emmender2

35 ms·

1.
by emmender2 2y ago
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.
2.
by emmender2 2y ago
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 entering these
3.
by emmender2 2y ago
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.
4.
by emmender2 2y ago
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.
5.
by emmender2 2y ago
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 ?
6.
by emmender2 2y ago
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, the
7.
by emmender2 2y ago
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 iteratio
8.
by emmender2 2y ago
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. eventually pe
9.
by emmender2 3y ago
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
10.
by emmender2 3y ago
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
11.
by emmender2 3y ago
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 requir
12.
by emmender2 3y ago
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 help
13.
by emmender2 3y ago
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 wi
14.
by emmender2 3y ago
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.
15.
by emmender2 3y ago
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 models our computations. A
16.
by emmender2 3y ago
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.
17.
by emmender2 3y ago
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
18.
by emmender2 3y ago
> 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
19.
by emmender2 3y ago
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) -
20.
by emmender2 3y ago
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"
21.
by emmender2 3y ago
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.
22.
by emmender2 3y ago
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 ?
23.
by emmender2 3y ago
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 dolla
24.
by emmender2 3y ago
anovikov: work on your biases. your beliefs (and internal narratives) are going to make you bitter as they are discordant with the real-world.
25.
by emmender2 3y ago
generic first-order shallow argument
26.
by emmender2 3y ago
the ceo has driven a unique culture as seen in his recent interviews. some of these run counter to prevailing business wisdom: long term projects, hard problems that could build platforms/markets vs easy problems that gain market share
27.
by emmender2 3y ago
cost, expense ? i can buy locks for the doors in my house, but if thieves can break locks, i need the police too to serve as a deterrent. ie, there needs to be a negative incentive for the thieves too.
28.
by emmender2 3y ago
attack the incentives... have dedicated govt agencies going after the crypto money trail, and disrupt the theives. this happened in the pipeline hack. otherwise, the thieves will continue thieving with nothing to stop them. thats why we hav
29.
by emmender2 3y ago
lets go back to C.S.Peirce (who invented fallibilism). humans are "concept" learning machines with a sign language to communicate said concepts to others. 1. there is a perceiver. 2. the perceiver apprehends a concept by its prope