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What Is Artificial Intelligence (AI)? Learning From The Big Names

Alsyd Eabidin
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 We've all had the same conversation. With this new and emerging technology, we're trying to understand what it is and how AI can help us. What Is Artificial Intelligence (AI)? Learning From The Big Names is a series of blog posts that answer your question by explaining the core principles of AI, the new ideas and concepts, how they relate to each other, and how they work.

What Is Artificial Intelligence (AI)? Learning From The Big Names


What Is Artificial Intelligence (AI)?


To answer that, it's important to understand what AI isn't. Contrary to popular belief, AI does not necessarily refer to machines becoming sentient or achieving human-like intelligence. Rather, AI is an attempt to create machines that are able to react and adapt to new situations like humans do.


And in order for a machine to be able to react and adapt as a human would, it needs to be able to "learn" in some way. There are two approaches here: either we can program the machine with some rules and logic so that it can make decisions based on those rules, or we can expose the machine to lots of examples so it can learn how humans make decisions in certain situations.


The first approach is called "rule-based" AI, and the second approach is called "machine learning." In this article, I'm going to focus on rule-based AI.


What is a Neural Network?



Artificial intelligence (AI) is the idea of a computer, or other device, being able to perform tasks that are considered to require human intelligence. AI is sometimes thought of as a machine being able to think for itself, but in reality, a machine would need to be programmed to think for itself before it could actually do so. The programming would be done with artificial neural networks.


A neural network is a system of interconnected nodes (or neurons) that can transmit data and carry out computations. The nodes are usually organized into layers; an input layer, one or more hidden layers, and an output layer. Neural networks are based on the structure of the human brain, where each node receives input from other nearby nodes and then outputs its own data. This data is passed on to other nearby nodes, until it reaches its destination node or nodes. Artificial neural networks can be used for many different purposes, including image processing and speech recognition.


Machine learning is simply using a computer program to learn from experience, without being explicitly programmed. For example, if you have a program that analyzes images, you could use machine learning to teach it what an image of a tree looks like by showing it hundreds of trees and having it make decisions about whether each image.


What is an Artificial Neural Network?


An artificial neural network (ANN) is a type of machine learning that is inspired by the structure and functionalities of biological neural networks. It simulates the brain's information processing, which consists of neurons, connected by synapses. It is a powerful computation tool that allows computers to learn from observational data without relying on any domain knowledge or rules.


Artificial Intelligence (AI) is a branch of computer science that builds systems capable of performing tasks that normally require human intelligence. AI research specializes in several areas, including machine learning, computer vision, and natural language processing.


Read also: What's in the Artificial Neural Network Book

What is Machine Learning?


Machine Learning is the "branch" of AI that gives computers a way to learn. It provides algorithms and statistical models that allow machines to solve problems by learning from past experiences. There are multiple types of machine learning functions, but they are generally divided into two groups: supervised and unsupervised learning.


Supervised learning is when your computer learns from example inputs you provide it. The algorithm analyzes the input data and makes predictions about the output data.


Unsupervised learning allows your computer to find patterns in a dataset without labels or targets.


What is an Artificial Neural Network (ANN)?


An artificial neural network (ANN) is a computing system inspired by the biological neural networks that constitute animal brains. The key element of this system is artificial neurons, which loosely model their biological counterparts and are connected in various ways to achieve certain functionalities. ANNs are the building blocks for deep learning (a branch of machine learning).


Common Neural Network Architectures



I'm glad you're interested in what makes something AI and what doesn't!


AI is a broad term, but we can narrow it down by defining intelligence as the ability to achieve goals in a wide range of complex environments. So, we can say that an AI system tries to accomplish these goals in a way that's similar to how humans do: by machine learning.


Machine learning is the process of training computer systems to make accurate predictions based on existing data.


In order for a machine learning system to learn from data, it needs to be able to change its own structure. These changes are made with artificial neural networks.


An artificial neural network (ANN) is a set of algorithms modeled after the human brain that have been designed to recognize patterns. They learn through a process called deep learning, which usually involves feeding an ANN lots and lots of data. ANNs are used in many AI applications today, such as image recognition and language processing.


What is Machine Learning?


Human beings are naturally curious and empathetic, so it makes sense that you would be wondering about [topic].


The idea of artificial intelligence (AI) has been around for a long time—probably since the beginning of human civilization. In modern times, scientists define AI as a machine's ability to simulate human intelligence by learning from observations or directions.


Machine learning is a subset of AI. It gives machines the ability to learn without being programmed explicitly or told what to do. Machine learning uses artificial neural networks to learn from data. Artificial neural networks are modeled after the human brain's neural structure and function.


What is Deep Learning?


Deep learning is a branch of machine learning, which is a branch of artificial intelligence.


Artificial intelligence (AI) refers to the work that computers do to pass as human. It's been around since the 1950s and has been strongly associated with science fiction, thanks to films such as Blade Runner. 


AI develops from computer science, which is the discipline that studies algorithms to help computers solve problems. Machine learning (ML) and deep learning (DL) are both subsets of AI.


Machine learning focuses on computers using algorithms to interpret data and make predictions based on their findings. It's been used in things like weather forecasting, medical diagnosis, and image recognition software. Deep learning is a specific subset of machine learning that involves algorithms called artificial neural networks that enable computers to learn through observation and experience, similar to the way children learn by observing their surroundings and seeing how they can interact with them. 


These algorithms can be trained on data sets so they can learn to recognize patterns and make predictions or decisions about new information they encounter in the future.


A Trip Down Memory Lane


Imagine an abandoned old factory from the 19th century, with peeling paint and a musty decay that permeates the air. Now imagine opening a door to a room inside. The room is dark. 


You flick on the light switch, and you see nothing but shelves lining every wall, filled with books. These are not ordinary books—each one has a title like "A Complete Introduction to Artificial Intelligence". There are so many that you can't even see the end of them. You reach for one, open it up, and it starts reading itself aloud. You don't understand anything coming out of its pages—the book is speaking in tongues!


But then you notice something: the book has lots of pictures in it. And at first they make absolutely no sense, but then slowly you begin to see recognizable patterns in the diagrams: little shapes that look like houses; some squiggles that look kind of like a river running through them; and lots of straight lines connecting everything together in an intricate web.


After staring for what feels like hours at the pictures in this book, you realize something: maybe this isn't an ordinary book after all. Maybe these pictures aren't just drawings—maybe they're instructions for how to build.


This article will help you understand the fundamentals of what AI is and how it works


These days, we hear a lot about artificial intelligence. But what is AI? What isn't AI? How do we define it?


To answer these questions, it's a good idea to start with some of the foundational ideas and principles of artificial intelligence. This will give you an introduction to the field and show you how it aligns with your own needs.


Artificial intelligence is a field that studies how to get computers to perform tasks that require human-like intelligence. Within this field, there are several subfields, including machine learning and artificial neural networks. Machine learning uses data to create algorithms that can make predictions or decisions without human intervention. Neural networks are a subset of machine learning where computers learn from examples rather than being programmed by humans.


The fundamental principle of AI is that computers are capable of performing tasks that require human-like intelligence. This means they can solve problems and make decisions without being explicitly programmed by people; instead, they learn from examples given by humans or other machines in order to develop their own strategies for solving problems efficiently and effectively.


It is easy to reach the end of a long article, especially one about something so speculative as artificial general intelligence, and feel that you have learned nothing. I hope this list will help you understand enough of the concepts involved to avoid feeling like that. I hope that it will help to convey the excitement and curiosity that drew me in originally. The ideas of artificial intelligence and machine learning are grand and fascinating. They are not yet fully formed but are being born before our very eyes. This is one of the most exciting things happening in our time, as machines begin to approach human cognition, and I hope this post has helped you understand why.


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