Generative AI is a subset of artificial intelligence that involves creating models capable of generating new content, such as images, videos, and text. This technology has been used by companies like Google and Microsoft to improve their software products, including Gmail and Microsoft Word. From creating photorealistic images and videos to mimic human-like reasoning, the potential applications for Generative AI in content creation are vast.
In recent years, Generative AI has emerged as a game-changing technology that is driving innovations and advancements in various fields. While Google and Microsoft are the biggest players in this field, other companies are also investing in the development of generative AI technology. This includes large companies like Salesforce Inc, as well as smaller startups like Adept AI Labs.
Generative AI, or GenAI, is a form of Artificial Intelligence that can generate diverse types of content including images, videos, audio, text, and 3D models, by acquiring knowledge from existing data sets and utilising it to create hyper-realistic and complex content that mimics human creativity. It has been used as a tool in many industries including gaming, entertainment, and product design and manufacturing.
Recent breakthroughs in the field, such as GPT (Generative Pre-trained Transformer) and Midjourney, have significantly advanced the capabilities of GenAI, opening new possibilities for using GenAI to solve complex problems, create art, and even assist in scientific research.
OpenAI GPT - The Generative AI Changing Content Creator Industry
What makes ChatGPT a new iteration in AI is its impressive performance in natural language generation tasks. Compared to earlier language models, ChatGPT is capable of generating much more complex and coherent responses to prompts. It achieves this by using a large number of parameters (175 billion, as of 2021) and being trained on a diverse range of data sources.
The development of ChatGPT represents a major milestone in the field of artificial intelligence and natural language processing. It has the potential to revolutionize a wide range of applications, from chatbots and virtual assistants to language translation and content creation.
OpenAI, the company behind GPT, is probably the one that has raised more concerns as to the extent and reasoning of their generative AI. Mira Murati, OpenAI's Chief Technology Officer, told ABC News recently that:
"The goal [with GPT] is to predict the next word – and with that, we're seeing that there is this understanding of language. We want these models to see and understand the world more like we do."
On the other hand, OpenAI’s CEO Sam Altman said:
"The right way to think of the models that we create is a reasoning engine, not a fact database. They can also act as a fact database, but that's not really what's special about them – what we want them to do is something closer to the ability to reason, not to memorize."
The new GPT-4 is OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%.
One of the major advantages is regarding AI behaviour, more specifically, steerability. Rather than the classic ChatGPT personality with a fixed verbosity, tone, and style, developers can now prescribe their AI’s style and task by describing those directions in the “system” message. System messages allow API users to significantly customize their users’ experience within bounds.
But GPT is not the only AI changing the current landscape.
Generative Adversarial Networks
One of the most significant innovations in Generative AI is the development of generative adversarial networks (GANs). GANs consist of two neural networks that work together to generate new content. The first network generates content, while the second network evaluates that content and provides feedback to the first network. This iterative process allows the model to continuously improve and generate increasingly realistic content.
GANs have numerous applications, such as creating photorealistic images, videos, and even music.
Some examples of GAN applications include:
- Image and video generation: GANs can generate realistic images and videos of people, animals, and objects. For example, NVIDIA's StyleGAN2 can generate high-resolution human faces that are nearly indistinguishable from real photos.
- Voice and music synthesis: GANs can be used to synthesize realistic speech and music. For example, Google's WaveNet can generate human-like speech with natural intonation and inflection.
- Data augmentation: GANs can be used to create new training data to improve the accuracy of machine learning models. For example, GANs can be used to generate new images of cars or people to train an image recognition system.
- Video game development: GANs can be used to generate new game assets such as characters, landscapes, and environments. For example, Unity's ArtEngine uses GANs to generate game-ready 3D assets.
- Fashion design: GANs can be used to generate new fashion designs and styles based on existing data. For example, the startup Heuritech uses GANs to analyze fashion trends and generate new designs for clothing brands.
Another innovation in the field of Generative AI is the use of reinforcement learning. Reinforcement learning is a type of machine learning that involves training models to make decisions based on trial and error. In Generative AI, reinforcement learning can be used to create models that generate new content based on user feedback. For example, a chatbot trained using reinforcement learning can learn to generate more realistic and human-like responses based on feedback from users.
Here are some examples of generative AI techniques that can be used in reinforcement learning:
- Generative Adversarial Imitation Learning (GAIL): GAIL is a technique that uses a GAN to learn a policy from expert demonstrations. The generator generates fake demonstrations, and the discriminator tries to distinguish between the real and fake data. The generator is trained to generate data that is indistinguishable from the expert demonstrations, which can then be used to train a reinforcement learning agent.
- Variational Autoencoder Reinforcement Learning (VAE-RL): VAE-RL is a technique that combines reinforcement learning with variational autoencoders (VAEs), which are a type of generative model. The VAE learns a compressed representation of the state space, and the RL agent learns to take actions to maximize the reward signal in this compressed space.
- Deep Q-Networks with Generative Models (DQGM): DQGM is a technique that combines deep Q-networks (DQNs) with generative models such as VAEs or GANs. The generative model is used to learn a compressed representation of the state space, and the DQN learns to take actions to maximize the reward signal in this compressed space.
- Adversarial Inverse Reinforcement Learning (AIRL): AIRL is a technique that uses a GAN to learn a reward function from expert demonstrations. The generator generates fake demonstrations, and the discriminator tries to distinguish between the real and fake data. The reward function is learned by training the discriminator to output a high score for the expert demonstrations and a low score for the fake demonstrations.
Natural Language Processing
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interactions between humans and computers using natural language. Generative AI is a subfield of artificial intelligence that involves creating new content that is similar to existing data. NLP and generative AI are closely related because generative AI can be used to create new language content, such as text, speech, or dialogue, that can be used in NLP applications.
Generative AI techniques can be used in NLP to create new language content in various applications such as chatbots, machine translation, summarization, and sentiment analysis. For instance, in chatbots, generative AI models can be used to generate responses that are more human-like and contextually appropriate for different user inputs. These models can be trained on large amounts of conversation data to learn patterns of language use and to generate responses that are more likely to be relevant and engaging for users.
In machine translation, generative AI models can be used to translate text from one language to another. These models can be trained on large amounts of parallel text data, which consists of pairs of sentences in two different languages, to learn patterns of language use and to generate accurate translations. The models can be further enhanced using techniques such as back-translation and iterative refinement to improve the quality of the translations.
As generative AI becomes more advanced, it is also becoming more accessible to developers and researchers who may not have a background in machine learning. New tools and platforms are being developed that allow anyone to create generative models without needing extensive knowledge of deep learning or other technical skills. This democratization of generative AI could lead to even more rapid advances in the field as a wider range of people are able to contribute to its development.