Generative AI: the great leap in artificial intelligence in 2024
What is generative AI and how does it work?
Generative AI is a type of AI that uses mathematical models to generate new data or content from existing data. These models are based on artificial neural networks, which are computer systems inspired by the functioning of the human brain, capable of learning from data and improving their performance with experience.
One of the most used models in generative AI is GPT-4, developed by OpenAI, a non-profit organization dedicated to AI research. The GPT-4 is capable of generating coherent and relevant texts on any topic, from a word, a phrase or an input paragraph. GPT-4 is based on a huge database containing billions of texts from the Internet, and uses a deep learning algorithm that allows it to analyze the context, meaning and style of texts, and generate new combinations of words.
Another very popular model in generative AI is DALL-E, also developed by OpenAI, which can generate images from textual descriptions. The DALL-E combines the GPT-4 with a computer vision model that allows it to interpret words and translate them into pixels. The DALL-E can create images of objects, scenes, people or animals that do not exist in reality, or that combine elements from different domains.
What benefits does generative AI have and how is it being applied? Generative AI has multiple benefits and applications in various sectors and fields.
Some examples are:
In the healthcare sector, generative AI can help create personalized diagnoses, treatments, medications or prostheses, based on patient data. It can also generate synthetic medical images to train professionals or to improve the quality of real images. In the education sector, generative AI can facilitate personalized learning, adapted to the level, interests and needs of each student. It can also generate educational content, such as books, exercises, exams or games, automatically and in a diverse way. In the innovation sector, generative AI can stimulate creativity, problem solving and the design of new products, services or processes. You can also generate prototypes, simulations or proofs of concept, quickly and efficiently.
In the entertainment sector, generative AI can offer immersive, interactive and personalized experiences, both digitally and physically. It can also generate artistic content, such as music, painting, literature or cinema, in an original and varied way. What risks does generative AI have and how are they being regulated? Generative AI also has risks and challenges that must be approached with responsibility and caution.
Some of them are:
The risk of generating false, misleading or malicious content, which may affect the veracity, trust and security of people and organizations. For example, false news, videos or audio can be generated that manipulate public opinion or defame people or entities. Offensive, discriminatory or illegal content may also be generated, which violates human rights or current legislation.
The risk of generating content that violates the privacy, intellectual property or authorship of people and organizations. For example, images can be generated that reveal personal or sensitive data, that infringe image rights or that impersonate other people. Content can also be generated that copies, plagiarizes or infringes copyright or patent rights, without the consent or recognition of its original creators.
The risk of generating content that has a negative impact on society, culture or the environment. For example, content can be generated that promotes addiction, dependence or isolation in people, that reduces their critical or creative capacity, or that alters their perception of reality. Content can also be generated that contributes to the excessive consumption of resources, the generation of waste or pollution.
To mitigate these risks, standards, laws and ethical principles are being developed to regulate the use of generative AI, both nationally and internationally.
