Explainer: A deep dive into how generative AI works

“Plain” autoencoders were used for a variety of purposes, including reconstructing corrupted or blurry images. Variational autoencoders added the critical ability to not just reconstruct data, but to output variations on the original data. DALL-E’s take on the subject is artistic and definitely futuristic, but much less conveniently aesthetic than MidJourney’s one. VC’s also demonstrate a particular interest in generative artificial intelligence startups this year. Experts say that their interest is motivated by the latest improvements in this area and real benefits that generative AI can bring across multiple industries.

This allows for using algorithms specifically designed to work with images like CNNs for our audio-related task. The more neural networks intrude on our lives, the more the areas of discriminative and generative modeling grow. Jokes aside, generative AI allows computers to abstract the underlying patterns related to the input data so that the model can generate or output new content. The entertainment industry benefits from generative AI models to streamline content creation processes for video games, film, animation, world-building, and virtual reality.

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Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data. Assisting programmers in expanding their datasets by generating synthetic data that can be used to train machine learning models.

Beyond content, generative AI can create new data to train other AI systems, compress data by removing redundant information and create new data as well as programming code. In its broadest sense, generative AI is a type of artificial intelligence that creates novel content based on patterns learned from existing data. Google trains a large language model (LLM) on billions of search queries made by users over the years, which then tries to predict the next word in your own search query. Generative AI is a transformative field that holds immense importance in driving innovation and unlocking new levels of creativity across industries. Through its ability to generate new and original content, generative AI empowers individuals and organizations to push the boundaries of what is possible. Artists can collaborate with AI to create unique and captivating works, while industries can leverage generative models to enhance their products and services.

How Generative AI Is Changing Creative Work

AI applications like ChatGPT work in a similar fashion, except that instead of being trained on image data, they’re trained on vast quantities of text. Evaluating the quality of that text can be a challenge, so the source text will have a huge impact on how effective the AI will be. Adversarial training techniques are one example of unsupervised AI training. On the surface that sounds simple, but there are a lot of muscles Yakov Livshits and joints and haptic feedback and proprioception that go into performing that seemingly simple task. Sanctuary is using physics to train the robots, essentially that same way that we learned to perform the same tasks. The down side to this is that those trained models are static; there’s no mechanism for evolving the AI with additional feedback and back-propagation, so updates are tied to new software versions.

There are various types of generative AI models, each designed for specific challenges and tasks. The popularity of generative AI has exploded in 2023, largely thanks to the likes of OpenAI’s ChatGPT and DALL-E programs. In addition, rapid advancement in AI technologies such as natural language processing has made generative AI accessible to consumers and content creators at scale. Programmers can train these models to identify abnormal or fraudulent patterns in various domains, such as finance, cybersecurity, or manufacturing. Developing personalized treatment plans for individual patients based on their unique medical history, genetic makeup, and lifestyle factors. By analyzing large datasets of patient data, generative AI can identify patterns and correlations that enable healthcare providers to create personalized treatment plans that are more effective than generic approaches.

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.

Explained: How AI Generates Images from Text Prompts

From a user perspective, generative AI often starts with an initial prompt to guide content generation, followed by an iterative back-and-forth process exploring and refining variations. The realm of generative AI is an exciting frontier, but its complexities can be daunting for newcomers. Whether you’re a hobbyist interested in exploring creative applications or a professional looking to integrate generative AI into your business, the first steps are crucial. For instance, the Inception Score is often used to evaluate the quality of images generated by GANs. In the case of text, coherence, and contextual relevance might be key indicators.

• Before diving into generative AI, it’s important to have a grasp of some basic concepts in machine learning and programming.
• However, it is also creating new roles and specialties, particularly in data science and AI ethics.
• This subset of machine learning has become one of the most-used buzzwords in tech circles – and beyond.
• The AI-powered chatbot that took the world by storm in November 2022 was built on OpenAI’s GPT-3.5 implementation.
• But generative AI only hit mainstream headlines in late 2022 with the launch of ChatGPT, a chatbot capable of very human-seeming interactions.

Generative models have been used in machine learning since its inception, to model and predict data. Generative AI, also known as generative modeling, involves training models to generate new data that exhibit similar patterns and characteristics as the training dataset. It aims to capture the underlying distribution of the data and generate new samples that are indistinguishable from real data.

Taking the cat image example we used earlier, let’s see how a VAE would process it. Variational Autoencoders (VAEs) take the image as input and processes it through two neural networks– an encoder and a decoder. The encoder compresses the image into a low-dimensional representation of the input data (which we call ‘latent space’), and the decoder uses it to generate a new image similar to the original one. CycleGAN works by using two generator networks and two discriminator networks that work together in a cyclic process to generate new images in a way that maintains the identity of the original image. It is a powerful tool that can be used to create new visual content and transform existing images in creative and unexpected ways.

The use cases of generative AI explained for beginners would also turn attention toward image generation. You can rely on generative AI models to create new images by using natural language prompts. Text-to-image generation protocols rely on creating images that represent the content in a prompt. Text generation has been one of the prominent topics of research in the field of AI. Most recently, AI researchers have started training generative adversarial networks or GANs for producing text that appears similar to human speech. ChatGPT is the best example of using generative artificial intelligence in text generation.

Through generative AI, computers can predict the most relevant patterns to input, allowing them to output corresponding content. During the training, a limited number of parameters are given to the generative AI models, enabling them to make their own conclusions and highlight features present in the training data. However, to get the most out of generative AI, human involvement is still essential, and that is both at the start and end of the training. Generative AI interactions with large language models are a centralizing gravitational force for the major CSPs because of their extremely high processing and infrastructure requirements. But AI data creation and LLM results consumption happen at the edge and on mobile devices. This is especially true when the application is end-user facing, and providing hyper-personalized content.