Artificial intelligence (AI) has transformed various industries, from healthcare to entertainment, bringing significant benefits. However, its implementation also raises concerns about its long-term impact. In this series of articles, we will explore the advantages and disadvantages of this technology and its effect on society in the coming years and decades.
What is AI?
Artificial intelligence (AI) is a branch of computer science that aims to create systems and machines capable of performing tasks that normally require human intelligence, such as reasoning, decision-making, pattern recognition, natural language processing, and learning. AI algorithms can analyze data, learn from it, and improve their performance over time without constant human intervention.
Types of Artificial Intelligence
Weak AI (Narrow AI or ANI – Artificial Narrow Intelligence)
This is the type of AI that exists today, designed to perform specific tasks very efficiently. It has no awareness or understanding beyond the domain in which it was trained.
Some Examples:
– Virtual assistants: Siri, Alexa, Google Assistant.
– Recommendation systems: Netflix, YouTube, Amazon (based on user preferences).
– Autonomous vehicles: Tesla cars that use AI for navigation and obstacle detection.
Strong AI (AGI – Artificial General Intelligence)
This is a theoretical concept of AI that would have the ability to perform any intellectual task that a human being can do. It would be able to understand, learn, and apply knowledge similarly to human intelligence. It has not yet been developed.
Examples: There are no practical examples today, as AGI remains a future goal in AI research.
Artificial Superintelligence (ASI)
This is a hypothetical type of AI that would surpass human intelligence in all aspects. ASI could think faster, solve complex problems better than any human, and even have advanced creative capabilities.
Examples: Like AGI, ASI is only a theoretical concept and has not yet been developed.
Subfields and Common AI Techniques
- Machine Learning: Algorithms that allow machines to learn from data without being explicitly programmed for each task. Example: facial recognition, fraud detection.
- Neural Networks: Models inspired by the human brain that are key in deep learning for recognizing patterns in large datasets. Example: image recognition, automatic translation.
- Natural Language Processing (NLP): Enables machines to understand and interact with human language. Example: chatbots, sentiment analysis, language translation.
- Computer Vision: Enables machines to interpret and understand images and videos. Example: autonomous vehicles, medical diagnostics.
In summary, AI today focuses on narrow AI, with specific applications in areas such as virtual assistants, process automation, and analyzing large volumes of data. The future aspirations for strong AI or general AI, and superintelligence, remain theoretical goals that, once achieved, could revolutionize the use of technology even further.
In a future article, we will delve into more technical aspects of this technology’s use in the financial world, and more specifically, in cryptocurrencies.