10 things you should know about Generative AI
This article is based on my talk at Google on 23 Mar 2024.
Generative AI has rapidly become a cornerstone of modern artificial intelligence, powering innovations from realistic image generation to sophisticated natural language understanding. This article delves into the intricacies of Generative AI, exploring its history, applications, and key technologies that have shaped its development. Here are 10 fundamental aspects to learn about Generative AI.
print "Attention Is All You Need"
print "Attention Is All You Need"
print "Attention Is All You Need"1. History of Generative AI
The journey of Generative AI began in the 1950s, with early experiments in neural networks. However, it wasn’t until the 1980s and 1990s that foundational models, such as Hopfield networks and Boltzmann machines, set the stage for later advances. The 2000s saw the introduction of Deep Learning, which led to the development of more sophisticated generative models. The 2010s marked a significant leap forward with the invention of Generative Adversarial Networks (GANs) and the refinement of neural network architectures, enabling more complex and realistic generation tasks.
2. Applications of Generative AI
As per the survey “A Survey of Generative AI Applications” by Roberto, Generative AI spans a broad spectrum of applications, including image and video synthesis, music creation, text generation, drug discovery, and more. These applications demonstrate the technology’s versatility in creating new content, augmenting human creativity, and solving complex, real-world problems.
3. Discriminative vs Generative AI
Generative AI models generate new data instances, while Discriminative AI models differentiate between different types of data instances. For example, a Generative model can create images of cats never seen before, whereas a Discriminative model can classify images as either ‘cat’ or ‘not cat’. The analogy often used is that Generative models are like painting a picture, while Discriminative models are like telling you if the picture is of a cat or a dog.
4. GANs
Generative Adversarial Networks (GANs) consist of two neural networks, the Generator and the Discriminator, which are trained simultaneously. The Generator creates data resembling the training data, while the Discriminator evaluates its authenticity. An analogy is a counterfeiter (Generator) trying to make fake currency, and the police (Discriminator) trying to detect it. GANs are used in applications like photo-realistic image generation, art creation, and even video game environments.
5. Transformers
Transformers are a type of model that process data in parallel, making them significantly faster and more efficient than their predecessors for tasks involving sequential data, like language processing. Introduced in the paper “Attention is All You Need”, Transformers have become the backbone of models like GPT (Generative Pre-trained Transformer) for text generation and BERT for understanding. They differ from earlier models by using an “attention mechanism” to weigh the importance of different parts of the input data.
6. Attention Mechanism
The Attention Mechanism allows models to focus on specific parts of the input data, improving the efficiency and effectiveness of processing. This was a breakthrough in AI research, enabling the solving of complex tasks such as machine translation, text summarization, and question-answering more effectively. Its importance lies in its ability to handle long-range dependencies in data.
7. Encoder-Decoder Architectures
These architectures are essential for tasks that require an understanding of input data to generate output data, like translating languages or summarizing texts. Examples include encoder-only models like BERT, which are great for tasks that require understanding context; decoder-only models like GPT, which excel in generating text; and encoder-decoder models like T5, which can both understand and generate text.
8. Diffusion Models
Diffusion models are a class of generative models that generate data by gradually refining random noise into a structured output. They are important for creating high-quality images and have applications in image synthesis, super-resolution, and denoising. Despite their impressive capabilities, diffusion models require significant computational resources, which can be a limitation.
9. Hallucinations
In the context of Generative AI, hallucinations refer to instances where the model generates false or nonsensical information. These can range from minor inaccuracies to completely fabricated content. Overcoming these limitations involves refining model architectures, improving training datasets, and implementing better evaluation techniques.
10. RAG Technique
Retrieval-Augmented Generation (RAG) combines the power of large-scale language models with the specificity of retrieved information from databases or the internet. This technique enables models to produce more accurate, detailed, and contextually relevant outputs by leveraging external information sources. Models like RAG-Token and RAG-Sequence demonstrate the effectiveness of this approach in enhancing the capabilities of Generative AI.
