AI Details: Classification, Types, How AI Works and Examples

what is Artificial Intelligence ?

Technological progress has brought many changes in human life. This brings a good change in the future in the industrial sector, one of which is AI.

Artificial Intelligence operates with an algorithm that allows it to analyze large amounts of data, process data quickly and iteratively, and learn data patterns automatically . Check out the things below.

What is Artificial Intelligence ?

AI is computer system (machine) that has human-like intelligence.

In this case, AI is capable of learning to acquiring information and rules for using information, reasoning using rules to reach conclusions, and self-correcting independently. In short, it can imitate human thought patterns and can ease the work like a support system.

AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). 

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of Artificial Intelligence, a phenomenon known as the AI effect.

For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.

Artificial Intelligence Classification

AI is classified into 2, weak AI and strong AI.

Weak AI

Weak AI is a technology that is designed and used to do certain jobs. For example, virtual personal assistants such as Apple Siri, Amazon Alexa, Google Assistant and so on.

Strong AI

Strong AI, also known as generalized artificial intelligence, is a system with general human cognitive abilities so that when presented with an unknown task, it has sufficient intelligence to find a solution.

Examples of applying this category are recommendation systems in eCommerce, social media, and other examples.

Where, the algorithm is able to provide search results requests from users when accessing applications or websites on the internet.

Types of AI

1. Neural Network
what is neutral network on AI?

Neural Network is a type of system that is organized into a layer that is interconnected with each other through simulation. The input here is the top layer which has the same function as the sensor.

And at least, there are two or more systems in a larger set of systems arranged hierarchically. This layer will send and classify information through the connection.

The development of Neural Network science has been around since 1943 when Warren McCulloch and Walter Pitts introduced the first computational neural network model.

They combine several simple processing units together which can provide an overall increase in computing power.

2. Neural
Definition of neural AI

Neural is a type of Artificial Intelligence that works based on artificial neurons and their connections. It known as the most popular type of AI among computer researchers in the late 80’s.

Neural AI is able to present knowledge to artificial neurons and their connections like a reconstructed brain.

3. Symbol-Manipulating AI
Definition of Symbol Manipulating on AI

The last type, is a system that works with abstract symbols. Where, the connections formed are abstract and the conclusions are logical.

Symbol manipulating is a type of AI that works with abstract symbols. So, the information embedded in this type of AI is hierarchical or processed from the top level.

How AI works

AI works

The way artificial intelligence works is by combining a fairly large amount of data, with a process that is fairly fast, iterative and has an intelligent algorithm.

This will allow a software to learn automatically on a pattern/feature in the data.

AI needs a system that is support by algorithm . This algorithm has several types, namely deep learning, machine learning, and other predefined instructions. The most common algorithm used in AI is machine learning.

This machine learning will enter data into the computer system so that the AI ​​can work according to its task. Using statistical techniques, machine learning drives it to learn and cuts down on lengthy coding processes.

Reporting from Brookings, artificial intelligent programming algorithms require large and robust data for computers to distinguish useful patterns.

With so much data and complex algorithms, machines seem to be able to think for themselves, make decisions, learn, and adapt.

Examples of Artificial Intelligence Applications

1. Health Services and Banking

In the medical field, artificial intelligence also plays a role in providing personalized medicine and X-ray readings. Personal assistants can also act as reminders to always take medication regularly, and exercise regularly.

2. Robotics field

And the last example is the development in the robot industry. Of course, the development of robots is more devoted to helping human work to be faster and optimal.

3. Virtual Assistant

There are many examples of platforms or hardware that provide this virtual assistant-based technology, such as Google Assistant, Amazon Alexa, Siri, and others.

The task of this virtual assistant is to be able to record any information you need, as well as provide information related to the time of your event.

4. Maximizing Smartphone Camera

However, the presence of Artificial Intelligecence technology on phones can improve the ability of the phone lens to take pictures, record, detect photo objects, and maximize a number of settings so that the capture looks quality.

5. E-Commerce Recommendations

When shopping through e-commerce, you may come across recommended products. These recommendations are the result of an AI process obtained through data on products that you have purchased before.

6. Facebook Deepface

Developed by the giant company Facebook which has a feature to detect and recognize faces in photo posts. With this technology, you don’t have to bother marking photos manually.

It can recognize faces based on the data that has been obtained, which comes from recommendations or suggestions when you approve a photo that has been successfully tagged.

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