Artificial Intelligence and Machine Learning are the buzzwords of today. AI/ML is an ever-evolving technology that enables machines to process similary to humans by interpreting data.
While Artificial Intelligence is the all-encompassing umbrella, Machine Learning is its sub-part.
These words are almost synonymous. However, they cater to different functioning.
Have you heard of driverless cars? How do OTT platforms recommend the show according to your taste? How does Google know what text are you going to write (with a click of a tab)?
Have you wondered how it happens? AI/ML is the answer to it.
This article is a comprehensive insight into everything related to AI/ML– Types of AI and ML, their functions, applications, and more.
Continue reading for a dive into the basics of AI/ML and its uses.
What is Artificial Intelligence?
Artificial Intelligence is the technology through which digital devices comprehend data to sense, study, respond, and synthesize any information to produce the same results as a human.
It involves the conceptualization of data followed by algorithms that comprehend the patterns and information of a particular thing or action.
As it perceives more and more data, AI builds a solid foundation for functioning tasks similar to humans.
It is like a simulation of the human mind- but on a machine.
What are the types of AI?
There are two broad categories of Artificial Intelligence:
- Narrow AI: The more commonly used AI is Narrow AI. It is also called weak ‘AI’. In this stage, the machines and devices only follow a specific pattern. These devices are, to a degree, only exposed to a portion of the data. So, in this category, the electronic device does not possess thinking capabilities similar to that of a human. It is, therefore, a safer option to accomplish any task efficiently without any threat of AI surpassing the human mind.
2. General AI: Also regarded as a ‘powerful AI’. The goal of general artificial intelligence is to broadly replicate human intelligence. The programming would be such, that it would be able to respond to different circumstances and things just like a “thinking person”.
Super AI is another classification that exists as well. It’s frequently compared to a point where artificial intelligence (AI) could also outpace humans.
Due to its powerful computational capabilities, AI can interpret data in a way that is ideally suited for human interaction. The more data, the better the AI would get familiar with the problem statements it is assigned.
How do Artificial Intelligence and Machine Learning work together?
Machine Learning works broadly in four different stages: Problem Identification, Data Processing, Model Building, and Final Feedback learning.
Artificial Intelligence employs a large amount of data. It interprets the data and classifies it to provide an output.
Various machine learning algorithms provide structure to data processing.
Neural networks simulate the perception of the input. AI enforces simulated thinking. Therefore, it has to adapt to the pattern of a situation, thing, place, or any kind of input.
Applications of AI/ML
Here are some common applications of AI/ML:
AI/ML has transformed various healthcare systems for the better. Technology has strengthened and made information safer; from the automation of prescriptions to patient records.
AI detects the potential problem. Subsequently helps in anticipating issues that can be addressed before it’s too late.
The E-commerce industry portals leverage AI/ML to provide relevant recommendations for your likes. Have you noticed how you get the sought-after recommendations for your favorite products that you either wishlisted or looked for all over the internet?
It is the AI algorithms that delve deep down into your tastes and recommend what you may need or want.
AI-based tests and data management systems are promoting efficient processing of education tasks.
Today, educational coachings are developing smart AI exams that assist students in selecting a worthwhile course for further study.
- Smart Apps
The embedded AI technology for behavioral analysis is present in all social media apps, food apps, etc. On your social media app, every search yields a result that is somehow relevant to your search.
Each suggestion on that meal app relates in some manner to your needs.
The algorithm learns about your behavior and possible queries in this way.
Aside from these, AI/ML is crucial for OTT platforms, social media apps, chatbots, fraud protection, and more.
What is Machine Learning?
First, let’s define what Machine Learning is and will mean in this article. The terms artificial intelligence and machine learning are interchangeable. Both, nonetheless, are unique. Like Natural Language Processing, Machine Learning is a component of AI (NLP).
Machine Learning involves the machine comprehending and analyzing a set of data to fulfill real-life tasks.
Let’s take an example. Consider a cat. Now, humans know how a cat looks—its eyes, face, ears, etc. Now, a machine has to learn what a cat looks like. Be trained to learn how a cat looks. It is fed with about a cat’s features; then whenever a cat’s image pops up, it not only recognizes it is a cat, but it will begin to distinguish between the different features that make up a cat.
The goal is to make a machine learn by itself. Therefore, Machine Learning is divided into three categories as follows:
- Supervised ML
As the name suggests, there is training data which acts as a supervisor. In this instance, both the input and the output are pre-defined. New data is inputted based on this setup, to see if the output aligns with the expected result or not. The system is programmed to anticipate an output using tagged data.
- Unsupervised ML
A machine is a clean slate. You may call it a blank mind. Through sensors and input modes, the machine forms clusters (the process of grouping similar pattern data). Now, the machine has no clue about the output. The machine self-learns. With the help of algorithms like K-mean, the device observes similar patterns of clusters and puts them into different categories to produce and output.
2.1. If we were to train machines about men and women.
2.2. This implies, different pictures of men and women would function as the data point.
2.3. The system will be able to forecast the desired outcome based on the user’s general appearance and physical attributes.
- Reinforced ML
The basis of this model is the penalty-reward-based policy. It forms clusters and learns through feedback. As you enter the input, there is a possibility of multiple outputs. Now the machine gets feedback in the form of a reward or a penalty, based on the accuracy.
On the contrary, Reinforced ML can’t be used in every situation as it is a bit time-consuming. When the output is already defined, Supervise ML is preferred.
Future Insights: Artificial Intelligence and Machine Learning
“Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology-enabled service, thing, or application.” as reported by Gartner.
Gartner also reported, “Leading organizations expect to double the number of artificial intelligence (AI) projects in place within the next year, and over 40% of them plan to actually deploy AI solutions by the end of 2020.”.
Human-machine interaction has been transformed in ways never imagined before, thanks to Artificial Intelligence!
It has promoted automation to a completely new level. With their extensive usage in almost every industry today; Artificial Intelligence and Machine Learning are two ever-evolving technologies that both together, and separately have bright futures ahead. From healthcare to entertainment, from transport to farming, these solutions have catered to ease out various processes like never before. Now, every task is possible with a click or even a voice command.
Stay tuned to https://www.rapidinnovation.io/ for more technical dives!