Discover what is machine learning, examples, and the difference between machine learning and artificial intelligence!
With a fast technology growing world, there’s no denying the fact Machine learning algorithms are responsible for the majority of the advancements that have been seen in AI. The question that is common debate is that what is machine learning? How does it work? How is it applied in various scenarios?
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What is Machine Learning?
Machine learning is a modern innovation that has not just helped to enhance the efficiency of industries but also helped to create a lot of ease in everyday living. But the real question is that what is machine learning? Actually, it’s a subset of Artificial intelligence that majorly focuses on using statistical data to build its intelligence level, enabling it to be interactive at the same time.
In order to get the in-depth detail of what is Machine Learning, we need to dive in deep and look at its different sub-components.
What is Machine Learning Exactly?
Machine learning is the process of enabling the computer system to make appropriate predictions out of the data it has been fed. The data encompasses a lot of things here- images, sound, numbers, whatever that can be digitally stored can be used in the machine learning algorithm for learning purposes.
The key difference that separates Machine Learning from a traditional artificially intelligent system is that a human developer hasn’t written the code that enables the machine to work. Instead, a machine learning model has been though how to differentiate between objects by looking at the specific elements that have been gathered from the stored data.
What is machine learning; Explained With Examples
If we look around ourselves, we can observe that we’ve been fully immersed with machine learning in almost every aspect of technology we’ve made use of. ML empowers many of the services that we use daily- the recommendation systems on Netflix, YouTube, Spotify; common search engines like Google; voice assistance like Siri, Bixby, Alexa, etc., and the list goes on.
Here are some of the most common real-life examples where machine learning is assisting in creating better technology to empower newly emerging ideas:
- Image Recognition
This is one of the common and efficient uses of Machine learning. The use of digital images enables these ML algorithms to use certain elements of the image to extract the required information. That information can then be used to identify what the object is or check whether it is what it’s claiming to be. This is how Face Detection is being used to authenticate the identity of a person.
- Speech Recognition
Translation of spoken words in text form is called speech recognition. The sound of the speech and the actual text is fed to the machine learning algorithms, which allows them to replicate these actions to generate a textual file.
This feature is used in applications like voice UI, searching, etc.
- Medical Diagnosis
In today’s world, machine learning is being used to analyze clinical parameters and prognosis to extract medical knowledge for research, patient monitoring, and many more. This type of assistance can be a significant boost towards increased reliability in the healthcare sector.
Machine learning makes use of data groups to predict the probability of a fault accurately. These computations allow the systems to make predictions across all the sectors for various purposes. This is said to be one of the best use of machine learning allowing long-term savings.
These examples give a clear answer to what is machine learning and where it is being used in real-life.
What is Machine Learning, and how is it being used?
The combination of Machine learning and Artificial intelligence creates one of the most significant technological duos in recent history. Here’s the list of some of the companies making use of Machine Learning in unique ways:
It is one of the most popular social media platforms out there that is significantly focusing on making use of AI and ML for introducing chat-bots, filtering out spam, and poor-quality content. In the future, Facebook is looking to integrate computer vision algorithms to read images for visually impaired people.
Being amid several controversies, no one can deny that twitter’s algorithmic feed is a blessing in disguise. The AI, along with Machine learning, evaluates each tweet on various metrics and then scores them accordingly. Then they display the tweets based on those scores meaning that a higher scored tweet is likely to have higher engagement and thus is placed above other tweets.
The most visited website on the World Wide Web has invested the most in machine learning. The company is continuously researching to explore all the aspects of machine learning to improve its previous algorithms.
After Google, this Chinese search engine is also putting a lot of interest in machine learning. The feature that distinguishes Baidu from others is what the company calls “Deep Voice.” It can generate entirely synthetic human voices that are very similar to that of humans.
What is the difference between Machine Learning and Artificial Intelligence?
At the moment, Machine learning and Artificial intelligence are the most trending and efficient pieces of technology at human hand. Being commonly interchanged with one another, they are not quite the same. The best and simple answer to the misconception is that:
Artificial intelligence is a broader concept of machines that are able to carry out tasks as per the given set of rules. These actions are considered “smart,” and the system can replicate these tasks on command. Without altering the ways of achieving an outcome, these machines are restricted to obey the instructions they have strictly provided.
Machine learning is a subset of Artificial intelligence developed around the idea of allowing the systems to think and learn independently. The computer’s tendency to adapt to the situation and find efficient ways on their own to achieve the desired outcome is relatively higher compared to the pre-programmed machines.
These are the main points to thoroughly understand the basic answer to machine learning and how it helps us in our daily lives.