Introduction
Artificial Intelligence (AI) encompasses a wide range of subfields, used for different purposes and in different contexts. Let us have a more in-depth look at some of these subfields of AI.
Source: https://www.youtube.com/watch?v=2ePf9rue1Ao
Machine Learning
As we have already discussed, Machine learning (ML) is the process through which a computer learns to think logically and draw conclusions based on available data, without being explicitly programmed to do so. ML is currently one of the most in-demand and demanding disciplines in advanced technology. It is actively employed in everyday life, including ML applications that many people are unaware of. As such, it has become the science that allows machines to interpret, execute and examine data in order to solve real-world issues.
To create a complete ML system, programmers use complex mathematical expertise to construct machine learning algorithms that are programmed in a machine language. Machine learning enables us to categorise, analyse, and estimate data from a dataset in this approach. ML has enabled the development of self-driving cars, picture and speech recognition, demand forecasting models, useful web search and a variety of other use cases. It essentially converges on applications that learn from their mistakes and improve their decision-making ability or forecast accuracy over time.
A number of ML techniques exist:
- Supervised Learning: Data professionals supply labelled training data to algorithms and defined variables to algorithms for accessing and detecting correlations in this form of learning. The algorithm's input and output are both particularised/defined.
- Unsupervised Learning: Algorithms that train on unlabeled data and analyse datasets to generate meaningful connections or inferences are examples of this sort of learning. Cluster analysis, for example, is a technique that employs exploratory data analysis to uncover hidden or grouping patterns or groups in datasets.
- Reinforcement Learning: This is a technique for teaching a machine to complete a multi-step process with precisely stated criteria. Programmers create an algorithm to fulfil a task and provide it with positive and negative signals to act as the algorithm executes. Occasionally, the algorithm will decide for itself what step to take in order to proceed. This differs from other types of ML techniques as in Reinforcement Learning, the system isn’t trained on a sample data set, but is instead learning through a trial-and-error process.
Source: https://www.youtube.com/watch?v=f_uwKZIAeM0
Consequently, Deep Learning, as a subset of ML, structures algorithms in layers to create Neural Networks that are able to learn and make independent intelligent decisions, without the need of human input. These networks function in the same way that human neural cells do. They're a set of algorithms that capture the relationship between several basic factors and interpret the data in the same way that a human brain does. Consequently, a Neural Network identifies fundamental linkages across enormous volumes of data using an approach that mimics the operation of the human brain. These are widely used for fraud detection, risk analysis, stock-exchange prediction and sales prediction.
Procesamiento natural del lenguaje
Como ya hemos discutido, el Procesamiento del Lenguaje Natural (NLP) es la ciencia de la lectura, comprensión e interpretación de un idioma por parte de una máquina. En consecuencia, cuando una máquina reconoce la intención del usuario, responde adecuadamente. En consecuencia, la PNL permite que las computadoras y los humanos se comuniquen usando lenguaje natural en términos sencillos. Es un método para buscar, analizar, comprender y extraer información de la entrada de texto. Los programadores utilizan las bibliotecas NLP para enseñar a las computadoras cómo extraer información significativa de los datos de texto. Por ejemplo, una aplicación popular de NLP se encuentra en el filtrado de correo electrónico, mientras que los algoritmos informáticos pueden evaluar si un correo electrónico es basura o no al observar el tema de una línea o el texto de un correo electrónico.
El uso de PNL tiene una serie de ventajas, que incluyen:
- aumento de la corrección y eficiencia de los documentos
- generación automática de salida textual de resumen legible
- provisión de marco para asistentes digitales personales y chatbots
- facilitación del análisis de sentimiento
Como tal, la traducción de texto, el reconocimiento de voz y el análisis de sentimientos son ejemplos de aplicaciones de PNL. Twitter, por ejemplo, emplea NLP para filtrar el lenguaje terrorista de varios tweets, mientras que Amazon usa NLP para leer los comentarios de los clientes y mejorar la experiencia general del usuario.
Computer Vision
As we have already discussed, Computer Vision is an important subfield of AI, as it allows the machine to detect, analyse, and explain real-world visual inputs. It uses Deep Learning and pattern recognition to extract visual information from any data, including images or video files within text documents and images, amongst others. For instance, robots use Computer Vision to see the world around them and make real-time decisions.
Computación cognitiva
Como ya hemos discutido, el propósito de los algoritmos de Computación Cognitiva es pensar y funcionar de la misma manera y junto a los humanos. La computación cognitiva puede comunicar esta información y, por lo tanto, puede ayudar a los humanos en la toma de decisiones en tiempo real. Esto se logra a través de la capacidad de las máquinas para aprender y comprender el comportamiento humano mientras interactúan con los humanos y luego duplican el proceso de pensamiento humano en un modelo de computadora. Las aplicaciones más comunes de la computación cognitiva se encuentran en los asistentes virtuales impulsados por IA que se encuentran en dispositivos móviles y domésticos inteligentes.