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Two classes of AI systems contributing to current AI success stories are generative AI and discriminative AI. Generative AI systems create things, such as pictures, audio, writing samples and anything that can be built with computer-controlled systems like 3D printers. Discriminative systems identify things like people in pictures, words in speech or handwriting and -- most importantly -- what's real vs. what's fake. The two are paired in a generative adversarial network (GAN) model.
For example, a GAN for creating realistic yet fake yearbook photos might use a generative model to synthesize human faces and then pass them, along with real photos, through a discriminative model to see if it can tell which are fake and which are real. The exercise trains both models. The discriminator gets better at identifying fakes, as it is told which images were created by the generator. The generator gets better at creating realistic photos, as it is told which fakes the discriminator successfully identified.
Generative AI examples show success in multiple industries
Pharmaceuticals. Pharmaceutical companies (including Amgen, Insilico Medicine and others) and academic researchers are working with generative AI in areas such as designing proteins for medicines. Predicting the folding of proteins has been an enormous challenge for geneticists and pharmaceutical developers for decades. GANs are increasing researchers' abilities to understand and utilize protein synthesis.
Genetics research. AI is also contributing to genetics research. Geneticists are learning to understand gene expression -- how specific genes and combinations of genes get turned on and off -- and what genes do when they are active. AI is also helping researchers predict how a gene expression will change in response to specific changes in the genes. This shows enormous promise for the development of gene therapies. It also optimizes treatments by predicting which medicines a person's genetics will best respond to.
Manufacturing. In manufacturing, Autodesk, Creo and other products use generative AI to design physical objects. In some cases, they also create those objects through 3D printing or computer-controlled machining and additive manufacturing. Generative AI can create machine parts and sub-assemblies of larger objects, for example, and can sometimes optimize designs for the following aspects of the manufacturing process: materials efficiency (minimizing waste), simplicity (fewest parts) and speed of production.
Other forward-looking generative AI examples
Image synthesis. OpenAI's Dall-E 2 and other products (Midjourney, Deep Dream Generator, Big Sleep, etc.) use AI to create pictures based on text descriptions. If you tell one to create a ridiculous picture of fourteen lemmings and a talking cantaloupe wearing a trench coat and pretending to be a private investigator, it will do so. Dall-E and its many competitors have taken a huge leap forward, in both their image quality and their ability to translate arbitrary text into images. Such systems are finding their way into advertising, product design, set design, film and other industries. Worryingly, they are also showing potential as engines of misinformation, as they can generate "deep fake" images of events that never happened.
Space synthesis. As is the case with images, this kind of synthesis can occur with 3D spaces and objects, both real and digital. On the real side, applications like AutoDesk or Spacemaker can help design buildings and the spaces within them or urban landscapes incorporating built and natural elements. In these situations, AI supplements human designers' work by filling in missing details or proposing solutions to fit specific code requirements or space and material constraints. Numerous companies -- most notably Meta and all the major game creators -- are developing applications to generate virtual spaces for game designs. These AI systems can constantly generate new spaces and possibly even make them infinitely expandable.
Future generative AI examples. While there is no way to predict which generative AI examples and use cases show the most promise for the future, there are certainly some, like image generation and speech synthesis, that have shown enormous progress in the last few years. Other aforementioned areas, like medicine and manufacturing, have also proven enormously promising. Progress in physical use cases appears somewhat slower, which makes sense given the inherent limits imposed by manipulating matter instead of data.
It is important to remember, though, that such progress builds on itself. As the base tools become cheaper, more widely available and easier to use, the pool of people harnessing those tools broadens and the pace of change accelerates. What is equally important to remember is that there are also antisocial and dangerous applications of AI that will become easier in the same ways.