Explainer: What is Generative AI, the technology behind OpenAI’s ChatGPT?

It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs. A neural network is a type of model, based on the human brain, that processes complex information and makes predictions. This technology allows generative AI to identify patterns in the training data and create new content. GANs are made up of two neural networks known as a generator and a discriminator, which essentially work against each other to create authentic-looking data. As the name implies, the generator’s role is to generate convincing output such as an image based on a prompt, while the discriminator works to evaluate the authenticity of said image. Over time, each component gets better at their respective roles, resulting in more convincing outputs.

As a music researcher, I think of generative AI the same way one might think of the arrival of the drum machine decades ago. The drum machine generated a rhythm that was different from what human drummers sounded like, and that fueled entirely new genres of music. Language models are already out there helping people — you see them show up with Smart Compose and Smart Reply in Gmail, for instance. 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.

A Brief History of Generative AI

Gen AI has the potential to address some of the biggest challenges in education today. Nonetheless, as educators and students, we face a new frontier as we navigate a world in which the distinction between content generated by AI and humans is rapidly blurring. For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. Generative AI is a disruptive technology that can generate artifacts that previously relied on humans, delivering innovative results without the biases of human experiences and thought processes. Another limitation of zero- and few-shot prompting for enterprises is the difficulty of incorporating proprietary data, often a key asset.

In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of applications. For businesses, efficiency is arguably the most compelling benefit of generative AI because it can enable enterprises to automate specific tasks and focus their time, energy and resources on more important strategic objectives. This can result in lower labor costs, greater operational efficiency and new insights genrative ai into how well certain business processes are — or are not — performing. 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. The concept of generative AI is still expanding and has a lot of innovations and technologies coming up.

How generative AI—like ChatGPT—is already transforming businesses

One is generating (for instance images) while  the second is verifying the results, for instance if the images are natural and look true. There is news, almost every month, about a new scandal related to fake images, fake news, or fake videos whose intention is to fool people into believing fake stories and making wrong decisions, including voting decisions. Or, at least to humiliate famous people with fake nudes, putting false words in their mouths, etc. Better grammar and spelling is something we use everyday without even thinking about. Definition based rule engines are augmented or even replaced by machine learning (ML) algorithms and they have proved to be more effective and accurate than previous ones.

Generative art is art that has been created (generated) by some sort of autonomous system rather than directly by a human artist. Nowadays, the term is commonly used to refer to images created by generative AI tools like Midjourney and DALL-E. These tools use neural networks to create art automatically based on a prompt from the user (e.g., “an elephant painted in the style of Goya”). Most generative AI is powered by deep learning technologies such as large language models (LLMs). These are models trained on a vast quantity of data (e.g., text) to recognize patterns so that they can produce appropriate responses to the user’s prompts. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.

Some Uses of Generative AI

It’s a large language model that uses transformer architecture — specifically, the generative pretrained transformer, hence GPT — to understand and generate human-like text. Generative AI models are increasingly being incorporated into online tools and chatbots that allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response. Generative AI is an exciting field that has the potential to revolutionize the way we create and consume content. It can generate new art, music, and even realistic human faces that never existed before.

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.

Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used. The advanced machine learning that powers gen AI–enabled products has been decades in the making. But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month.

Similarly, business teams will use these models to transform and label third-party data for more sophisticated risk assessments and opportunity analysis capabilities. The recent progress in LLMs provides an ideal starting point for customizing applications for different use cases. For example, the popular GPT model developed by OpenAI has been used to write text, generate code and create imagery based on written descriptions. As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny.

Through fill-in-the-blank guessing games, the encoder learns how words and sentences relate to each other, building up a powerful representation of language without anyone having to label parts of speech and other grammatical features. genrative ai Transformers, in fact, can be pre-trained at the outset without a particular task in mind. Once these powerful representations are learned, the models can later be specialized — with much less data — to perform a given task.

• Finally, it’s important to continually monitor regulatory developments and litigation regarding generative AI.
• For instance, a traditional AI could analyze user behavior data, and a generative AI could use this analysis to create personalized content.
• But I think we’re poised for even more ambitious capabilities, like solving problems with complex reasoning.
• But it was not until 2014, with the introduction of generative adversarial networks, or GANs — a type of machine learning algorithm — that generative AI could create convincingly authentic images, videos and audio of real people.
• A neural network is a type of model, based on the human brain, that processes complex information and makes predictions.

Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. The net change in the workforce will vary dramatically depending on such factors as industry, location, size and offerings of the enterprise. Artificial intelligence has a surprisingly long history, with the concept of thinking machines traceable back to ancient Greece. Modern AI really kicked off in the 1950s, however, with Alan Turing’s research on machine thinking and his creation of the eponymous Turing test. 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.

Generative AI holds enormous potential to create new capabilities and value for enterprise. However, it also can introduce new risks, be they legal, financial or reputational. Many generative models, including those powering ChatGPT, can spout information that sounds authoritative but isn’t true (sometimes called “hallucinations”) or is objectionable and biased. Generative models can also inadvertently ingest information that’s personal or copyrighted in their training data and output it later, creating unique challenges for privacy and intellectual property laws. This deep learning technique provided a novel approach for organizing competing neural networks to generate and then rate content variations.

This ability to generate novel data ignited a rapid-fire succession of new technologies, from generative adversarial networks (GANs) to diffusion models, capable of producing ever more realistic — but fake — images. LLMs, especially a specific type of LLM called a generative pre-trained transformer (GPT), are used in most current generative AI applications—including many that generate something other than text (e.g., image generators like DALL-E). This means that things like images, music, and code can be generated based only on a text description of what the user wants.

Over the last decade, GPUs and advances in deep learning have ushered in far more advanced AI. Today, these recurrent neural networks can generate content in a way that approximates—and in some cases exceeds—human artists, musicians and writers. Generative AI models combine various AI algorithms to represent and process content. Similarly, images are transformed into various visual elements, also expressed as vectors. One caution is that these techniques can also encode the biases, racism, deception and puffery contained in the training data.