Background

Large Language Models have been with us for a couple of years. OpenAI released the GPT-3 model on June 11, 2020. It generated a lot of buzz in technical circles, but the model was not packaged in a way that was usable by the masses. That changed on November 30, 2022 when OpenAI released ChatGPT, running an improved version of the GPT-3 Model (GPT 3.5) and bringing AI / large language model capabilities to the masses.

ChatGPT is set up with a simple message-based interface, like iMessage or WhatsApp, that makes it easy for users to send text to the model and for the model to generate a response. It immediately showed promise writing essays, generating summaries, answering questions and performing basic computer programming tasks. Pretty quickly users realized that these models generate plausible text that is mostly, but not completely correct.

On March 14, 2023, OpenAI released GPT-4, a major upgrade from GPT-3. It accepts much longer input for questions, can do real math and analysis and it is much less likely to lie in its responses to a user.

From Hope to Hype back to Hope again

GPT-4 makes one heck of a first impression. It can take dozens of pages of context and extract information instantly. It can summarize and reason. It can generate websites and simple apps from a prompt and then incorporate suggestions. It came out 15 weeks after GPT 3.5. My feeling was, if this is the pace of progress now, the world is suddenly a different place than it was the day before.

After some time playing around with it, it still makes things up, often enough that it’s not possible to blindly trust it. It’s not going to turn every industry around overnight. It also seems like we are not careening towards a singularity any time soon. The industry’s focus is on understanding and optimizing capabilities and making models more focused or reliable.

Meanwhile, I’ve found the GPT models to be a great productivity boost for the parts of my work that are tedious, and I have been poking around the edges trying to find unexpected places where these models perform well because that’s where there will be unexpected advances. For example, large language models can gather information, formulate plans and guide their execution in a number of settings, such as chemical engineering. With some teasing, they can perform well at reasoning tasks

Stay tuned, we’ll dig into these and more, providing examples along the way.