McKinsey on ChatGPT

McKinsey comments on this, with more at the link.  I say we are about to get a big new flow of such products, and will have to quickly adapt. 

Generative AI is here: How tools like ChatGPT could change your business

December 20, 2022 | Commentary

By  Michael Chui  ,  Roger Roberts , and  Lareina Yee

Generative AI and other foundation models are changing the AI game, taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users.

In the spirit of this article’s topic, we asked ChatGPT—a generative AI language model that can create original content in response to a user prompt—how it would set the context. Our prompt to the system:

Write the opening paragraph for an article about how transformative generative AI will be for business, in the style of McKinsey & Company.

ChatGPT’s response:

The rise of generative AI has the potential to be a major game-changer for businesses. This technology, which allows for the creation of original content by learning from existing data, has the power to revolutionize industries and transform the way companies operate. By enabling the automation of many tasks that were previously done by humans, generative AI has the potential to increase efficiency and productivity, reduce costs, and open up new opportunities for growth. As such, businesses that are able to effectively leverage the technology are likely to gain a significant competitive advantage.

Not perfect but overwhelmingly impressive.

For us and many executives we’ve spoken to recently, entering one prompt into ChatGPT, developed by OpenAI, was all it took to see the power of generative AI. In the first five days of its release, more than a million users logged into the platform to experience it for themselves. OpenAI’s servers can barely keep up with demand, regularly flashing a message that users need to return later when server capacity frees up.

Products like ChatGPT and GitHub Copilot, as well as the underlying AI models that power such systems (Stable Diffusion, DALL·E 2, GPT-3, to name a few), are taking technology into realms once thought to be reserved for humans. With generative AI, computers can now arguably exhibit creativity. They can produce original content in response to queries, drawing from data they’ve ingested and interactions with users. They can develop blogs, sketch package designs, write computer code, or even theorize on the reason for a production error.

This latest class of generative AI systems has emerged from foundation models—large-scale, deep learning models trained on massive, broad, unstructured data sets (such as text and images) that cover many topics. Developers can adapt the models for a wide range of use cases, with little fine-tuning required for each task. For example, GPT-3.5, the foundation model underlying ChatGPT, has also been used to translate text, and scientists used an earlier version of GPT to create novel protein sequences. In this way, the power of these capabilities is accessible to all, including developers who lack specialized machine learning skills and, in some cases, people with no technical background. Using foundation models can also reduce the time for developing new AI applications to a level rarely possible before.   ;;;'