Generative AI refers to artificial intelligence models designed to generate new content in the form of written text, audio, images or videos. The applications and use cases are very broad. Generative AI can be used to create a short story based on the style of a particular author, generate a realistic image of a person who does not exist, compose a symphony in the style of a famous composer, or create a video clip from a simple textual description.

To better understand the uniqueness of generative AI, it is useful to understand how it differs from other types of AI, programming and machine learning.

Traditional AI refers to AI systems that can perform specific tasks following predetermined rules or algorithms. They are primarily rule-based systems that cannot learn from data or improve over time. Either way, Generative AI can learn from data and generate new instances of data.

Generative AI and the different models designed to generate new content.

Generative AI refers to artificial intelligence models designed to generate new content in the form of written text, audio, images or videos. The applications and use cases are very broad. Generative AI can be used to create a short story based on the style of a particular author, generate a realistic image of a person who does not exist, compose a symphony in the style of a famous composer, or create a video clip from a simple textual description.

To better understand the uniqueness of generative AI, it is useful to understand how it differs from other types of AI, programming and machine learning.

Traditional AI.

Traditional AI refers to AI systems that can perform specific tasks following predetermined rules or algorithms. They are primarily rule-based systems that cannot learn from data or improve over time. Generative AI, on the other hand, can learn from data and generate new instances of data.

Machine learning.

Machine learning allows a system to learn from data rather than through explicit programming. In other words, machine learning is the process in which a computer program can independently adapt to and learn from new data, resulting in the discovery of trends and strategic information. Generative AI makes use of machine learning techniques to learn and create new data.

Conversational AI.

Conversational AI allows machines to understand and respond to human language in a human-like manner. While generative AI and conversational AI may appear similar, particularly when generative AI is used to generate human-like text, their main difference lies in their purpose. Conversational AI is used to create interactive systems that can engage in human-like dialogues, while generative AI is broader, encompassing the creation of various types of data, not just text.

Artificial general intelligence.

Artificial general intelligence (AGI) refers to highly autonomous systems – currently hypothetical – that can outperform humans in more economically valuable jobs. If realized, AGI would be able to understand, learn, adapt and implement knowledge in a wide range of tasks. While generative AI may be a component of such systems, it is not equivalent to AGI. Generative AI focuses on creating new instances of data, while AGI denotes a broader level of autonomy and capability.

AI tools in imaging, another dilemma for the photography industry.

If the ability to create realistic – but not real – finishes through artificial intelligence has reached this point in less than 8 months and continues to evolve at the rate it does, it stands to reason that getting an answer to the question “And now ?” could be as complex as squaring the circle symbolically embodied in St. Augustine’s parable, the Trinity and the boy who, bucket after bucket, was trying to empty all the water from the sea into a hole in the sand.

AI photo editor.

Any technological advance that directly affects the fiber of any productive sector is never without controversy, nor without consequences, both positive and negative. All of us who have dedicated ourselves to photography, and even more so those of us who were trained in the analog era of celluloid and chemistry, have had our moral dilemmas with the irruption of digital cameras and the democratization of the use of digital retouching such as Photoshop. Not to mention cell phones and their increasingly sophisticated integrated cameras.

There was no shortage of voices shouting “photography is dead”. But the fact is that this is not the case. More pictures are taken today than ever before, and although a smartphone may be equipped with Leica or Carl Zeiss lenses, DSLR cameras continue to evolve and conquer the market, to the point of saturating it with such a wide range of offerings.