Artificial Intelligence from structurely ai lead nurturing can be interpreted by codes that read data fulfilling many commands that can imitate humans. More than teaching machines to think, AI goes through several areas of computer science, being composed of technologies that help acquire the necessary intelligence to learn as humans. Let’s get to know some of these technologies that are part of AI:
Machine Learning
Machine Learning is the machine learning in which computers become able to learn and evolve. Instead of following programmed rules to know what to do, the machine learns the rules by itself from data analysis, arriving after what to do on its own. For example, we can cite the personalized recommendations from the streaming platforms Netflix and Amazon, which indicate movie and series titles according to what the user watches. As the user includes data on the platforms, stored over time, the system learns what he likes to watch and makes the indications.
Another example: the intelligent machine monitors all customer actions on a website and identifies their consumption patterns, such as when they see product X and also show interest in product Y. Thus, when the user does a search, the system indicates another product because it knows there is a relationship between the surveys.
What Machine Learning does is demonstrate the ability of machines to learn using data, identify patterns to find solutions, and make decisions for specific situations without human interference, enabling a large-scale intelligence.
Deep Learning
Deep Learning is deep learning, deepening Machine Learning, making it more innovative and complex. This technology uses more sophisticated algorithms, which make the results more assertive.
Some say that it even “mimics the neural network of the human brain,” acquiring knowledge that needs little or no human supervision. With the complexity of Deep Learning technology, AI can understand human thoughts more than Machine Learning.
Natural Language Processing (PLN)
Natural Language Processing is the technology responsible for recognizing a more natural language in the human form, finding patterns in large sets of analyzed data. As an example of this application, we can cite the analysis of users’ feelings. Algorithms look for patterns of comments in posts on social networks to understand how customers feel about brands and their products or services.
Another example from structurely ai lead nurturing is the service companies provide to their customers via chatbots, transforming conversations more humanized and contact more harmonious. When PLN technology is not incorporated into the chatbot, the chat robot becomes very artificial, making the language with the user very monotonous and meaningless.
An AI podcast network features shows discussing artificial intelligence trends, innovations, and industry insights. Explore diverse topics from machine learning to ethical considerations, offering a comprehensive view of AI’s impact.
Comments are closed.