Yes - we train only on a subset of human communication, the one using written symbols (even voice has much much more depth to it), but human brains train on the actual physical world.
Human students who only learned some new words but have not (yet) even began to really comprehend a subject will just throw around random words and sentences that sound great but have no basis in reality too.
For the same sentence, for example, "We need to open a new factory in country XY", the internal model lighting up inside the brain of someone who has actually participated when this was done previously will be much deeper and larger than that of someone who only heard about it in their course work. That same depth is zero for an LLM, which only knows the relations between words and has no representation of the world. Words alone cannot even begin to represent what the model created from the real-world sensors' data, which on top of the direct input is also based on many times compounded and already-internalized prior models (nobody establishes that new factory as a newly born baby with a fresh neural net, actually, even the newly born has inherited instincts that are all based on accumulated real world experiences, including the complex very structure of the brain).
Somewhat similarly, situations reported in comments like this one (client or manager vastly underestimating the effort required to do something): https://news.ycombinator.com/item?id=45123810
The internal model for a task of those far removed from actually doing it is very small compared to the internal models of those doing the work, so trying to gauge required effort falls short spectacularly if they also don't have the awareness.
I'm not sure what point you are trying to make. Are you saying in order to make LLMs better at learning the missing piece is to make the capable to interact with the outside world? Give them actuators and sensors?
I have several chat groups with friends and even with my wife to avoid this topic. With my wife, I have several now: one to share recipes, one to share the weeks meal plan, one to talk about the activities of our two year old and one to share houses we might be interested in buying.
It’s great to separate for record keeping, but mostly to avoid forcing the conversation on some organisational thing when the other just needs eg to vent about something at work
Most novel AI serves the user perfectly well. And since this type of AI requires lots of labelled data, these users are typically large data harvesting organisations.
Some other applications that you may use daily are translate and face unlock/recognition.
It’s interesting that you mention search and spam filtering which both include an adversarial component. It seems to me that the adversarial AI has become better, in line of expectation from the democratisation of AI tools and knowledge.
Sounds like excel flash fill might be similar to what you’re interested in. It’s a well documented feature described in papers and talks, it comes from a pretty cool group in MS research.
My data is a 600 char string of letters and following some very loose rules you slice 7-10 substrings of the string. The string is an RNA sequence and the substrings vary depending on the genus/species/etc. I know loosely how far down the string each might be, but it’s not perfect and it varies. I definitely know in what order they are, so that helps. Right now I have a script that works 90% of the time, but I’d love to make it learn from the data it reads so it can guess the other 10% or be ready for new discoveries. So I’m not sure how flash fill would work in this scenario.
Stochastic methods may not be able to do so in isolation, but they can be used in tandem with other approaches such as in
“A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving”
https://ojs.aaai.org/index.php/AAAI/article/view/16780/16587
A colleague of mine recently published a paper on ML-based cell counting for human cancer cells.
It's open access and available here
https://www.mdpi.com/2076-3417/11/11/4912
