Generative AI 2023 vs. Traditional AI: What’s the Difference and Why It Matters

Generative AI is a type of artificial intelligence that can create new content, such as text, images, videos, or audio, based on some input data or prompt. For example, you can ask a generative AI system to draw a picture, compose a song, or code an app. Generative AI is one of the most exciting and innovative fields of AI today, with many potential applications and benefits for various industries and domains.

How does generative AI work?

A generative AI system is constructed by applying unsupervised or self-supervised machine learning to a data set. The capabilities of a generative AI system depend on the modality or type of the data set used. For instance, if the data set consists of text, such as books, articles, or tweets, the generative AI system can learn to generate new text in response to a query or a topic. If the data set consists of images, such as photos, paintings, or cartoons, the generative AI system can learn to generate new images that match a description or a style.

There are different types of generative AI models that use different techniques and architectures to learn and generate data. Some of the most popular and powerful generative AI models are:

• Large language models (LLMs): These are neural networks that are trained on large amounts of text and learn to predict the next word in a sentence. For example, “peanut butter and ___” is more likely to be followed by “jelly” than “shoelace”. LLMs can not only generate new text, but also answer questions, summarize documents, translate languages, and perform other natural language processing tasks. Some examples of LLMs are GPT-3 and GPT-4 by OpenAI, LaMDA by Google, and ChatGPT by Microsoft.
• Generative adversarial networks (GANs): These are neural networks that consist of two components: a generator and a discriminator. The generator tries to create realistic data that can fool the discriminator, while the discriminator tries to distinguish between real and fake data. The generator and the discriminator compete with each other and improve over time. GANs can generate realistic images, videos, or audio from noise or low-resolution inputs. Some examples of GANs are StyleGAN by NVIDIA, BigGAN by Google, and WaveGAN by University of Washington.
• Variational autoencoders (VAEs): These are neural networks that encode data into a latent space and then decode it back into the original space. The latent space is a lower-dimensional representation of the data that captures its essential features and variations. VAEs can generate new data that is similar but not identical to the original data by sampling from the latent space. VAEs can also perform tasks such as denoising, inpainting, interpolation, and anomaly detection. Some examples of VAEs are DRAW by Google, SketchRNN by Google, and MusicVAE by Google.

Why is generative AI important?

Generative AI has many advantages and applications for various domains and industries. Some of the benefits of generative AI are:

• Creativity: Generative AI can help us create new content that is novel, diverse, and original. Generative AI can also inspire us with new ideas and perspectives that we may not have thought of before.
• Productivity: Generative AI can help us save time and effort by automating tasks that require generating content, such as writing reports, designing logos, composing music, or coding apps.
• Education: Generative AI can help us learn new skills and knowledge by providing feedback, guidance, examples, and explanations.
• Entertainment: Generative AI can help us enjoy new forms of entertainment and art by creating immersive experiences, interactive stories, personalized content, and expressive media.
• Innovation: Generative AI can help us discover new solutions and possibilities by exploring different scenarios, generating hypotheses, testing assumptions, and optimizing outcomes.

Where can I learn more about generative AI?

If you are interested in learning more about generative AI, here are some useful links and sources:

1. Google Generative AI – Google AI: This website showcases how teams at Google are using generative AI to create new experiences.
2. Generative artificial intelligence – Wikipedia: This article provides an overview of generative AI and its history.
3. Generative Artistry: This website teaches you how to create generative art using code and mathematics.
4. Generative Design: This website introduces you to the concept and practice of generative design, which is a design method that uses generative AI to create optimal solutions.
5.  Generative.fm: This website lets you listen to endless streams of generative music that are created by AI.

Conclusion

Generative AI is a fascinating and powerful technology that can enrich our lives in many ways. It can help us unleash our creativity, boost our productivity, enhance our education, entertain us in new ways, and drive innovation in various fields. If you want to learn more about generative AI or try it for yourself, you can check out some of the links and sources I have provided in this blog post. I hope you found this blog post informative and enjoyable. Thank you for reading!