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Generative Artificial Intelligence: beyond deepfake, the new frontiers of innovation

September 22, 2022

Generative Artificial Intelligence is an application of this technology that is as promising as it is disturbing, which requires companies to adopt an approach based on a strong ethical foundation.

Among the many emerging applications of Artificial Intelligence, Generative AI is one of the most interesting and at the same time most disturbing, given that creative activity has always been thought of as a distinctive feature of the human mind.

In fact, Generative AI is known today to the general public above all for the phenomenon of deep fakes: audio or video contents that are able to replace the real ones with a degree of precision that was previously unthinkable (the replacement of a face in a video is among the most striking examples).

But according to Gartner, who included it in the Top Strategic Technology Trends of 2022, Generative AI is first of all a “revolutionary technology, capable of generating artefacts that were previously based on human creativity, guaranteeing results. innovative without those prejudices typical of human experience and its thought processes”. Gartner also states that: “By 2025, Generative AI will be responsible for 10% of all data produced, up from less than 1% today, and by 2027, 30% of manufacturers will use AI. Generative to improve the efficiency of the development process”.

Generative AI will rethink the operation of many business areas, from product and content development to customer experience, from analytics to software engineering and, given the potential of the medium, a strong ethical approach on the part of the companies involved is essential.

Not surprisingly, Gartner also states that “IT leaders globally must use appropriate governance to exploit its extraordinary creative potential”.

Among the most interesting and useful applications of Generative AI are the reconstruction of higher resolution images, the remastering of old films, the generation of natural language codes to accelerate software engineering cycles, simulations in the medical field, support to diagnostic activity and microbiological research and also to the design of buildings and vehicles. In the latter case, we speak in particular of generative design, useful for example to redesign an object starting from a given shape (i.e.; lighten a frame) or even create completely new concepts in terms of architecture or product.

In the medical field, on the other hand, Generative Adversarial Networks are considered a particularly promising family of technologies for the computational creation of new molecules, thanks to the high level of innovation in the virtual synthesis of images. Pharmaceutical research requires in fact to simulate different combinations in a chemical environment so that key factors such as the level of toxicity, the ease of synthesis or the stability of the drug can be evaluated and these technologies seem to have excellent potential.

In other less critical areas, Generative AI is used to take content personalisation processes to new levels. For example, a University of California student developed a potentially infinite podcast with AI that creates unique storylines and characters. And marketing agencies are also starting to generate personalised emails and promotional ads using Generative AI techniques, based on their customer data.

Finally, Gartner recommends companies not to lose the advantage of the first mover, in the face of such disruptive technology, and to identify which business areas are the most suitable for starting pilot projects for the use of AI capable of bringing significant improvements.