Artificial Intelligence (AI) is a smart technology that is being embraced by several businesses belonging to different industries. What is this smart technology, Artificial Intelligence; that has created so much buzz?
What is Artificial Intelligence?
- Artificial Intelligence is a program that exhibits cognitive (intellectual) ability just like human beings do.
- The main objective of artificial intelligence is to make computers and machines think and solve complex problems, just like we humans do.
- An AI-powered computer program shows features such as image recognition, language processing, self-improvement, and of course learning abilities as well.
What is machine learning (ML)?
- Whenever we hear ‘Artificial Intelligence’, the specific smart technology that is working is machine learning.
- Initially, AI could just do the tasks which it was programmed to do. However, with ML algorithms, AI programs can do a lot more, as ML has the power to evolve.
- Machine learning shifts the learning away from humans and forces the computer to figure out things on its own.
- Machine learning helps a computer program to improve on a particular set of tasks it performs.
Different types of learning
Artificial Intelligence being a smart technology has the ability to learn. There are three different types of machine learning. These include:
- It is also known as task-driven learning since it focuses on a particular task, giving the algorithm more and more examples before it can start performing the task with accuracy
- It is the most popular and basic type of machine learning.
- Supervised learning can be compared to teaching a child the names of fruits, vegetables, plants, animals, etc. with the help of flash cards.
- This type of learning is quite simple to understand and can be implemented easily.
- In supervised learning, the input data is labelled for training the machine learning algorithm.
- The ML algorithms are trained using small datasets.
- The dataset used for training is a small portion of the larger dataset.
- The training dataset used is similar to the larger dataset, and gives an idea to the ML algorithm about the problem, solution, and data points it needs to handle.
- This type of machine learning is known as supervised learning because it takes place under the supervision of a human being.
- Supervised learning provides accurate results, and is used when a researcher or scientist known what information he/she is looking out for from the data
- The possible use cases of supervised learning include image/face recognition, email filtering (spam/not spam), price prediction, demand forecasting, and others.
- It is also known as data driven learning and is the exact opposite of supervised learning.
- There is no labelled data used in this type of machine learning.
- In this learning type, the algorithm is fed with a lot of data, and given the tools to find out the properties of the data on its own.
- The algorithm tries to find out the relationships between the data on its own by creating certain patterns.
- Based on the patterns, the ML algorithm is able to sort and organize the data just like human beings can do.
- For example, if we input data that contains different fruits like apples, bananas, oranges, etc., the ML algorithm will be able to group the different fruits based on their similarities, and come up with the result that shows all apples grouped together, all bananas grouped together, and all oranges grouped together.
- The advantage of unsupervised learning is that it can work on unlabeled data without human intervention.
- The researchers and scientists use this type of learning when they do not know what they are looking for in a data.
- The possible use cases of supervised learning include recommender systems like we see in the case of YouTube, Netflix, etc., customer segregation, and anomaly detection.
- It is quite different from supervised and unsupervised learning.
- It involves learning from mistakes by trial-and-error method.
- Reinforcement learning functions just like humans do, it continuously improves itself when placed in an environment, and constantly learns from new encounters.
- In reinforcement learning, favorable (positive) outcomes are encouraged, while the unfavorable (negative) outcomes are punished.
- Reinforcement learning is driven by behavior.
- This type of learning involves an agent, an environment, and a feedback loop to connect the two.
- The possible use cases of reinforcement learning include video games, resource management, and robotics in industrial automation.
How does AI work?
- AI, the smart technology, makes use of machine learning to mimic human behaviour and activity.
- The computer learns how to respond to certain actions, and makes use of algorithms and data to create models.
- Once the model is created, it can begin to make predictions.
- It handles huge volumes of data with intelligent algorithms, and comes up with quick decisions.
- Based on intelligence searches, it interprets the images and text to identify patterns in intricate data, and responds accordingly by acting on the learnings.
In today’s tech world, smart technology like artificial intelligence is quite pervasive in various industries. It is time for AI researchers to experiment with AI and find out new combinations that work best for the industry.