| What I've noted is that GPT is really good at things that have really good documentation. The example of kube-tf in this repo is a perfect example. The Kubernetes documentation and all of Hashicorp's documentation is excellent. GPT will have infinite examples of good code to stitch together code for the task in this example. Now I've been running a private cloud at work on the OpenNebula platform, which has documentation that is definitely lacking. I tried to ask GPT to write some basic code in Python such as "Give me a list of VMs from the OpenNebula API in a powered off state that have a start time older than 30 days." What I noted was that it would spit out code that looked correct on the surface, but would not run. It would take a decent amount of me modifying the code until I got my desired result. Since there was no documentation, I was just reading through the OpenNebula packages themselves to understand what to do. The nice thing, though, is that it was a great starting point. Much in the same way I might take a code snippet from StackOverflow and modify it to suit my own needs. I listened to a great podcast titled "The Trouble with AI" on Making Sense with Sam Harris. One of the key takeaways I grabbed from it was that GPT is an LLM not an AI. What it is very good at is predicting the next correct character or word in a sequence based off of other examples. But it does not actually fundamentally understand what it is outputting. In order to demonstrate, open up a session with ChatGPT really quickly and ask it a single digit multiplication question. Such as "What is 3 multiplied by 4?" and you will see a correct answer. Next, ask it something a bit larger, like "What is 12366 multiplied by 981632?" and you will get an incorrect answer but one that looks pretty close to correct. Validate with a calculator yourself. The reason being, as an LLM it doesn't actually understand multiplication. Instead it has just seen 3 multiplied by 4 countless times in the data it ingested when it was being "trained", but never has seen larger number examples of multiplication. Not that it knows multiplication in the first place. GPT is fantastic, but as of right now it needs to be used as a starting point towards knowledge or something concrete. I wouldn't trust it as an authoritative source on anything quite yet. It is fantastic for generating a bit of code and then allowing the developer to tweak that code until it actually works. |
As a technical communicator, I interpret this to mean that yes, we are all going to die at the hands of GPT-13.