Application of Artificial Intelligence Technology

In the development of the industrial era, from the steam industry to the age of Artificial Intelligence (AI), this certainly has a strong role in technological development. We need to know that Artificial Intelligence, Machine Learning, and Deep Learning have their respective roles. Many still don’t realize that the three have different parts and focuses. Artificial Intelligence, often referred to as artificial intelligence, is a field of science that houses Machine Learning and Deep Learning. AI consists of several techniques that can make computers mimic human intelligence by using logic, if-then rules, and machine learning, which includes deep understanding. Meanwhile, Machine Learning is a part of AI with statistical techniques that can take advantage of machines to improve in all tasks with experience or knowledge (including deep learning techniques in it). Deep Learning is part of machine learning whose algorithms can help software train itself in completing tasks such as speech and image recognition by exposing several layers of the neural network to several amounts of data.
AI has several differences from conventional programmings, such as:

1. AI represents and manipulates symbols, while conventional programming uses algorithms.
2. AI can tell the computer about a problem, while conventional programming orders the computer to solve the problem.
   3.AI can provide computer inference capabilities while conventional programming provides data to computers and programs.
   We also know that AI technology, based on its scope of work, can be divided into two, namely:
3. Artificial Narrow Intelligence (ANI) – weak AI. Here, machines can produce a task very well and even better than humans.
4. Artificial General Intelligence (AGI) – strong AI. Appliances can be made to think and function as humans do.
   The division of this type of AI Technology can also be differentiated based on the problem domain into branches of the problem domain being solved. For example, Natural Language Processing (input/output in the form of language), Speech Processing (input/output in the format of sound signals), and Image Processing/Computer Vision (input/output in the form of images).
   In addition, AI technology is also differentiated based on how it works, namely:
5. Problem-Solving Agent. AI technologies can bring analytics to Industry, improve the performance of existing analytics technologies such as computer vision and time series analysis, leverage existing capabilities and make them better and provide more vision, understanding, memory, and many others. It can be solved using search management, such as Blind Search, Informed Search, and Local Search (for optimization problems).
6. Knowledge-based agents. Agents seek solutions based on their knowledge, where knowledge can come from experts or information sources. It can also be found in knowledge learned (learning agent) from data.

Read also : What is Big Data?

In Knowledge-based Agent can represent techniques:

1. Production Rules. Rules can be a pair of conditions and actions. In addition, it can be forward and backward chaining.
2. Logical Representations. It can be proportional logic, first-order logic, default logic, and others.
3. Semantic Networks. Knowledge as a form of network graphics.
4. Frame Representation. Such a structure consists of a collection of attributes and values ​​to describe an entity in the world.

From year to year, the development of AI technology is developing rapidly. Now the algorithm has developed into deep learning using computing devices like GPUs, TPUs, and clouds. In addition, data availability from IoT devices and social media continues to experience a significant increase.

Monitoring
Even this AI technology can be utilized in monitoring, such as when the COVID-19 pandemic hit. AI technology can play a role in finding out which people are wearing masks or not by checking through videos connected to machines that have carried out data training. In addition, it can be used to monitor the distance between one person and another.

Verification and Identification
Here AI technology can be used to verify and identify data based on personal data more quickly, accurately, and safely and can also be applied in various applications such as banking, health insurance, and other industries. Apart from utilizing verification data from identity cards, it can also be from voice or individual facial images.

Document Digitization
To support paperless use, digitization, and security from the past, present, and future. This is used to reduce clerical work, be faster and more accurate when inputting data, reduce operational costs, automate, improve user experience, and save time. This process can be seen on electronic IDs, passports, credit cards, etc.

Read also : What is Big Data Analytics (BDA)?

Meeting Analytics
Now when holding a meeting or meeting, you can easily take minutes and analyze during meetings, speeches, discussions, and others in real-time from the sound of a microphone or recording. This product utilizes Automatic Speech Recognition technology, Speaker dialysis with voice biometrics, and Text Analytics.

Currently, there are still frequent challenges in the development of AI, such as:

1. Regulation. Ethical regulations and the use of AI must be more accountable.
2. Privacy. This is related to the use of data used for building AI models.
3. Lack of explanation of how AI models arrive at certain decisions or conclusions regarding accountability and trust.
4. Availability of data to determine the extent to which the data used is representative and unbiased.
5. Lack of talent in developing.

It may be helpfull!!