Automating tiring and repetitive activities to leave more space for human creativity: this is one of the concrete consequences of implementing AI

what is artificial intelligence?

IA analyzes data deeper and deeper using neural networks that have many hidden layers.

A lot of data is needed to train deep learning models as they learn directly from them. The more data you can feed them, the more accurate they become.

AI achieves incredible accuracy thanks to the depth of neural networks - which was previously impossible.

For example, your interactions with Alexa, Google Search, and Google Photos are all based on deep learning and keep getting more accurate as you use them.

AI to get the most out of your data

When the algorithms are self-learning, the data itself can become intellectual property. In any market sector, even if all competitors applied similar analysis techniques, the owner of the best data would still be winner




AI automates continuous learning and discovery through data.

But AI behaves differently than hardware-based robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume tasks reliably and effortlessly.


AI adds intelligence to existing products.

Instead, products already in use are enhanced with AI capabilities, just as Siri was added as a new feature to a generation of Apple products.


AI adapts through progressive learning algorithms and lets the data do the programming.

The AI finds the structure and regularities in the data so that the algorithm acquires a skill: the algorithm becomes a classifier or a predictor. Retro propagation is an artificial intelligence technique that allows the model to adapt through training and additional data, when the first answer is incorrect.

what is artificial intelligence?

Machine learning

automate the construction of analytical models. It uses methods from neural networks, statistics, operations research and physics to find hidden information in data without having been explicitly programmed as to where to look or what conclusions to come to.

Cognitive computing

it is a branch of artificial intelligence that wants to achieve a natural interaction with machines, similar to that of humans.
Using artificial intelligence and cognitive computing, the ultimate goal is a machine that simulates human processes through the ability to interpret images and speech and is then able to respond consistently.

Neural networks

they are a type of machine learning made up of interconnected units (such as neurons) that process information by responding to external inputs and relaying information between each unit.
The process takes the data multiple steps to find connections and achieve
meaning from the undefined data.

Natural language processing

(NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural linguistic interaction, which allows humans to communicate with computers using normal, everyday language to carry out their activities.

Deep learning 

uses huge neural networks with many layers of processing units. Leverage advances in computing power and improved learning techniques to learn the complex patterns found in large amounts of data. The most common applications include image and voice recognition.

Other technologies enable and support artificial intelligence:

Graphics processing unit

they are critical to AI because they provide the computing power needed for iterative processing. Learning neural networks requires big data and high computing power.

The Internet of Things

generates huge amounts of data from connected devices, most of it not scanned. Automating models with AI will allow us to use more of them.

Advanced algorithms

have been developed and combined in new ways to analyze more data faster and at more levels. This intelligent processing is critical for identifying and predicting rare events, understanding complex systems, and optimizing scenarios.

APIs, the programming interfaces of an application

are code packages that allow you to add AI capabilities to existing products and software packages.
They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and titles or recall patterns and information about the data.
In summary, the goal of AI is to provide software that can think about inputs and explain about outputs.


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