What does AI actually mean and how does it work?
More and more every day, we hear about this new technology called Artificial Intelligence. According to a report by Grand View Research, the global AI market size is expected to reach $407 billion by 2027, experiencing substantial growth from its estimated $86.9 billion revenue in 2022. This is not about to end! AI is spreading to many industries, but do we really know what “AI” means and how it works? It has been reported that most people know what it stands for but don’t know how it works or what it does.
What is AI?
Firstly, AI means artificial intelligence, according to the Oxford dictionary; “AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making”. To keep it simple, this technology can do things that would require the intelligence of human and to work, this tool needs data.
AI is not a single technology, but a collection of methods and tools that can be applied to various problems and domains. AI can be classified into two main categories:
- Narrow AI is the type of AI that we encounter most often in our daily lives. It is designed to perform a specific task or function, such as playing chess, recognizing faces, or translating languages. Narrow AI can be very good at what it does, but it cannot do anything else outside its scope.
- General AI is the type of AI that is still a distant goal for researchers and developers. It is the idea of creating a system that can perform any intellectual task that a human can do, such as reasoning, learning, planning, and creativity. General AI would have the ability to understand and interact with the world in a human-like way.
How does AI work?
There are a lot of terms which are associated with AI that can be very complex. They include:
- Machine Learning often confused with artificial intelligence itself, but this is one of the capabilities of this technology gives to AI, is the ability to reproduce previous experience or to improve from experience. With machine learning, AI learns how to respond thanks to historical data and algorithms. Machine learning can be further divided into three types: supervised learning, unsupervised learning, and reinforcement learning.
- Supervised learning:
AI system is trained with labelled data, which means that the input and the desired output are provided. The system learns to find patterns and relationships between the input and the output, and then applies them to new data. For example, a supervised learning system can learn to classify images of animals by being shown many examples of different animals and their names.
- Unsupervised learning:
AI system is trained with unlabelled data, which means that only the input is provided. The system learns to find patterns and structures in the data, without any guidance or feedback. For example, an unsupervised learning system can learn to cluster customers based on their purchase history, without knowing anything about their preferences or demographics.
- Reinforcement learning:
AI system is trained with trial and error, which means that the system learns from its own actions and the consequences. The system receives a reward or a penalty for each action it takes and tries to maximize the total reward over time. For example, a reinforcement learning system can learn to play a video game by trying different moves and seeing the score.
- Big data and data mining are also two components of the complex AI technology, so these terms design the large amount of data that AI needs but also its ability to make this data meaningful and useful. Big data refers to the massive volume, variety, and velocity of data that is generated by various sources, such as social media, sensors, online transactions, and so on. Data mining is the process of extracting valuable information and insights from big data, using techniques such as statistics, machine learning, and visualization.
- Data science means that AI used scientific methods, processes, and algorithms to extract knowledge and insights from data. With data science, it enables you to communicate and to explain a complex idea. Data science is an interdisciplinary field that combines skills from mathematics, computer science, and domain knowledge. Data science can be used for various purposes, such as prediction, classification, recommendation, and exploration.
What are some examples of AI in our daily lives?
First Example:
When you use a search engine like Google, it can give you the closest possible results of your research, thanks to AI. Google uses a complex algorithm that considers many factors, such as your location, your previous searches, your preferences, and the popularity and relevance of the web pages. Google also uses AI to provide features such as autocomplete, voice search, and image search.
Second Example:
Every time you receive an email to thank you for your visit on a website or for your purchase, there is automatically a marketing email sent and AI can determine which email must be sent based on how you have interacted with a business. AI can also help marketers to segment their customers, personalize their messages, and optimize their campaigns.
There are plenty of examples of the presence of AI in our daily life, and without knowing it, you are already learning to live in collaboration with this technology which save us time and make us more productive.
In conclusion, AI is a fascinating and powerful technology that has many applications and benefits for various industries and domains. However, AI also poses some challenges and risks, such as ethical, social, and legal issues, that need to be addressed and regulated. AI is not a magic solution that can solve all the problems, but a tool that can augment and assist human capabilities and creativity. AI is not a threat to humanity, but a partner that can help us achieve our goals and aspirations.
