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Artificial Intelligence Vs Machine Learning

Artificial intelligence and machine learning are two buzzwords in computer science today. Even in the tech field in general, everyone talks about artificial intelligence vs machine learning and many are eager to learn about them. They have become so ubiquitous that both technologies are used interchangeably. 

Reports have shown that many companies have falsely claimed the usage of Artificial intelligence and machine learning in their products and services. But you can’t blame them. There’s so much misconception as regards artificial intelligence and machine learning in the public domain. And if you, as well think that they mean the same thing, don’t be too hard on yourself. You’re not alone. But then, is artificial intelligence and machine learning really the same thing? We’d be answering this fundamental question in this article. 

You will understand what machine learning and artificial intelligence are. Furthermore, you will see how these two technologies are linked, and importantly, the key differences between AI and machine learning Without further ado, let’s begin. 

What is Artificial Intelligence 

Artificial intelligence is the science of building systems that mimic human behavior. Alan Turing, a mathematician, computer scientist, and pioneer of Artificial Intelligence said that a computer can be regarded as intelligent, if it exhibits properties that are similar and indistinguishable from that of humans. This is popularly known as the Turing test. 

In other words, artificial intelligence is the science of making computers that can fool humans to think it’s a human as well.

How AI is Classified

There are two approaches to artificial intelligence called weak AI and strong AI. 

  • Weak AI (otherwise called narrow AI): A weak AI system is one that is designed to do a specific task. This is the most common kind of AI application. The field has grown in leap and bound since inception. Although weak AI systems are built to perform one task, they are extremely proficient at it. You may have heard about the AlphaGo project. In that project, Google DeepMind created a computer that can play complicated game called Go. The name of Google’s AI system was called Alpha Go. In March 2016, AlphaGo went on to defeat the greatest Go player of the decade, Lee Sedol by 4 to 1. This goes to show how weak AI can be so powerful in its given field. 
  • Strong AI (otherwise called artificial general intelligence): In this field of AI, computer scientists attempt to build systems that can do every task as humans. The reality of this is still farfetched as the level of development is nowhere close to the end goal. We however stay optimistic that in some years’ time, there will be breakthroughs in this field. 

Here’s one thing to take home before we move further. Artificial Intelligence machines do not need hardcoded instructions to do a given task. They are rather dynamic such that they work with machine learning algorithms to inform whatever decision they take, based on the systemic condition.

Now let’s discuss machine learning. 

What is Machine Learning

Machine learning has been defined in different ways by a couple of practitioners. We will go with a popular and formal definition by Tom Mitchell in his book. He said a machine is said to have learned if it learns a task T from an experience E, measured by a performance P if its performance P at the task T gets better with experience E. This definition may seem like a handful but it very much covers what machine learning is about. Let’s break it down.

Let’s take an example. Say, I expose a system to many labeled pictures of cats and dogs. Afterward, I ask the system to make a guess for unlabeled pictures of cats or dogs (T). The system may go wrong by calling a dog a cat in its first attempt (low P). The machine however takes note of its error in its first attempt (E) and learns not to make such mistakes going forward. Hence, making a better prediction (increased P) over time (with increased E). Succinctly, you can say machine learning makes use of algorithms to learn independently when exposed to historical data. This is the process of machine learning. 

Machine learning is classified as a branch under artificial intelligence since its end game is to ensure that a system becomes artificially intelligent. Learning can either be supervised, unsupervised or reinforced. Now let’s check out the difference between ai and machine learning (AI Vs ML) battle. 

Artificial Intelligence vs Machine Learning?

  • Artificial intelligence is poised at creating systems that can act like humans. Machine learning, on the other hand, creates systems that can automatically learn from data over time without being hardcoded 
  • The success of AI is measured by how much a system can mimic human behaviors to get to the bottom of complex problems. Whereas, machine learning is measured by how the system can learn from data to produce a more accurate result. 
  • Artificial intelligence is a very broad field while machine learning is narrower in scope.
  • AI has two subfields – machine learning and deep learning whereas machine learning has one subfield – deep learning. Hierarchically, you can view AI as the grandparent, machine learning has the parent and deep learning as the child. 
  • The major aim of artificial intelligence in decision making while machine learning is aimed at getting insights from data. 
  • Due to the fact that AI mimics humans, AI goes for solutions that are optimal while machine learning cares about the solution alone. Machine learning does not really care whether the solution is the optimal solution. 
  • Artificial intelligence encompasses learning and self-correction. Whereas, machine learning involves learning and self-correction when exposed to a new dataset. 
  • Artificial intelligence is mostly applied in physical systems such as self-driven cars, Cortana, Google Assistants, AlphaGo, etc. Machine learning on the other hand finds its application in language translation, recommendation systems, classification and prediction problems, web search algorithms, etc.
  • AI can be divided into strong and weak AI while machine learning can be classified as unsupervised learning, supervised learning, and reinforcement learning. 
  • You can see AI as a system that is wise. Whereas, a machine learning system is a system that is knowledgeable. 

 In conclusion, you have seen how artificial intelligence vs machine learning plays out in the tech world. They are two fields that are intertwined, yet have peculiar purposes. Very importantly, we explained that AI involves a system that acts like humans while machine learning involves a system that learns from data. 

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