Deep Learning Technology Inside Artificial Intelligence

Artificial intelligence (AI) is a hot topic, especially in the technology industry. AI is touted to be the future of technology, with many companies striving to develop AI that can think like humans. Let's delve deeper into AI in this material:

deep learning

Source: commsult Digital Assets

What is AI or Artificial Intelligence?

So, what exactly is AI? AI, or artificial intelligence, is a branch of computer science that explores machines with artificial intelligence capable of solving problems that require human intelligence.

How does AI work?

AI works by combining a large amount of data with algorithms created to learn patterns and process the data. In each process, they learn and test their own processes, acquiring new knowledge from these processes. 

AI is essentially a computer, so it will never rest unless the computer is turned off. Even with data in the billions, they can process it and learn in a short time.

The main goal of creating AI is to mimic human behavior, make more rational decisions without emotions, and solve more complex problems. To achieve this goal, AI uses many techniques and processes.

AI vs Algorithm, Is it the Same?

Many people might still think that AI and algorithms are the same thing, but that's incorrect. While AI and algorithms are indeed interconnected, these two technologies are not the same.

AI is a technology trained and designed to mimic human thinking processes, enabling it to make increasingly accurate decisions as it's trained with data. On the other hand, an algorithm is a set of rules created to perform a task or provide the best possible outcome. Essentially, algorithms are mathematical calculations and cannot learn autonomously.

Concepts in AI

1. Machine Learning: A feature of AI that allows programs to learn independently and improve results through experience.

2. Deep Learning: One way AI learns and improves learning outcomes by processing data.

3. Neural Network: A process of analyzing a set of data repeatedly to find meaning in undefined data.

4. Cognitive Computing: A component of AI systems that mimics the interaction between humans and machines.

5. Natural Language Processing: A part of AI systems that recognizes, analyzes, interprets, and truly understands human language, both written and spoken.

Understanding Deep Learning in Depth

Earlier, we briefly looked at what deep learning is; now, let's delve deeper into deep learning.

Deep learning is a part of machine learning whose algorithms resemble the structure of the human brain, essentially learning like humans. In this era, deep learning is already used in many things such as YouTube video recommendations, virtual assistants, chatbots, and translation systems.

So, When We Can Use AI and When to Use Algorithm?

AI and algorithms each have their own strengths and weaknesses, and they play very different roles. As explained earlier, AI is trained to think like a human, while algorithms are mathematical. Therefore, AI can be used in situations requiring deep thinking, such as using AI to make decisions on the most suitable job candidates based on available data.

On the other hand, algorithms can be employed when dealing with problems that can be solved mathematically, such as calculating distances between locations. However, there's an interesting aspect: algorithms are also used by AI. This relates to the concept of neurons or interconnected thinking similar to human nerves. In this case, algorithms provide a set of rules to AI, enabling it to perform activities that will serve as the basis for its learning process.

Algorithms in Deep Learning

1. Convolutional Neural Network (CNN)

CNN in deep learning can be distinguished from other networks by its superior ability to input images, speech, and audio signals. CNN consists of three layers: 

1. Convolutional layer 

2. Pooling layer

3. Fully-connected (FC) layer.

2.Recurrent Neural Network (RNN)

RNN is a type of artificial neural network based on sequential or time-series data. The algorithm is usually used for ordinal or temporal problems, and RNN is applied in language translation, image captioning, or speech recognition.

3. Self Organizing Maps (SOM)

SOM is a type of artificial neural network that operates with unsupervised learning. This network can define low dimensions (2 dimensions), commonly referred to as a map.

Benefits of Deep Learning

Here are the benefits what you can get if you used deep learning on your AI:

1. Maximum utilization of unstructured data

2. Elimination of dependence on feature engineering

3. Ability to provide high-quality results

4. Reduction of unforeseen costs

5. Elimination of data labeling

So, do you now understand deep learning? if you wanted to develop AI with deep learning, commsult Indonesia can help you achieve it.

consult with us Now!

Blog by Avryanz Azell, 22 January 2024

Other Blogs