Generative AI: Definition, Tools, Models, Benefits & More

“General AI” is again an umbrella for more traditional types of artificial intelligence that have long been used for different tasks. For instance, the AI used in selfies-turned-into-portraits, classifying your online purchasing habits, self-driving cars, or forecasting weather patterns are everyday examples of AI many of us use. Generative AI can produce new pieces of music or sound based on learned patterns. It can even mimic the style of specific genres or instruments, which can be used in the entertainment industry or for creating sound effects. Generative AI is a relatively new category that became wildly popular in the early 2020s. ChatGPT, which creates seemingly original text, is the poster child for this category.

The image you see has been generated with the help of Midjourney — a proprietary artificial intelligence program that creates pictures from textual descriptions. Given the computational power of modern AI models, generative AI can significantly accelerate the design process and development time, especially when compared to manual or more traditional computer-aided methods. Generative AI can be trained on large datasets, drawing insights and patterns that might not be apparent to human designers. This can lead to the generation of design solutions that are informed by vast amounts of historical data, industry best practices, and even aesthetic trends. Traditional AI simply analyzes data to reveal patterns and glean insights that human users can apply. Generative AI takes this process a step further, leveraging these patterns and insights to create entirely new data.

What Are Some Popular Examples of Generative AI?

The noticeable advancement in creating large language models focuses on access to large volumes of data with the help of social media posts, websites, and books. The data can help in training models, which can predict and generate natural language responses in different contexts. Part of the umbrella category of machine Yakov Livshits learning called deep learning, generative AI uses a neural network that allows it to handle more complex patterns than traditional machine learning. Inspired by the human brain, neural networks do not necessarily require human supervision or intervention to distinguish differences or patterns in the training data.

Gartner has included generative AI in its Emerging Technologies and Trends Impact Radar for 2022 report as one of the most impactful and rapidly evolving technologies that brings productivity revolution. AI algorithms can design shoes optimized for performance, comfort, and aesthetics, sometimes leading to structures or forms that might be unconventional yet functionally superior. OpenAI also unveiled its much-anticipated GPT-4 in March 2023, which will be used as the underlying engine for ChatGPT going forward. In addition, the company has started selling access to GPT-4’s API so that businesses and individuals can build their own applications on top of it. Its mass adoption is fueling various concerns around its accuracy, its potential for bias and the prospect of misuse and abuse. The speed, efficiency and ease of use permitted by generative AI is what makes it such an appealing tool to so many companies today.

Examples of generative AI

DLSS samples multiple lower-resolution images and uses motion data and feedback from prior frames to reconstruct native-quality images. This approach implies producing various images (realistic, painting-like, Yakov Livshits etc.) from textual descriptions of simple objects. The most popular programs that are based on generative AI models are the aforementioned Midjourney, Dall-e from OpenAI, and Stable Diffusion.

ChatGPT is considered generative AI because it can generate new text outputs based on prompts it is given. Generative AI refers to deep-learning models that can take raw data — say, all of Wikipedia or the collected works of Rembrandt — and “learn” to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data. Key concepts in generative modeling include latent space, training data, and generative architectures.

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.

Microsoft’s decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system. Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior. Google has since unveiled a new version of Bard built on its most advanced LLM, PaLM 2, which allows Bard to be more efficient and visual in its response to user queries. Similarly, if you have trained a model with a large corpus of images with image tagging, captions, and lots of visual examples, the AI model can learn from these examples and perform image classification and generation. This sophisticated system of AI programmed to learn from examples is called a neural network.

To generate text, natural language processing techniques are used to transform raw characters into sentences, parts of speech, entities, and actions. LaMDA (Language Model for Dialogue Applications) is a family of conversational neural language models built on Google Transformer — an open-source neural network architecture for natural language understanding. First described in a 2017 paper from Google, transformers are powerful deep neural networks that learn context and therefore meaning by tracking relationships in sequential data like the words in this sentence. That’s why this technology is often used in NLP (Natural Language Processing) tasks.

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But generative AI only hit mainstream headlines in late 2022 with the launch of ChatGPT, a chatbot capable of very human-seeming interactions. In March 2023, Bard was released for public use in the United States and the United Kingdom, with plans to expand to more countries in more languages in the future. It made headlines in February 2023 after it shared incorrect information in a demo video, causing parent company Alphabet (GOOG, GOOGL) shares to plummet around 9% in the days following the announcement. DALL-E can also edit images, whether by making changes within an image (known in the software as Inpainting) or extending an image beyond its original proportions or boundaries (referred to as Outpainting).

• For instance, a traditional AI could analyze user behavior data, and a generative AI could use this analysis to create personalized content.
• Generative AI models use a combination of AI algorithms to represent and process content.
• After the incredible popularity of the new GPT interface, Microsoft announced a significant new investment into OpenAI and integrated a version of GPT into its Bing search engine.
• The process begins with a prompt that could be in the form of text, image, video, design, or musical notes.
• We have already seen companies such as Reddit, Stack Overflow, and Twitter closing access to their data or charging high fees for the access.
• Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the “When inside of” nested selector system.

In fact, it has its roots in the early days of artificial intelligence.The first generative models were simple algorithms designed to create basic patterns. However, with more advanced machine learning techniques, these models have grown exponentially more powerful. Nevertheless, generative AI is showing it can create new content such as marketing content, social media posts, scripts and books to name a few. 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. Advanced chatbots, virtual assistants, and language translation tools are mature generative AI systems in widespread use.

– What is generative artificial intelligence?

Today, using a generative AI system usually requires nothing more than a plain language prompt of a couple sentences. And once an output is generated, they can usually be customized and edited by the user. The implementation of generative artificial intelligence is altering the way we work, live and create. It’s a source of entertainment and inspiration, as well as a means of convenience. And if a business or field involves code, words, images or sound, there is likely a place for generative AI.