What’s Generative AI? Explore Underlying Layers of Machine Learning and Deep Learning

*Generated by AI

Overview

Generative AI is a type of artificial intelligence that is capable of generating new and original content such as images, music, video, or text that did not previously exist. Generative AI systems are designed to learn and mimic the patterns and characteristics of a particular type of data, and then use that knowledge to create new content that is similar to the original data. It provides AI models to build powerful applications.

Let’s first explore where AI models fits into the landscape.

AI Landscape

• Artificial Intelligence: It is a technological discipline that involves emulating human behavior by utilizing machines to learn and perform tasks without the need for explicit instructions on the intended output.
• Machine learning: It is a subset of AI that refers to systems that can learn by themselves. ML models take in data and fit the data to an algorithm, to make predictions like how much money a store might make in a given day.
• Deep learning: It is a subset of machine learning that’s based on artificial neural networks. The learning process is deep because the structure of artificial neural networks consists of multiple input, output, and hidden layers. Each layer contains units that transform the input data into information that the next layer can use for a certain predictive task. It operates on massive, large datasets.
• Generative AI: It is a subset of deep learning models that can produce new content based on what is described in the input. The collection of generative AI models that can produce language, code, and images.

In Machine learning, the algorithm requires explicit instruction on how to make an accurate prediction by consuming more information. In contrast, in Deep learning, the algorithm can learn how to make an accurate prediction through its own data processing, facilitated by the artificial neural network architecture.

Below table presents a more detailed comparison of the two techniques:

Comparision between Machine Learning and Deep Learning

Examples of generative AI systems include:

• StyleGAN: A system that generates realistic images of human faces
• OpenAI: ChatGPT-3, a language model that can generate natural language text. Refer my other article for more insight and architecture on OpenAI ChatGPT: Architecture of OpenAI ChatGPT
  GPT-4, a large multimodal that can accept image and text inputs and emit outputs. Refer my other article for more insight and architecture for GPT-4: What’s new in GPT-4: Architecture and Capabilities
  DALL-E: A generative model that creates images from textual descriptions, such as “Sky made up of cotton candy” or “a helicopter on a rocky cliff.” Refer my other article for more insight and details for DALL-E: Digital Art with AI OpenAI DALL·E 2
• AIVA: A generative AI platform uses deep learning algorithms to compose original music in various genres, such as classical, cinematic, and pop. AIVA can generate new music pieces based on different parameters, such as tempo, key, and mood.
• Synthesia: A generative AI platform for creating video content. Synthesia uses a combination of deep learning algorithms and natural language processing to generate realistic video content based on text inputs. Users can enter a script or text, and Synthesia’s AI system will automatically generate a video that matches the content of the text, including audio, visual, and animation elements.

Types of Generative AI

1. Variational Autoencoders: Variational Autoencoders (VAEs) are a type of generative AI system that are trained to learn a low-dimensional representation of a dataset. VAEs use this representation to generate new data points that are similar to the original dataset, but with variations.
2. Generative Adversarial Networks: Generative Adversarial Networks (GANs) are a type of generative AI system that consist of two neural networks: a generator and a discriminator. The generator creates new data points, while the discriminator tries to distinguish between the generated data and the original data. Over time, the generator improves its ability to create data that is more realistic and harder for the discriminator to distinguish from the original data.
3. Autoregressive Models: Autoregressive models are a type of generative AI system that generate new data points one at a time, based on the probabilities of previous data points in the sequence. These models are often used for generating text, as they can learn the patterns of language and generate new sentences that are grammatically correct and coherent.

Applications of Generative AI

1. Image Synthesis Generative AI systems are capable of generating high-quality images that are often indistinguishable from real images. This technology can be used in a variety of applications, such as creating realistic images for video games or simulating medical images for training purposes.
2. Music Generation Generative AI systems can also be used to create new music compositions. These systems can learn the patterns and characteristics of different types of music, and then use that knowledge to create new compositions that are similar in style or genre.
3. Text Generation Generative AI systems such as GPT-3 can generate natural language text that is often difficult to distinguish from text written by humans. This technology can be used in a variety of applications, such as generating news articles, creating chatbots, or even writing creative fiction.
4. Video Generation Generative AI systems can be used to create video content from scratch. AI algorithms can generate realistic animations, characters, and backgrounds based on user input, making it easy to create videos for marketing, training, and entertainment purposes. It has tremendous potential to transform the way videos are created. From automatic video editing to personalized content creation, businesses and creators can leverage generative AI to save time, reduce costs, and increase creativity. As AI technology continues to advance, we can expect to see even more exciting applications for generative AI in the future.

Challenges and Future of Generative AI

While generative AI has made significant strides in recent years, there are still several challenges that must be addressed to fully realize its potential and ensure its responsible use.

One of the biggest challenges of generative AI is its inherent bias. Generative AI models are only as good as the data they are trained on, and if that data is biased or incomplete, the resulting model will be as well.

For example, a generative AI model is trained on a dataset of job applications and resumes that is biased towards men, with fewer examples of resumes from women or non-binary individuals. The model may learn to associate certain words or phrases with male applicants, such as “assertive” or “competitive,” while ignoring or downplaying similar qualities in resumes from female or non-binary applicants.
This could lead to biased hiring decisions if the model is used to screen resumes for a company.

To address this challenge, it is important to ensure that the training data used for generative AI models is diverse and representative of the real world, including a variety of genders, races, ages, and backgrounds. This can help to reduce bias and ensure that the resulting models are fairer and more accurate.

Another challenge of generative AI is its lack of transparency. Because generative AI models learn on their own, it can be difficult to understand how they arrived at a particular output. This lack of transparency can make it challenging to diagnose and fix errors or biases in the model. To address this challenge, researchers are working to develop tools and techniques for interpreting and visualizing the inner workings of generative AI models.

A related challenge is the ethical implications of generative AI. As generative AI becomes more advanced, it raises important ethical questions about its use and impact on society. For example, if generative AI can create realistic images or videos of people who don’t actually exist, how will this impact issues like identity theft or privacy? To address these challenges, researchers and policymakers must work together to establish ethical guidelines for the development and use of generative AI.

Finally, one of the biggest challenges of generative AI is its scalability. While generative AI has shown impressive results in generating content like images or music on a small scale, it is still limited in its ability to scale up to more complex tasks like generating entire films or novels. To address this challenge, researchers are exploring new architectures and techniques for generative AI that can handle more complex and sophisticated tasks.

Despite these challenges, the future of generative AI is bright. As researchers continue to make progress in addressing the challenges of bias, transparency, ethics, and scalability, we can expect to see even more impressive and impactful applications of generative AI in the years ahead. From creating more lifelike virtual environments to generating new works of art and literature, the possibilities for generative AI are endless, and it will undoubtedly play a critical role in shaping the future of technology and society.

*Note: Some of the contents from this article generated by Chat GPT 3.5 & Bing search engine.