examples of AI. In fact, the global Artificial Intelligence market is expected to grow to a $126 billion market by 2025. The demand for AI solutions is growing unexpectedly and the trait is expected to continue in the years to come. However, the growing popularity of AI, ML, DL, etc. has also caused a huge confusion and we have received many questions if they are all the same.
No doubt, AI, ML, DL, or even NL (Neural Language) are connected, they are different in effect. So, keeping eyes on trending technologies like Artificial Intelligence, Machine Learning, and Deep Learning, we have decided to end the dilemma of many. Let’s explore;
What is Artificial Intelligence —An Overview
When a machine is built or developed with the ability to act or perform like a human. In technical terms, AI is a science in which machines are programmed with cognitive ability. That means once the data is fed to the machine, it can mimic and act like humans or animals. Based on data, (predefined) parameters, and conditions, machines can help businesses carry out most tasks, including customer care, planning processes, understanding complex business communication (verbal context), recognizing complex images/sounds (AI voice assistant is an example), and helping to create wise strategies.
Simply put, Artificial Intelligence is a machine that acts and performs like a human with little or no involvement of humans.
Even though Artificial Intelligence is widely associated with the Fourth Industrial Revolution, the AI term was coined in 1956 by John McCarthy. No doubt, earlier it was visualized through movies, such as Terminator, Star Wars, and others. However, in the last few years, AI has experienced a resurgence through real-world application. Now, healthcare, fashion, and eCommerce to manufacturing, construction, and scientific research centers are heavily relying on AI.
The core function of Artificial Intelligence is to;
- Improve, Correcting
It has three broad categories;
1. Artificial Narrow Intelligence or Narrow AI or Weak AI
2. Artificial General Intelligence or General AI or Strong AI
3. Artificial Super Intelligence or Active AI
When an AI-powered machine is destined or deployed to perform one specific task. Chatbots, Google Assistant, Siri, Alexa, Cortana, etc. are known as narrow or weak AI. They all answer questions based on the inputs they receive from users. You cannot see Narrow AI mimicking humans rather it simulates human behavior based on the set parameters. It’s just like an infant who acts as per the instructions received from an adult.
When a machine is powered to interpret and understand human emotion and tone called General or Strong AI. Understanding the emotion and tone, Strong AI acts accordingly. Artificial General Intelligence (AGI) is designed to be at par with other humans. For example, it can learn and teach itself. Strong AI example can be seen In the Poker game where it teaches itself to act or outsmart its human opponents.
Active AI or Artificial Super Intelligence (ASI) is when a machine (powered by AI) becomes self-aware and surpasses human intelligence and ability. No doubt, Active AI is super intelligent, though its implementation has been questioned by top scientists. Some of the top examples of Active AI includes;
- Disease mapping and prediction tools in healthcare.
- Image / facial recognition software, like smartphones and Google Image Search.
- Manufacturing and drone robots to support manufacturing sectors.
- Rankbrain by Google / Google Search.
What is Machine Learning —An Overview
Machine Learning is the subset of AI that enables machines with learning capabilities through statistical methods and algorithms. Machine Learning powers machines to learn automatically from their previous experiences. Simply put, ML empowers computers to train themselves and automate tasks that are exhaustible or impossible for humans to do.
Comparing Artificial Intelligence vs. Machine Learning, ML is basically a language that focuses on the use of data and algorithms to imitate, learn and automate just like a human does.
Arthur Samuel is said to be the founder of the term Machine Learning in the 80s. Based on data, ML can perform various tasks, such as clustering, regression, or classification. In simple terms, the stronger the data, the highly accurate results you will get out of ML. When AI is science, ML is its subset —a study of computer algorithms.
What Machine Learning Does to Support Business
There are plenty of things that Machine Learning can do to help businesses grow. For example, sales forecasting, gauging customer sentiments, analyzing customer behavior, and helping businesses act, such as offering services based on their records. Another example of ML is the OTT platform, such as Netflix and Amazon that use machine learning to help users with a recommendation based on the movies they viewed in the past.
In short, Machine Learning is evolving and will continue to grow over time. So, machine learning and artificial intelligence can be the same in method, but different in action.
What is Deep Learning —An Overview
It’s an extensive part of Machine Learning —it’s the evolution of machine learning and hence can solve a complex business problem that Machine Learning can’t. When the data is huge and unstructured, Machine Learning can take the time or may not function while Deep Learning can easily do.
In another context, Machine Learning needs human support to solve complex business problems out of complex datasets while machine learning can do without any human support. Its algorithms use a complex multi-layered neural network that can mimic how the human brain works.
Machine learning, deep learning, and neural networks are different in names and action, but they are all sub-fields of artificial intelligence and are designed to function differently based on the complexity of data. For example, Machine Learning is the sub-field of AI, similarly, Deep learning is a sub-field of machine learning.
The trend follows with Neural Networks as well. For example, it’s a sub-field of deep learning. In ML, as we have mentioned earlier, human intervention may require while in Deep learning, manual human intervention is eliminated. Machine Learning aka Classical, or “non-deep”, machine learning is widely dependent on manual human intervention.
How AI, ML and DL Correlated
AI is science, it’s clear. Whereas Machine is the subset of AI and Deep learning is the sub-field of ML. We have explained how these things work and what differentiates them. You can refer to this article again for more clarity. Besides, you can also connect without an expert if you want to build any solution-based AI.
As we have analyzed the three terms, AI vs. ML vs. DL, we can say that they are all the same, being used interchangeably. However, they are designed to solve different problems. We at Dash design, build and implement AI solutions for healthcare, manufacturing, and retail sectors. Let’s connect and explore the possibilities of Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks in your business.
We’re technology experts with a passion for bringing concepts to life. By leveraging a unique, consultative process and an agile development approach, we translate business challenges into technology solutions.
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