Generative AI: Creating New Data with Incredible Accuracy
What is Generative AI and How It Works
Generative AI, also known as generative adversarial networks (GANs), is a form of artificial intelligence that creates new data by learning from existing datasets. Unlike traditional AI systems that rely on explicit instructions, generative AI operates by learning patterns and structures within a given dataset and using that knowledge to create new outputs.
Generative AI models typically consist of two key components: a generator and a discriminator. The generator network takes random noise as input and generates new data that is similar to the training data. The discriminator network, on the other hand, tries to distinguish between the generated data and the real data. The two networks are trained together in a process called adversarial training, where the generator tries to fool the discriminator, and the discriminator tries to correctly classify the data as real or fake.
The result of this training is a generator network that can create new data that is similar to the training data but is not an exact copy. This makes generative AI useful in a wide range of applications, including image and video synthesis, music generation, and natural language processing.
The Crucial Role of Data Labeling
Data labeling plays a pivotal role in the training of generative AI models. Before training can begin, a large dataset with accurately labeled examples is required. These labels serve as the ground truth, allowing the models to understand the desired patterns and characteristics of the generated content. For instance, in image generation, the dataset may include labeled images of specific objects or scenes, enabling the model to learn and generate new images of the same nature.
The labeling process involves annotating the data with relevant tags or attributes. This step requires human annotators who possess domain knowledge and follow specific guidelines. Their expertise ensures that the training data accurately reflects the desired outcomes, leading to better results in the generated content.
Can Machines Learn on Their Own?
While generative AI models showcase remarkable abilities to create content, it is important to note that these machines do not learn completely on their own. The training process involves human supervision and intervention at various stages. From curating the initial dataset to defining evaluation metrics and fine-tuning the models, human expertise is indispensable.
However, it is worth mentioning that once trained, generative AI models can operate independently to produce novel and creative outputs. These outputs may exhibit a level of creativity beyond what was explicitly present in the training data. Nevertheless, the models are still limited by the quality and diversity of the training data, as well as the biases inherent in the dataset.
For good illustrations that explain how generative AI works, we compiled a list below.
- This YouTube video by Two Minute Papers provides a brief overview of GANs and includes some great visuals: https://www.youtube.com/watch?v=Sw9r8CL98N0
- This article by NVIDIA includes some fantastic examples of GAN-generated images: https://blogs.nvidia.com/blog/2018/08/02/what-is-a-gan/
- This interactive article by The New York Times allows you to explore some of the latest advancements in generative AI: https://www.nytimes.com/interactive/2018/10/12/opinion/artificial-intelligence-deep-learning-art.html
- This Medium article includes some great illustrations that explain the basics of GANs: https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09
- This article by OpenAI includes some amazing examples of GAN-generated images, including faces, animals, and landscapes: https://openai.com/blog/glow/
