- an undoubtedly very intelligent person. in the course of their studies, they have read about different chess strategies, openings, etc. but they never actually played the game themselves
- an average person with a year of chess playing experience
who do you think is going to win? of course, you could give the LLM time to think and consider its opponents potential next moves, but this is a computationally expensive way to play the game that doesn't scale
which is all beside the point that chess isn't a very good proxy for general intelligence. there is a correlation, but it's very weak
It's an incredibly smug poem that has the vibe of someone fantasizing about winning an argument against someone who doesn't even exist.
The narratives about people not calling their friends for advice, but instead using AI... these are basically unfalsifiable. How could he possibly know that this is a thing that commonly occurs? That's right, he doesn't.
>Be sure to use AI when making your next, I don’t know, meal plan, for example. Definitely do not call your friend who loves to cook and ask her for her favorite recipes or tips or ways to save time making meals
I don't know about you, but if a friend were to call me for meal prep advice, I'd honestly be worried that they're having some sort of crisis and just need to talk to someone.
>Definitely do not text your friend who has fly-fished every river in Pennsylvania and biked every backwoods trail
Personally, I consider it kind of rude to pester someone who is an expert in a subject with extremely basic questions. Yes, sometimes they wont mind and will even be glad to answer your questions, but they would probably appreciate that you took the time to do research and aren't just using them as human Google search. The more genuine way to reach out to this person would be to learn as much about the subject as you can, and ask them to sanity check what you've learned. This is a much more considerate way to go about things.
>Be sure to use AI
>and while you do I’ll be over here in my 50th
>year, my youngest daughter asleep on my chest,
>my arm falling asleep because I dare not move
>lest I scare away this moment,
What? Does using AI disqualify you from having this experience? This post is so ridiculous man.
You're reacting to a very level, honest examination of what we lose with AI by insulting and berating and inventing other scenarios. You're exemplifying both the thing you are accusing the author of, and also the thing that the author is lamenting.
But it's not really an examination of what we lost with AI as all. Nor is it "level" - we can disagree on interpretations but it's very obviously written to be emotionally charged. You even called it a lamentation!
Like the person you replied to said, AI doesn't prevent you from holding your napping daughter. And most people who need a recipe would have used the Internet, and then recipe books in the pre-AI era. Plenty of people who ask Claude about fly fishing probably would have gone to Google or Reddit, or maybe even the library.
qwen3.5 9b outperforms granite 4.1 30b by a huge amount (32 vs 15 on artificialanalysis benchmark)... i have no idea what made the writer of this article say so many demonstrably incorrect things
isn't object recognition essentially solved? AI models were beating humans at image classification (in terms of error rate) back in 2016. even if this particular model isn't the best at it, they can always call out to an API or have a secondary on-device VLM that has stronger object recognition capabilities
$899? The bladeRF 2.0 micro xA9 has identical or even better specs -- for $860. Aren't crowdfunded projects supposed to be a bit cheaper to make the gamble on a new product of unknown quality and performance worth it?
The bladeRF 61.44MHz sampling rate can be effectively doubled (although they both use the AD9361, so it principle also possible on the SignalSDR) and the FPGA on board has 301KLE, versus the SignalSDR 85KLE.
The comparison matrix is also being a bit dishonest by not mentioning that the bladeRF also supports USB 3.0 and can fully saturate the 5Gbps link -- no such information about the SignalSDR.
It also states the PlutoSDR frequency range as 325MHz-3.8GHz and RF bandwidth as being 20MHz, but it can easily be increased to 70MHz-6GHz and 56MHz RF bandwidth by running two commands on the device. This process is approved by the manufacturer, and is actually outlined on AD's official wiki for the PlutoSDR.
The crying shame is that none of this potential can be utilised due to the limitations of the USB 2.0 port. The oscillator precision is also lacking, but modifications are possible.
But what I'm trying to say is, the PlutoSDR is $200, sometimes on sale for $99. If you added USB 3.0 and improved the oscillator precision, it would be practically on par with the SignalSDR, and that wouldn't cost $700 extra to do.
What a terrible article. There was one part that may have actually communicated some information though
>But by using entangled photons, the quantum photonic-dimer laser can maintain precision and strength over greater distances and in adverse conditions, the scientists said in a statement.
This makes me think the "photonic-dimer laser" has very narrow linewidth/extremely long coherence length, but also at very high power. There's usually a big tradeoff between laser power and spectral purity. A laser with a longer coherence length has favourable beam characteristics which allow it to be less divergent and less susceptible to diffraction from the atmosphere. The ideal propagation characteristics combined with the high output power is the story I guess.
Quantum entanglement contributes to the longer coherence length, which I guess could described poetically as photons protecting each other...
Have you read the additionalinformation liked in the article? They make no sense.
> “The unique thing about this project is its dual focus on generating these novel strongly correlated quantum photonic states and developing the theoretical framework and advanced algorithms for their efficient detection, potentially revolutionizing quantum imaging and communication,” Shen said.
> “Our goal is to support the advancement of imaging technologies and provide access to and training on these state-of-the-art tools so that researchers can drive towards discoveries,” said CZI Imaging Program Officer Stephani Otte. “By collaborating closely with the imaging community and providing both funding and expertise in technology development, we hope to help make the next breakthroughs in imaging possible.”
It looks like they have some kind of new type of laser, but they are overpromising aplications results. I can't find any of the fantastic new features tested experimentaly.
(And I stil can't imagine how they may work in theory. In particular fog is made of a lot of small drops of water that scater light in all directions, and entanglement can't magicaly solve that.)
if your not a survivor with the intention of finding each other and communicating, you should take dissahc out of your name and stop making these posts.
i don't think having weaknesses like these say too much about the general capabilities or potential of LLMs. try this yourself: generate a novel pangram without any sort of iterative process or revisions -- the first thing that pops into your head you must commit to paper. it's very hard. it's a lot easier if you go in alphabetical order, as you don't need to keep track of which letters you've already used. interestingly, gpt-4o also performs better at this task when you ask it to go in alphabetical order.
LLMs have known weaknesses, many having to do with an inability to "think" without using tokens. so for tasks like these, you can dramatically increase their performance by getting them to think out loud. this was the crux of the "let's verify step by step" paper.
i ran this prompt three times:
### prompt start ###
your goal is to create a novel pangram in the spanish language that sounds natural. it should be grammatically correct and coherent. it shouldn't be just technically coherent and grammatically correct, it should sound like a normal sentence.
first, print out the spanish alphabet. these are your $remainingletters. $sentence = "". then enter a loop where you do the following:
loop 1:
- select a random letter from $remainingletters choose based on which one allows you to add the most natural sounding word to the sentence, do NOT go in alphabetical order
- eliminate the letter you chose from the remaining letters
- ensure the word you chose actually begins with that letter! very important
- add the word you chose for that letter to $sentence
- are there letters in $remaining? if so, go back to start of loop 1. otherwise move on to loop 2.
loop 2:
- go through the spanish alphabet in order and ensure your $sentence contains a word starting with that letter
- once every letter is accounted for, translate the sentence to english
- does it sound like a natural sentence?
- if not, go back to the start of loop 2
- if so, print $sentence as well as its translation in english
think out loud, keep track of your work as you go
i'm not asking you to generate code, i'm just explaining how you should accomplish the task
### prompt end ###
i ran this prompt three times, and each time it generated a valid pangram that was coherent and grammatically correct. i can't be bothered to run it a bunch of times to get an accurate success rate, but i'm fairly sure there exists a prompt with a success rate of 100%. there are a lot of output tokens available, and by asking the model to iterate, it will arrive at something correct far before 128,000 tokens are exhausted.
sidenote: when i asked gpt-4o to generate 5 novel pangrams in english, and it got them all right. so language definitely matters when it comes to getting things like this right in one shot.
thanks for the very thorough reply! it's fascinating to see the techniques used to improve LLM outputs :)
i'll reply to some specific points, but i think your main argument of trying to find ways of working effectively with LLMs is spot-on.
> try this yourself: generate a novel pangram without any sort of iterative process or revisions -- the first thing that pops into your head you must commit to paper. it's very hard.
yes, it would be indeed very hard. and i don't know why i would try to do it that way. notice also that i didn't instruct ChatGPT to do it that way either.
to generate a pangram, i'd probably start with some random phrase, count the letters i've used and which ones i'm missing, and then iteratively tweak the phrase to use more letters of the alphabet until i've used them all. that at least seems like a reasonable strategy. and i would expect any intelligent agent to do the same. not that particular strategy, but "the same" as in: to try to find a strategy that works. after all, isn't that a fundamental part of intelligence? being able to find solutions to novel problems.
i know that LLMs don't work that way. and that's fine. but that was also the main point i tried to make in the post: we're being sold LLMs as "intelligent", but they don't work in any way like what we would intuitively say it's intelligent.
I think that shows how LLMs lack an important part of what we as intelligent agents have, as the parent comment pointed out, the innate ability to have some sort of train of thought or self check mechanism. In a human, as you said, we don't immediately blurt out or write out a phrase, since if someone gave us this problem we would immediately start considering the constraints and possibilities. By contrast, LLMs do not have this and the ability has to be "bolted on" through a pre prompt like "consider if your answer is correct" and "think step by step" etc. As far as actually choosing a viable strategy, such as figuring out to go alphabetically, this probably emerges as they get larger and larger; i.e, they need both the bolted on train of thought ability and also an actual good sense of reasoning and logic, which could be compared to a person (maybe a child) who can't come up with a good solution, let's say, to making a pangram (in this case).
>One exercise that helps every time is I’ll draw a diagram of all the major characters and ask myself, “Does each character have at least one scene with every other character?” Or “Am I missing moments with three or four characters together?”
this sounds like a great way to insert a pointless and boring scene into a TV show. why not just focus on making good television instead of servicing characters because... ???
hey, i might have a good solution... i wrote https://github.com/waveplate/spotifm which allows you to stream spotify over icecast2 and control the playblack with a REST API
imagine a hypothetical chess match between:
- an undoubtedly very intelligent person. in the course of their studies, they have read about different chess strategies, openings, etc. but they never actually played the game themselves
- an average person with a year of chess playing experience
who do you think is going to win? of course, you could give the LLM time to think and consider its opponents potential next moves, but this is a computationally expensive way to play the game that doesn't scale
which is all beside the point that chess isn't a very good proxy for general intelligence. there is a correlation, but it's very weak