Introducción
El aprendizaje automático (ML) es un subcampo de la inteligencia artificial (IA) que brinda a los sistemas la capacidad de aprender y mejorar automáticamente a partir de la experiencia sin ser programados explícitamente. Se centra en el desarrollo de programas informáticos que pueden acceder a los datos y utilizarlos para aprender por sí mismos. El proceso de aprendizaje comienza con la observación de datos como ejemplos, experiencia directa o instrucciones. Luego buscará patrones y datos y tomará mejores decisiones en el futuro en función de los ejemplos que proporcionamos. El objetivo principal es permitir que las computadoras aprendan automáticamente sin intervención o asistencia humana, y se ajusten en consecuencia.
Classification
Source: https://www.youtube.com/watch?v=R_5BTa8PVdg
One of the main uses of ML is classification. As you can see from the video, there is a simple classification method for onions. In this case, the classification is made up of those two rods and essentially it is dividing the onions based upon their size. However, classification can become much more complex.
Source: https://www.youtube.com/watch?v=7o6iWffuuTQ
Classification is the process of predicting the class of given data points. Classes are sometimes called labels or categories. For example, in the coin video, the classes are 50, 20, 10, 5, 2 and 1. The task is to categorise our coins or sort them in those classes so we get the 50s together, the 20s together, etc. Another example might be spam detection, spam detection in the email service providers can be identified as a classification problem. This is a binary classification since there are only two classes and email can be spam or not spam. Classifier utilizes some training data to understand how given input variables relate to the class. In this case, spam emails and non-spam emails have to be used as a training data. When the classifier trains accurately, it can be used to detect an unseen email.
Clasificadores
La clasificación es extremadamente útil y podemos usarla para varias aplicaciones. Por ejemplo, tenemos un conjunto de mascotas y podemos usar sus funciones de datos. Entonces, ¿cuáles son los elementos que los describen? Tal vez podría ser su tamaño, su color, la forma de su cabello o incluso su cola, y luego podemos entrenar un algoritmo para reconocerlos.
Finalmente, el algoritmo aprenderá cómo colocarlos en categorías específicas y luego podremos usarlos para diferentes procesamientos.
El sistema de IA, que se utiliza para clasificar estos datos, normalmente se conoce como clasificador. Entonces, en el ejemplo de las mascotas, un '1' significaría que tiene esa característica y el '0' significaría que no tiene esa característica. Algunas de las funciones, por supuesto, se dispararán o activarán, y eso significa que ese animal en particular pertenece a esa clase de animales. Una puntuación alta significaría que pertenece a una clase y la puntuación baja significaría que pertenece a otra clase. Por ejemplo, si tengo una clase de animales pequeños y tienen orejas puntiagudas que son cortas, lo más probable es que pertenezca a la clase de gatos. Y este proceso continúa para todos y cada uno de los animales. Existen diferentes tipos de clasificadores, como regresión logística, máquinas de vectores de soporte, base ingenua y redes neuronales.
¿Cómo funciona la clasificación?
En esta etapa, es posible que se pregunte cómo funciona realmente la clasificación. Por ejemplo, si se quiere categorizar gatos y perros, se crearía un algoritmo que logre reconocer si una imagen representa un perro o un gato. Lo primero que hacemos es extraer el número de características, por ejemplo, tamaño, color, forma de las orejas, etc.
En segundo lugar, necesitamos mapearlos en un espacio bidimensional. Entonces, dividimos ese espacio en función de las características de esos animales.
El tercero es entrenar el algoritmo. En varios casos, el algoritmo logra reconocer a todos los gatos y a todos los perros. Esto se llama sobreajuste. El sobreajuste ocurre cuando el algoritmo logra reconocer todos los objetos dentro de nuestro conjunto de entrenamiento, pero por supuesto, solo esos objetos. Entonces, imagina que tenemos una nueva imagen de un gato, algo que el algoritmo nunca vio, y nos gustaría mostrárselo al algoritmo. En ese caso, lo más probable es que el algoritmo falle y fallará porque el algoritmo aprendió a reconocer solo esas imágenes en el conjunto de entrenamiento. Sin embargo, queremos que el algoritmo aprenda a reconocer todos los perros y gatos existentes, y no solo los presentes en nuestro conjunto de entrenamiento. Entonces, al elegir un modelo, debemos elegir algo menos preciso, pero que logre generalizar mejor. Al hacerlo, si alimentamos al algoritmo con una imagen invisible, probablemente la reconocerá y la clasificará en el grupo correcto de animales.
Machines that Learn: The Amazon Case Study
AI, and in particular ML, are being increasingly used by businesses to enhance their operations and subsequently offer better services.
Source: https://www.youtube.com/watch?v=2DtyjC0UxTw
Amazon is using AI in practically everything! But did you ever wonder how your shopping experience on the Amazon website actually works? Which part of Amazon is actually learning how you are interacting with the website and giving you back information which is relevant to your search? Did you ever stop and wonder how does Amazon recommend that particular item which you are interested in? In fact, it is fair to say that Amazon excels in the way in which it uses AI.
So, how does Amazon help you locate the items you want to purchase? Sign in on https://www.amazon.com/ and then have a look at the different components which exist on the website. Try to locate those parts of the website which are actually offering recommendations.
The first thing you'll probably notice is the section which offers you personalised recommendations. This is a page full of products recommendations just for you. In fact, they have been customized based upon your past experiences. Amazon recommends a range of products from different categories which you have been browsing and places products that you are likely to click.
You'll also probably notice something called ‘Frequently bought together’. This recommendation has one main goal to increase average order value. So, it's not just about buying one product, but you might also want to buy an additional product which is related to that particular product. Recommendations aim to up-sell and crosssell products by providing suggestions based on the items in the shopping cart or based on products you’re currently looking at on the site.
Another relevant section is entitled ‘Customers who bought this item’. Over here you can see items which were bought together with the item which you actually bought, and Amazon is recommending that you also buy them. The company displays items that have been purchased together in the past with the goal of increasing average order value through up selling and cross-selling. These items are purchased together a little less often, then frequently bought together and is a way for Amazon to sell items that are not as popular to help retailers move their inventory.
The ‘Inspired by’ section is used to show items which are related and might be interesting. Amazon looks at products you've been browsing and recommends very similar products of different shapes, sizes and brands to help you find something very similar to a product you've already shown an interest in. It shows you different brands, colours, shapes and sizes with the hope that you will proceed to buy.
In the section entitled ‘A newer version’, Amazon shows you a new version of an item which you already own. If for instance, I look at an old e-book reader I bought from Amazon, there is a recommendation underneath the invoice letting me know that there is a newer version of the product that I can upgrade to. It's almost like a replenishment campaign, but for electronic devices.
Recommendations based upon previous purchases look at what you already bought and suggests alternative items that you might need, based upon that purchase. For instance, after purchasing an e-book reader, Amazon recommends a variety of different covers and cases for the exact product I had just purchased in an attempt to encourage a second purchase with a highly relevant cross-selling offer.
Best sellers form a specific product category which help people find popular products and buy from new categories that they may never have purchased from before, which opens up a whole range of up selling and cross-selling opportunities as well.
These are real life examples of how AI is being used in practice. In this case, Amazon is using it to recommend products. AI is basically crunching all the data about what you do and even what others do on the Amazon website, and presenting recommendations based upon the data.