Large language models like ChatGPT are part of a class called foundation models, which have the potential to drive value in a business setting. LLMs have been trained on vast amounts of unstructured data, which enables them to transfer what they have learned to different tasks and generate new content. While potential benefits for business include productivity gains and high performance levels, risks include high training costs and issues with trustworthiness. Watch this eight-minute-46-second video for an explanation of this emerging AI field and how businesses can use it to their advantage.
Generative AI
Background
Now that artificial intelligence can automate complex tasks, extract useful insights from large data sets, and help workers boost their productivity, AI has crossed over from science fiction to the mainstream.
Generative AI, meanwhile, is an emergent form of AI that can actually mimic human imagination and creativity. The term “generative” refers to models’ ability to generate original content, including text, video, audio, and more.
How it works
Prediction is at the heart of how generative AI systems work. Most users interact with these systems through different applications—for example, by typing a prompt into OpenAI’s ChatGPT.
In the case of ChatGPT, the chatbot relies on a large language model that has been “trained” on huge data sets. This underlying model has analyzed much of the public internet, giving it a general understanding of which words are most likely to go together in a sequence. See a more technical breakdown of how ChatGPT works here.
Other AI tools, particularly those creating images and videos, rely on a different approach known as generative adversarial networks. The machine-learning framework involves two systems working against each other—one, the “generator,” produces something like an image to try and trick the other model, the “discriminator.”
In the process (deep dive here), the generator’s output gets incrementally more realistic. This approach is particularly useful for applications that create images and videos, like Midjourney and OpenAI’s popular DALL-E.
How are people using generative AI?
More than 100 million people are now using ChatGPT each week for tasks that range from the mundane to the creative. ChatGPT can do everything from writing an essay to drafting an email, debugging computer code, summarizing articles, and explaining complex topics in a human-like style (try ChatGPT here).
The use of text-to-image and text-to-video applications has also become increasingly popular. These tools allow the rapid creation of content ranging from abstract art to hyper-realistic footage based on simple inputs (Midjourney, DALL-E, and Sora).
Beyond creating text-, image-, and video-based content, generative AI underpins new products for searching the web (Perplexity), digital assistants (Google’s Gemini, Microsoft’s Bard), and more.
What are the risks?
Estimates vary, but due to generative AI, it’s believed that jobs involving largely rote, repetitive tasks will eventually shift from humans to machines, potentially displacing a large number of jobs across a number of industries.
From a technical perspective, generative AI programs sometimes “hallucinate” erroneous or made-up information (for more on AI hallucinations, what they are, and why they happen, Google offers this guide).
Meanwhile, hackers are also already using generative AI to augment their efforts. Generative AI makes some things easier—like lowering the barrier to content creation, for example, in terms of both cost and speed. But lowering that barrier for everybody, by definition, also means lowering it for bad actors.
Generative AI has also made it easier to create content like fake news articles as well as disinformation and propaganda.
OpenAI, the $86 billion company behind the chatbot ChatGPT, helped kickstart an AI arms race in Silicon Valley. Led by CEO Sam Altman, OpenAI’s mission is right there in the name — to “ensure AGI benefits all of humanity,” by making its products open for all to use. This article presents a history of the company, including an explanation of its unusual governance structure.
Microsoft and Google are now among the companies worldwide with public-facing generative AI products, which means big changes for how the world searches for information online. Both companies have integrated AI into their search products, moving away from merely surfacing a collection of links. Read this article to learn more about the impact of generative AI simply summarizing the web for users.
For all of the sophistication of modern AI technology, chatbots like ChatGPT and Google’s Gemini at times still make bizarre errors. The chatbots will make up facts out of whole cloth, while aI image creators like OpenAI’s DALL-E sometimes show humans with more than ten fingers. Read more in this article about the causes of AI “hallucinations,” and why they’re important to be aware of and to fix.
Suno, a new web-based text-to-music generator, offers a good example of how generative AI works in practice. Give the Suno website a simple prompt, such as a music style along with a general lyric idea, and it can crank out a highly polished, realistic-sounding tune in a matter of seconds. The AI creates everything, from the words to the music, from scratch based on the user prompt.
The work of generative AI systems like chatbots, powered by large language models, can sound a bit daunting to the average person, but these systems can also be used for fun and games, too. One example is Infinite Craft, an in-browser game (powered by AI software) in which a player combines the elements of earth, wind, fire, and water into an infinite number of new elements. A whole universe, in other words, created from just four elements.
Less than a year after the debut of powerful AI platforms, the creative minds at Waymark debuted a film created by the AI platform DALL-E. "The Frost," set in an apocalyptic future where weather has been stalled, is both uncanny and surreal, leaning into the bizarre yet fascinating output of the AI platform.
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