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

These real-world use cases demonstrate the transformative potential of generative AI in the business world. For example, an architectural firm could use generative AI to create 3D models of building designs. These models can be used to visualize the final product, make necessary adjustments, and even create virtual tours for clients. For example, a healthcare company could use generative AI to create synthetic patient data, enabling them to build more robust AI models without compromising patient privacy. For instance, a marketing company could use generative AI to draft promotional content, a design firm could use it to create new design concepts, or a music production company could use it to compose new melodies.

The ability for generative AI to work across types of media (text-to-image or audio-to-text, for example) has opened up many creative and lucrative possibilities. No doubt as businesses and industries continue to integrate this technology into their research and workflows, many more use cases will continue to emerge. That said, the impact of generative AI on businesses, individuals and society as a whole hinges on how we address the risks it presents.

By the end of this article, you’ll have a solid understanding of what is generative AI and how it can be a game-changer for your business. We’ll dwell on the nuts and bolts of this cutting-edge technology, explore real-world use cases, and discuss how businesses can use its power for operational efficiency. This is a question that many businesses are starting to ask as they explore new ways to leverage technology for growth. PCMag.com is a leading authority on technology, delivering lab-based, independent reviews of the latest products and services. Our expert industry analysis and practical solutions help you make better buying decisions and get more from technology.

Industrial Setting

In this comprehensive guide, we will demystify what is generative AI, shedding light on its capabilities, applications, and potential impact on businesses. Artificial intelligence, in a general sense, describes all kinds of autonomous technology. It includes physical computing, such as robotics and autonomous vehicles, as well as screen-based or software-based autonomous technology.

Architects could explore different building layouts and visualize them as a starting point for further refinement. Design tools will seamlessly embed more useful recommendations directly into workflows. Training tools will be able to automatically identify best practices in one part of the organization to help train others more efficiently.

What are the generative AI use cases in generative design?

Transformer-based models are trained on large sets of data to understand the relationships between sequential information, such as words and sentences. Underpinned by deep learning, these AI models tend to be adept at NLP and understanding the structure and context of language, making them well suited for text-generation tasks. ChatGPT-3 and Google Bard are examples of transformer-based generative AI models.

They are commonly used for text-to-image generation and neural style transfer.[31] Datasets include LAION-5B and others (See Datasets in computer vision). As good as these new one-off tools are, the most significant impact of generative AI will come from embedding these capabilities directly into versions of the tools we already use. We have already seen companies such as Reddit, Stack Overflow, and Twitter closing access to their data or charging high fees for the access.

D. Video Synthesis and Deepfakes

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.

First, many generative models are sensitive to how their instructions are formatted, which has inspired a new AI discipline known as prompt-engineering. A good instruction prompt will deliver the desired results in one or two tries, but this often comes down to placing colons and carriage returns in the right place. A prompt that works beautifully on one model may not transfer to other models. 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.

Master your role, transform your business and tap into an unsurpassed peer network through our world-leading virtual and in-person conferences. As with any technology, however, there are wide-ranging concerns and issues to be cautious of when it comes to its applications. Many implications, ranging from legal, ethical, and political to ecological, social, and economic, have been and will continue to be raised as generative AI continues to be adopted and developed.

To talk through common questions about generative AI, large language models, machine learning and more, we sat down with Douglas Eck, a senior research director at Google. Doug isn’t only working at the forefront of AI, but he also has a background in literature and music research. That combination of the technical and the creative puts him in a special position to explain how generative AI works and what it could mean for the future of technology and creativity. These types of General AI might produce content as a by-product while performing their primary tasks. Whether text, images, product recommendations, or any other output, Generative AI uses natural language to interact with the user and carry out instructions. Generative AI is an exciting field that has the potential to revolutionize the way we create and consume content.

This form of AI is a machine learning model that is trained on large data sets to make more accurate decisions than if trained from a single algorithm. Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

Is Tencent’s ChatGPT rival, Hunyuan, Set to Disrupt the AI Market?

Latent space is a compressed representation of data that captures its essential features. Training data serves as the foundation for learning and helps models understand the underlying patterns. Generative architectures, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), auto-regressive models, and flow-based models, are the building blocks that enable generative modeling. It’s a powerful technology that uses machine learning to generate new, original data. With applications ranging from content creation to data enhancement, it’s already driving innovation in various industries. Despite some challenges, the future of generative AI for businesses looks promising, with increased adoption, improved quality, and new applications on the horizon.

To navigate this, it’s important to consult with legal experts and to carefully consider the potential risks and benefits of using generative AI for creative purposes. The introduction of pre-trained foundation models with unprecedented adaptability to new tasks will have far-reaching consequences. According to Accenture’s 2023 Technology Vision report, 97% of global executives agree that foundation models will enable connections across data types, revolutionizing where and how AI is used. Yakov Livshits To operate in tomorrow’s market, businesses will need to lean on the full capabilities that generative AI provides. Those two companies are at the forefront of research and investment in large language models, as well as the biggest to put generative AI into widely used software such as Gmail and Microsoft Word. By leveraging this learned knowledge, generative AI models can generate new text that follows grammatical rules, maintains coherence, and aligns with the given context or topic.

• Write With Transformer – allows end users to use Hugging Face’s transformer ML models to generate text, answer questions and complete sentences.
• For instance, consider the riddle “What weighs more, a pound of lead or a pound of feathers?
• As we navigate the future, AI generative models will continue to shape creativity and drive innovation in unprecedented ways.
• By staying informed and prepared, businesses can benefit from generative AI to drive innovation, efficiency, and growth.
• The output of generative AI, however, is content—music, text, video, code, etc—generated from a corpus of content.
• Therefore, researchers can train new models on massive collections of text, which would ensure better accuracy and depth in the operations.

Below you’ll find some of the most popular generative AI models available today. Keep in mind that many generative AI vendors build their popular tools with one of these models as the foundation or base model. Many types of generative AI models are in operation today, and the number continues to grow as AI experts experiment with existing models. 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.

The two sub-models cycle through this process repeatedly until the discriminator is no longer able to find flaws or differences in the newly generated data compared to the training data. First, the generator creates new “fake” data based on a randomized noise signal. Then, the discriminator blindly compares that fake data to real data from the model’s training data to determine which data is “real” or the original data.