All Eyez On Data

Artificial Intelligence vs. Machine Learning

Artificial Intelligence and Machine learning are terms that are often used interchangeably. They are definitions that have confused many people. Artificial Intelligence is the broader umbrella under which machine learning comes. We can say that they’re just the subset of each other. But how exactly do they differ from each other?

Artificial Intelligence

The term Artificial Intelligence (AI) was first coined in 1956. The concept is pretty old, but it has gained popularity in the recent years. Earlier, we had quite a low amount of data. That data was not enough to predict accurate results. Now, there is a tremendous increase in the amount of data. Statistics suggest that by the end of 2020, the accumulated volume of data will increase to 44 trillion gigabytes of data. Along with such an enormous amount of data, now we have a more advanced algorithm and high-end computing power and storage that can deal with such large amount of data.

But what is AI? It is a tool or a technique that enables machines to act like humans by replicating the behaviors and the nature. With AI it is possible for machines to learn from experience. The machines are just the response based on new input, thereby performing human-like tasks. AI can be trained to accomplish specific tasks by processing large amount of data. It also has the ability to recognize and to follow patterns in them.

Some examples of AI systems are self-driving cars, chess-playing computer, Siri, Alexa and Google Assistant and so much more.

Artificial Intelligence

Machine Learning came into existence in the late 80s and early 90s. It is a branch of Artificial Intelligence. We can define it as the study of algorithms. These algorithms give permission to computer programs to automatically improve by using experience. It involves machines finding ways on how to perform tasks by not being programmed by humans to do so. By providing them with data, they are able to learn how to carry our specific tasks. If you assign them to do simple tasks, it would be possible to program instructions so the machines know how to execute them.

For more complicated tasks, it might be difficult for humans to set instructions to the machines. It would be more effective and easier to make the machines develop their own set of algorithms and commands. This won’t have to force humans to specify and program every step. This discipline uses many techniques and approaches to teach machines to achieve and perform tasks and activities where a fully satisfactory algorithm isn’t available.

Differences between AI and Machine Learning

• Artificial Intelligence
• Machine learning
• It is a tool or a technology that makes machines learn and copy human behavior.
• It is a subset of AI. It gives machines the ability to learn from past experiences and data without any human programming.
• The primary goal of AI is to make machines behave like humans and solve human-like problems.
• Machine Learning’s goal is to create technologies that learn from past data in order to improve themselves without the need of the humans.
• These systems are created to perform tasks like humans.
• Here, the machines not only they do tasks, but they also give accurate results and outcomes.
• AI has a very wide range of scope.
• Machine learning has a limited scope.
• AI system is concerned about maximizing the chances of success.
• Machine learning is mainly concerned about accuracy and patterns.
• Some examples of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc.
• Some examples of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.
• Based on the capabilities, AI is divided into:
• Weak AI, Strong AI and General AI
• Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.
• It includes learning, reasoning, and self-correction.
• It includes learning and self-correction when introduced with new data.
• AI completely deals with Structured, semi-structured, and unstructured data.
• Machine learning deals with Structured and semi-structured data.

The truth is that we will ultimately produce human-like AI that has regularly been treated as something of an inevitability by technologists. Certainly, today we are closer than ever, and we are progressing towards that aim at a rising rate. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envisage AI working, which have been brought about by ML. I hope this piece has corrected a few people who know the difference between AI and ML.