Metadata
- Author: Davo
- Full Title: Thoughts on AI
- URL: https://davetang.org/muse/2025/03/07/thoughts-on-ai/
Highlights
- Since I’m going to give my thoughts on Artificial Intelligence (AI) I thought I’ll give you some idea of my (limited) background so you can put my thoughts into perspective. Also I just wanted to write something a bit more personal, since I don’t do that so often nowadays. The main point is that I don’t have formal training in machine learning (or mathematics or statistics for that matter). I studied microbiology and biochemistry in university and learned bioinformatics pretty much by myself. (View Highlight)
- With my background description out of the way, I can give my first and foremost thought: I don’t like the term AI. I didn’t like it before and now with all the buzz, I dislike it even more. The main reason is because AI is a vast field and saying AI could mean various things. (View Highlight)
- I guess nowadays AI is just synonymous with ChatGPT and/or Large Language Models (LLMs) but I would still prefer it if people said ChatGPT and/or LLMs instead of AI. (View Highlight)
- LLMs are built using something called Transformer architectures, which was introduced in the Attention Is All You Need paper that I mentioned earlier. Transformers use a type of deep neural network (a neural network with a lot of layers and parameters) called deep learning. Deep learning is a subset of machine learning. And finally, machine learning is a subset of AI. (View Highlight)
- Secondly, I do believe that LLMs are useful for certain tasks (debugging code, generating templates, etc.) and as you may have seen in my recent blog posts, I’ve been exploring different ways to use LLMs offline. (View Highlight)
- I believe that AI should really stand for Augmented Intelligence and as nicely illustrated in The LLM Curve of Impact on Software Engineers, LLMs have varying degrees of usefulness depending on your work/position. (View Highlight)
- But I feel that AI companies don’t want to advertise AI as augmented intelligence because they are supposed to completely replace some task/role. This brings me to my major gripe, which is all the hype surrounding AI. (View Highlight)
- I think LLMs are useful but would it impact me significantly if it didn’t exist today? Not really. Perhaps I’m not making the most out of them (and I am actively looking into different avenues of using LLMs) but I don’t know a single person who absolutely relies on LLMs or have been replaced by LLMs. And we may never be fully reliant on LLMs until hallucinations disappears because until then we always need to manually double-check the output. In order to be able to double-check the output, we need to have some sort of expertise in the first place. (View Highlight)
- I was listening to the 404 Media Podcast where they were discussing the Microsoft publication: “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers”. The title of the publication pretty much summarises the findings. (View Highlight)
- In the podcast, they discussed an example of how we no longer remember phone numbers (at least for people born before the advent of mobile phones) because mobile phones have built-in phone books. Like the podcasters, I still remember my home phone numbers (3258831 and 3257747) that we had when I was a kid; nowadays I can’t even remember my own phone number. Remembering phone numbers is probably not a good use of our mental capacity and it’s fine to outsource this task to our phones. (View Highlight)
- Remembering code syntax (and deciphering error messages) can probably be outsourced to LLMs in the same manner. The higher level tasks such as the logic and design of a program, tasks that require critical thinking, should be done by us. (View Highlight)
- Recently I have been checking out ellmer and I have pretty much the same stance on LLMs as the authors of {ellmer}:
In general, we recommend avoiding LLMs where accuracy is critical. That said, there are still many cases for their use. For example, even though they always require some manual fiddling, you might save a bunch of time ever with an 80% correct solution. In fact, even a not-so-good solution can still be useful because it makes it easier to get started: it’s easier to react to something rather than to have to start from scratch with a blank page. (View Highlight)