In recent years, the world has witnessed a surge in interest and development of AI - Artificial Intelligence. This groundbreaking technology has rapidly infiltrated various sectors, from healthcare and finance to transportation and entertainment. Despite the growing popularity of AI, many people still wonder what AI actually is and how to get involved in such a large field of progress that surrounds us more and more and leaves us breathless with its possibilities. As far as we understand, AI - Artificial Intelligence is a broad field of computer science that deals with creating intelligent agents, which are systems that can reason, learn, and act autonomously. AI is used to solve a wide range of problems, from medical diagnosis to self-driving cars. The advancements in AI have been nothing short of remarkable.
From sophisticated language models capable of generating human-quality text to self-driving cars navigating complex urban environments, AI has demonstrated its immense potential. Its applications are vast and ever-expanding, promising to revolutionize industries and improve our daily lives in countless ways. Artificial intelligence is a branch of computer science that deals with the development of computer systems that have the ability to perform tasks that require intelligence, similar to or even surpassing the capabilities of the human mind. Artificial intelligence focuses on the development of algorithms, techniques and models that enable computers to think, learn, understand and make decisions and communicate in a human-like manner. It is having an increasing impact on different industries and sectors, transforming the way work is done, decisions are made and technology is interacted with.
Simply put, artificial intelligence allows computers to automate and simulate human intelligence, relying on the principles and ways in which the human brain processes information so that computers can think and act like humans and even better. Using artificial intelligence, computers are able to perform tasks that normally require human intelligence. But how does artificial intelligence work? It essentially imitates human thought processes while at the same time stimulating human senses such as listening, speaking, understanding language, memory, thinking, vision and movement. Essentially, artificial intelligence attempts to mimic the cognitive abilities of the human mind using algorithms and models that allow computers to make decisions, learn from experience, and perform complex tasks in a manner similar to human thinking and action.
There are many ways to categorize artificial intelligence, but some distinctions have become more widely accepted than others. Let's start with the most basic and common division:
NAI - Narrow Artificial Intelligence or Narrow AI: This refers to AI systems designed to perform a specific task with a high degree of efficiency and accuracy. These systems are narrowly focused and lack the ability for general understanding or performing other types of tasks. For example, a narrow AI system could be developed to recognize objects in images, recommend products, perform automatic translation, or drive a self-driving car.
GAI - General Artificial Intelligence GAI: This refers to a hypothetical AI system that would possess human-level intelligence and be able to understand, learn, and carry out a wide range of tasks at or above the level of human intelligence. A general AI would be able to apply knowledge and skills acquired in one domain to solve problems in other domains. The idea of general AI is that a machine would have the ability to think and act like a human, adapting to new situations, learning from experience, and making creative decisions.
Superintelligence: This refers to a level of AI that surpasses the intellectual capacity of the most intelligent humans in all domains. A superintelligence would be vastly more intelligent than humans and would have the potential to surpass human capabilities in all aspects. This is a futuristic concept and has not yet been achieved, but it is often discussed in the context of the long-term development of artificial intelligence.
Beyond this basic classification, artificial intelligence encompasses various branches and areas of research. Let's take a look at some of the important branches of artificial intelligence.
Key Branches of Artificial Intelligence: Growth and Development
You might think that artificial intelligence and machine learning are the same, but there is more to the story! There is a common misconception that AI - Artificial Intelligence and machine learning are interchangeable terms. In reality, machine learning is a specialized subset of the broader field of artificial intelligence. In this tutorial we will go into different branches of artificial intelligence. Let's first explore the different branches of artificial intelligence:
- Machine Learning: This branch of AI involves developing algorithms that enable computers to learn from data and improve their performance over time without being explicitly programmed. It's like teaching a computer to learn from experience, similar to how humans do.
- Deep Learning: A subset of machine learning, deep learning utilizes artificial neural networks with multiple layers to process complex data patterns. It's akin to building a multi-layered brain for computers, allowing them to learn intricate relationships.
- NLP - Natural Language Processing: NLP equips computers with the ability to understand, interpret, and generate human language. Think of it as teaching computers to speak and understand our language.
- Computer Vision: Computer vision grants computers the power to perceive and interpret visual information, such as images and videos. It's like giving computers eyes that can see and understand the world around them.
- Robotics: Robotics combines AI with mechanical engineering to create autonomous machines capable of performing tasks in the physical world. Imagine robots that can move, interact with objects, and even assist humans in various activities.
- Decision Making and Optimization: This branch focuses on developing algorithms that help computers make informed decisions and optimize resources. It's like giving computers the ability to weigh options, solve problems, and find the best solutions.
- ANN - Artificial Neural Networks: Inspired by the human brain, ANNs are computational models that can process information, recognize patterns, and make decisions. Think of them as artificial brains that can learn and adapt.
- Etc.
Therefore, based on these divisions, you can conclude
that when it comes to artificial intelligence, some things are interconnected.
For example, you can say that machine learning belongs to the category of
Narrow AI, but it can also be a key tool in achieving General AI. Through
machine learning, systems can acquire the ability to learn from experience and
improve their performance over time. This is an important step towards
developing more intelligent systems, but the concept of General AI and Super AI
goes far beyond machine learning and includes various aspects such as
understanding, creativity, consciousness, and other cognitive abilities. Given
all this complexity, it's natural to wonder where and how to start learning to
create artificial intelligence.
Let's assume you want to get involved in artificial intelligence, open a company, and hire the right people to create your own AI and turn it into a big business. Who would you hire? You would probably look for:
Data Scientist: An expert responsible for collecting, cleaning, analyzing, and interpreting data. They apply statistical and mathematical methods to extract meaning from data and build machine learning models.
Machine Learning Engineer: An expert focused on implementing and maintaining machine learning models. They are responsible for building, training, and evaluating models, as well as optimizing performance.
AI Researcher: An AI researcher is involved in advanced research in the field of artificial intelligence. They explore new techniques, algorithms, and models to advance the field of artificial intelligence and develop innovative solutions.
Software Engineer: A software engineer is responsible for developing software systems and platforms for artificial intelligence. They are involved in implementing and optimizing software, managing infrastructure, and scaling systems.
NLP Engineer: A natural language processing engineer focuses on understanding and processing human language. They develop algorithms for entity recognition, sentiment analysis, machine translation, and other language processing tasks.
Computer Vision Engineer: A computer vision engineer is involved in analyzing and interpreting visual information. They develop algorithms for object recognition, face detection, image segmentation, and other tasks related to image and video processing.
Robotics Engineer: A robotics engineer is an expert responsible for designing, developing, implementing, and maintaining robotic systems that use AI technology. Their duties are diverse and can vary depending on the specifics of the project, industry, and area of application.
Ethical AI Specialist: An ethical AI specialist is an expert who considers the ethical issues related to the application of artificial intelligence. They deal with safety, transparency, privacy, laws, dilemmas, and risk analysis and assessment.
Etc.
This is the minimum of a minimum team of experts needed to build your own artificial intelligence. This is a smaller list with one expert from each necessary area, although in practice, it is much longer. Each of the listed experts probably has a whole team of people working with them. Can you replace all these experts yourself? We think not, even if you have finished several faculties. But you can enter the world of artificial intelligence through a smaller door. Which one? It depends on what you like to do and what you do. The right entrance, even for us in this complex world, is like for many, the programming language Python.
Python is rightly considered the best programming language for machine learning. Even if you're unfamiliar with this programming language, Python's syntax is so simple, clean, clear, and intuitive that it makes code easy to understand and projects quick to develop. The only potential annoyance for C# programmers might be the lack of curly braces { }. Imagine the JavaScript programming language, where you have to guess when to use curly braces. With Python, you always know where you stand. Python is a high-level interpreted language that allows for rapid implementation of ideas without the need for complex programming syntax. It's versatile, being used for the development of a wide variety of applications.
However, what truly makes Python the best programming language for machine learning is the abundance of libraries, many of which are specifically designed for machine learning, such as NumPy, SciPy, TensorFlow, PyTorch, scikit-learn, Keras, and others. Before we even start learning machine learning, it's essential to install the Anaconda distribution on your operating system. Why? Anaconda has over 7500 packages, with over 250 already pre-installed. This will be incredibly helpful at every step of your learning journey. Naturally, the ideal environment for developing AI is Linux operating systems and servers. But since you'll be learning and working on smaller projects, you can also use the Windows operating system. Check out how to install Anaconda on Windows and how to start using Jupyter Project.
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