Page Content
Artificial
Intelligence
About
There is still no widely accepted unified definition of artificial intelligence, despite the term's introduction in 1956 and the recent spread of its technologies. This is because it is difficult to define what human intelligence is, in addition to the various viewpoints that can be used to describe artificial intelligence.
Definition
Many theoretical definitions of artificial intelligence center on a machine's capacity to mimic human behavior or carry out tasks that call for intelligence, but given the majority of current applications, artificial intelligence can be described as: Systems that employ methods that can gather data and use it to predict, suggest, or make decisions with varying degrees of autonomy and select the best course of action to accomplish particular objectives.
Importance
AI is one of the most important modern technologies that contribute significantly to rapid technical development and increase opportunities for innovation and growth in various fields.AI plays an important role in raising quality, increasing capabilities, business efficiency and improving productivity. Despite the widespread use of AI technologies and the frequent exaggeration of their capabilities, there is still a lot of uncertainty surrounding these technologies, which could lead to unrealistic expectations. This makes it difficult for many decision-makers or executives in the public and private sectors to comprehend artificial intelligence, its technologies, and the reality of its capabilities.
The following infographic outlines the key milestones in AI’s development from its conception in early 1940 to 2020, highlighting major launches and releases throughout its evolution.
Principles and Controls of AI Ethics
Given the rapid growth of practices and technologies in the field of AI and the diversity seen in the use of various applications in many fields, which has an effective role in accelerating the pace of decision-making and its optimal use, the principles and controls of AI ethics contribute to the application of ethics during the stages of the development lifecycle of AI systems. These principles also help support initiatives of research, development and innovation in the Kingdom, which is reflected in the quality of services provided by the Kingdom to individuals to ensure the responsible use of AI technologies.
Generative Al Guideline
There are two versions of this guideline. The first version directed toward government employees. The second version is for the public. Both versions provide guidelines regarding adoption and use of generative artificial intelligence systems. It also include examples based on common scenarios that entities may address. It also highlights the challenges and considerations associated with the use of generative artificial intelligence, proposes principles for responsible use, and presents recommended practices
For more, see the Generative Al Guidelines:
Generative Al Guideline for Government
Generative Al Guideline for Public
AI Adoption Framework
The AI adoption framework is a guiding reference that provides a comprehensive approach for AI adoption across all sectors. It aims to establish directions, guidelines, and identify key steps in according with best practices. The AI adoption framework helps ensure achievement of desired goals through the optimal and responsible use of AI..
For more, see the AI Adoption Framework:
History
The first proposals for an artificial neuron model appeared at the beginning of the 1940s, which is when artificial intelligence first emerged. When British scientist Alan Turing questioned whether a machine could think, the idea of artificial intelligence started taking off in the early 1950s. Since then, Al has experienced waves of growth and decline, known as "AI Winter," before it gained the widespread adoption that we are seeing today in many industries. The most important advancements in the development of AI capabilities are depicted in Figure 1 as a timeline.
The following infographic illustrates the evolutionary stages that culminated in the emergence of AI, machine learning, and deep learning.
AI Technologies
A number of technologies fall within the AI field, the most prominent of which are at present:
01
Machine Learning
- Supervised Learning: Learning the relationship between inputs and outputs using a user-categorized dataset.
- Undirected Learning: Derive patterns from a non-user-categorized dataset.
- Reinforcement Learning: Attempting to achieve the best outcomes while interacting with the environment around them.
- Deep Learning: The use of neural networks with multiple layers of data processing, which may be direct, indirect or supported by human capabilities in accomplishing tasks
02
Natural Language Processing
- Text Generation: Create useful texts that comply with the requirements.
- Answering questions: Automated respond to users inquires.
- Machine Translation: Translate texts into different languages.
03
Computer Vision
- Object recognition: Recognize objects in photos or videos.
- Face recognition: Face recognition by images, audio, or video.
04
Speech Recognition
- Speech-to-text: Sounds recognition and convert them to text.
- Text-to-speech: Text recognition and convert them into sounds.
05
Robots
- Industrial Robot: It is used in industrial fields to automate processes and applications.
- Service Robot: Used in commercial or personal fields to accomplish certain tasks or services.
Recent technological advancements and the abundance of data have led to a rapid spread of AI applications. Currently, machine learning technologies are the most popular, particularly: Deep learning. This due to its high capabilities in data processing, understanding patterns and relationships, accuracy of conclusions, and quality of decision-making in specific tasks. Data analysis capabilities have advanced dramatically as a result of machine learning.
The following infographic introduces machine learning, encompassing descriptive, predictive, and analytical methods.
Machine learning can be employed in a variety of fields depending on the needs and capabilities of those technologies. The figure shows the different applications of machine learning types depending on the learning method.
In general, the purpose of using AI techniques can be classified into two main purposes:
Reinforcement
Automation
AI for Humanity and Global Community
Many developed nations are rushing to adopt AI as one of their national components in order to use it to their advantage in creating strong economies based on data and cutting-edge technologies. Establishing the Saudi Data & AI Authority (SDAIA) came to strengthen the Kingdom's position among the best leading nations in sustainable economies based on data and AI. The Kingdom of Saudi Arabia was one of the first nations to utilize data and artificial intelligence technologies to achieve the goals of its Vision 2030.
Foresight
58.8
T SAR
Size of the projected contribution of AI to the world economy by 2030.
412.5
B SAR
Projected global spending on AI by 2024.
97
M
New jobs created by AI by 2025.
69
%
Percentage of daily tasks currently performed by managers will be fully automated by 2024.
#6459a7
Small
Yes
Theme1
No