However, AI drifted into specific fields. Over time, science evolved into , and it was then that AI began to generate significant results in our lives. It was a combination of image recognition, language processing, neural networks, and auto mechanics that made an autonomous vehicle possible. The market sometimes refers to these kinds  AI in business. In this publication we are going to focus on a fundamental aspect of developments as “weak AI.” The following table shows some important events in the history of Artificial Intelligence. Year Event 1842 Lovelace: Programmable Analytical Engine 1950 Turing: the Turing test 1956 McCarthy, Minsky, Rochester and Shannon hold the first .

AI conference 1965 Weizenbaum: “ELIZA”, the first specialist system 1993 Horswill: “Polly” (behavior-based robotics) 2005 TiVo: recommendation technology 2011 Apple, Google and Microsoft: mobile recommendation applications 2013 Miscellaneous: Technological advances in machine and deep learning 2016 Google DeepMind: AlphaGo beats Lee Sedol in “Go” game 3. Main techniques of Artificial Intelligence Now that you know the definition of AI and more of its history, the best way to delve into the topic is to learn about the main techniques of AI, specifically, the cases in which Artificial Intelligence is used for business. machine learning Generally.

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Machine Learning is confus with that of “weak AI”. It is Bolivia phone numbers in this field that the most important advances in AI are taking place. In practical terms, “Machine Learning is the science of making computers perform actions without the for explicit programming.” The main idea here is that Machine Learning algorithms can be given data and then us to know how to make  or guide decisions. Some examples of Machine Learning algorithms include the following: decision diagrams, clustering algorithms, genetic algorithms, Bayesian networks, and Deep Learning. deep learning Remember when Google announced an algorithm that found cat videos on YouTube?

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(If you want to refresh your memory click here ). Well, this is Deep Learning, a Machine Learning technique that uses neural networks (the concept that neurons can be simulat  by computational units) to perform classification tasks (think of classifying an image of a cat, dog or people, for example). Some examples of practical Examples of the use of artificial intelligence in business “It sounds interesting.. applications of Deep Learning are: vehicle identification, pedestrian and autonomous vehicle license plates, image recognition, translation and natural language processing. Smart data discovery It is the next step in IE (Business Intelligence) solutions.

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Full automation of the EI cycle: data ingest and preparation, predictive analytics and patterns, and hypothesis identification. This is an interesting example of smart data recovery in action. The information that no IE tool had discovered. predictive analytics . Think of the moment when you are hiring insurance.  For your car and the agent asks you a series of questions. These questions are related to the variables that influence risk. Behind these questions is a predictive model that reports the probability of an. Accident occurring based on your age, zip code, gender, car brand, etc. It is the same principle that is  in predictive credit models to identify good and bad payers.

Thus, the main concept of predictive analytics (or modeling) means that a number of variables (income, zip code, age, etc.).  Combin with outcomes (for example, good payer or poor payer) can be used to generate a model. That provides a score (a number between 0 and 1) that represents the probability of an event (for example. The use cases in business are broad: credit models, customer segmentation models (grouping), purchase probability models and customer migration models, among others. 4. . But what does the AI ​​offer us that we don’t already have?” There are many applications for: the client . AI is transforming customer expectations. Uber, Google and Amazon.

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