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

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.

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.

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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.

AI-generated art models like DALL-E (its name a mash-up of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza. Other generative AI models can produce code, video, audio, or business simulations. The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end.

The great acceleration: CIO perspectives on generative AI

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 Yakov Livshits 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.

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.

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.

When he was 19 he dropped out of university to set up Muslim Youth Helpline, a telephone counseling service. He says he brings many of the values that informed those efforts with him to Inflection. The difference is that now he just might be in a position to make the changes he’s always wanted to—for good or not. This content was produced by Insights, the custom content arm of MIT Technology Review. Register to view a video playlist of free tutorials, step-by-step guides, and explainers videos on generative AI. Nothing, although there is concern about the technology’s potential abuse.

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.

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 Yakov Livshits 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.

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

Another website has  more than two million photos, royalty free, of people who never existed but look like real people. You can select different parameters to get images that fit the specific criteria, and all Yakov Livshits this is generated by AI; none of these people even exist. Now the typical use case is the intelligent upscaling of low resolution images to high resolution images using complex AI image generation techniques.