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.
I asked gpt for giggles and the comparison is much more thorough, it has written also power per watt improvements, benefits of denser packing, and sustainability of moving toward more energy efficient solution.
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.
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.
Seeing a problem you've seen many times and have memorized and plowing through it without "concentrating" enough to see the subtle differences is a failure mode that occurs in humans as well. We don't say "humans can't reason" just because this happens so it makes little sense to say the same for LLMs. The important bit is that it can solve it if nudged from memory, same as people.
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.
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
I respect OpenAI for creating a comprehensive cookbook, and my tooling uses OpenAI for embeddings and chat completion which I have mentioned in the Readme. However, it was not built using a single reference or code example, and rather it is a combination of ideas from huggingface, openAI and langchain documentation.
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.
Many of us feel this way about his essays. He bloviates often as if on a pedestal without realizing how transparently arrogant he sounds. Many of his ideas inherently contradict his other ideas, or are simply vague and shallow. But he’s rich, so of course he must be an astute philosopher.
Thank you for your note, and for all you do to help moderate HN. Moderators like you help keep the site honest.
I have reviewed the link you provided. I agree with the guidelines noted therein. I asked some of my peers to give an objective read of my comment and they found it an accurate reflection of how they too responded to the posted essay. I cannot identify anything about it that was contrary to what is observed by many members of this community.
I do apologize to you though if you read it differently.
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.
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
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.
I disagree. E-graphs as a data structure are used to represent an exponential combination of terms in a manageable (ie polynomial) structure. Thus, it is interesting to know what the limits of that "compression" are - what we can do in the polynomial representation and when we have to fall back to an exponential (or make compromises).
This is a breathtakingly rude comment. The blog post author is not some random grad student looking at another's work; he is actively working on egglog & is one of the key people driving work in the e-graphs space.
Heck, Bitcoin is incredibly unsafe for illegal activities since everything is publicly recorded on a ledger. There is a reason people moved to Monero for such transactions.
