Definition and overview Generative AI in the Enterprise Dell Technologies Info Hub

What is generative AI? Artificial intelligence that creates

Say, we have training data that contains multiple images of cats and guinea pigs. And we also have a neural net to look at the image and tell whether it’s a guinea pig or a cat, paying attention to the features that distinguish them. Generative AI has proven to be a powerful technology with many revolutionary applications across various industries. From content creation to healthcare, generative AI has the ability to generate sophisticated and personalized outputs that can help us work smarter and more efficiently. As much as we want it to be, artificial intelligence isn’t perfect, even with the advanced tools of intelligent technology and a computer’s ability to do deep learning.

The output of generative AI, however, is content—music, text, video, code, etc—generated from a corpus of content. The accuracy of generative AI is dependent upon massive troves of training data from diverse sources. Many ethical questions about AI involve how data sets are gathered and cleaned, and biases that might emerge through these methods. This app mostly helps people to edit photos, for example, using AI to automatically color old photos, remove objects and background from photos.

Technology Vision 2023

This policy applies to all applications for IMD programs from individuals or organizations, and any commercial or non-commercial partnerships. Explore the transformative power of business innovation with real-world examples and strategies for success. Generative AI has made Yakov Livshits considerable strides in the recent past, marking its position as one of the most prominent technologies in the AI landscape. From amplifying creative capabilities to facilitating superior product and service offerings, generative AI promises a wealth of opportunities.

Autoencoders work by encoding unlabeled data into a compressed representation, and then decoding the data back into its original form. “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.

Language generation

This approach reduces labeling costs by generating augmented training data or learning data representations, enabling AI models to excel with minimal labeled data. This process is facilitated through various methods, including utilizing techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These tools employ machine learning to generate new content mirroring established patterns. It creates a replica of the human brain to understand the structures and patterns of the data.

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.

• A parameter is a network component, and when people in the AI world talk about parameters in a neural network, they’re referring to scale.
• Generative AI means that we can add a new role to that list – prompt engineers.
• Generative AI has come a long way since its early beginnings, and it continues to evolve at a rapid pace.
• In simple words, It generally involves training AI models to understand different patterns and structures within existing data and using that to generate new original data.
• Generative AI technology is evolving rapidly, as are the ways it is used to help people create, research, work, and play.

However, prompting “tell me the weather today in New York City, I need to know if I need my raincoat for my walk to the subway” will likely give you the answer you’re looking for. The specter of an intelligence explosion, unforeseen consequences that could radically alter our society under the banner of ‘progress,’ and even the daunting prospect of human extinction are realities we must grapple with. As we innovate in this exciting domain, we must tread cautiously, acknowledging human feedback, by maintaining the balance between technological advancement and our way of life. If something goes wrong, accountability mechanisms should be in place to determine responsibility.

The process is quite computationally intensive, and much of the recent explosion in AI capabilities has been driven by advances in GPU computing power and techniques for implementing parallel processing on these chips. One example might be teaching a computer program to generate human faces using photos as training data. Over time, the program learns how to simplify the photos of people’s faces into a few important characteristics — such as size and shape of the eyes, nose, mouth, ears and so on — and then use these to create new faces.

Using large language models to power conversations is a huge boost to a brand’s AI capabilities in today’s uber-competitive e-commerce marketplace. Conversational commerce represents the future of e-commerce as brands race to offer the most personalized experiences for customers without putting all the heavy lifting on their own internal marketers and merchandisers. Companies can also use generative AI to analyze customer behavior and use that analysis internally to develop potential areas of improvement for their own business practices. Conversational AI, such as chatbots, can provide shoppers with quick, helpful responses to their questions, while virtual assistants can help guide them through the shopping process. These technologies not only enhance the shopping experience, but also provide valuable data to retailers about customer preferences and buying behaviors. For starters, it’s already impacting your lives – from AI copywriting tools to AI art generators.

Although AI technology has been around for some time, in 2022, it was suddenly put in the hands of consumers with text-to-image models such as Stable Diffusion, Dall-E 2, and Midjourney. This was followed by ChatGPT – a large language model (LLM) that captivated the masses with its ability to generate very convincing text in response to any given prompt. The AI bug spread like wildfire, and other LLMs, such as LLaMA, LaMDA, and BARD, quickly followed. Generative AI is also able to generate hyper-realistic and stunningly original, imaginative content. Content across industries like marketing, entertainment, art, and education will be tailored to individual preferences and requirements, potentially redefining the concept of creative expression. Progress may eventually lead to applications in virtual reality, gaming, and immersive storytelling experiences that are nearly indistinguishable from reality.