Application of Machine Learning in Artificial Intelligence
Machine Learning and Artificial Intelligence are two of the hottest and trending topics in the Information Technology Industry since recently. Machine Learning and Artificial Intelligence are being applied to many day-to-day activities, that our lives will be entwined with these technologies so much that we will not be able to function without them.
If you are new to the terms of Machine Learning (ML) and Artificial Intelligence (AI), you need not worry. Let us get into the technical terms and definitions of ML and AI and then dive into more interesting details.
Artificial Intelligence refers to the ability of a computer or machine to think and perform actions similar to a human being according to IBM Cloud [1]. The Expert.ai team defines Machine Learning as the ability of a system to learn from past experience and data without being programmed each time [2]. In basic terms, Machine Learning is an application of Artificial Intelligence that develops programs to access data, identify patterns and learn on their own. The Machine Learning model follows the fascinating and complex design of the Human Brain.
The concept of Machine Learning has been circulating since the time of the Ancient Greek Civilization, but the first Machine Learning model was created in the year 1949 and was based on the model of how the brain cell interacts. It was created by Donald Hebb and was included in the book “The Organization of Behavior”. Alan Turing, in the year 1920 published a research paper named “Computing Machinery and Intelligence” which probes into the question “Can machines think?” Since the publication of this book and report, Machine Learning has evolved immensely and is now unbelievably advanced.
Some confuse the terms Machine Learning and Artificial Intelligence and some believe they are the same. It needs to be noted that Artificial Intelligence strives to make devices smart and be able to solve complex tasks just as humans do, and the objective of Machine Learning is for machines to learn and give accurate results by analyzing the data that they have been fed. Machine Learning focuses mainly on the patterns that can be identified from the given data and producing an accurate output for future predictions.
Machine Learning creates algorithms using the available historical data, and these data are mostly structured or semi-structured data. There are various types of Machine Learning algorithms, and they can be mainly categorized into 3 main subparts: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised Learning – this algorithm learns from data that has been categorized or labeled and helps in predicting an outcome for an unforeseen instance of data.
Unsupervised Learning – the data that is being used is not labeled, and the model discovers similar and hidden patterns in the dataset given and classifies them into groups.
Reinforcement Learning – the main goal of this algorithm type is to maximize the reward and minimize the risk. It interacts with the environment continuously and learns from the consequences of actions. This is similar to how children learn from mistakes made.
Now that we have gone deep into the technicalities, let us get into a more interesting section and find out about real-world applications of Machine Learning in Artificial Intelligence.
Speech Recognition – It is the ability of a program to recognize a voice and distinguish the words that are spoken, often converting them to text. It is also called Computer Speech Recognition or Speech to Text. Some famous examples where Speech Recognition is used are Alexa on Apple devices, Cortana on Windows as well as Google Assistant.
Image Recognition – It is one of the most famous applications of machine learning and is now extremely advanced. Image Recognition programs can identify faces, objects, signs, places, and many more and has been exceptionally valuable in helping the blind identify things around them. One of the more common applications of Image Recognition is the project “Deep Face” by Facebook, which uses face detection and provides automatic friend tagging suggestions.
Medical Diagnosis – Another valuable asset to all, medical diagnosis using Machine Learning has been trending and easing the lives of doctors as well as patients. Machine Learning helps in the analysis of bodily fluids, recognition of Cancerous tissues as well as recommendation of treatment options after a diagnosis.
Prevention of Online Frauds – Online transactions can be quite dangerous at times and online monetary frauds are more common. Machine Learning can aid in detecting fraudulent activities that may take place when doing online banking or online payment, as they are trained to detect authorized and unauthorized transactions.
Currently, human lives are dependent on Machine Learning and Artificial Intelligence more than ever, and the above are only a very few of its applications. Machine Learning and AI is a vast field and most definitely is the future of our world. I encourage you to look into ML and AI even further and be fascinated by how advanced Information Technology has become!
References
[1] IBM Cloud Education, What is Artificial Intelligence, June 03rd 2020, Accessed on: March 6, 2021. [Online]. Available: https://www.ibm.com/.../what-is-artificial-intelligence
[2] Expert.ai Team, What is Machine Learning: A Definition, May 06th 2020, Accessed on: March 6, 2021. [Online]. Available: https://www.expert.ai/blog/machine-learning-definition/
Other sources
https://pub.towardsai.net/differences-between-ai-and...
https://www.dataversity.net/a-brief-history-of-machine.../
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