The Fourth Industrial Revolution is based on the digital revolution, where technology becomes an integral part of societies and even human bodies themselves.
This revolution is characterized by the emergence of innovative technologies in various fields, including:
- Robotics
- Nanotechnology
- Quantum Computing
- Bio-technologies
- Internet of Things (IoT)
- 3D Printing
- Autonomous and self-driving vehicles
- Artificial Intelligence (the main focus of our discussion today).
Today's computers are capable of solving highly complex mathematical operations millions of times faster than the human mind. However, they are still far from replicating tasks that a young child can do as a result of learning, such as recognizing family members, learning to communicate with others, or learning through trial and error.
Unlike the human mind, which is an immensely intricate network of interconnected billions of neural cells, computers, as the name suggests, calculate and work with numbers and algorithms but do not think or perceive like the human brain.
Human intelligence is considered one of the most complex things in the universe, making current attempts to fully replicate human intelligence beyond human capabilities at present.
Nevertheless, researchers are striving to imitate some characteristics of human intelligence that could enhance machine intelligence. In this context, researchers focus on two main goals:
- Understanding how the human mind processes acquired information.
- Understanding the general principles of intelligence.
Efforts have converged in various fields to achieve these goals, including philosophy, psychology, cognition, logic, linguistics, and biology. Over the years, these efforts have borne remarkable applications of artificial intelligence.
Artificial intelligence is now utilized in military, industrial, economic, technological, medical, educational, and service-related domains. It is expected to open doors to boundless innovations and lead to further industrial revolutions, radically transforming human life in the future.
With the immense and rapid technological advancements amid the Fourth Industrial Revolution, Artificial Intelligence will be the driving force for progress and prosperity in the coming years. It has the potential to lay the groundwork for a seemingly fantastical world that current indications suggest is within reach.
What is Artificial Intelligence?
Artificial Intelligence, often abbreviated as AI, is a branch of Computer Science that enables the creation and design of programs that simulate human intelligence, cognitive abilities, and work patterns.Through AI, machines can simulate human learning from past experiences and then perform certain tasks instead of humans. These tasks require logical and organized thinking, as well as the ability to understand, hear, speak, and move.
Since the development of digital computers in the 1940s, it has been demonstrated that computers can be programmed to perform extremely complex tasks. For example, they can prove mathematical theorems or play chess with great expertise.
On the other hand, some programs have achieved levels of performance comparable to those of human experts and professionals in specific tasks. You can find AI with this limited meaning in various applications, such as medical diagnosis, search engines, voice recognition, or handwriting recognition.
To differentiate, here's the distinction between a regular computer and a computer built with AI technology:
- Regular Computer: It can carry out various calculations and operations based on pre-defined commands and relatively fixed algorithms.
- AI-enabled Computer: It can perform a variety of tasks flexibly, similar to the human ability to handle diverse information. It can modify data based on experience and experimentation to produce more intelligent and flexible outputs, as well as solve problems in innovative and creative ways.
A Historical Overview of Artificial Intelligence
The formal emergence of Artificial Intelligence (AI) began at Dartmouth College in the United States in 1956.Due to the material problems faced by the industry sector at that time, there was a need to leverage modern technologies to address these issues.
AI surfaced, and a simplified version of a humanoid robot was produced. However, this progress was insufficient to satisfy the investors, leading to a reduction in funding for humanoid manufacturing.
In the mid-1980s, researchers managed to develop computer systems capable of making decisions based on pre-programmed problem-solving solutions. Yet, they failed to utilize this invention in practical applications effectively.
With continuous technological advancements, computers capable of learning and processing problems autonomously emerged. In 1997, a computer defeated a human for the first time in a game of chess. Subsequent inventions and improvements propelled AI to become an urgent necessity and an indispensable tool with applications in almost all fields.
The Artificial Intelligence Family
The AI family encompasses several main areas and diverse applications that one should be familiar with when entering the world of Artificial Intelligence.The AI family tree branches into four fundamental categories:
1. Natural Interface Application
This branch comprises three primary areas: Natural Language Processing, Speech Recognition, and Multi-use Sensory Interface.2. Robotics
This branch includes the field of Visual Perception.3. Computer Science Applications
This branch necessitates the availability of the following: 8th-generation computers (8K), Parallel Processing, Symbolic Processing, and Artificial Neural Networks.4. Cognitive Science
This branch requires knowledge of Expert Systems, Knowledge-Based Systems, Fuzzy Logic, and Intelligent Agents.The Impact of Artificial Intelligence on Recruiting
Recruitment is one of the most challenging issues faced by both large and small companies. Hiring the wrong person can be highly costly and can negatively impact organizations.
Here, Artificial Intelligence plays a crucial role in human capital. AI can enhance employee efficiency by automating certain lower-level operational tasks.
AI systems provide additional information to operations managers when starting a process, as there is a significant amount of data that needs to be stored. This prevents entities from reinventing the wheel each time an operation takes place.
Moreover, most companies use AI-integrated programs to create models that can assemble successful employee attributes, understand weaknesses and shortcomings in lower-level employees, and then work on strengthening these weaknesses to make the most of the human elements within any institution.
AI-powered testing programs can also assess candidates' abilities for new positions, measure their performance in specific roles, and determine whether their personality traits are suitable for the organization.
Furthermore, AI systems can enhance the chances of finding suitable talents that can benefit the organization and contribute to the operations. In cases where the required employee is not found, AI can suggest alternatives and offer alternative studies.
Companies That Are at the Forefront of Artificial Intelligence
Here are more companies that are at the forefront of Artificial Intelligence:
Google:
Google is considered one of the leading companies in the field of Artificial Intelligence. It extensively uses AI in its search engine to deliver more accurate search results and in image search capabilities.
Furthermore, Android phones equipped with AI can understand user commands and provide instant translations for foreign language phrases written on papers, signs, and roads, among other things.
Facebook:
The social media giant, Facebook, is one of the companies that heavily utilizes Artificial Intelligence. AI enables Facebook to recognize faces in photos, label them with the owner's name, select appropriate and preferred content to display on the user's news feed, suggest old friends, and perform various other impressive tasks.
Amazon:
Amazon uses AI extensively for its e-commerce platform, recommendation systems, customer service, and logistics optimization. AI-powered virtual assistants like Alexa are also a prominent example of Amazon's AI applications.
Microsoft:
Microsoft integrates AI in various products and services, including its search engine Bing, voice recognition software Cortana, and machine learning platforms like Azure Machine Learning.
IBM:
IBM is a major player in the AI industry with its Watson platform, which provides AI solutions for various industries, including healthcare, finance, and customer service.
Apple:
Apple employs AI in its virtual assistant Siri, facial recognition technology for unlocking devices, and personalized app recommendations.
NVIDIA:
NVIDIA specializes in AI hardware and provides high-performance GPUs used in deep learning and AI research.
OpenAI:
OpenAI is an organization dedicated to advancing AI research and development, with a focus on promoting ethical and safe AI practices.
Salesforce:
Salesforce incorporates AI in its customer relationship management (CRM) platform to improve sales forecasting, customer insights, and personalized marketing.
Baidu:
Often referred to as the "Google of China," Baidu is a leading Chinese tech company heavily invested in AI research and applications, including natural language processing and autonomous vehicles.
Tesla:
Tesla is known for its advancements in self-driving technology, which heavily relies on AI algorithms for autonomous driving.
Intel:
Intel is another company involved in AI hardware, providing processors and accelerators optimized for AI workloads.
These are just a few examples of companies actively utilizing AI technologies to enhance their products and services across various industries. The field of AI is constantly evolving, and many other companies are also contributing to its growth and innovation.
Important Concepts Related to Artificial Intelligence
In order to better understand Artificial Intelligence (AI), you need to learn some concepts that technology pioneers rely on in various applications and fields. These concepts represent fundamental pillars in the field of AI.
Machine Learning
Machine Learning is a subfield of AI that focuses on providing machines with the ability to learn. This is achieved by using algorithms that detect patterns from data and information presented to the machine, enabling it to apply that knowledge in the future to make decisions and predictions. This process allows programmers to avoid the need to manually program machines for every possible scenario.
Machine Learning is just an approach to embodying AI, eliminating or reducing the need for extensive coding when dealing with a wide range of possibilities. In the 1960s, American electrical engineer Arthur Samuel made significant contributions to AI by shifting its focus from pattern recognition to learning from experience. Working with IBM, Samuel used the game of Draughts (similar to chess) in his research, paving the way for the development of AI in early IBM computers.
Today, Machine Learning continues to evolve and is increasingly applied in complex medical applications. Examples include analyzing genomes to prevent diseases, diagnosing depression based on speech patterns, and identifying individuals with suicidal tendencies.
Deep Learning
Deep Learning is a more advanced subfield of Machine Learning and is considered one of the most sophisticated areas of AI. It aims to bring AI closer to its ultimate goal: enabling machines to learn and think like humans.
Going beyond the higher and more complex levels of Machine Learning, Deep Learning requires complex structures that mimic interconnected neural networks in the human brain. The objective is to understand patterns and uncover hidden details.
Although the capabilities of Deep Learning are vast, it also demands significant requirements, such as vast amounts of data and immense computational power. This means that future AI intelligence doesn't need much additional effort in programming, as all the potential lies within the program itself, resembling the flexibility of an incomplete but boundless child's mind.
Artificial Neural Network
This technology simulates the neurons in the brain by building interconnected units that receive information from multiple sources simultaneously and process it sequentially, similar to the human brain.
Artificial Neural Networks are a fundamental technique used in the technology of Machine Learning, as explained earlier.
Natural Language Processing
This term represents the ability of computers and operating systems to analyze and process the texts and languages used by humans. It is the same technology your phone uses to understand what you want to type or search for.
There are more terms, but I'm trying to keep it simple so you can grasp some aspects of Artificial Intelligence, the technology that will significantly change the world in the near future.
Pros and Cons of Artificial Intelligence
Pros of Artificial Intelligence:
1. Achieving high rates of economic, human, and social development.
2. Improving and elevating healthcare standards for humans.
3. Saving a significant amount of time in the process of human development.
4. Extending human progress achievements globally.
5. Reducing production costs.
6. Ensuring efficient and cost-effective transportation services.
Cons of Artificial Intelligence:
1. Dominance of large corporations in industrial production, leading to the decline of medium and small-scale companies in the production process.
2. Widening the scope of unemployment, as automation of industries and rapid technological advancements may lead to a 50% reduction in job opportunities.
3. Exacerbating inequality and widening the gap between the rich and the poor.
4. Imposing unprecedented challenges on human societies, necessitating comprehensive economic restructuring.
5. Requiring social and political restructuring, as achieving the goals of the fourth industrial revolution demands advanced economic, social, and political frameworks aligned with the new concepts of inclusive and sustainable development.
6. Changing cultural and social values, which will be affected on the sidelines of the fourth industrial revolution.
Challenges Facing Artificial Intelligence Technology
Despite the ongoing and daily advancements in artificial intelligence technology and its applications, there are still obstacles hindering its progress, including:
1. High costs of research and implementation of artificial intelligence technology in various fields.
2. The need for a strong infrastructure of computers and operating systems to store and transfer data quickly and easily. For instance, self-driving cars currently being developed require the analysis of 1 GB of data per second, which demands significant time and effort.
3. Any technology in the world has dual purposes. While artificial intelligence can be used in medical fields and assist humans, it can also be utilized in manufacturing weapons, which poses a challenge for the global scientific community to control and resist such applications.
4. Developing and improving educational curricula and raising awareness among people worldwide are crucial to ensure the proper development and deployment of artificial intelligence. Like any other technology, humans may use modern inventions correctly or incorrectly. Some jobs may disappear, while new ones emerge.
How to Effectively Learn Artificial Intelligence?
There are numerous resources and training courses available for beginners to learn artificial intelligence (AI). It may seem challenging at first if you want to delve into this field and learn AI on your own. Therefore, it's essential to follow the tips and steps we will provide to ensure a good understanding and successful learning experience.
Steps to learn artificial intelligence:
1. Strengthen Your Foundation First
Before diving into AI and entering this realm, it's best to build a strong foundation. This can begin with mastering the basics of programming (Python is one of the recommended languages for AI learning) and mathematics (linear algebra, statistics, and calculus).
In this way, you'll also enhance your algorithmic and abstract thinking. You don't need an advanced degree to master AI and machine learning, but you do need boundless passion!
2. Understand Machine Learning Mathematics
Professionals and AI developers need to comprehend the underlying probabilities behind machine learning concepts. Regular developers use math libraries to facilitate complex and intricate calculations. However, AI should be able to write and understand complex algorithms effortlessly.AI specialists should be capable of diving into data and discovering patterns.
3. Learn Python Proficiently
To enter the world of AI, individuals can set themselves above the competition by working in this field. Python is a recommended programming language as it is easy to understand and write.
Moreover, it has essential libraries and a strong community support. Python supports advanced machine learning and implements deep learning systems (popular frameworks like TensorFlow, PyTorch, and Keras).
4. Research Online for Resources and Training Courses
If you are genuinely interested in learning, you can start simply. There are thousands of free resources, articles, and training courses available on Google that you can use to enter this field. These free resources can provide you with the basics to determine if this is truly what you want.
5. Start Building Simple Things Using AI Algorithms
One key to success in learning AI is to develop a strong intuition about how AI systems work. One way to create such an intuition is by simplifying things.
For example, start a project that interests you and write AI algorithms from scratch. Rest assured, you will learn a lot along the way, and the long-term benefits are significant.
6. Learn How Human Vision and Computer Programming Interact
To become a robust developer, you need a solid foundation in data science and statistics. You need to know effective programming languages in AI.
You need to understand more about core mathematics and be able to analyze data easily.
You need to combine computer programming with human insight to become a successful developer.
7. Learn How to Gather the Right Data
AI is excellent at processing vast amounts of data simultaneously. When thinking about creating AI applications, consider tasks that require data (like customer service and marketing) and create programs that make handling heavy data easier and faster.
8. Engage in Online Forums
Kaggle is an online forum for data scientists and AI students. Its core system allows users to find and publish datasets, create web-based models, and interact with other AI engineers.
This can be a great way to get familiar with AI. The website also offers AI competitions where you can participate to improve your skills.
9. Familiarize Yourself with Current AI Technologies
AI comprises several sections that you can study. It's best to enter this field with a plan and a goal, knowing the area you want to work in. Research different AI types and determine what exactly you want to learn, as there are many concepts to master.
10. Have Reasonable Expectations
There's a lot of hype surrounding the development of AI today, which can lead people to overestimate its current capabilities. Although it's an incredibly exciting field for software development and business, as you learn more about this technology, you'll quickly discover its limitations.
The important thing is, if you don't want to lose interest, deal with these issues with reasonable expectations!
Conclusion
I hope this article has helped you understand one of the most critical concepts in our modern era, which is artificial intelligence. It will fundamentally reshape the world in the future.
