AI simplified: What computers are good at

Home Technology AI simplified: What computers are good at

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. Investment banks can use AI in six critical ways

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

Sentiment analysis aims to determine the attitude or emotional reaction of a person with respect to some topic – e.g. positive or negative attitude, anger, sarcasm. It is broadly used in customer satisfaction studies (e.g. analyzing product reviews).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

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Joel Gómez Treviño

Soy Abogado egresado del Tecnológico de Monterrey y tengo una Maestría en Derecho Internacional por la Universidad de Arizona. Recibí en 2019 un Doctorado Honoris Causa. Cuento con 25 años de trayectoria como especialista en derecho de las tecnologías de la información. Soy Presidente Fundador de la Academia Mexicana de Derecho Informático. Coordino el Comité de Derecho de las Tecnologías de la Información y Protección de Datos Personales de la Asociación Nacional de Abogados de Empresa, Colegio de Abogados. También Coordino el Comité de Compliance Digital, Ciberseguridad y Protección de Datos Personales de la Comisión Nacional de Compliance. Soy Socio Fundador y Director de Lex Informática Abogados. Soy miembro honorario de Phi Delta Phi, la Sociedad Legal Internacional de Honores más grande del mundo. Soy Perito Forense Digital honorario de la Red Latinoamericana de Informática Forense.

Comments (2)

  1. septiembre 11, 2020
    Lina
    Reply

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    1. septiembre 11, 2020
      Jayz Marcop
      Reply

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