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How Generative AI Works ?
From Imagination to Reality
AI has come a long way from just being able to calculate numbers and perform logical operations. Now AI systems can create new things - images, text, music and more. This "creative" aspect of AI is known as generative AI.
Let me start by explaining what AI is. AI stands for artificial intelligence - computer systems that can perform tasks normally requiring human intelligence. An AI system is "trained" using large amounts of data and examples, allowing it to identify patterns and make intelligent predictions.
Generative AI takes this a step further by creating new content that follows the patterns it has learned. Instead of just analyzing data, generative AI systems produce original outputs - images, text or music - that isn't copied from the training data. They generate something new that is influenced by what they have learned.
How a Generative AI model works:
The basic idea behind a generative AI model is to train a neural network to learn the patterns and relationships that exist in a dataset, and then use that knowledge to generate new data that is similar to the original dataset.
The model is trained on a dataset of real examples. For example, if the goal is to generate new human faces, the model would be trained on thousands of photos of real human faces.
During training, the model learns the patterns and statistical distributions present in the training data. It essentially learns what makes something look like a realistic human face.
The model learns a "latent space" representation of the data. This means it finds a way to represent the data as points in a continuous multidimensional space. Similar concepts are located close together in this space.
When the model (generator) generates new data, it starts with a random point in the latent space. This point acts as a code that represents some concept - for example, what kind of face to generate.
The model also takes random "noise" as input. This causes variation in the output, so different noise inputs combined with the same latent point can generate different but similar outputs.
Using the latent point and noise as inputs, the model then generates an output - in this case, an image of a novel human face.
However, the initial generated face is often unrealistic or flawed in some way. So the model uses techniques like "adversarial training" to refine its outputs.
During adversarial training, a "discriminator" network evaluates the generated faces and determines how realistic they are. It provides feedback that the generative model can use to improve.
The generative model adjusts its weights and generation process based on this feedback. When it generates new faces, they should be of higher quality.
This process of generate->evaluate->adjust is repeated iteratively, with the generated faces becoming more and more realistic with each iteration.
In summary, the generative AI model learns the statistical properties of real data, represents concepts in a latent space, generates initial outputs that it then refines using techniques like adversarial training, and improves its outputs through an iterative process.
The website This Person Does Not Exist uses generative AI to generates a random, photo of a fictional person every time you visit it. These are all fictional persons who do not actually exist.
Let us take another simple example:
Imagine you show a computer lots of different photos of animals like dogs, cats, birds and fish. The computer studies all the shapes, colors, textures and patterns in the animal photos.
Now the computer has an "idea" of how animals generally look based on the photos it saw.
When you ask the computer to make up its own animal photo, it can randomly generate an original image of an animal that follows the patterns it learned from the real animal photos. The computer's generated animal photo will be different from any real animals you showed it, but it will use similar shapes, colors and textures in a style influenced by the real animal photos.
This is how generative AI image models work. They are trained on lots of real images, then when you "trigger" the generative AI, it produces original images that follow learned patterns but are still new and unique.
The key points to remember:
The computer learns patterns from real data (in this case, animal photos)
Based on what it learned, the generative AI can then create original outputs (new animal photos) that follow learned patterns but are still different from the training data.
Though the generated images are influenced by real images, they are not actually copies of any specific training image. They are novel creations based on learned patterns.
Recent breakthroughs like GPT and Midjourney have significantly progressed Generative AI capabilities. These advances have opened up new possibilities for using Generative AI to solve complex issues, create art and assist in research.
ChatGPT, with its 100 million users, demonstrates how quickly Generative AI is being adopted and its wide-ranging impact. Its availability on GitHub shows its transformational potential, even at an early stage. Generative AI is already reshaping different fields and its influence is set to grow exponentially. Embracing this powerful technology will open doors to unimaginable possibilities, heralding a new era of creativity, efficiency and progress.
Applications of Generative AI are:
Text generation - Machine learning models generate new text based on patterns learned from existing text data. This has many uses in natural language processing, chatbots and content creation. ChatGPT, developed by OpenAI, uses text generation to provide human-like responses in conversations.
Image generation- Deep learning algorithms like GANs and Stable Diffusion create new images that look similar to real photos. This can be used for data augmentation, creating art, generating product images and more. Platforms like MidJourney and DALL-E use image generation to produce realistic images.
Video and speech generation - Techniques like GANs and video diffusion generate new videos by predicting frames. This can be used in entertainment, sports analysis and self-driving cars. Speech generation uses Transformers for text-to-speech conversion, virtual assistants and voice cloning.DeepBrain and Synthesia use video and speech generation to create realistic videos.
Data augmentation - Various image transformations are applied to existing training data to increase its diversity and avoid overfitting, leading to better machine learning model performance.
Generative AI can be used for music generation, code generation, gaming, healthcare and more. In healthcare, it can help generate synthetic medical data, develop new drug candidates and design clinical trials. As the technology advances, more uses will emerge.
Pros and Cons of Generative AI:
Creativity and productivity - Generative AI can produce a large volume of novel and creative content like art, music, text, etc. This can improve productivity and efficiency.
Personalization at scale - Generative AI models can personalize content at a large scale by taking input parameters. This can help produce more targeted and relevant content.
Time savings - Generative AI can produce content much faster than humans, saving time and effort.
Continuous improvement - Generative AI models can improve over time as they are trained on more data.
Bias - Generative AI models can inherit and replicate the biases in the data they are trained on. This can lead to issues of discrimination, stereotyping, etc.
Lack of control - There may be a lack of control over the specific types of content generative AI produces and no guarantees of quality. Some "junk" content may also be produced.
Ethical and safety issues - There are concerns around generative AI being used to produce harmful or unsafe content like deepfakes,misinformation, etc. Strict precautions need to be taken.
Risk of job loss - As generative AI replaces human creative work, there are concerns about content creators losing their jobs.
Unrealistic expectations - There are sometimes unrealistic expectations of generative AI producing human-level creativity, which is still a long way off.
While generative AI offers much promise and potential benefits, there are also risks and challenges that need to be addressed to ensure its safe, ethical, and beneficial development and use. With proper governance, incentives, and safeguards, we maximize the pros while mitigating the cons of this emerging technology.