Introduction
Artificial Intelligence (AI), once viewed as a distant future technology, was once a distant dream. AI is spreading beyond the research labs and into the real world. AI is now everywhere. It's in your factory, at school, banks, hospitals, and even on your smartphone. They are the eyes and ears of self-driving cars, Alexa and Siri's voice, and the scientists behind weather forecasting and robotic-assisted surgical procedures. AI has become an integral part of modern society. AI has been a part of many IT technologies almost immediately over the past few years.
Machine learning, which allows computers to learn directly from data without any programming needed, emerged later as an effective approach to AI. This isn't all. Deep learning is a computational algorithm that provides the foundation for programming methods. Deep learning was developed in the 1960s by a biologically-motivated neural network field. However, it was first coined a decade ago. Recent years have seen neural networks resurface with new names and meanings. However, they are used in different contexts in the current wave of deep learning and artificial intelligence.
Computer science is a broad field that sees new concepts emerge. Computer science is home to two concepts: AI and neural networks. They are, in some ways, related. They should not be mistaken for one another.
AI vs. Neural Network
The major difference between AI/neural network and AI is that AI, or artificial intelligence, is a branch of computer science that studies and creates smart machines with their intelligence. A neural network refers to a network of artificial nodes that are in harmony with the actual brains of animals and can mimic their intelligence.
AI is a term that refers to the intelligence that machines have and can demonstrate. They perceive and assess their environment. These inferences are used to guide them in taking actions that increase the likelihood of reaching a particular goal. This idea is heavily rooted in an artificial neural network.
A neural network is an entire network that uses artificial neurons or nodes. It works in the same way as neurons do in the brains of animals. This neural network can perform functions such as classification, categorization and pattern recognition, language processing, named entity identification, and many more. This solves many AI problems.
Difference between AI and Neural Network in Tabular Form
| Parameters for Comparison | AI | Neural Network |
| Significance | It is the layer of neural networks smart machines have. | It's a network of artificial nodes, which are used in conjunction with an animal brain. |
| Nature | It refers to machines that have their own intelligence. | It imitates intelligence found in an animal's brain. |
| Dependency | Artificial neural networks are required for this to happen. | It isn't dependent on AI. |
| Applications | It's used in machine learning and machine vision, knowledge reasoning, clinical diagnoses, and many other areas. | It can be used for categorization and classification, pattern recognition, language process, named entity recognition, as well as other purposes. |
| Training | It is very easy to train. | Training neural networks take longer. |
| Performance | It has a very high level of performance. | It has a low performance. |
The table shows the tabular difference between ai and neural network. Let’s know more about the topic.
What is AI?
In a broad sense, AI is any system capable of recognizing and analyzing its environment. It must also be able to draw inferences from the past to make decisions. This must be done to maximize the chance of reaching a specific objective. This technology was established in 1956 and later became an academic discipline.
Many smart machines, with their intelligence, can use AI. This technology can be found in many web search engines, self-driving cars, recommendation systems, and systems that understand human speech. It can also be used to create automated decision-making systems.
Artificial neural networks are the basis of this form of intelligence. These smart machines can use the cognitive abilities of animals to create their intelligence. This intelligence can be used for many tasks. Machine learning, machine vision, and knowledge reasoning are just a few of the many applications.
AI is the latest technology. It is also one of the easiest to train compared to other learning technology. It is also the most efficient and effective in terms of performance. Many laws and regulations govern their use around the globe.
Artificial Intelligence - Some more Details
Artificial Intelligence, also known as machine intelligence, is a term that refers to machines capable of mimicking human cognitive skills. Artificial Intelligence (AI) is the idea of creating intelligent machines that are smarter than or as smart as humans. Although this idea dates back centuries, it is now mainstream due to the advent of digital computers. AI is a branch of computer science that focuses on creating computer programs that can perform the same tasks as humans. It doesn't matter if these programs think like humans. AI can come in many sizes and algorithms. AI is now everywhere: from factories and schools to hospitals and banks, to your TV and toothbrush, to your smartphone's chip, to the cars you drive, to Siri and Alexa, and everything in between.
Different types of Artificial Intelligence
Reactive Machines: These systems only react. These systems don’t have memories and don’t use past experiences to make new decisions.
Limited Memory: These systems refer to the past and can add information over time. The information referenced is temporary.
Theory of Mind: Systems that can understand emotions and their impact on decision-making. They learn to adapt their behavior accordingly.
Self-awareness: These systems were designed to help people become more aware of their inner world. They can predict the feelings of others and understand their internal state and their own, and then act accordingly.
Artificial Intelligence Applications
- Google Translate is a machine translation service.
- Google Waymo is a self-driving vehicle
- with Artificial Intelligence Robots like Sophia and Aibo
- Speech Recognition apps like OK Google or Apple's Siri
We have covered the basics of artificial Intelligence. Let's now move on to machine learning to see how it works.
What is a Neural Network?
A neural network is a system of artificial neurons that imitate the intelligence of the brain of any animal, including humans. After its theoretical foundation was laid in 1873, many studies were conducted on the concept. All AI is based on neural networks.
Technology is composed of groups of neurons that function together. A neuron can be connected to many neurons, making a large network. These neurons work together to mimic how a brain's cognitive abilities are displayed. It has inspired many cognitive modeling designs.
There are many uses for neural networks. These include pattern recognition, sequence detection, e-mail filtering, data mining, and medical diagnosis. They can also be used for strategic game-playing, decision-making, and even medical diagnosis. This technology is used in many machines around the globe because of its abilities.
Neural networks have some limitations in comparison to AI. This network takes longer to train to become capable of performing functions. It is also less efficient than the former. The network is constantly improving to become a top-of-the-line system.
Neural Network - More Details
"Neural networks" is a term that refers to artificial neurons or nodes which are vaguely inspired by the biological neural network that makes up the animal brain. In a sense, it was the foundation for much of modern artificial intelligence. Studies show that AI's current implications and applications result from the unique characteristics of neural networks (e.g., machine learning, deep learning). Neural networks are a well-rounded paradigm rooted in many disciplines, including engineering, computer science, psychology, and information science. Neural networks are a system of interconnected nodes whose functionality is loosely inspired by animal neurons. Neural networks are widely used today in many areas, including customer research, problem-solving, and data validation. They also help with sales forecasting and risk management.
How does machine learning work?
Machine learning uses large amounts of structured and unstructured data to learn from them and predict the future. Multiple algorithms and techniques are used to help it learn from the data. Here is a diagram showing how a machine learns data.
Different types of machine learning
There are three main categories of machine learning algorithms:
- Supervised Learning
Supervised learning uses data that has been labeled, meaning you already know the target variable. This method of learning allows systems to predict future outcomes using past data. It must have at least one input and one output variable to train the model.
Here is an example of a supervised learning algorithm. The algorithm is trained with labeled data from cats and dogs. The model is trained to predict whether the new image of a cat or a dog will be.
One example of supervised learning is logistic regression, linear regression, support vector machines, and Naive Bayes.
- Unsupervised Learning
Unsupervised learning algorithms use unlabeled data to find patterns in the data. These systems can identify hidden features in the input data. The patterns and similarities are more obvious when the data becomes more easily readable.
Here is an example unsupervised learning method that trains a model with unlabeled data. The data is made up of different vehicles. The model's purpose is to categorize each type of vehicle.
Unsupervised learning can be described as k-means clustering and hierarchical clustering.
- Reinforcement Learning
Reinforcement learning is a method that trains an agent to perform a task in an uncertain environment. The environment provides reinforcement learning by providing observations and rewards to the agent. In return, the agent sends actions back to the environment.
Q-learning and Deep Q–learning Neural Networks are two examples of reinforcement learning algorithms.
Machine Learning Applications
- Forecasting sales for different products
- Bank fraud analysis
- Recommendations for products
- Stock price prediction
After exploring machine learning and its uses, let us now turn our attention toward deep learning. What is it, and how is it different from AI or machine learning?
Differences Between AI and Neural Network in Points
Definition
Artificial Intelligence, also known as AI, is a branch of computer science that focuses on creating intelligent machines that possess a type of intelligence. This is different from the natural intelligence that humans have. Artificial Intelligence refers to machines capable of mimicking human cognitive skills. Neural networks are made up of artificial neurons or nodes that are vaguely inspired by the biological neural networks found in the animal brain. Neural networks are a collection of interconnected nodes whose functionality is loosely inspired by animal neurons.
Goal
AI works in a combination of strong AI and weak AI. Weak AI uses sophisticated algorithms to solve certain problems within a restricted set of functions. Strong AI supports the belief that machines can truly develop human consciousness equal to humans. Neural networks are a well-rounded paradigm rooted in many disciplines, including psychology, engineering, computer science, and information science. Neural networks are designed to help you recognize patterns in your data and make decisions.
Applications
AI is now a common part of modern society. AI is used in various areas, including self-driving cars, Siri, and Alexa, and forecasting and clinical diagnosis. AI is used across many industries, including entertainment, education, health, and transport. Neural networks use the brain to create algorithms that recognize complex patterns. Artificial neural networks are used for text classification and categorization and named entity recognition (NER), paraphrase detection, and pattern recognition. They also help detect fraud and natural language processing.
Main differences between AI and Neural Network in points
The following are the difference between AI and Neural Network:
- Artificial intelligence is a layer of neural networks that smart machines have. In contrast, a neural network refers to a system of artificial nodes that work together in harmony with an animal brain.
- Artificial intelligence refers to machines that have their intelligence, while a neural network is similar to an animal's brain.
- Artificial neural networks are dependent on AI, but the neural network is independent of AI.
- AI is used in machine vision, machine learning, knowledge reasoning, and clinical diagnosis. A neural network is used for categorization and classification, pattern recognition, and language processing. Named entity recognition is another use of AI.
- While AI can be trained quickly, neural networks take longer to learn.
- Artificial intelligence shows high performance, while neural networks show low performance.
Conclusion
Artificial intelligence and neural networks can be somewhat related. AI was largely inspired and used some functionality of neural networks to create its design. There are many differences between them. AI is more advanced, simpler to train, and has higher performance.
Their meaning is that AI is the intelligence smart machines have, and it is their own. On the other hand, a neural network is designed to mimic how an animal's brain might function. It is not its intelligence.
References
- https://thejns.org/view/journals/j-neurosurg/113/3/article-p585.xml
- https://pubs.rsna.org/doi/abs/10.1148/radiol.2017170236
