What Does Generative AI Mean?
While the term generative AI is often associated with ChatGPT and deep fakes, the technology was initially used to automate the repetitive processes used in digital image correction and digital audio correction.
Techopedia Explains Generative AI
Any time an AI technology is generating something on its own, it can be referred to as “generative AI.” This umbrella term includes learning algorithms that make predictions as well as those that can use prompts to autonomously write articles and paint pictures.
How Generative AI Works
Once a generative AI algorithm has been trained, it can produce new outputs that are similar to the data it was trained on. Because generative AI requires more processing power than discriminative AI, it can be more expensive to implement.
In a GAN, two machine learning models are trained at the same time. One is called the generator and the other is called the discriminator. The generator’s job is to create new outputs that resemble training data. The discriminator’s job is to evaluate the generated data and provide feedback to the generator to improve its output.
In a VAE, a single machine learning model is trained to encode data into a low-dimensional representation that captures the data’s important features, structure and relationships in a smaller number of dimensions. The model then decodes the low-dimensional representation back into the original data. Essentially, the encoding and decoding processes allow the model to learn a compact representation of the data distribution, which it can then use to generate new outputs.
Some generative AI models can even use random noise as input to generate new outputs. In this approach, the model takes a random noise vector as input, passes it through the network and generates output that is similar to the training data. The new data can then be used as additional, synthetic training data for creative applications in art, music and text generation.
When generative AI is used as a productivity tool to enhance human creativity, it can be categorized as a type of augmented artificial intelligence.
Popular Free Generative AI Apps for Art
Art AI generators provide end users with a fun way to experiment with artificial intelligence. Here are some of the most popular and free art AI generators:
DeepDream Generator – An open-source platform that uses deep learning algorithms to create surrealistic, dream-like images.
DALL·E2 – This AI model from OpenAI generates new images from text descriptions.
Pikazo – This mobile app uses AI filters to turn digital photos into paintings of various styles.
Popular Free Generative AI Apps for Writers
The following platforms provide end users with a good place to experiment with using AI for creative writing and research purposes:
GPT-3 Playground – allows end users to interact with OpenAI’s GPT-3 language model and generate text based on prompts the end user provides.
Write With Transformer – allows end users to use Hugging Face’s transformer ML models to generate text, answer questions and complete sentences.
AI Dungeon – this online adventure game uses a generative language model to create unique storylines based on player choices.
Writesonic – this writing and image generation platform is a popular choice for ecommerce product description.
Popular Free Generative AI Apps for Music
Here are some of the best generative AI music apps that can be used with free trial licenses:
Amper Music – creates musical tracks from pre-recorded samples.
AIVA – uses AI algorithms to compose original music in various genres and styles.
Ecrette Music – uses AI to create royalty free music for both personal and commercial projects.
Musenet – can produce songs using up to ten different instruments and music in up to 15 different styles.
Business Uses for Generative AI
Today’s generative AI can create content that seems to be written by humans and pass the Turing test established by notable mathematician and cryptographer Alan Turing. That’s one reason why people are worried that generative AI will replace humans whose jobs involve publishing, broadcasting and communications.
Here’s an example of how generative AI might replace a human copywriter:
The task: Putting together an insurance brochure from a list of policies, along with their costs, benefits and other details.
The traditional way this would work is that a human writer would take a look at all of that raw data, take notes and write a narrative. With generative AI, learning algorithms can review the raw data programmatically and create a narrative that appears to have been written by a human.
In addition to creating deliverables for marketing, other popular uses for generative AI in business include:
- Web publishing – generative AI models can be used to create engaging non-fiction text, digital images, video and audio content.
- Art and Entertainment – generative AI models can be used to create immersive Web3 experiences.
- Portfolio management – generative AI models can be used to optimize investment portfolios by analyzing a wide range of market data and then generating detailed predictions based on past performance and current market trends.
- Healthcare – AI models can be used to generate personalized treatment plans and synthetic images that can be used to fine-tune medical image analytics applications.
- Customer Experience Management – generative chatbots can be used to answer customer questions and provide personalized marketing messages.
Will Generative AI Replace Humans in the Workplace?
Proponents of the technology argue that while generative AI will replace humans in some jobs, it will actually create new jobs because there will always be a need for a human in the loop (HiTL).
Humans are still required to select the most appropriate generative AI model for the task at hand, aggregate and pre-process training data and evaluate the AI model’s output.
Generative AI and Ethics
Some people are concerned about the ethics of using generative AI technologies, especially those technologies that simulate human creativity.
Generative AI can produce outputs that are difficult to trace back to the responsible parties, which in turn, can make it challenging to hold individuals or organizations accountable for fake news or deepfake videos generated by AI.
This has led to a more general debate about responsible AI and whether restrictions should be put in place to prevent data scientists from scraping the internet to get the large data sets required to train their generative models.
Currently, the legality of scraping the internet to acquire free data for training depends on several factors — including specific laws and regulations in the jurisdiction where the data is being collected, the type of data that’s being collected and how the data is being used.
As the value of high-quality data sets continues to increase, and data owners become more aware of their web content’s worth to data scientists, machine learning engineers (MLEs) may need to pay web publishers for the data they use to train their generative models.