Comment by 3abiton
1 year ago
The article should be titled " What can LLM never do, yet". By definition, Large Language Models would keep growing larger and larger, to be trained on faster and more advanced hardware, and certain points like "completing complex chains of logical reasoning" tasks, would be just a time hurdle. Only time will tell.
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.
Well said. This has always been my fundamental problem with the claims about large language models' current or eventual capabilities: most of the things people claim it can or will be able most of the things people claim it can or will be able to do require a neural architecture completely different from the one it has, and no amount of scaling up the number of neurons and the amount of training data used will change that fundamental architecture, and at a very basic level the capabilities of any neural network are going to be limited by its architecture. We would need to add some kind of advanced recursive structure to large language models, as well as some kind of short-term and working memory, as well as probably many other structures, to make them capable of the kind of metacognition necessary to properly do a lot of the things people want them to be able to do. Without metacognition, the ability to analyze what one is currently thinking and think new things based on that analysis, and therefore to look at what one is thinking and error correct it, consciously adjust it or iterate on it, or consciously ensure that one is adhering to certain principles of reasoning or knowledge, we can't expect large language models to be able to actually understand Concepts and principles and how they are applicable and reliably perform reasoning or even obey instructions.
>will not change it from being a language model into something else,
This is a pretty empty claim when we don't know what the limits of language modelling are. Of course it will never not be a language model. But the question is what are the limits of capability of this class of computing device?
Some limit's are pretty obvious, even if easy to fix.
For example, a pure LLM is just a single pass through a stack of transformer layers, so there is no variable depth/duration (incl. iteration/looping) of thought and no corresponding or longer duration working memory other than the embeddings as they pass thru. This is going to severely limit their ability to plan and reason since you only get a fixed N layers of reasoning regardless of what they are asked.
Lack of working memory (really needs to be context duration, or longer, not depth duration) has many predictable effects.
No doubt we will see pure-transformer architectures extended to add more capabilities, so I guess the real question is how far these extensions (+scaling) will get us. I think one thing we can be sure of though is that it won't get us to AGI (defining AGI = human-level problem solving capability) unless we add ALL of the missing pieces that the brain has, not just a couple of the easy ones.
Thanks for that final paragraph! I'm going to quote you from now on, when trying to explain to someone (for the thousandth time) why ChatGPT isn't about to become super-intelligent and take over the world.
I think that the article is correct. There are indeed things that LLMs will never be able to do, at least not consistently, however much the hardware improves or on how much more material they are trained.
How come? Note my emphasis on the 2nd 'L'. I'm not saying that there are things that AI models will never be able to do, I'm saying that there are things that Large Language Models will be unable to do.
Training LLMs is often argued to be analogous to human learning, most often as a defence against claims of copyright infringement by arguing that human creativity is also based on training from copyrighted materials. However, that is a red herring.
The responses from ever more powerful LLMs are indeed impressive, and beyond what an overwhelming majority of us believed possible just 5 years ago. They are nearing and sometimes surpassing the performance of educated humans in certain areas, so how come I can argue they are limited? Consider it from the other side: how come an educated human can create something as good as an LLM can when said human's brain has been "trained" on an infinitesimal fraction of the material which was used to train even the 1st release of ChatGPT?
That is because LLMs do not learn nor reason like humans: they do not have opinions, do not have intentions, do not have doubts, do not have curiosity, do not have values, do not have a model of mind — they have tokens and probabilities.
For an AI model to be able to do certain things that humans can do it needs to have many of those human characteristics that allow us to do impressive mental feats having absorbed barely any training material (compared to LLMs) and being virtually unable to even remember most of it, let alone verbatim. Such an AI model is surely possible, but it needs a completely different paradigm from straightforward LLMs. That's not to say however that a Language Model will almost certainly be an necessary module of such an AI, but it will not be sufficient.
I don't think values, opinions or things like that are needed at all. These are just aspects we have in order to perform in and together with the society.
Also doubt is just uncertainty, and can be represented as a probability. Actually all values and everything can be presented as a numerical probability, which I personally prefer to do as well.
Values and opinions drive human attention, which as transformers demonstrate, is relevant to reasoning.
The big question is if LLMs are capable enough to converge to AGI. It might very well be that as we pour in more resources that they converge to something only slightly more useful but similar as we have today.
> The article should be titled " What can LLM never do, yet".
I don't think it should. It's more interesting to know what LLMs will _never_ be able to do (if anything).
Yes, but the article doesn't really answer this question.
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.
Math reasoning is still a non solved problem even if the rest of the capabilities are getting better. This means the transformers architecture may not be the best way to approach all problems
Maybe the wording is correct. Looks like a hard limit on doing what a LLM just do. If it goes beyond that, then is something more, or at least different, than a LLM.