Are you familiar with the term ‘AI vs Machine Learning’? Do you know what is the difference between the two?
Technology has revolutionized the world to the next level. We can now see, do, and learn all the things which were not even existed a few decades ago. Examples- Smart glasses, Virtual Reality, and self-driven cars. Artificial Intelligence and Machine Learning have made the life of customers/businesses easy and precise.
Let’s discuss it with a few examples to get a clear picture.
Artificial Intelligence
Artificial Intelligence also known as AI, refers to the tasks that can be done by machines without the help of humans. For example- Playing a tennis game on PlayStation, live chatbots that reply to your conversation just like a person, and human-like virtual assistants that respond to your queries like Alexa, Siri, or Cortana.
AI can be defined as a process that enables a machine to copy human-like behavior using advanced technological algorithms.
Machine Learning
Machine Learning can be defined as ‘training machines to learn on their own using algorithms to deliver smart services’.
For Example- when we save something in our online shopping cart without checking out. We can see similar products being displayed to us next time, it is because of machine learning as the app learns our preferences from our cart and then displays the products to us. It helps in increasing sales and delivers better customer service.
Moreover, we are no longer dependent on software tools that gather statistical data to predict the outcomes. ML gathers the data, learns from it, and then depicts something. Before ML, AI used to do very limited work and could automate low-level business tasks only. But with the help of ML, a machine can now move past what it is programmed for.
Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning (Task Driven)
In this, the information boundaries are named and the ML calculation is given a smaller dataset to prepare with. It is the piece of the greater dataset and is allowed to find out about the problems, conclusions, and other helpful focuses. The calculation then portrays the connection between the boundaries and lays out a reason and impact connection between the variable of the boundaries. Eventually, the machine has thought of how the information functions between the inputs and outputs.
Unsupervised Learning (Data-Driven)
Unsupervised learning works with unlabeled data. This means that a person is not required to make the data readable for the machines, making it easy to work with a large set of data. It helps to know the relationships between the two points more accurately as there is no input required.
Reinforcement Learning (Trial & Error)
It means learning about data gradually from errors using a trial and error method. Here, the good outcomes are reinforced and the bad outcomes are discouraged. The algorithm works using an interpreter and a reward system. With every repetition of the algorithm, the outcomes are sent to the interpreter who decided whether it is a good or a bad outcome. The reward system is linked to the effectiveness of the outcome, which means until the result is a favorable one, it keeps on repeating the cycle.
Summary
AI and ML work together. We can say that ML is necessary to provide the inputs to make the AI achieve new heights:
- It helps in providing better customer journeys, services, and business strategies.
- It helps in eliminating the duplication of efforts, cuts the workload, and provides insights for any improvement.
- It can boost the learning and e-commerce experience to a new level.
- It defines a powerful source of useful machines/computers but may come at a very high cost.
- AI can also be a reason for unemployment and layoff of employees.
- Sometimes the data collected by the ML can be too larger and may take time to get the best possible result.
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