Generative AI: The Next Big Thing
Generative artificial intelligence, or AI, has been making waves in recent years, leading to a surge in headlines about powerful machine-learning models that can create new data rather than make predictions. This technology isn’t entirely new, drawing on research and computational advances from the past few decades.
Increase in Complexity
Generative AI took a huge leap forward with the development of more complex deep-learning architectures. These include the Generative Adversarial Network (GAN) and diffusion models, as well as Google’s transformer architecture, which powers models like ChatGPT. Each of these approaches allows for the generation of new data that looks similar to the training dataset.
A Variety of Applications
Generative AI has a wide range of applications, from creating synthetic image data to training intelligent systems to recognize objects, to designing novel protein and crystal structures for materials science. However, it has limitations and might not be the best choice for all types of data. Although generative AI is powerful, it’s not perfect and has the potential to amplify biases and raise red flags related to issues such as worker displacement and copyright concerns.
In conclusion, generative AI is a fascinating and powerful field with the potential to revolutionize many industries, but it also raises important ethical and practical considerations.