Differences Between Artificial Intelligence (AI) and Machine Learning (ML) That You Need To Know Now!!

May 12, 2021
4 min read

Artificial Intelligence and Machine Learning are making sweeping changes in the technological world and are always making a buzz in the tech industry. Companies are using both terms as synonyms to create hype around their products and brands but they are doing it all wrong.

What Is Artificial Intelligence (AI) and Machine Learning (ML)?

Machine Learning
Tom Mitchell defines Machine Learning as, “Machine learning is the study of computer algorithms that allow computer programs to improve through experience automatically.

In simpler words, Machine Learning is a subset of AI that works based on huge amounts of data where the machine learns from patterns and experience and does not require external human coding.

Artificial Intelligence
AI is a technology that aims at creating smart machines that possess human intelligence and thinking patterns. This technology is applied to systems to simulate human emotion into a machine. They use emotionally intelligent data to grasp a better understanding of the working of the human brain.

These AI systems use smart algorithms such as Computer Vision, Robotics, Machine Learning, Deep Learning, Neural Networks, and Voice Technology.

How Is Machine Learning Different From Artificial Intelligence?

  1. AI is the superset of ML
    Machine learning and Artificial Intelligence are neither synonymous nor two different entities. Machine learning is the subset of AI. It is used for creating intelligent systems to generate predictions and make decisions based on historically fed data. The ultimate goal of AI is to produce smart machines, whereas Machine Learning is a method to achieve it.
  2. AI is used in system software and ML is put in service for Online Recommender Systems.
    The term AI has been on every tech-junkies' mouth since the development of system software such as Siri, Google Assistant, Alexa, and Alpha Go. These systems in their early days created a buzz in tech industry, and with this, the term, “Deep learning” gained popularity.

Deep learning is the classic case of, “Necessity is the mother of all inventions.” The term AI was coined in the year 1956 and since then the subject has constantly been evolving and becoming more and more complex. Fifty years ago, any chess game on a computer was considered a high level of AI whereas now it's nothing more than a basic coding formula.

Deep learning delivered results when typical rule-based coding was unable to perform tasks, and it also took over new fields such as voice and face recognition, natural language processing, and image classification. Deep learning combined with profound learning, which is a subset of machine learning, establishes a system that can be termed as smart.

Machine learning is the technique that is used to create recommender systems from Google search algorithm, email spam filter to Spotify and Netflix recommendations.

Machine learning on the other hand works for a specific domain only.  For example: If I create a machine learning model to detect fish images the system might become unresponsive towards images of octopus. This was until a new and stronger type of machine learning was created. This newer type and stronger version of Machine learning is called Reinforcement Learning.

Reinforcement Learning focuses on Maximising accuracy and minimising error. It is done by collecting observations from monitoring the interactivity within its environment continuously. Thus the agent ( reinforcement learning algorithm) learns continuously through iteration. The most advanced example for reinforcement learning is superhuman computers that are capable of beating humans at video games.

Types of Artificial Intelligence and Machine learning

Artificial Intelligence and Machine Learning are broad fields of study and have types and subtypes.

Artificial intelligence is broadly divided into three broad types:

  • Narrow/ Weak Artificial Intelligence
    This is designed to perform a single task and to get better at its execution with time.It can include any task which has already been performed and to minimise the inconveniences while its execution. Most AI that exists today comes under the category of Narrow AI.
  • General Artificial Intelligence
    Often mentioned as “The True AI”, General AI prioritizes machine Comprehension rather than a single task-to-task performance. It focuses on making a machine perform on a wider level just like a human being would do!!!
  • Strong Artificial Intelligence
    We are yet to become WestWorld (a TV show based on AI). All the mystic aura that surrounds the word Artificial Intelligence (AI) in general comes from the myths of AI from stuff like robots, fully automated cars and to make a machine with equal thinking and functioning capability.

Machine learning has the following types:

  • Supervised Learning
    This machine learning model works based on explicit examples. It learns from the past and uses it to make future predictions. This works in an environment where the data is marked with correct answers and the algorithm produces the already known correct outputs.
  • Unsupervised Learning
    Unsupervised Learning lacks correct outputs in the environment because it is impossible to attain, obtain or observe the correct labels and therefore it generates output based on data exploration and patterns (if there are any).
  • Semi-Supervised Learning (SSL)
    As the name suggests it takes a mid-way between supervised and unsupervised learning. Most AI systems, require a combination of both the techniques to solve a problem and Semi-Supervised learning works best in those cases.

Scope of Artificial Intelligence (AI)  and Machine Learning (ML)

Machine Learning is a faster-growing area of study, especially some of its interesting subtypes like reinforcement learning giving better results than ever whereas Artificial Intelligence on the parallel encompasses a lot as a subject and has a vast scope.

The future of Machine Learning ( ML) can be the generation of recommender systems that are a hundred per cent (absolute state) accurate and have zero error frequency. Although the future of Artificial Intelligence (AI) is quite uncertain as this world still hasn’t seen house guard robots that the early Artificial Intelligence (AI) manifesto promised.

What  the future holds for AI ?
As companies use the term Artificial Intelligence (AI) to market their brand, services and products to create hype in the tech market without the complete understanding of it, their systems will eventually falter. According to TechTalks  (a major tech website) when their systems start to fail, they will be hiring humans for doing the jobs. This mistrust can cause, “ another winter of Artificial Intelligence ( AI) in the market”.

Elon Musk says that AI will be “ vastly smarter” than humans in the next 5 years. He also said that it was the biggest threat for humans.

Although the future of Artificial Intelligence ( AI) does not look negative to everyone. A positive outlook also exists. As The Verge another big player in the tech industry believes that AI might be the powerful way to discover the universe.