ChatGPT, Lensa, Stable Diffusion, and DALL-E: Generative AI, explained
He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth Yakov Livshits of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
- This approach implies producing various images (realistic, painting-like, etc.) from textual descriptions of simple objects.
- Like other breakthrough technologies — things like the computer and the smartphone, but also earlier inventions, like the air conditioner and the car — generative AI could change much of how our world operates.
- Walmart aims to learn from the people who are “doing the work at the ground level,” said David Glick, senior vice president of enterprise business services at Walmart.
- I urge you to add Tree of Thoughts to your prompt engineering repertoire.
OpenAI’s president demonstrated on Tuesday how it could take a photo of a hand-drawn mock-up for a website he wanted to build, and from that generate a real one. Investor caution and increased conservation of capital have contributed to the lack of unicorn exits. As of the second quarter of 2023, just eight unicorns in the U.S. exited. These include Mosaic ML, an artificial intelligence startup, and carbon recycling firm LanzaTech. In total, unicorn exits within 11 years or less accounted for just over three-quarters of tracked exits from 1997 to 2022.
Synthetic data generation
ChatGPT can produce what one commentator called a “solid A-” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Other generative AI models can produce code, video, audio, or business simulations.
Generative AI will significantly alter their jobs, whether it be by creating text, images, hardware designs, music, video or something else. In response, workers will need to become content editors, which requires a different set of skills than content creation. In customer support, AI-driven chatbots and virtual assistants help businesses reduce response times and quickly deal with common customer queries, reducing the burden on staff. In software development, generative AI tools help developers code more cleanly and efficiently by reviewing code, highlighting bugs and suggesting potential fixes before they become bigger issues. Meanwhile, writers can use generative AI tools to plan, draft and review essays, articles and other written work — though often with mixed results.
Gartner Experts Answer the Top Generative AI Questions for Your Enterprise
Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content. Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts.
Generative AI is a type of artificial intelligence that can create new content and ideas, including conversations, stories, images, videos, and music. Like all artificial intelligence, generative AI is powered by machine learning models—very large models that are pre-trained on vast amounts of data and commonly referred to as Foundation Models (FMs). Apart from content creation, generative AI is also used to improve the quality of digital images, edit video, build prototypes quickly for manufacturing, augment data with synthetic datasets, and more.
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.
Examples of products with generative AI features
It’s still available to try out, but after seeing what ChatGPT can do, it feels like a clunky, slow, and weird step backward. One of the first major generative AI products for the consumer market is Microsoft’s new AI-infused Bing, which debuted in January to great fanfare. The new Bing uses generative AI in its web search function to return results that appear as longer, written answers culled from various internet sources instead of a list of links to relevant websites.
Initially created for entertainment purposes, the deep fake technology has already gotten a bad reputation. Being available publicly to all users via such software as FakeApp, Reface, and DeepFaceLab, deep fakes have been employed by people not only for fun but for malicious activities too. In 2022, Apple acquired the British startup AI Music to enhance Apple’s audio capabilities. The technology developed by the startup allows for creating soundtracks using free public music processed by the AI algorithms of the system.
What kinds of output can a generative AI model produce?
Researchers, for example, tasked ChatDev to “design a basic Gomoku game,” an abstract strategy board game also known as “Five in a Row.” The eco-friendly EVs demonstrate the latest in next-gen electric technology and underscore BYD’s position as a leading global car brand. China’s emerging EV makers — which have been quick to embrace the shift to electric powertrains and software-defined strategies — were also in force at IAA as they set their sights on the European market. Lotus conducted test drives at IAA of its Lotus Eletre Hyper-SUV, which features an immersive digital cockpit, a battery range of up to 370 miles and autonomous-driving capabilities powered by the NVIDIA DRIVE Orin system-on-a-chip. With DRIVE at the wheel, the all-electric car offers server-level computing power that can be continuously enhanced during the car’s lifetime through over-the-air updates. For example, if the response said that the ball “was” indeed in the cup in the bedroom, such a statement is correct.
Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content. 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. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate.
It isn’t an overstatement to say that generative AI is going to have as much impact on the business world as the internet. And marketing use cases are the most common way businesses start using generative AI. Like Yakov Livshits other breakthrough technologies — things like the computer and the smartphone, but also earlier inventions, like the air conditioner and the car — generative AI could change much of how our world operates.