What Is Generative AI? Definition, Applications, and Impact

Nearly as many say generative AI will help them do more work (89%) and create better quality work (88%)—and 9 out of 10 business leaders surveyed say the same. The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies.

Popular examples of generative AI include ChatGPT, Bard, DALL-E, Midjourney, and DeepMind. Companies will have thousands of ways to apply generative AI and foundation models to maximize efficiency and drive competitive advantage. But they’ll need to reinvent work to find a path to business value from this technology.

What does it take to build a generative AI model?

Photo sessions with real physical human models are expensive and require lots of logistical effort. There is also a complex law behind this activity, such as copyrights, etc. The results are impressive, especially when compared to the source images or videos, that are full of noise, are blurry and have low frames per second.

Once developers settle on a way to represent the world, they apply a particular neural network to generate new content in response to a query or prompt. Techniques such as GANs and variational autoencoders (VAEs) — neural networks with Yakov Livshits a decoder and encoder — are suitable for generating realistic human faces, synthetic data for AI training or even facsimiles of particular humans. Generative AI models combine various AI algorithms to represent and process content.

AI existential risk: Is AI a threat to humanity?

Business leaders must lead the change, starting now, in job redesign, task redesign and reskilling people. The coming years will see outsized investment in generative AI, LLMs and foundation models. What’s unique about this evolution is that the technology, regulation, and business adoption are all accelerating exponentially at the same time. Every role in every enterprise has the potential to be reinvented, as humans working with AI co-pilots becomes the norm, dramatically amplifying what people can achieve.

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.

The same applies to computer games which can upscale the resolution to 4K while maintaining high frames per second based on lower resolution textures. The results are impressive, much better than from traditional techniques, and textures are sharp and look natural. Machine learning (ML) is of great help here as well, as it can detect suspicious behavior without predefined rules and it can discover rules which were not known when the attack comes. There are well-known algorithms for trends analysis that the mathematicians have known for tens of years and they are still being used today.

Discover the potential of Microsoft 365 Copilot to streamline tedious processes and uncover critical insights. GANs are not the only approach, but also Variational Autoencoders (VAEs) and PixelRNN (example of autoregressive model). In other words, one network generates candidates and the second works as a discriminator. The role of a generator is to fool the discriminator into accepting that the output is genuine. With the advancements of technology, such as the famous GPT-3 which we covered in a different article, many people are simply stunned.

Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites. There are a variety of generative AI tools out there, though text and image generation models are arguably the most well-known.

Meanwhile, writers can use generative AI tools to plan, draft and review essays, articles and other written work — though often with mixed results. There are AI techniques whose goal is to detect fake images and videos that are generated by AI. The accuracy of fake detection is very high with more than 90% for the best algorithms. But still, even the missed 10% means millions of fake contents being generated and published that affect real people. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.