What is generative AI
If you’ve found your way to this blog, chances are you’re already well aware of generative AI and its many applications. But let’s take a minute to get clear on exactly what we mean when we talk about ‘generative AI’.
Generative AI refers to a subset of AI techniques that involve the creation or generation of new and original content, such as images, texts, videos, and even music. Unlike traditional AI models that are primarily designed for classification or prediction tasks, generative AI models are capable of producing novel outputs that have not been explicitly programmed in the training data. For instance, if we train a traditional AI model to classify animals, it may be able to recognize and label images of cats, dogs, and birds. In contrast, a generative AI model can generate an entirely new image of a cat that does not exist in reality but appears realistic and consistent with the characteristics of cats in the training data.
Significance of generative AI
So why is generative AI important, beyond its ability to provide novel images of cats? With its ability to generate original content and push the boundaries of what is possible, generative AI will transform industries, foster creativity, and shape the future of human-machine collaboration. Programmers will be able to code more efficiently, with generative AI tools playing the role of a ‘code copilot’. Creative teams will be able to generate designs, images, and copy with unprecedented speed. Marketers will use generative AI to ideate personalized campaigns and create tailored content and targeted recommendations. Scientists will use it to aid in complex data analysis, drug discovery, and medical imaging. I could go on and on. The truth is, the potential applications and implications of generative AI are limitless, and we’re only beginning to make sense of what that means for individuals, businesses, and society.
For more context on the significance of Generative AI, hear from AI expert and Udacity Co-Founder Sebastian Thrun:
Generative AI Solutions on Google Cloud
There’s a lot of big names involved in the research and development that led to the emergence of generative AI, and unsurprisingly Google is at the center of it all. Google has made some historic breakthroughs in AI, such as the 2017 paper that introduced Transformer, and just this month Google released a variety of exciting AI features for their suite of products at the annual Google I/O conference.
Since then, we’ve been collaborating with Google to create training for professionals eager to take advantage of Generative AI with Google Cloud solutions. We’re excited to share the following seven free courses covering topics and technology that are foundational to generative AI, available for you to start learning right away. All courses can be covered in under an hour and vary in the prerequisite experience that’s required:
- Introduction to Generative AI
- This introductory course explains what Generative AI is, how it is used, and how it differs from traditional machine learning methods.
- Introduction to Large Language Models
- Another introductory course that explores what large language models (LLM) are, their use cases, and how you can use prompt tuning to enhance LLM performance.
- Transformer Models and BERT Model
- This course introduces you to key concepts at the heart of Generative AI: the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.
- Attention Mechanism
- This course offers an introduction to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence.
- Introduction to Image Generation
- Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. In this course, you’ll explore the theory behind diffusion models and how to train and deploy them on Vertex AI.
- Create Image Captioning Models
- Discover the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model, and learn to create an image captioning model by using deep learning.
- Encoder-Decoder Architecture with Google Cloud
- Get a synopsis of encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering.
If it feels like everybody in your orbit is talking about generative AI, it’s because they are. And if it feels like there’s some new tool or AI development you’re seeing every day, it’s because there is. And if you feel overwhelmed and stressed about keeping up with the generative AI hype cycle, you’re not alone.
So what to do about it? I recommend focusing less on all the clickbaity articles (except this one, definitely still focus on this one) and talking heads postulating what generative AI means for your job, and instead focus on understanding the technology itself. By demystifying the tools and technology, you’ll be best positioned to draw your own conclusions about what generative AI means for your unique context, and what to do about it.
And how can you do that? You can start by browsing our collection of free courses on fundamental Generative AI concepts. These courses cover the fundamentals in <1 hour, and several of them assume no prior knowledge of the topic.