What Is Generative AI? The Tech Shaping the Future of Content Creation

Multimodal models can understand and process multiple types of data simultaneously, such as text, images and audio, allowing them to create more sophisticated outputs. An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt. Generative AI has been around for years, arguably since ELIZA, a chatbot that simulates talking to a therapist, was developed at MIT in 1966. But years of work on AI and machine learning have recently come to fruition with the release of new generative AI systems. You’ve almost certainly heard about ChatGPT, a text-based AI chatbot that produces remarkably human-like prose. DALL-E and Stable Diffusion have also drawn attention for their ability to create vibrant and realistic images based on text prompts.

Third, it would benefit from editing; we would not normally begin an article like this one with a numbered list, for example. The last point about personalized content, for example, is not one we would have considered. Red Hat is also using our own Red Hat OpenShift AI tools to improve the utility of other open source software, starting with Ansible Lightspeed with IBM Watson Code Assistant. Ansible Lightspeed helps developers create Ansible content more efficiently.

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One example is from CarMax Inc (KMX.N), which has used a version of OpenAI’s technology to summarize thousands of customer reviews and help shoppers decide what used car to buy. Diffusion models require both forward training and reverse training, or forward diffusion and reverse diffusion. Read on to learn more about what a generative AI model is, how they work and compare to other types of AI, and some of the top generative AI models that are available today. One lawyer got in serious trouble when he trusted ChatGPT to write an entire brief and then didn’t take the time to edit its output. From there, a generative adversarial network (GAN) algorithm constantly competes with itself within the gen AI model. This means that the AI will try to outdo itself to produce an answer it believes is the most accurate.

Text-to-image generation protocols rely on creating images that represent the content in a prompt. Transformer-based models are designed with massive neural networks and transformer infrastructure that make it possible for the model to recognize and remember relationships and patterns in sequential data. Then, once a model generates content, it will need to be evaluated and edited carefully by a human. He then improved the outcome with Adobe Photoshop, increased the image quality and sharpness with another AI tool, and printed three pieces on canvas. Overall, it provides a good illustration of the potential value of these AI models for businesses. They threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications.

They enable the generation of realistic images, art synthesis, and interactive exploration of latent spaces. VAEs are generative models that utilize an encoder-decoder architecture to map input data into a latent space and reconstruct it back to the original data domain. They balance reconstruction accuracy and regularization to generate new samples that follow the learned data distribution.

To recap, the discriminative model kind of compresses information about the differences between cats and guinea pigs, without trying to understand what a cat is and what a guinea pig is. In logistics and transportation, which highly rely on location services, generative AI may be used to accurately convert satellite images to map views, enabling the exploration of yet uninvestigated locations. As for now, there are two most widely used generative AI models, and we’re going to scrutinize both. Catch up on the latest tech innovations that are changing the world, including IoT, 5G, the latest about phones, security, smart cities, AI, robotics, and more.

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Once the generative AI consistently “wins” this competition, the discriminative AI gets fine-tuned by humans and the process begins anew. Data augumentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering. The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models.

Pfizer used AI to run vaccine trials during the coronavirus pandemic1, for example. Notably, some AI-enabled robots are already at work assisting ocean-cleaning efforts. Google Bard is another example of an LLM based on transformer architecture.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

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It is important to understand how it works in the context of generative AI. While GANs can provide high-quality samples and generate outputs quickly, the sample diversity is weak, therefore making GANs better suited for domain-specific data generation. Recent progress in LLM research has helped the industry implement the same process to represent patterns found in images, sounds, proteins, DNA, drugs and 3D designs. This generative AI model provides an efficient way of representing the desired type of content and efficiently iterating on useful variations.

Diffusion is at the core of AI models that perform text-to-image magic like Stable Diffusion and DALL-E. Language models basically predict what word comes next in a sequence of words. We train these models on large volumes of text so they better understand what word is likely to come next. One way — but not the only way — to improve a language model is by giving it more “reading” — or training it on more data — kind of like how we learn from the materials we study. We recently expanded access to Bard, an early experiment that lets you collaborate with generative AI.

These predictions are based off the data the models are fed, but there are no guarantees the prediction will be correct, even if the responses sound plausible. Transformers processed words in a sentence all at once, allowing text to be processed in parallel, speeding up training. Earlier techniques like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks processed words one by one. Transformers also learned the positions of words and their relationships, context that allowed them to infer meaning and disambiguate words like “it” in long sentences. Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media).

Another deep learning technique, the diffusion model, has proven to be a good fit for image generation. Diffusion models learn the process of turning a natural image into blurry visual noise. Then generative image tools take the process and reverse it—starting with a random noise pattern and refining it until it resembles a realistic picture. Beneath the AI apps you use, deep learning models are recreating patterns they’ve learned from a vast amount of training data.

Imagine a world where AI can write a best-selling novel, design a skyscraper, or even create a blockbuster movie. It’s not just about creating content; it’s about pushing the boundaries of creativity and innovation. Describe what you want in natural language and the app returns whatever you asked for—like magic. Some of the well-known generative AI apps to emerge in recent years include ChatGPT and DALL-E from OpenAI, GitHub CoPilot, Microsoft’s Bing Chat, Google’s Bard, Midjourney, Stable Diffusion, and Adobe Firefly.

• These models use large language models (LLMs) and natural language processing to generate unique outputs, with applications ranging from image and video synthesis to text and speech generation.
• ChatGPT is the best example of using generative artificial intelligence in text generation.
• DALL-E is a foundation model that can combine text and image inputs and generate images.
• StyleGAN is also a good option when generative AI tools for images are discussed.
• An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt.

These nodes use mathematical calculations (instead of chemical signals as in the brain) to communicate and transmit information. This simulated neural network (SNN) processes data by clustering data points and making predictions. Generative AI can create a large amount of synthetic data when using real data is impossible or not preferable. For example, synthetic data can be useful if you want to train a model to understand healthcare data without including any personally identifiable information. It can also be used to stretch a small or incomplete data set into a larger set of synthetic data for training or testing purposes.

By using advanced data analysis tools, generative AI can identify customer behavior patterns and preferences, allowing businesses to create dynamic product recommendations and offers that speak directly to each customer. In many cases, businesses may not even have to specifically ask their customers for preferences or demographic information. By analyzing customer interactions and datasets generated by each individual interaction, Yakov Livshits generative AI can pick up on small cues that indicate what a customer is interested in or what they may be looking for. Generative AI models offer a wide range of possibilities, paving the way for innovative applications across various industries. By understanding the different types of generative AI, we can appreciate their unique capabilities and harness their potential to create groundbreaking solutions.