Artificial intelligence is a deep and convoluted world. Scientists who work in this field often trust jargon and jargon to explain what they are working on. As a result, we often have to use those technical terms in our coverage of the artificial intelligence industry. That is why we think it would be useful to build a glossary with definitions of some of the most important words and phrases we use in our articles.
We will regularly update this glossary to add new entries as researchers continually discover novel methods to boost the border of artificial intelligence by identifying emerging security risks.
An AI agent refers to a tool that uses AI technologies to perform a series of tasks in its name, beyond what a more basic chatbot could do, such as presenting expenses, booking tickets or a table in a restaurant, or even writing and maintaining code. However, as we have explained before, there are many mobile pieces in this emerging space, so different people can mean different things when they refer to an AI agent. The infrastructure is also being built to deliver planned capabilities. But the basic concept implies an autonomous system that can resort to multiple AI systems to carry out several steps tasks.
Given a simple question, a human brain can answer without even thinking about it too much: things like “What animal is higher between a giraffe and a cat?” But in many cases, you often need a pen and a paper to find the correct answer because there are intermediary steps. For example, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, it is possible that he should write a simple equation to obtain the answer (20 chickens and 20 cows).
In a context of AI, the reasoning of the chain of thought for large language models means breaking a problem in smaller and intermediate intermediate steps to improve the quality of the final result. Usually, it has been obtaining an answer longer, but the answer is more likely to be correct, especially in a logic or context of coding. The so -called reasoning models are developed from traditional models of large languages and optimized for thinking chain thinking thanks to reinforcement learning.
(See: Large Language Model)
A subset of britcommerce britcommerce administration learning in which the AI algorithms are designed with an artificial neuronal network structure (ANN) of several layers. This allows them to make more complex correlations compared to simpler systems based on britcommerce learning, such as linear models or decision trees. The structure of deep learning algorithms is inspired by interconnected neurons in the human brain.
Deep learning Ais can identify important characteristics in the data themselves, instead of demanding human engineers to define these characteristics. The structure also admits algorithms that can learn from errors and, through a repetition and adjustment process, improve their own results. However, deep learning systems require many data points to produce good results (millions or more). Usually, it also takes longer to train deep learning in front of the simplest britcommerce learning algorithms, so development costs tend to be higher.
(See: Neuronal network)
This means a greater training of an AI model that is intended to optimize performance for a more specific task or area than previously a focal point of your training, usually feeding in new and specialized data (that is, task oriented).
Many new AI companies are taking large language models as a starting point to build a commercial product but competing to expand the usefulness for an objective sector or task by complementing the previous training cycles with fine adjustment based on their own specific knowledge and experience of domain.
(See: Large Language Model (LLM))
Large language models, or LLM, are the models of AI used by popular AI attendees, such as Chatgpt, Claude, Google’s Gemini, Meta’s ai Llama, Microsoft Copilot or Mistral’s le chat. When chatting with an AI assistant, interacts with a large language model that processes its application directly or with the help of different tools available, such as web navigation or code performers.
IA and LLM attendees may have different names. For example, GPT is Openai’s big language model and Chatgpt is the assistant product of AI.
LLM are deep neuronal networks made of billions of numerical parameters (or weights, see below) that learn the relationships between words and phrases and create a representation of language, a kind of multidimensional words map.
Those are created from the coding of the patterns they find in billions of books, articles and transcripts. When a LLM is applied, the model generates the most likely pattern that fits the application. Then evaluate the next word more likely after the last one depending on what was said before. Repeat, repeat and repeat.
(See: Neuronal network)
The neuronal network refers to the algorithmic structure of multiple layers that supports deep learning and, in general, the entire boom in generative the tools after the appearance of large language models.
Although the idea of inspired by the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates back to the 1940s, it was the much more recent increase in graphic processing hardware (GPU), through the video game industry, which really unlocks the power of the theory. These chips proved to be very suitable for training algorithms with many more layers of the possible in previous times, allowing AI systems based on the neuronal network to reach a much better performance in many domains, either for voice recognition, autonomous navigation or the discovery of drugs.
(See: Large Language Model (LLM))
The weights are central to the training of AI, since they determine how much importance (or weight) is given to different characteristics (or input variables) in the data used to train the system, thus configuring the output of the AI model.
In other words, the weights are numerical parameters that define what is most prominent in a data set for the given training task. They achieve their function by applying the multiplication to the entries. The model training usually begins with weights that are randomly assigned, but as the process develops, the weights are adjusted as the model seeks to reach an exit that matches the objective more closely with the objective.
For example, an AI model to predict housing prices that is trained in historical real estate data for an objective location could include weights for characteristics such as the number of rooms and bathrooms, whether a property is separated, semi-semi-determined, whether it has or does not have parking, a garage, etc.
Ultimately, the weights that the model adheres to each of these inputs is a reflection of how much they influence the value of a property, depending on the given data set.