emmender

Humans are wired fundamentally to be irrational - our perceptual/cognitive apparatus is deeply flawed - umpteen studies show this - so this is a given.

But, we also discovered a way to think/model which seems to work amazingly - which is the scientific method or reasoning. But this language is not natural to the way humans operate at all. It is a struggle for most of us to think in that manner. thats why math/science is difficult for most of us, and these were discovered only in the last 2000 years.

LLMs cannot yet represent conceptual relationships deterministically/symbolically. At some point in the future, perhaps they can, but the current generation has a long way to go.

how can you say reasoning is fine - when it fails at basic logic.. ?

we need to coax-it with the right prompts for it to come up with an answer - so, basically it cant reason.

looks like you have an incentive to ignore what you see.

take 3 ideas that are hot: chiplets, cloud, and LLM - remix them into the title of a paper that describes a hypothetical machine.. academia playing catch up and trying to stay relevant in my cynical eye.

disagree, their creators are hyping these things to no end - to get their next rounds of funding.

prove that there are no non negative numbers less than 3

bullshits an answer with confidence (all llms do this)

stupid monty hall

Suppose you're on a game show, and you're given the choice of three transparent doors...

stupid river crossing

A farmer with a wolf, a goat, and a koala must cross a river by boat....

basically, these LLMs have ingested canned solutions and cant reason with newly defined concepts. Anything "out-of-the-box" and they BS canned answers - like the rote student. The BS is particularly distasteful because of the confidence projected in the answer...

So, they are great for looking-up commonly understood "in-the-box" narratives, but are poor at reasoning where there is some novelty. this is what we can expect from a probabilistic "deep" autocompleting machine. unlike a child which can learn ideas and metaphors from a few examples and anomalies.

failed all the logic puzzles with slight tweaks - including stupid monty hall (with transparent doors). BSs with confidence. agi is not knocking at the door.

enterprise software architects trying to wedge into this emerging area, and you soon start hearing of: provenance, governance, security postures, gdpr, compliance.. give it a rest architects, LLMs are not ready yet for your wares.

Please provide this reference in your readme / blog as it is the original source for your work... and provides the background for the tradeoff between the 2 approaches: 1) fine-tuning vs 2) Search-ask

https://github.com/openai/openai-cookbook/blob/main/examples...

imo, it is injustice to present a clean capsule of the subject in a book, and expect students to digest it - whereas it took hundreds of years of discovery to arrive at the concepts in that book.

i would love books that illustrate the struggles, and problems that drove advancements and how or why the ideas were invented in the first place.

to that end, i think gilbert strang's linear algebra is decent.

No, I did not.

The question is ill-posed imo. I would invert the question and ask: "How not to suck at your work" as that would lead to similar conclusions, and is more actionable.

This essay has too many weasel sentences like:

"Boldly chase outlier ideas,"

"Husband your morale"

"Doing great work is a depth-first search whose root node is the desire to. "

"Curiosity is the best guide."

This is woolly-feel-good writing that chatgpt and folks like steve pinker, deepak chopra etc specialize in, ie: a bag-of-words about fuzzy feel-good ideas we all want to hear.

As Mark Twain said: "I wrote a long essay because I didn't want to put the effort writing a shorter one" - applies to this piece.

It is not confirmation bias. It is a different tendency.

It is a tendency to absorb whatever we read or hear - regardless of its information content.

IMO: Human languages evolved more for bonding than for conveying information. So, words exchanged were more to soothe the emotions of individuals involved rather than convey useful information about the state of their environment.

Hence we are fundamentally wired not to process the information content critically but to be soothed emotionally by whatever we read or hear

Such despicable behavior from everyone involved.

1. Professors and MIT students attempting to gain fleeting fame riding on the bandwagon without trying to do deep work

2. Professors at MIT getting upset and lash out that GPT-4 can now get a MIT degree devaluing said degree.

What has become of academia these days ?

My apologies, I didnt intend to be rude..

Any discovery is a step forward (whether it is news or not)..

Proving a problem is np-complete should not be news. what should be news is when a problem has a P algo. (example, primes is in P)

my cynical eye sees this as an over-eager grad student rushing out his/her discovery onto hacker news. Next thing you know, an FPTAS for it may rear its ugly head.

where is the study on "bitcoin as a tool in ransomware" ?

how can they make a sale to enterprise IT without a giant architecture diagram ? The next thing you know, java may rear its ugly head as well just when we thought python eliminated it completely for these applications.

are VCs doing architecture now ? huh... architecture astronauts much.

looks like they are playing catchup and trying to stay relevant.

What happened to their web3 vision ?

agree with you 100%..

some philosophy (plato's dialogues) establish the underpinnings of reasoning and meaning - in that everything can be critiqued and debated.

and some other philosophy (spinoza's emmendations of the intellect / ethics) attempt an axiomatic approach..

however, much of philosophy/psychology is just a narrative with lot of words but little coherence.

loved the "dumb monty hall" problem, and inspired me to create this "stupid birthday paradox".

"In an event for twins, How many people should attend so that the likelihood of finding two people with the same birthday is over 50%"

the slightest tweaks to the standard problem narratives trips up these stochastic parrots.

if there is one thing chatgpt has opened my eyes to, it is that we live in a world teeming with BS. In particular, philosophical essays and books are full of it.

It seemingly makes sense, because, we contort the narrative to squeeze meaning out of it. But a skeptical eye sees no coherence.

For example, this passage:

"We grow, we heal, we are reborn. Nothing is impossible. The galaxy is bursting with chaos-driven reactions. You and I are storytellers of the world."

Surely, all of us can squeeze meaning out of this passage, but it is actually produced by this BS generator: https://sebpearce.com/bullshit/

If there is anything that should be taught in schools, it is the triumph of the reason in the forms of math/science thinking - over all the BS narratives that abound - only because, that kind of thinking is unnatural, whereas stream of consciousness BS comes naturally to us.

there is nccl, gpudirect, nvlink and so on and so forth.. It is not just matmul on gpus.

python is syntactic sugar - the heavy lifting is done by c/c++ bindings.

many ML experts are not software engineers. They just want syntax to get their job done. fair enough.

In addition to the above:

1) function decorators etc have made the code unreadable

2) while code is succinct, a lot of abstraction is hidden in some C/C++ language binding somewhere, so, when there is a problem, it is hard to debug

3) Pytorch has become a monolithic monster with practically no-one understanding its functionality e-2-e

ChatGPT is hot news. Professor wants in on the action. He and his grad-student come up with a thesis. Then they cherry-pick evidence supporting the thesis and concoct a catchy headline that makes it to hacker news.

the stock market for short-term traders is a zero-sum game.

if there is a chatgpt trick that produced alpha, it would be adopted by every hedge-fund reducing its effectiveness (and alpha generation).

so, these sorts of tricks depend on suckering retail traders who as a group are the losers. but the stimmies have run out, and so will these tricks.

"GIGO at GBps" as someone put it.

LLMs remix and sling BS in infinite ways to create more of the same.

We are entering a golden-age of Bull

the range of technology that needs to come together for ai training is underestimated. there is cuda of course, but there is also nccl, infiniband, gpudirect, each of which requires years of sw and hw maturity. unlike the cpu which has a clean interface (instruction set) the gpu has no such thing - it is more like an octopus with tentacles into networking, compute, storage etc.

The value added by the paper is to direct the readers attention to BS - and make them think about it.

This is a fair take - ie, the authors are saying what their readers want to hear using some pseudo-statistics.

In this sense, the authors are bullshitting their readers - by telling them what they want to hear (as any politician might)