What can LLMs never do?

It’s an auto regressive model so it can’t do anything that requires planning tokens.

It can’t do anything which implies a large or infinite token space (eg video understanding).

It’s also limited to a reasonable response length since token selection is probabilistic at each recursion. The longer you make it the more likely it is to veer off course.

In the Danish public sector we provide services based on need assessments of citizens. Then we subsequently pay the bills for those services. Which amounts to thousands of small invoices having to be paid by a municipality each month. An example of this could be payments for a dentist visit, transportation and similar. Most of these are relatively small in size, and we've long since automated the payments of anything below a certain amount through automation. Systems which are faster and less error prone as far as putting valid data everywhere goes. They are more prone to decision making errors, however, and while fraud isn't an issue, sometimes citizens have invoices approved that they aren't entitled to. Since it's less costly to just roll with those mistakes than to try and fix them, it's an accepted loss.

The systems are hugely successful and popular, and this naturally leads to a massive interest in LLM's as the next step. They are incredibly tools, but they are based on probability and while they're lucky enough to be useful for almost everything. Decision making probably shouldn't be one of them. Similarly ML is incredibly helpful in things like cancer detection , but we've already had issues where they got things wrong and because MBA's don't really know how they work, they were used as a replacement instead of an enhancement for the human factor. I'm fairly certain we're going to use LLM's for a lot of things where we shouldn't, and probably never should. I'm not sure we can avoid it, but I wouldn't personally trust them to do any sort of function which will have a big influence on peoples lives. I use both Co-pilot and OpenAI's tools extensively, but I can still prompt them with the same thing and get extremely different quality outputs, and while this will improve, and while it's very to get an output that's actually useful, it's still a major issue that might never get solved well enough for what we're going to ask of the models way before they are ready.

I hope we're going to be clever enough to only use them as enhancement tools in the vital public sector, but I'm sure we're going to use them in areas like education. Which is going to be interesting... We already see this with new software developers in my area of the world, where they build things with the use of LLM's, things that work, but aren't build "right" and will eventually cause issues. For the most part this doesn't matter, but you really don't want the person designing your medical software to use a LLM.

If I tell GPT-4 to print something, it understands it needs to check if my printer is turned on first and turn it on if it's not, so, yes?

Also, if the generated text contains reasoning, what's your definition of "understanding"? Is it "must be made of the same stuff brains are"?

> If the text that the text-generating tool generates contains reasoning, then the text generation tool can be said to be reasoning, can't it.

I don't know... you're still describing a talking parrot here, if you'd ask me.

We really don't need time to tell.

Just making a transformer bigger and bigger, and feeding it more and more data, will not change it from being a language model into something else, anymore than scaling up an expert system such as Cyc will transform it into something other than an expert system. "Scale it up and it'll become sentient" is one of the recurring myths of AI.. a bit odd that people are falling for it again.

As an aside, it seems reasonable to consider an LLM as a type of expert system - one that has a broad area of expertise (like Cyc), including (unlike Cyc) how to infer rules from language and generate language from rules.

If you want to create a brain-like AGI, then you need an entire cognitive architecture, not just one piece of it which is what we have currently with LLMs. Compared to a brain, an LLM is maybe just like the cortex (without all the other brain parts like cerebellum, hippocampus, hypothalamus and interconnectivity such as the cortico-thalamic loop). It's as if we've cut the cortex out of a dead person's brain, put it in a mason jar to keep it alive, and hooked it's inputs and outputs up to a computer. Feed words in, get words out. Cool, but it's not a whole brain, it's a cortex in a mason jar.