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Artificial Write For Us – Artificial Intelligence is currently one of the maximum mentioned terms in the world (search for AI (initials for Artificial Intelligence) on Google alone returns more than a billion results). It should not be any different: we are in the middle of the fourth industrial revolution, and whoever aspires to at least remain in force for a while longer must know and understand what it is about, especially its importance.

There are many questions around this topic. Today I want to share five key questions (and answers) to understand what we are talking about when we refer to Artificial Intelligence and its transformative power for life and business (questions collected from what are usually the questionnaires sent by journalists in recent times to a leading company in Artificial Intelligence issues such as SAS in the world):

How has Artificial Intelligence changed the lives of human beings?

Artificial intellect (AI) makes it possible for machines to absorb skills, adjust to new inputs, and perform tasks like humans. Most of the examples of artificial intelligence you hear about today – from computers that play chess to self-driving cars – use deep learning algorithms and techniques, better known as Deep Learning, natural language processing, and image recognition. Employing the immense computational processing resources, large volumes of data can be processed to train these algorithms giving way to Applied Artificial Intelligence that changes the lives of human beings.

In what aspects of our life is Artificial Intelligence present?

It is imperative to take into account two aspects in particular:

AI adds knowledge and decision autonomy to existing products and services. In most cases, artificial intelligence will not be sold as a stand-alone application or product. Instead, the solutions we already use will be enhanced with artificial intelligence capabilities, for example, as Apple did when it added its intelligent assistant Siri as an embedded feature across all products in its portfolio. Automation, conversational platforms, bots, and intelligent machines can be combined with big amounts of data to recover many technologies at home and in the workplace, from security intelligence to asset analytics.

Today, artificial intelligence harnesses the capabilities of Big Data and achieves incredible precision through the use of machine learning algorithms such as deep neural networks to analyze vast amounts of information. For example, the assistants we use every day like Alexa, Google Search and Google Photos, are based on the use of deep neural networks and learn with every interaction and become more accurate the more we use them. In the medical field, computer vision-based artificial intelligence techniques for image classification and object recognition can be used to detect cancer on MRIs (magnetic resonance imaging) with the same precision as highly trained radiologists.

What is machine education and why is it so important in AI?

Machine Learning is a fundamental field of artificial intelligence and focuses on enabling machines the ability to learn similar to how humans do. Machine Learning algorithms look for relationships between different variables to model patterns in the data and automatically deliver results to the machine so that it detects these patterns, simulating the ability to learn to recognize them. Once the algorithm has learned it will be able to apply that knowledge to a new set of data. The life cycle is asking the question, collecting the data, training the algorithm, testing it, collecting the feedback, and using this knowledge to improve the algorithm for more accuracy.

Why is Machine Learning so popular now?

Machine Learning is not something new, in fact, many of the algorithms we use today (eg Neural Networks, Decision Trees, Bayesian Networks, etc.) were invented decades ago, but the resurgence of interest in machine learning machine and other modern deep learning techniques, has increased in recent years due to the increasing volume of data available, the reduction in the cost of computational processing, and the ease of accessing and storing information from any device.

Although many machine learning processes have been around for a long time, the ability to automatically apply composite mathematical calculations to big data – over and over again, faster and faster – is a recent achievement. Here are some widely published examples of machine learning applications:

  • Google’s self-driving car is based on Machine Learning: Deep learning and reinforcement learning techniques.
  • Online recommendation offerings such as those from Amazon and Netflix use machine learning algorithms such as factoring machines and models for sequence analysis and similarity analysis.
  • Knowing the opinion that your customers have on a social network like Twitter is based on the use of natural language processing (NLP) techniques, recurrent neural networks, and deep neural networks.
  • Automatic systems for fraud detection, use anomaly detection analysis algorithms, neural network models and relationship network models (graphs).

What are the technologies that enable Artificial Intelligence?

Several technologies enable and support artificial intelligence:

  • Graphics Processing Units (GPUs) are critical to artificial intelligence because they provide the vast computing power required for iterative processing. Training neural networks requires big data, as well as computing power.


  • The Internet of Things generates enormous amounts of data from related devices, most of it unanalyzed. Mechanizing models with artificial intelligence allows us to use more of them.


  • Advanced algorithms are being industrialized and combined in new ways to analyze more data sooner and at multiple levels. This brainy processing is key to identifying and anticipating rare events, understanding complex systems, and optimizing unique scenarios.


  • APIs, or application programming lines, are portable correspondences of cryptogram that make it possible to add artificial intelligence functionality to existing software products and packages. They can add image credit capabilities to home security systems and question and answer capabilities that describe data, create captions and headers, or highlight interesting patterns and insights in the data.


Why is there talk that it is necessary for more and more people to study related careers such as AI?

Artificial Intelligence together with data analytics are part of one of the most important innovations that have revolutionized the world, which is why it is important that more and more people acquire knowledge related to this matter. Many data scientists started their careers as statisticians or data forecasters. But as big data (and big data storing and dispensation skills like Hadoop) began to grow and grow, those roles also evolved. Data is no longer an afterthought for IT to deal with. It’s key information that requires analysis, creative nosiness, and a knack for translating high-tech thoughts into new ways to generate profit in different environments.

What are currently those careers linked to AI?

The role of the data scientist has academic origins. A few ages ago, universities began to recognize that employers wanted to hire people who were programmers and good team players. Faculty modified their classes to accommodate this requirement – ​​and some programs, such as North Carolina State University’s Institute for Advanced Analytics, developed and prepared to churn out the next cohort of data experts. There are now more than 60 alike programs at universities across the country.

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