what I would like to see is a parameterized class of prompts which can never be solved by the LLMs even when a finite number of them are manually added to the dataset.
did data-herald not find usecases or user problems to solve using its tech ?
are any startups applying LLMs profitable at all ? or is it just a mirage - ie, in the real world, startups are not able to solve users problems well using LLMs.
Researchers are trying their damndest to build a "reasoning" layer using LLMs as the foundation. But, they need to go back to the drawing-board and understand from first principles what it means to reason. For this in my view, they need to go back to epistemology (and refer to Peirce and logicians like him).
this proves that all llm models converge to a certain point when trained on the same data. ie, there is really no differentiation between one model or the other.
Claims about out-performance on tasks are just that, claims. the next iteration of llama or mixtral will converge.
LLMs seem to evolve like linux/windows or ios/android with not much differentiation in the foundation models.
all human knowledge is created by a small number of people. most of us just regurgitate and use it.
think euclid, galileo, newton, maxwell, etc...
and all human knowledge is mathematical in nature (galileo said this).
what is meant here is that, facts and events in the world we perceive can be compressed into small models which are mathematical in nature and allow a deductive method.
human genius comprises of coming up with these models. This process is described by Peirce (and Kant before him) ie, inventing concepts and relations between them to comprise models of the world we live in.
imagine compressing all observed motion into a few equations of physics. or compress all electromagnetic phenomena into a few equations. and then use this machinery to make things happen.
imagine if we feed a lot of perceived motion data into a giant black-box (which could be a neural net) - and out comes a small model of that data comprising newton's equations (and similarly maxwellian equations).
But, this giant knowledge edifice is built on solid foundations of mathematical reasoning (newton said this).
human genius is to invent a mathematical language to describe imaginary worlds precisely, and then a scientific method to apply that language to model the real world.
thinking step-by-step requires 100% accuracy in each step. If you are 95% accurate in each step, after the 10th step, the accuracy of the reasoning chain drops to 59%. this is the fundamental problem with llm for reasoning.
reasoning requires deterministic symbolic manipulation for accuracy. only then it can be composed into long chains.
there are facts,events,narratives and there is knowledge.
knowledge consists of models of the world we have constructed and learnt, which abstract patterns of facts.
facts,narratives make for banter with friends (bonding) but knowledge helps with action (decision).
when reading, demarcate narratives from models, and/or layout the facts against known mental models. this may point to deficits in mental models, or missing models altogether.
most of my reading unfortunately is mindless soaking up of pointless narratives.
i see many startups deeply understanding end-customer-workflows and usecases,
and then experimenting with how LLM may improve that.
customer-service, code-assist, call-center are a few areas which show early promise wherein customers are willing to pay for the added value. outside of these areas, i am yet to see breakthrough applications for which people are willing to pay. let me know if this is mistaken.
ie, things we construct by the computer are deterministic. the turing machine (and other equivalent models like the lambda calculus etc) being the canonical machine that models our computations. Arguably, all human knowledge is symbolic and determistic - even though it may model probabilistic phenomena.
the key is to be able to traverse the abstraction hierarchy all the way from the physics of the hardware to the end-user, and that arguably is what any engineer must learn.
Joel Spolsky had a great article on leaky abstractions.
LLMs for code are leaky abstractions. They work many-a-time. But when they break, good luck fixing it.
yann-lecunn also put it well. If something works 95% of the time, and you compose it 10 times, it only works 59% of the time.
In the real world of software engineering, we cannot build on something that works 95% of the time reliably. And LLM apologists will immediately say that code written by humans has bugs too. Of course it does.
> Hugely inconvenient and disrespectful of my time.
dealing with dealers and repair shops is not fun. but gas-cars are still the known-devil - masses understand their issues and are habituated to them.
evs come with unknowns which hinder fast mass adoption.
Is it true that luxury (or any) car manufacturers extend their oil-change periods so that the engine wears out earlier and their customers will replace their cars sooner. So, BMW wants you to replace your car every x miles (eg 100K miles) - whereas the mechanism can last way longer (300K miles) if maintained better.
the car maker has an inherent incentive to reduce the lifespan of the vehicle which conflicts with the customer's incentive to extend the lifespan.
this goes into the heart of what it means to "know".
All human knowledge is "symbolic". that is, knowledge is a set of abstractions (concepts) along with relations between concepts. As an example, by "knowing" addition is to understand the "algorithm" or operations involved in adding two numbers. reasoning is the act of traversing concept chains.
LLMs dont yet operate at the symbolic level, and hence, it could be argued that they dont know anything. LLM is a modern sophist excelling at language but not at reasoning.