According to John McCarthy, a researcher at Stanford, Artificial Intelligence (AI) is the technology used to build robots, computers, or machines that exhibit intelligence similar to humans. These machines are self-sufficient and involve sophisticated algorithms.
Artificial Intelligence can be classified into two kinds.
Table of Contents
1. Vertical Artificial Intelligence:
Vertical AI emphasizes developing algorithms that perform one specific job. These tasks include repetitive task automation. Computers or bots developed using vertical AI can serve you in just one way. For instance, you cannot use an algorithm designed for planning meetings to order food online.
2. Horizontal AI:
Horizontal AI, on the other hand, is designed to perform different tasks across different businesses. Siri, Google Assistant, and Alexa are some examples of Horizontal AI. These services are often used in the form of questions and answers. For example, they can respond with equal skill to questions like “What is the temperature in New Delhi?” or instructions like “Call Alex.” They do multiple tasks rather than focus on a single job.
What is Machine Learning?
Machine learning (ML) is a branch of artificial intelligence. In machine learning, computers are taught to learn a program using past experiences.
ML is divided into three parts based on the three types of framing concepts:
1. Supervised Learning:
In supervised learning, a machine gets a certain dataset for training, and this data is then analyzed using ‘supervised learning algorithms’. This means that each input data is mapped to a specific output data. The machine is trained with human intervention till it learns the right answer to each input question. A classic example of the supervised learning method is warning calls and push notifications for fraudulent credit card payments.
2. Unsupervised Learning:
In the unsupervised learning method, the machine is provided with input data and output information, but they aren’t mapped to each other. Thus, the machine needs to identify the relationship between these two data without external interference. An example use case of unsupervised machine learning is when an e-commerce website throws up suggestions of products you might like based on your previous purchases.
3. Reinforcement Learning:
In reinforcement learning, the machine has to solve a problem by deciding on the best course of action. The machine does not get any human help and has to use previous experiences. Reinforcement learning states to focusing more on learning issues than learning techniques.
You can learn these methods in any artificial intelligence courses offered by Great Learning
Applications of AI and ML in the real world
1. Autonomous Vehicles:
We’ve all heard that self-driving cars are the way of the future in the automotive business. Machine learning, artificial intelligence, and deep learning play a major role in making self-driving possible and efficient. The following are some of the most often used machine learning algorithms in autonomous driving :
● Scale-invariant feature transform (SIFT)
● AdaBoost
● TextonBoost
● You only look once (YOLO)
2. Machine Learning-based Stock Market Signals:
You read that correctly. Machine learning applications are also often used to get indications that assist in making sensible stock market trading selections. Before introducing ever-evolving machine learning algorithms in financial markets, stock market price prediction was a difficult undertaking. Now, traders can make consistent choices, thanks to AI. Machine learning frameworkscurrently being developed to discover social sentiment scores, assess technical indicators, and provide stock traders with actionable results.
3. Professional Virtual Assistants:
VPA is one of the most well-known machine learning applications. Machines are growing smarter in adopting human habits as the use of smart gadgets increases. Have you told Google Assistant that you want to be woken up at 6 a.m.? Have you asked Siri to provide you with directions to your favourite restaurant? Machine learning can be seen in all of these situations. Machine learning algorithms are at the heart of everything from revolving on smart appliances to ordering an Uber pickup.
4. Recognition of images:
It is relatively easy for people to recognize any image. Imagine an automobile, for example. I’m sure you can recall the picture of a car, its brand, and even the colour. However, pictures are merely a collection of numerical data for a computer. So image processing algorithms search for patterns in digital images (videos, graphics, or still images). Computers identify patterns by means of algorithms, and machine learning algorithms may recognize any picture.
5. Recommendation of a Product:
When you purchase a product on eCommerce sites like Amazon and Flipkart, you will be given choices with the tag ‘customers who bought this product also bought…’ or ‘users also bought this with this product…’ All of this is the consequence of powerful machine learning training, in which the system learns specific user habits and recommends additional items for purchase.
6. Chatbots for Online Support:
When you use any program that involves customer support, such as a banking app, you will see an option that says, “talk with us.” When you choose the option, you interact with chatbots that use machine learning concepts. These bots can distinguish between a wide range of inquiries and, as a result, provide speedy responses that address the query by extracting the appropriate input.
7. Google Translate is a tool that allows you to translate the text:
Travelling to a new area is always exciting, but the only riddle is figuring out how to communicate in the local language. Google has released an app that allows for quick translation of any language to address this issue.
Conclusion
According to India’s Future Scope of Artificial Intelligence 2021, the country has expected to have more than 8 billion AI-powered voice assistants by 2023. AI businesses worldwide will be worth more than 55 billion dollars by 2025.
Enrolling in a Machine learning course by Great Learning is the best thing you can do if you want to achieve this in-demand skill. Each Great Learning course includes a hands-on lab experience and in-depth course evaluations to help you learn and apply the most crucial skills in Artificial Intelligence.
