Machine learning (ML) and Artificial intelligence (AI) are two of the most trending topics these days. ML & AI are both a part of the computer science discipline used for developing intelligent and smart systems. People often use these two terms interchangeably, but do you know there are many differences between the two? Before diving into those differences, let’s see what each of these terms means.
AI means incorporating human intelligence into a machine. You provide a machine with an artificial brain with which it can think and make decisions like humans. AI has been around in academic study for about 65 years now. The aim then was to get machines to accomplish assignments seen as uniquely human. With AI, machines could play checkers and solve logical problems. But today, it is not just about playing checkers or doing a few logical puzzles, but a lot more than that. Today, with AI, a machine can answer inquiries related to sales, fraud disclosure, inventory and many others. With AI, a computer knows what data is required, and it looks at relations among all the variables. It then forms a result that communicates with you.
Machine learning is a subset of AI centered on developing apps and machines that receive data and learn from it. The term Machine Learning itself says that a machine learns on its own. The learning starts with the collection and observation of data. It then scans for patterns in the sample data to make more reliable judgments in the future. With ML, the apps or machines can increase their efficiency over time without being programmed to do so. The principal object is to enable the machines to learn on their own with no human interference or support and adjust actions accordingly. So now that you know what these two terms mean, let’s understand the main differences between them in depth.
What is Machine Learning and How it Works?
As said by Tom M. Mitchell, ML uses computer algorithms that enable computer programs to improve on their own through experience. It is one of the many ways in which we can achieve Artificial Intelligence. ML relies on working with both small and large datasets by analyzing and correlating the data to find familiar patterns and search nuances.
For example, if you present a machine-learning model with many movies that you enjoy watching. It will use the supervised machine learning model to develop a recommender system. That will recommend to you all the movies that you may like to watch in the future. You may have used Netflix or Amazon Prime. It has a similar system that keeps recommending movies based on your watching history. Let us look at another example, say you load an ML application with a substantially huge dataset of X-ray images along with the details of the symptoms and other things to consider for a proper diagnosis. That will have the potential to aid and provide a better data analysis of X-ray pictures later on. The ML model will scan every image in the dataset and find a similar pattern. When you put in new images, the ML app will compare the parameters with the existing dataset.
The three types of machine learning are; supervised learning, unsupervised learning and reinforcement learning. The two examples given above are of the supervised machine learning model. Unsupervised learning has the main use in pattern detection and detailed modeling. It does not have output classifications or tags on the data, unlike that of supervised learning. Whereas, in reinforcement learning, the machine or computer application continuously studies its surroundings using multiple iterations. An example of this machine learning method is machines attaining a super-human status. Such machines can even beat humans in various computer games.
So Then What is Artificial Intelligence?
AI is a vast field that aims at making computers intelligent enough to perform super-human tasks. Many years back, a chess-playing application was regarded as a form of AI. These days, it includes more advanced technologies, and machine learning is one of the many. You may have heard about AI, Deep Blue, which in 1997, beat the world’s chess champion using the tree search algorithms that could judge millions of moves of the opponent.
Today we use AI in our day-to-day life. Siri, Alexa, Smart home devices and many others are a few examples of human-AI interaction gadgets. We also have online video streaming platforms like Amazon Prime, Netflix and others, that use AI to provide a personalized experience to its users. All this technological progress in AI has become necessary in our lives. These AI-enabled gadgets and devices provide us with day-to-day assistance, thus making us more productive.
In opposition to ML, Artificial Intelligence is a moving target whose description keeps changing with technological advancements. After a few decades, it may happen that all the technological advances in AI that we are in awe of right now may get outdated. Just the way it happened with flip phones and TVs with big backs.
A lot of businesses today use AI-based machine learning to improve their efficacy and productivity. As per research conducted by Forrester, the investment in AI has been increasing exponentially. Domains such as healthcare, data analytics, security, telecommunications and many others have already leveraged ML software to stay ahead. ML is majorly used in speech and image recognition applications, statistical strategies regarding finance & trading, studying customer insights, market behavior predictions, consumer behavior predictions, extraction of data from unstructured raw data, evaluating output data based on multiple input variables by regression methodology, and many others. Hire ML Developer now to leverage the benefits of Machine learning to aid your business.
In the present scenario, all the major business processes involve huge data and it is practically impossible for humans to analyze it and conclude anything based on it. With Machine Learning, you will be able to process the data faster and with less cost, get real-time for better marketing and, improve insight-driven decisions. You can thus, Hire a machine learning developer for increasing our competitive edge and ROI.