Generative AI Models Explained
Generative AI models are an emerging technology with the potential to revolutionize how we interact with our environments and even how we think. The algorithms that power these systems can be used to create compelling content and understanding, but they can also be used to generate adversarial examples that fool you into seeing fake objects in real-world videos. In this post, we'll explain what generative models are and why they're so important.
What is generative AI?
Generative Adversarial Networks (GANs) are a type of deep learning model that use two neural networks to generate data that look real. A GAN is made up of two parts: an encoder and a generator. The encoder takes in random noise and attempts to encode it into an image or text, while the generator takes in this encoded data and attempts to generate new content based on what it knows about real images or texts.
GANs were first developed in 2014 by Ian Goodfellow, who was then working as a postdoctoral fellow at Google Brain Research Group. At the time, he was focused on designing algorithms for computer vision tasks such as object classification and image generation. He soon realized that his encoder/generator design could not only be applied to computer vision but also speech synthesis as well as language translation.
GANs are currently being used by researchers looking at ways AI can be used to improve our everyday lives. In logistics and transportation, GANs have been used by companies including Mercedes-Benz Research & Development North America LLC (MBRDNA) which partnered with NVIDIA GPU Technology Conference 2019 exhibitor Stanford University School of Medicine to develop technology that uses deep learning models trained on thousands upon thousands of CT scans from cancer patients' bodies so they can detect tumors faster than ever before. This technology has also been used by Tesla Inc., which recently debuted its latest vehicle called Roadster 3 that features self-driving capabilities thanks not only neural networks but also Generative Adversarial Networks too.
Discriminative vs generative modeling
While discriminative models are best known for their ability to classify existing data points, they can also be used to predict class labels and make predictions on new data. In contrast, generative models are more focused on understanding the dataset structure and generating similar examples of the same type.
Generative models have higher accuracy on certain tasks than discriminative models, but they don't have as much utility in a commercial setting because they require a lot of data preparation before training or inference.
In many cases, a generative model can be used to train a discriminative model. This process is known as training with an auxiliary variable (VAE), and it's often used in data science to provide additional information that helps generate more accurate results.
Generative Adversarial Networks
Generative adversarial networks, or GANs, are a type of neural network that consists of two models: a generator that generates data and a discriminator that classifies it. The generator and discriminator play a game against each other to improve their respective abilities.
GANs were invented by Jan Goodfellow and his colleagues at the University of Montreal in 2014. They were first applied to natural language processing (NLP), but have since been used for other tasks such as image generation and music synthesis.
GANs can be implemented using CNNs (convolutional neural networks) as either the generator or discriminator model, with each being optimized during training through supervised learning on labeled examples from the same domain as they will be applied in practice.
GANs are trained to be either discriminative or generative. When applied to image generation, GANs can be used as a black box that takes in an input (a random noise vector) and returns an output (an image). In this case, the generator is generating fake images from scratch based on its own internal parameters.
Transformer-based models are a type of neural network that has been incredibly successful at solving machine translation problems. They were first described in a 2017 paper from Google, where it was used to create the best language model ever built at the time.
Previously, language models worked by generating words one after another based on their probability of occurring together and some linguistic rules about grammar and syntax. Transformer-based models take this approach further by learning context, which means they're better able to predict what comes next in a sentence than previous approaches could predict.
Google has also released their own transformer model called GPT-3. Another example of how transformers can be used is OpenAI's LaMDA project which uses them for dialogue applications like chatbots.
You might ask yourself: "What exactly is going on inside these mysterious black boxes?" The answer is simple: An encoder takes input words and generates vectors representing their position in sentences; then those vectors get passed through decoders which generate new sentences based on them. In English we call this process "translation" because it means transforming one sequence into another sequence with similar meaning but different content; but technically speaking what happens inside these black boxes isn't necessarily translating per se – just changing one type of data format into another type (like text into speech or images etc..).
Types of generative AI applications with examples
Generative AI applications are used to generate new data that is similar to a given dataset. In other words, they can be used to create fake photos and videos by creating completely new content that's indistinguishable from the real thing.
Here are some common ways generative models are being applied today:
- Image generation: Generating images of people, buildings, animals, or objects that don't exist in the real world but look realistic
- Image-to-image translation: Altering an existing image in order to change its style or genre (such as turning a landscape photo into an oil painting)
- Text-to-image translation: Turning text into images (such as turning "We will have sunny weather on Saturday" into pictures of sunshine)
- Text-to-speech conversion: Turning written sentences into spoken words
The dark side of generative AI
While generative AI is an amazing technology, it also carries some challenges.
One of the biggest concerns is that this type of approach can be used for malicious purposes as well. In fact, there are already reports of creating fake videos and deep fakes in real-time with generative models. It has been used in several instances where someone wanted to create a deep fake video that would make someone look like they were doing something they weren't or saying things they didn't say (i.e., politicians).
Another challenge with this type of AI is that its outputs cannot be controlled easily by humans because it tends to learn on its own and doesn't follow rules set by humans or any other external condition/factors like other AIs do; therefore, making decisions based on what you want isn’t possible yet.
Generative AI models are becoming more common, but they're still relatively new technology. As you can see from the examples above, there are many different types of generative AI models and applications. Some of them can be used for good (such as creating realistic art), while others could have scary implications for humanity in the future (like making fake news). It's important that we understand what these models do so we can make informed decisions about whether or not they should be used in certain situations.
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