What is artificial intelligence?

3 min read

A definition in simple terms

Artificial intelligence (AI) is a technology that allows computers and machines to simulate human capabilities such as learning, understanding, problem-solving, decision-making, being creative, and acting autonomously [1]. Put simply, it is the ability of a machine to imitate the capabilities of the human mind [2]: artificial devices perform tasks that require human intelligence through learning, perception, reasoning, and natural language processing [3].

Other complementary definitions: Stanford describes it as the science and engineering of making intelligent machines, especially computer programs [4], and it can also be seen as a discipline of computer science that develops systems capable of making decisions autonomously [5].

AI applications can see and identify objects, understand human language, learn from new information and experience, make detailed recommendations, act without human intervention (such as a self-driving car), and generate original content through generative AI [6]. In effect, AI learns and improves thanks to the texts, statistics, and information that we upload into its system [7].

AI, machine learning, and deep learning

A simple way to think about AI is as a series of nested concepts that have emerged over more than 70 years [1]:

• Artificial Intelligence: the broadest concept; machines learn from data to reason, predict, and act [8].
• Machine learning: a subfield of AI that creates models by training an algorithm to make predictions or make decisions based on data, without explicitly programming every rule [1].
• Deep learning: a subset of machine learning that uses multi-layer neural networks, with an input layer, hundreds of hidden layers, and an output layer. Since it does not require human intervention, it processes large volumes of unlabeled data and today powers most AI applications [1].

Generative AI operates on this basis: deep learning models that create original text, images, video, or audio in response to user instructions [6].

Applications and benefits

AI is present in daily life: voice assistants, music and video recommendations, spam filters, maps, facial recognition, fraud detection, and customer service chatbots. Among the documented use cases, notable examples include chatbots that answer queries with natural language processing, fraud detection by analyzing transaction patterns, personalized marketing based on purchase history, and human resources platforms that filter resumes [1]. Its benefits include automating repetitive tasks, improving decision-making, reducing human errors, operating consistently 24 hours a day, and decreasing physical risk in dangerous jobs [1].

Risks and ethics

It is fundamental to understand how this technology works to leverage its benefits and mitigate its risks [9]. The main risks are:

• Data: vulnerable to poisoning, manipulation, bias, or cyberattacks that cause leaks.
• Models: attackers can compromise their integrity by altering architecture, weights, or parameters.
• Operation: model drift, bias, and governance failures.
• Ethics and legal: biased training data can reinforce gender or racial stereotypes, for example in hiring decisions [1].

Therefore, AI ethics studies how to optimize its beneficial impact and reduce risks through a governance system that ensures secure and ethical tools [1].

Conclusion

AI is the ability of machines to simulate human intelligence [2], a constantly evolving discipline [10] built upon successive layers of machine learning and deep learning [1]. Knowing how it works is the best way to leverage its benefits and mitigate its risks [9].