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.
I was waiting for: "but humans do that too" and bingo. on another note: an entire paper written on one prompt - is this the state of research these days ? finally: a giant group of data-entry technicians are likely…
yawn, we need ai bust to get these folks to see sense. until then, they have a free pass to get away with such scare-mongering bs.
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…
When you tell them it is a fake, they will believe more strongly that it is real. Dont we all live in the joyful bubble of beliefs many of which have no basis ?
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…
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,…
the parable also has a "separating perception from reality" flavor for the science types. that is, the market is perception of what is out there, reinforced by the herd mentality. the reality is what actually exists.…
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).…
wut the average theorem in euclids' elements (written 2000 years back) would have a reasoning chain of at least 10 steps. all of the mathematical machinery humans build need 100% accuracy in each step
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…
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…
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…
lot of investment being pumped into the shovel makers (nvidia), and the shovel sellers (csps). those panning for gold (app devs, startups etc) may or may not find it. remains to be seen and i remain skeptical.
indeed, and that goes into the heart of it. 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…
100% agree. 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…
> 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…
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…
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…
Happy to see such a huge team collaborating on a project at any company. Perhaps becoz, it involves LLM and LLMs are hot, and everyone wants a piece of it.
these workers met your hiring bar. why lay them off when there is hiring in other teams ? why not move workers from unproductive projects to more promising ones ?
pytorch/tensorflow etc are becoming the "OS" for inference and training. users interact with pytorch - not with hardware libraries. so, if pytorch can abstract the hardware, users wont care. all users will care about is…
anovikov: work on your biases. your beliefs (and internal narratives) are going to make you bitter as they are discordant with the real-world.
generic first-order shallow argument
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.
I was waiting for: "but humans do that too" and bingo. on another note: an entire paper written on one prompt - is this the state of research these days ? finally: a giant group of data-entry technicians are likely…
yawn, we need ai bust to get these folks to see sense. until then, they have a free pass to get away with such scare-mongering bs.
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…
When you tell them it is a fake, they will believe more strongly that it is real. Dont we all live in the joyful bubble of beliefs many of which have no basis ?
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…
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,…
the parable also has a "separating perception from reality" flavor for the science types. that is, the market is perception of what is out there, reinforced by the herd mentality. the reality is what actually exists.…
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).…
wut the average theorem in euclids' elements (written 2000 years back) would have a reasoning chain of at least 10 steps. all of the mathematical machinery humans build need 100% accuracy in each step
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…
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…
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…
lot of investment being pumped into the shovel makers (nvidia), and the shovel sellers (csps). those panning for gold (app devs, startups etc) may or may not find it. remains to be seen and i remain skeptical.
indeed, and that goes into the heart of it. 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…
100% agree. 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…
> 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…
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…
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…
Happy to see such a huge team collaborating on a project at any company. Perhaps becoz, it involves LLM and LLMs are hot, and everyone wants a piece of it.
these workers met your hiring bar. why lay them off when there is hiring in other teams ? why not move workers from unproductive projects to more promising ones ?
pytorch/tensorflow etc are becoming the "OS" for inference and training. users interact with pytorch - not with hardware libraries. so, if pytorch can abstract the hardware, users wont care. all users will care about is…
anovikov: work on your biases. your beliefs (and internal narratives) are going to make you bitter as they are discordant with the real-world.
generic first-order shallow argument