Explainer: A deep dive into how generative AI works

Generative AI is important not only by itself but also because it makes us one step closer to the world where we can communicate with computers in natural language rather than in a programming language. With the help of generative AI, models become multimodal, which means they are able to process several modalities at a time, such as text and images, which expands their Yakov Livshits areas of application and makes them more versatile. The second element of the model (the discriminative NN) tries to distinguish between the real-world data and the ‘fake’ data generated by the model. Every time the first model succeeds in fooling the second one, it gets rewarded. Diffusion is commonly used in generative AI models that produce images or video.

Larger enterprises and those that desire greater analysis or use of their own enterprise data with higher levels of security and IP and privacy protections will need to invest in a range of custom services. This can include building licensed, customizable and proprietary models with data and machine learning platforms, and will require working with vendors and partners. Variational autoencoders or VAEs is a generative AI model well-known for its ability to offer variation in the data in a specific direction rather than just generating new content that resembles that training data. It’s important to note that the training process and the specific algorithms used can vary depending on the generative AI model employed. Different techniques, such as GANs, VAEs, or other variants, have unique approaches to generating content.

The ChatGPT list of lists: A collection of 3000+ prompts, examples, use-cases, tools, APIs…

Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs. Transformer-based models are another prominent type of neural network architecture with a self-attention mechanism at its base. They are particularly well-suited to perform tasks that involve sequential data.

Specifically, generative AI models are fed vast quantities of existing content to train the models to produce new content. They learn to identify underlying patterns in the data set based on a probability distribution and, when given a prompt, create similar patterns (or outputs based on these patterns). Training involves tuning the model’s parameters for different use cases and then fine-tuning results on a given set of training data. For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return. An image-generating app, in distinction to text, might start with labels that describe content and style of images to train the model to generate new images. The two models are trained together and get smarter as the generator produces better content and the discriminator gets better at spotting the generated content.

What to do when few-shot learning isn’t enough…

In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work. Once generative AI models go through training, they can start producing personalized content upon their users’ preferences. This can benefit businesses in creating content that is more likely to reach their target audiences, as they are specifically tailored to their own preferences. When looking at public sectors, there are no regulations concerning the growth of generative AI models, yet that does make more room for issues concerning the invasion of privacy, intellectual property, and copyright.

For example, it has a knowledge cut-off because it was trained using a dataset that only extended until September 2021. Artificial intelligence refers to computer systems or algorithms that can perform tasks which previously required human intelligence. Let’s start by considering the bigger picture of AI and then situate “generative AI” within it. They represent a massive advance in the possibilities—and our awareness—of generative AI. Imagine using AI chatbots to handle customer service inquiries, providing immediate responses and support. Or using AI to transcribe audio, making content more accessible to a wider audience.

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.

What Are Some Popular Examples of Generative AI?

The automotive industry uses generative AI tools to create 3D worlds and models for simulations and car development. A good generative model should also be able to capture the minority modes in its data distribution without sacrificing generation quality. This is known as diversity and helps reduce undesired biases in the learned models. Once the VAE is trained, it can generate new data by sampling from the learned distribution of the latent space. The VAE can also be used for other applications, such as data compression, denoising, and feature extraction. This blog will explore how generative AI works, types of generative AI models, and applications based on these models.

The objective is to produce totally unique artifacts that would seem authentic. To start with, a human must enter a prompt into a generative model in order to have it create content. “Prompt engineer” is likely to become an established profession, at least until the next generation of even smarter AI emerges. The field has already led to an 82-page book of DALL-E 2 image prompts, and a prompt marketplace in which for a small fee one can buy other users’ prompts.

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Generative AI is a type of artificial intelligence that involves using algorithms to create new data that
resembles training data. In this section, we’ll explore the key concepts behind Generative AI and how it
works. Among some of the most critical concerns are issues related to data privacy, model accuracy and the tendency to produce harmful content, and unethical use of LLMs and other generative models. Generative AI startups have collectively raised more than $17 billion in funding according to Dealroom, which maintains an excellent up-to-date visual landscape of funding in the field. However, for the purpose of this article, we’re going to focus on the machine learning models themselves. In order to effectively understand generative AI, we must understand the difference between generative and discriminative machine learning models.

Their work suggests that smaller, domain-specialized models may be the right choice when domain-specific performance is important. Another limitation of zero- and few-shot prompting for enterprises is the difficulty of incorporating proprietary data, often a key asset. If the generative model is large, fine-tuning it on enterprise data can become prohibitively expensive.

Curious about which Generative AI model will be the best fit for your application?

Not so many years ago, it was easy to conclude that AI technologies would never generate anything of a quality approaching human artistic composition or writing. Now, the generative model programs that power DALL-E 2 and Google’s LaMDA chatbot produce images and words eerily like the work of a real person. Dall-E makes artistic or photorealistic images of a variety of objects and scenes. While GPT-4 promises more accuracy and less bias, the detail getting top-billing is that the model is multimodal, meaning it accepts both images and text as inputs, although it only generates text as outputs.