Health - Resources and discussion for everything health-related

1. No. And I’ve lost my voice describing why this is the case - LLMs do not use training data in real time which is indicative of the fact that their reasoning chains are learned over many training epochs rather than something akin to a search engine which is parsing and aggregating results from direct sources. I wish I had a different answer but that is simply how the mathematics behind this kind of machine learning model work. The only way to properly manage it would be to limit and license the data appropriately during core model training, but that genie is out of the bottle.
2. We will eventually (soon hopefully) hit critical mass where the technology isn’t delivering value on the hardware it takes to run it. The limitations, like I detailed above, are core to the technology and are not something that we’re just around the corner from solving. Those are core limitations and a different technology will be needed to move the ball forward past what is essentially a calculator with words. When this happens, we’ll see a whiplash effect where a ton of (server) hardware hits the market from the small datacenters looking to capitalize on the current rush. It’ll cripple the market for new hardware, I’d expect, as they’re going to want to get that capital back ASAP as it’s a quickly deprecating asset if just sitting idle.
3. Similar to above, the current trajectory isn’t going to last. It’s going to hurt once the reality finally sets in for the economy.
4. Oh yes, and it’s already been there for years! Unfortunately, these applications are not the glamorous applications like a “Her”-style chat companion, but rather precise application of specific machine learning models for specific business needs. I.e. do you really need an LLM to upload a picture to ask what kind of cat is in the picture? NO! That’s what convolutional neural networks are for, or maybe some custom vision transformers. There are dozens of types of ML models that have clear applications and with fine tuning and proper process implementation, the models can produce production-ready results as any other means of solving this issue.

The core problem with this technology is the misuse/misunderstanding that:

1. AI does not yet exist. Full stop.
2. An LLM is just ONE TYPE of machine learning algorithm
3. An LLM does not possess the ability to understand OR interpret intent
4. An LLM CAN NOT THINK This is the point I can’t stress enough; the “thinking” models you see today are doing nothing much more than cramming additional data into it’s working context and hoping that this guides the inference to produce a higher-quality result. Once a model is loaded for inference (i.e. asking questions) it is a STATIC entity and does not change.

Thank you for coming to my autistic TED talk <3

Edit: Also, fantastic question and never apologize for wanting to learn; keep that hunger and run with it