I Asked AI to Count My Carbs 27,000 Times. It Couldn’t Give Me the Same Answer Twice.
Ask ChatGPT to estimate the carbs in your lunch. Now ask it again. And again. Five hundred times. You’d expect the same answer each time. […]
LLMs
ChatGPT Health: Promise, Practicality, and the Reality for People with Diabetes
Artificial intelligence has been hovering around diabetes care for a while now, usually wrapped in glossy demos and ambitious claims. Carb-counting apps that “just work”. […]
In conversation with… Unpacking the “Why” Behind the Errors
Here we discuss the LLMs design and purpose in the context of interacting with and advising on health data, and demonstrate why the two may be at odds with one another:
https://wp.me/p7O2EL-2BM
In conversation with… File uploads and interpretation. Where the wheels fall off!
In this final section of the “In conversation with” experiment, we provided “access” to complete Nightscout data for the LLM models.
Unfortunately, this is also where the wheels rather fell off, and whilst the results gave an initial appearance of credibility, upon further investigation it became clear that what was presented was rather less then effective and could have been very unsafe.
Read on to learn more: https://wp.me/p7O2EL-2BE
In Conversation with…. Reviewing user data and system settings using AI
In this article, we look at the abilities of the LLMs to review data charts in image form from Nightscout and see what they suggest in terms of changing treatments, and how the contextualise those changes.
As a key way that users are interacting with these models, this is a critical insight into what you might get.
It’s a big read with lots to go through, but we’d highly recommend taking the time. It revealed a lot of unexpected items that are potential gotchas…
In Conversation with…. Carb Counting with AI: A Deep Dive into How Different Models Stack Up
In the dynamic world of diabetes management, anything that can help us accurately estimate carbohydrates in our meals is a game-changer. Imagine a future where […]
In conversation with… Factual questions for Large Language Models
As I stated in the last “In conversation with” article, I split the questions into three sets. Factual, picture analysis and data analysis. This is […]