9 key concepts related to AI
Knowing key concepts of AI is the first step in understanding how AI systems work. This understanding is the basis for evaluating the use, capabilities and limitations of AI. It ensures effective use of these tools in different situations. Understanding AI concepts supports better communication between technical and non-technical stakeholders, facilitating project management and collaboration.
Artificial intelligence (AI): AI refers to the simulation and automation of human intelligence in machines that are programmed to think and learn like humans. These systems can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision making and language translation. A distinction can be made between strong (Artificial General Intelligence) and weak (Narrow AI) AI; the former would be able to interact similarly to intelligent humans, while the latter is used to solve individual tasks.
Algorithm: This is a predetermined set of rules or steps that are followed by a computer to complete a task or solve a problem. These instructions are designed to be clear and unambiguous to ensure that the computer can perform them efficiently and accurately. Algorithms can range from simple, such as sorting a list of numbers, to complex tasks, such as recognizing patterns in large data sets. By breaking down tasks into smaller individual steps, algorithms enable computers to process information and make systematic decisions.
Machine learning (ML): A branch of AI that focuses on training algorithms to make predictions or decisions based on data. As these algorithms are exposed to more data, they learn and improve their performance, making them more accurate and efficient over time. This learning process allows ML systems to adapt to new information and refine their outputs without explicit programming for each task. ML is widely used in various applications, including recommendation systems, image recognition and predictive analytics.
Neural networks: These are computer systems inspired by the structure and function of the neural networks of the human brain. They consist of interconnected nodes, known as neurons, which process data through several layers. Each layer analyzes the data, recognizes patterns and passes the information to the next layer for further processing. This layered approach allows neural networks to make complex decisions and predictions based on the patterns they identify in the data.
Natural Language Processing (NLP): This field of AI focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret and generate human language, facilitating more intuitive and effective communication. NLP techniques are used in various applications, such as chatbots, language translation and sentiment analysis, allowing computers to process and respond to text or speech in a human way. By bridging the gap between human language and machine understanding, NLP plays a crucial role in making technology more accessible and user-friendly.
Large Language Models (LLMs): These are advanced NLP models trained on huge amounts of text data to understand and generate human-like text. These models have been exposed to large and diverse datasets, allowing them to capture context, nuance and variation in human language. This allows LLMs to perform a variety of language-related tasks such as translation, summarization and question answering with high accuracy and relevance.
Generative Pre-trained Transformers (GPT): A type of LLM developed by OpenAI that is designed to generate coherent and contextually relevant text based on the input they receive and the data on which they have been trained. This text is generated using statistical and probabilistic methods. These models are initially pre-trained on a diverse and large dataset, which allows them to learn a wide range of language patterns and information. After initial training, GPTs can be further trained for specific tasks such as translation, summarization or custom content creation by training them on specialized datasets. This combination of pre-training and fine-tuning enables GPTs to produce text comparable to that of humans.
Prompt: In the context of AI, particularly with language models such as GPT, a "prompt" refers to the initial text or input given to the model to guide its response. This prompt serves as the starting point for the model's text generation process and sets the context and direction for the output. The nature and specificity of the prompt can significantly affect the quality and relevance of the generated text, making it a critical component in achieving the desired results. Effective prompts can help the model generate accurate, coherent and contextually appropriate responses tailored to specific tasks or queries.
Ethics and bias: AI models can adopt existing biases based on the data on which they have been trained, which can lead to unfair or discriminatory results. These biases can occur in various ways, for example by favoring certain groups or reinforcing harmful stereotypes. It is crucial for the use of AI systems to be aware that the output may be shaped by stereotypes or biases. By reflecting on and reviewing AI responses, users can ensure that AI technologies are used responsibly.
