Generative AI with a historical context

Generative artificial intelligence (generative AI) is a subfield that focuses on creating models that generate new data with similar characteristics to the input training data. Generative AI models learn the patterns and structure of their input training data and then generate new data with similar characteristics. Generative AI has applications across various industries, including art, writing, script writing, software development, product design, healthcare, finance, gaming, marketing, and fashion[1].

Automated art dates back to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music[1]. The tradition of creative automatons has flourished throughout history, exemplified by Maillardet’s automaton created in the early 1800s[1].

The academic discipline of artificial intelligence was established at a research workshop held at Dartmouth College in 1956 and has experienced several waves of advancement and optimism in the decades since[1]. In the 1980s and 1990s, generative AI planning was used to refer to AI planning systems, especially computer-aided process planning, to generate sequences of actions to reach a specified goal[1]. Generative models have a long history of AI, dating to the 1950s. Early models like Hidden Markov Models and Gaussian Mixture Models generated simple data[2]. In NLP, traditional sentence generation used N-gram language models, but these needed help with long sentences[2].

In the ongoing AI boom, advances in transformer-based deep neural networks have enabled several generative AI systems notable for accepting natural language prompts as input. These include large language model (LLM) chatbots such as ChatGPT, Copilot, Bard, and LLaMA, and text-to-image artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E[1]. Investment in generative AI surged during the early 2020s, with large companies such as Microsoft, Google, and Baidu and numerous smaller firms developing generative AI models[1].

However, there are concerns about the potential misuse of generative AI, including cybercrime, the creation of fake news, or the production of deepfakes that can deceive or manipulate people[1]. Using generative AI responsibly and ethically is essential to ensure it benefits society.

?Generative AI is a rapidly evolving field with a long history from ancient times. With its ability to generate new data that has similar characteristics to the input training data, generative AI has applications across a wide range of industries and has the potential to revolutionize the way we live and work. However, using generative AI responsibly and ethically is essential to ensure it benefits society.

Examples of generative AI

Generative AI has a wide range of applications across various industries. Here are some examples of generative AI:

1. Chatbots: Chatbots are computer programs that use natural language processing (NLP) to simulate human conversation. Generative AI models like?ChatGPT?and?LLaMA?have been developed to create chatbots that can generate human-like responses to user queries[3].
2. Artificial intelligence art: Generative AI models like?DALL-E?and?Midjourney?have been developed to create images from textual descriptions[3]. These models can generate realistic images of objects, animals, and fictional characters.
3. Music composition: Generative AI models like?Amper Music?and?AIVA?have been developed to create original music compositions[3]. These models can generate music in various genres and styles.
4. Scriptwriting: Generative AI models like?ScriptBook?have been developed to create movie scripts[3]. These models can analyze existing movie scripts and generate new ones with similar characteristics.
5. Product design: Generative AI models like?Autodesk Dreamcatcher?have been developed to create product designs1. These models can generate designs that meet specific criteria, such as weight, strength, and cost.
6. Video game development: Generative AI models like?GAN Theft Auto?have been developed to create video game environments[3]. These models can generate realistic environments that can be used in video games.

These are just a few examples of the many applications of generative AI. We can expect to see more[4] innovative applications as technology evolves.

May the force be with you! ??

References:

1.-?Generative artificial intelligence - Wikipedia

2.-?History of Generative AI. Paper explained. | by iva vrtaric | Artificialis | Medium

3.-Generative AI: What Is It, Tools, Models, Applications and Use Cases (gartner.com)

4.-?Deep Learning in Healthcare: Latest Developments & Applications (indikaai.com)