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Deep learning is a sub-area of machine learning (machine learning) that is based on deep artificial neural networks, inspired by the functioning of the human brain.
These networks are composed of layers of interconnected “artificial neurons” working together to perform machine learning tasks.
It’s based on deep artificial neural networks, which are composed of multiple layers of artificial neurons called “units” or “nodes”.
These layers work together to automatically learn from training data, identifying complex patterns and relationships in data.
Machine Learning is a field of AI that focuses on machines’ ability to learn from data and improve their performance without being explicitly programmed.
The process begins with the collection and preparation of relevant data, which may come from different sources. After selection, the model is trained to identify models and relationships and improve predictions.
A successfully trained model can be used autonomously to make predictions about new data.
Robotic Process Automation (RPA) is a technology that allows organizations to automate business processes and repetitive tasks using digital software or “robots”.
Start with the identification of business processes suitable for automation (repetitive and regulated). Next, developers create digital “robots” that can replicate human activities, allowing them to navigate between applications, extract data, complete forms, and more.
The entire process is monitored in real time and managed by a control center, with exception handling by human operators.
General Artificial Intelligence (or GAI) is an AI with various abilities, capable of solving multiple problems without needing to be reprogrammed from scratch, as is the case with today’s algorithms.
In practice, an AI that is able to play chess, write texts, recognize images and more without difficulty passing from one task to another, just as humans do.
It aims to understand and learn from a wide range of cognitive activities, such as reasoning, language understanding, vision, and complex problem solving.
This form of AI aspires to reach a level of versatility similar to human, allowing you to face a multiplicity of tasks without the need to be specifically programmed for each of them. And then? Well, we just have to see what the future of technology will propose…
Predictive Artificial Intelligence is a branch of artificial intelligence that focuses on predicting future events or outcomes based on historical data.
Use algorithms and models to analyze large amounts of past data and detect patterns and trends. This information is used to make predictions about future events, helping to make informed decisions.
Generative Artificial Intelligence is a technology that aims to create original data or content, such as images, texts or sounds, independently.
Use generative models, such as generative neural networks (GAN), which learn from large amounts of data to generate new content based on existing ones.
They are able to produce creative works, simulate human natural language and even generate new ideas and concepts.
The chatbot is a computer program designed to simulate a human conversation.
It uses natural language processing algorithms to understand user input text and generate appropriate responses. They can be rule-based (following a script), or use machine learning to adapt to conversations in a more flexible way.
They are used in customer support services, websites, messaging apps to answer user questions, automate tasks and improve customer interaction.
Backpropagation is a key algorithm used for training neural networks. It is a type of machine learning.
This algorithm is used to update the weights of connections between “neurons” within a neural network so that the network can learn to perform a certain task or task.
The goal is to gradually reduce the error at each iteration of training, thus improving the network’s ability to capture pattern in data and improving its overall performance.
Big Data represents large amounts of complex data that exceed the capabilities of traditional data management technologies. This data can come from various sources, such as online transactions, connected devices, social media and much more.
Specialized systems and technologies such as distributed computing and parallel processing are used to manage big data. The main goal is to extract meaningful information from a database full of data.
By analyzing big data, organizations can identify patterns, trends, and hidden insights, using this data to make informed decisions, optimize operations, and improve their services.
An algorithm, in AI, is a set of mathematical instructions or procedures that allow a computer to solve a specific problem or perform a task.
Algorithms in AI can vary greatly, but generally involve processing incoming data in an organized process to produce a desired result. Algorithms can be designed to learn from data, adapt to changes, and improve their performance over time.
Augmented Intelligence is a design model that fosters collaboration between humans and AI, allowing you to experience, learn and make better decisions.
Collaborate with humans to increase their abilities and abilities. This can be done by automating repetitive tasks, advanced data analysis, providing AI-based recommendations or suggestions, and intuitive human-machine interaction.
The Semantic Web is an evolution of the World Wide Web that aims to provide meaning and structure to the data on the web.
Through the use of metadata and semantic marking standards, such as the Resource Description Framework (RDF) and ontology, it connects data so that machines can understand the meaning and relationship between information.
This allows machines to perform smarter searches, extract meanings from texts, connect data between different sources, and perform natural language processing tasks more accurately.
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