Computer science is a vast field with new concepts that emerge frequently, that too at a rapid pace. AI and neural networks are two such concepts in the field of computer science.
It is true that they are related to each other in some ways. However, they must not be confused as the same thing.
Key Takeaways
- Artificial intelligence (AI) is a broader concept encompassing various techniques and algorithms to create intelligent systems, while neural networks are a specific type of AI inspired by the human brain.
- AI systems can use different approaches, such as rule-based systems or genetic algorithms, while neural networks rely on interconnected layers of nodes to process information.
- Neural networks excel in pattern recognition and complex data analysis, while other AI techniques may be more suitable for problem-solving or decision-making tasks.
AI vs Neural Network
The difference between AI and neural network is that AI or artificial intelligence is an entire branch of computer science that works on studying and creating smart machines that possess their own intelligence, whereas a neural network refers to a system of artificial nodes that are made up in coherence with actual brains of animals for somewhat mimicking their intelligence.
In a narrow sense, AI refers to intelligence that machines possess and demonstrate. They do so by perceiving and assessing their surroundings.
They further take actions based on these inferences to maximise the chances of achieving a certain objective. This concept is largely rooted in artificial neural networks.
A neural network refers to an entire network system that is made up using nodes or artificial neurons. It mimics the way neurons work in the brain of an animal.
In doing so, this neural network can carry out functions such as categorization, classification, pattern recognition, language processing, named entity recognition, and much more. This helps solve a lot of AI problems.
Comparison Table
| Parameters of Comparison | AI | Neural Network |
|---|---|---|
| Meaning | It is a layer of neural networks that smart machines possess. | It is a system of artificial nodes that are used together in coherence with an animal brain. |
| Nature | It refers to machines possessing their own intelligence. | It mimics the intelligence that an animal brain possesses. |
| Dependency | It is dependent on artificial neural networks. | It is not dependent on AI. |
| Applications | It is used in machine learning, machine vision, knowledge reasoning, clinical diagnosis, and much more. | It is used for categorization, classification, pattern recognition, language processing, named entity recognition, and much more. |
| Training | It can be trained very quickly. | It takes a relatively longer duration to train neural networks. |
| Performance | It shows very high performance. | It shows low performance. |
What is AI?
AI, according to a broad definition, is any system that is able to perceive and analyse its environment. Furthermore, it must be able to take actions based on previous inferences.
This should be done to maximise the chances of achieving a particular objective. This technology was founded in 1956, after which it became an academic discipline.
AI is functional in numerous smart machines that possess their own intelligence. The form of technology is present in various web search engines, self-driving cars, recommendation systems, systems that understand human speech, strategic gaming systems and even automated decision-making systems.
This form of intelligence is largely based on artificial neural networks. The cognitive abilities of an animal’s brain are used as a basis for these smart machines to have their own intelligence.
This intelligence can further be applied to numerous tasks. Some of them include machine learning, machine vision, knowledge reasoning, clinical diagnosis, and much more.
AI is one of the most advanced technologies out there right now. Compared to other learning technologies, it is one of the fastest to train.
Moreover, the performance that it displays is the most effective and efficient. However, some various laws and regulations regulate their use across the world.
What is Neural Network?
A neural network is an entire system of artificial neurons that mimic the intelligence of an animal’s brain, including humans. Its theoretical base was first laid out in 1873, after which various studies have been conducted regarding its concept.
The entire mechanism of AI has neural networks at its roots. The technology is made from groups of neurons that are functionally connected to each other. One neuron may be connected to several other neurons which together form an extensive network.
They work in coherence with how a real brain displays its cognitive abilities. This has inspired many designs of cognitive modelling.
Neural networks can be used for a variety of applications. Some of them include pattern recognition, sequence recognition, e-mail spam filtering, data mining, medical diagnosis, strategic game-playing and even decision-making.
By dint of these abilities, this technology has been adopted in numerous machines across the world. However, there are certain limitations of neural networks in comparison with AI.
It takes a far longer duration to train this network for it to be able to perform functions. Moreover, as compared to the former, it is not as efficient in its performance.
However, the network goes through several improvements constantly to make it a top-of-the-line system.
Main Differences Between AI and Neural Network
- AI is a layer of neural networks that smart machines possess, whereas a neural network is a system of artificial nodes that are used together in coherence with an animal brain.
- AI refers to machines possessing their own intelligence, whereas a neural network mimics an animal brain’s intelligence.
- AI depends on artificial neural networks, whereas neural networks do not depend on AI.
- AI is used in machine learning, machine vision, knowledge reasoning, clinical diagnosis, and much more, whereas neural network is used for categorization, classification, pattern recognition, language processing, named entity recognition, and much more.
- AI can be trained quickly, whereas neural networks take a relatively longer duration to train.
- AI shows very high performance, whereas neural network shows low performance.
- https://thejns.org/view/journals/j-neurosurg/113/3/article-p585.xml
- https://pubs.rsna.org/doi/abs/10.1148/radiol.2017170236
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