{"id":16228,"date":"2025-10-13T11:56:14","date_gmt":"2025-10-13T09:56:14","guid":{"rendered":"https:\/\/www.panter.ch\/?p=16228"},"modified":"2025-10-13T11:56:14","modified_gmt":"2025-10-13T09:56:14","slug":"what-is-generative-ai","status":"publish","type":"post","link":"https:\/\/www.panter.ch\/en\/stories\/what-is-generative-ai\/","title":{"rendered":"What is generative AI?"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”40px” top_padding_phone=”12vw” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”20px” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”default” gradient_type=”default”][nectar_highlighted_text color_type=”regular” highlight_color=”#7000FF” style=”regular_underline” highlight_expansion=”default” underline_thickness=”default” custom_font_size=”2vw” text_direction=”default”]Have you ever wondered how a machine can write a poem or create a photorealistic image from a simple text description? This is possible thanks to generative AI<\/em>.<\/strong>[\/nectar_highlighted_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”40px” top_padding_phone=”12vw” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”10px” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”default” gradient_type=”default”][vc_column_text css_animation=”fadeInUp” css=”” text_direction=”default”]\n

Basics of Generative AI
\n<\/h2>\n[\/vc_column_text][nectar_highlighted_text color_type=”regular” highlight_color=”#7000FF” text_color=”#000000″ style=”regular_underline” highlight_expansion=”default” underline_thickness=”1px” text_direction=”default”]Generative AI (Generative Artificial Intelligence) refers to AI systems that can generate new, original content similar to that on which they have been trained. Data is not just interpreted, but recreated<\/strong>. In essence, AI systems learn patterns and structures<\/strong> from existing data and generate plausible variations<\/strong> – be it text, images, music, code or even videos. The focus is on creativity and generation<\/g>, as opposed to pure analysis or prediction. To better understand this, we differentiate it from two other prominent types of AI.
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Difference to other AI models
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Discriminative AI<\/span><\/h4>\n[\/vc_column_text][nectar_highlighted_text color_type=”regular” highlight_color=”#7000FF” text_color=”#000000″ style=”regular_underline” highlight_expansion=”default” underline_thickness=”1px” text_direction=”default”]This model learns to differentiate between data points and assign them to categories. It therefore classifies or differentiates existing data. A classic example is a spam filter that decides whether an email is “spam” or “not spam”. It does not generate anything new, but makes a yes\/no decision based on learned patterns.
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Predictive AI<\/span><\/h4>\n[\/vc_column_text][nectar_highlighted_text color_type=”regular” highlight_color=”#7000FF” text_color=”#000000″ style=”regular_underline” highlight_expansion=”default” underline_thickness=”1px” text_direction=”default”]This AI makes predictions – in the sense of the Latin praedicere (to predict) – by evaluating historical data. It answers the question “What will happen next?”. Examples of this are weather forecasts<\/strong> or the prediction of share prices<\/strong>. Here too, no new content is generated, only probabilities<\/strong> and estimates<\/strong>.
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Generative AI<\/span><\/h4>\n[\/vc_column_text][nectar_highlighted_text color_type=”regular” highlight_color=”#7000FF” text_color=”#000000″ style=”regular_underline” highlight_expansion=”default” underline_thickness=”1px” text_direction=”default”]This model, on the other hand, learns the underlying patterns and structures in a data set so well that it can generate completely new but plausible examples from them. It answers the question: “What would a new example look like that fits this data?”. <\/p>\n

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Core technologies of generative AI
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[\/vc_column_text][vc_column_text css=”” text_direction=”default”]There are several established approaches that vary depending on the application domain (e.g. text vs. images). The most common are
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Generative Adversarial Networks (GANs)
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[\/vc_column_text][vc_column_text css=”” text_direction=”default”]GANs consist of two neural networks that “compete” against each other: the generator<\/strong> and the discriminator<\/strong>. Imagine an art forger (generator) trying to paint a perfect duplicate of a Rembrandt, and an art detective (discriminator) who has to distinguish real from fake paintings.
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