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I'm not sure why people on HN (of all places) are so divided regarding the perception of AI/ML. I have not seen anything like it before. We literaly had not sy

I'm not sure why people on HN (of all places) are so divided regarding the perception of AI/ML. I have not seen anything like it before. We literaly had not system or way of even doing things like code generation based on text input. Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. I could list tons of examples which are groundbreaking. The whole Image generation stack is completly new. That blog article is fair enough, there is hype around this topic for sure, but alone for every researcher who needs to write code for their research, AI can make them already a lot more efficient. But i do believe, that we have entered a new ara: An ara were we take data again very serious. A few years back, you said 'the internet doesn't forget' then we realized that yes the internet starts to forget. Google deleted pages, removed the cache feature and it felt like we stoped caring for data because we didn't knew what to do with it. Then ai came along. And not only is now data king again but we are now in the mids of reinforcment ara: We now give feedback and the systems incorporate that feedback into their training/learning. And the ai/ml topic is getting worked on on every single aspect of it: Hardware, Algorithm, use cases, data, tools, protocols, etc. We are in the middle of incorporating and building for and on it. This takes a little bit of time. Still the progress is crazy exhausting. We will only see in a few years if there is a real ceiling. We do need more GPUs, bigger Datacenters to do a lot more experiments on AI architecture and algorithm. We have a clear bottleneck. Big companies train one big model for weeks and month.

KurSix 1y ago
But on the flip side, the "AI will revolutionize science" narrative feels way ahead of what the evidence supports
callc 1y ago
> “I'm not sure why people on HN (of all places) are so divided regarding the perception of AI/ML.” Everyone is a rational actor from their individual perspective. The people hyping AI, and the people dismissing the hype both have good reasons. The is justification to see this new tech as ground breaking. There is justification to be weary about massive theft of data and dismissiveness of privacy. First, acknowledge and respect that there are so many opinions on any issue. Take yourself out of the equation for a minute. Understand the other side. Really understand it. Take a long walk in other people’s shoes.
Barrin92 1y ago
>but alone for every researcher who needs to write code for their research, AI can make them already a lot more efficient. scientists don't need to be efficient, they need to be correct. Software bugs were already a huge cause of scientific error, and responsible for lack of reproducibility, see for example cases like this (https://www.vice.com/en/article/a-code-glitch-may-have-caused-errors-in-more-than-100-published-studies/ https://www.vice.com/en/article/a-code-glitch-may-have-cause...) Programming in research environments is done with some notoriously questionably variation in quality, as is the case for the industry to be fair, but in research minor errors can ruin results of entire studies. People are fed up and come to much harsher judgements on AI because in an environment like a lab you cannot write software with the attitude of an impressionist painter or the AI equivalent, you need to actually know what you're typing. AI can make you more efficient if you don't care if you're right, which is maybe cool if you're generating images for your summer beach volleyball event, but it's a disastrous idea if you're writing code in a scientific environment.
- Flamentono2 1y ago
  I do expect a researcher to verify the way the code interacts with the data set. Still a lot of researchers can benefit from code tools for their daily work to make them a lot faster. And plenty of strategies exist to saveguard this. Tool use for example, unit tests etc.
whyowhy3484939 1y ago
> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. Thing is we just see that it's copy pasting stack overflow, but now in a fancy way so this is sounding like "I asked Google for a nearby restaurant and it found it in like 500ms, my C64 couldn't do that". It sounds impressive (and it is) because it sounds like "it learned about navigating in the real world and it can now solve everything related to that" but what it actually solved is "fancy lookup in a GIS database". It's useful, damn sure it is, but once the novelty wears off you start seeing it for what it is instead of what you imagine it is. Edit: to drive the point home. > claude just generated that What you think happened is AI is "thinking" and building a ontology over which it reasoned and came to the logical conclusion that this script was the right output. What actually happened is your input correlates to this output according to the trillion examples it saw. There is no ontology. There is no reasoning. There is nothing. Of course this is still impressive and useful as hell, but the novelty will wear off in time. The limitations are obvious by this point.
- Flamentono2 1y ago
  I'm following LLMs, AI/ML for a few years now and not just on a high level. There is not a single system out there today which can do what claude can do. I stil see it for what it is: A technology i can communicate/use with natural language and get a very diverse of tasks done. From writing/generating code, to svgs, to emails, translation etc. etc. etc. Its a paradigma shift for the whole world literaly. We finally have a system which encodes not just basic things but high level concepts. And we humans are doing often enough something very similiar. And what limitations are obvious? Tell me? We have not reached any real ceiling yet. We are limited by GPU capacity or how many architectural experiments a researcher can run. We have plenty of work to do to cleanup the data set we use and have. We need to build more infrastructure, better software support etc. We have not even reached the phase were we all have local AI/ML chips build in. We don't even know yet how a system will act if everyone of us has access to very fast inferencing like you already get with groq.
  - whyowhy3484939 1y ago
Retr0id 1y ago
Google never gave a good reason for why they stopped making their cache public, but my theory is that it was because people were scraping it to train their LLMs.
sanderjd 1y ago
HN is always divided on "how much is the currently hype-y technology real vs just hype". I've seen this over and over again and been on different sides of the question on different technologies at different times. To me, this is same as it ever was!
- aleph_minus_one 1y ago
  I basically agree, but want to point out two major differences to other "hype-y" topics that existed in the past that in my opinion make the whole AI discussions on HN a little bit more controversial than other older hype discussions: 1. The whole investment volume (and thus hope and expectations) into AI is much larger than into other hype topics. 2. Sam Altman, the CEO of OpenAI, was president of YCombinator, the company begind Hacker News, from 2014 to 2019.
  - sanderjd 1y ago
    On (1): Investment volume relative to what? To me, it looks like a very similar pattern of investors crowding into the currently hot thing, trying to get a piece of the winners of the power law. On (2): I'm honestly not sure I think this is making a big difference at all. Not much of the commentary here is driven by YC stuff, because most of the audience here has no direct entwinement with YC.
    - famouswaffles 1y ago
      >On (1): Investment volume relative to what? To me, it looks like a very similar pattern of investors crowding into the currently hot thing, trying to get a piece of the winners of the power law. The profile of investors (nearly all the biggest tech companies amongst others) as well as how much they're willing to and have put down (billions) is larger than most. Open AI alone just started work on a $100B+ datacenter (Stargate)
      - sanderjd 1y ago
        Yeah maybe I buy it. But it reminds me of the investment in building out the infrastructure of the internet. That predates HN, but it's the kind of thing we would have debated here if we could have :)
Workaccount2 1y ago
The ultimate job of a programmer is to translate human language into computer language. Computers are extremely capable, but they speak a very cryptic overtly logical language. LLMs are undeniably treading onto that territory. Who knows how far in they will make it, but the wall is breached. Which is unsettling to down right scary depending on your take. It is a real threat to a skill that many have honed for years and for which is very lucrative to have. Programmers don't even need to be replaced, having to settle for $100k/yr in a senior role is almost just a scary.
- kbelder 1y ago
  Yes, but the scale isn't 'unsettling' to 'scary'... it's from 'incredible' to 'scary'.
corytheboyd 1y ago
> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. I agree that this is useful! It will even take natural language and augment the script, and maybe get it right! Nice! The AI is combing through scraped data with an LLM, and conjuring forth some imagemagick snippets into a shell script. This is very useful, and if you’re like most people, who don’t know imagemagick intimately, it’s going to save you tons of time. Where it gets incredibly frustrating is tech leadership seeing these trivial examples, and assuming it extrapolates to general software engineering at their companies. “Oh it writes code, or makes our engineers faster, or whatever. Get the managers mandating this, now! Also, we need to get started on the layoffs. Have them stack rank their reports by who uses AI the best, so that we are ready to pull the trigger.” But every real engineer who uses these tools on real (as in huge, poorly written) codebases, if they are being honest (they may not be, given the stack ranking), will tell you “on a good day it multiplies my productivity by, let’s say, 1.1-2x? On a bad day, I end up scrapping 10k lines of LLM code, reading some documentation on my own, and solving the problem with 5 lines of intentional code.” Please, PLEASE pay attention to this details that I added: Huge, poorly written codebases. This is just the reality at most software companies that have graduated from series A startup. What my colleagues and I are trying to tell you, leadership, is that these “it made a script” and “it made a html form with a backend” examples ARE NOT cleanly extrapolating to the flaming dumpster fire codebases we actually work with. Sometimes the tools help! Sometimes, they don’t. It’s as if LLM is just another tool we use sometimes. This is why I am annoyed. It’s incredibly frustrating to be told by your boss “use tool or get fired” when that tool doesn’t always fit the task at hand. It DOES NOT mean I see zero value in LLMs.
evilfred 1y ago
most work in software jobs is not making one-off scripts like in your example. a lot of the job is about modifying existing codebases which include in-house approachs to style and services along with various third party frameworks like Spring driven by annotations, and requirements around how to write tests and how many. AI is just not very helpful here, you spend more time spinning wheels trying to craft the absolute perfect script than just making code changes directly.
dvfjsdhgfv 1y ago
There is no single reason. Nobody will argue that LLMs are already quite useful at some tasks if used properly. As for the opposing view, there are so many reasons. * Founders and other people who bet their money on AI try to pump up the hype in spite of problems with delivery * We know some of them are plainly lying, but the general public doesn't * They repeat their assumptions as facts ("AI will replace most X and Y jobs by year Z") * We clearly see that the enormous development of LLMs has plateaued but they try to convince the general public it's the contrary * We see the difference on how a single individual (Aaron Swartz) is treated when making a small copyright infringement, and how the consequences for AI companies like OpenAI or Meta who copied the whole contents of Libgen are non-existent. * Some people like me just hate AI slop - in writing and imaging. It just puts me off and I stop reading/watching etc. There are many more points like this.

KurSix 1y ago

But on the flip side, the "AI will revolutionize science" narrative feels way ahead of what the evidence supports

callc 1y ago

> “I'm not sure why people on HN (of all places) are so divided regarding the perception of AI/ML.” Everyone is a rational actor from their individual perspective. The people hyping AI, and the people dismissing the hype both have good reasons. The is justification to see this new tech as ground breaking. There is justification to be weary about massive theft of data and dismissiveness of privacy. First, acknowledge and respect that there are so many opinions on any issue. Take yourself out of the equation for a minute. Understand the other side. Really understand it. Take a long walk in other people’s shoes.

Barrin92 1y ago

>but alone for every researcher who needs to write code for their research, AI can make them already a lot more efficient. scientists don't need to be efficient, they need to be correct. Software bugs were already a huge cause of scientific error, and responsible for lack of reproducibility, see for example cases like this (https://www.vice.com/en/article/a-code-glitch-may-have-caused-errors-in-more-than-100-published-studies/ https://www.vice.com/en/article/a-code-glitch-may-have-cause...) Programming in research environments is done with some notoriously questionably variation in quality, as is the case for the industry to be fair, but in research minor errors can ruin results of entire studies. People are fed up and come to much harsher judgements on AI because in an environment like a lab you cannot write software with the attitude of an impressionist painter or the AI equivalent, you need to actually know what you're typing. AI can make you more efficient if you don't care if you're right, which is maybe cool if you're generating images for your summer beach volleyball event, but it's a disastrous idea if you're writing code in a scientific environment.

Flamentono2 1y ago
I do expect a researcher to verify the way the code interacts with the data set. Still a lot of researchers can benefit from code tools for their daily work to make them a lot faster. And plenty of strategies exist to saveguard this. Tool use for example, unit tests etc.

whyowhy3484939 1y ago

> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. Thing is we just see that it's copy pasting stack overflow, but now in a fancy way so this is sounding like "I asked Google for a nearby restaurant and it found it in like 500ms, my C64 couldn't do that". It sounds impressive (and it is) because it sounds like "it learned about navigating in the real world and it can now solve everything related to that" but what it actually solved is "fancy lookup in a GIS database". It's useful, damn sure it is, but once the novelty wears off you start seeing it for what it is instead of what you imagine it is. Edit: to drive the point home. > claude just generated that What you think happened is AI is "thinking" and building a ontology over which it reasoned and came to the logical conclusion that this script was the right output. What actually happened is your input correlates to this output according to the trillion examples it saw. There is no ontology. There is no reasoning. There is nothing. Of course this is still impressive and useful as hell, but the novelty will wear off in time. The limitations are obvious by this point.

Flamentono2 1y ago
I'm following LLMs, AI/ML for a few years now and not just on a high level. There is not a single system out there today which can do what claude can do. I stil see it for what it is: A technology i can communicate/use with natural language and get a very diverse of tasks done. From writing/generating code, to svgs, to emails, translation etc. etc. etc. Its a paradigma shift for the whole world literaly. We finally have a system which encodes not just basic things but high level concepts. And we humans are doing often enough something very similiar. And what limitations are obvious? Tell me? We have not reached any real ceiling yet. We are limited by GPU capacity or how many architectural experiments a researcher can run. We have plenty of work to do to cleanup the data set we use and have. We need to build more infrastructure, better software support etc. We have not even reached the phase were we all have local AI/ML chips build in. We don't even know yet how a system will act if everyone of us has access to very fast inferencing like you already get with groq.
- whyowhy3484939 1y ago

Retr0id 1y ago

Google never gave a good reason for why they stopped making their cache public, but my theory is that it was because people were scraping it to train their LLMs.

sanderjd 1y ago

HN is always divided on "how much is the currently hype-y technology real vs just hype". I've seen this over and over again and been on different sides of the question on different technologies at different times. To me, this is same as it ever was!

aleph_minus_one 1y ago
I basically agree, but want to point out two major differences to other "hype-y" topics that existed in the past that in my opinion make the whole AI discussions on HN a little bit more controversial than other older hype discussions: 1. The whole investment volume (and thus hope and expectations) into AI is much larger than into other hype topics. 2. Sam Altman, the CEO of OpenAI, was president of YCombinator, the company begind Hacker News, from 2014 to 2019.
- sanderjd 1y ago
  On (1): Investment volume relative to what? To me, it looks like a very similar pattern of investors crowding into the currently hot thing, trying to get a piece of the winners of the power law. On (2): I'm honestly not sure I think this is making a big difference at all. Not much of the commentary here is driven by YC stuff, because most of the audience here has no direct entwinement with YC.
  - famouswaffles 1y ago
    >On (1): Investment volume relative to what? To me, it looks like a very similar pattern of investors crowding into the currently hot thing, trying to get a piece of the winners of the power law. The profile of investors (nearly all the biggest tech companies amongst others) as well as how much they're willing to and have put down (billions) is larger than most. Open AI alone just started work on a $100B+ datacenter (Stargate)
    - sanderjd 1y ago
      Yeah maybe I buy it. But it reminds me of the investment in building out the infrastructure of the internet. That predates HN, but it's the kind of thing we would have debated here if we could have :)

Workaccount2 1y ago

The ultimate job of a programmer is to translate human language into computer language. Computers are extremely capable, but they speak a very cryptic overtly logical language. LLMs are undeniably treading onto that territory. Who knows how far in they will make it, but the wall is breached. Which is unsettling to down right scary depending on your take. It is a real threat to a skill that many have honed for years and for which is very lucrative to have. Programmers don't even need to be replaced, having to settle for $100k/yr in a senior role is almost just a scary.

kbelder 1y ago
Yes, but the scale isn't 'unsettling' to 'scary'... it's from 'incredible' to 'scary'.

corytheboyd 1y ago

> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. I agree that this is useful! It will even take natural language and augment the script, and maybe get it right! Nice! The AI is combing through scraped data with an LLM, and conjuring forth some imagemagick snippets into a shell script. This is very useful, and if you’re like most people, who don’t know imagemagick intimately, it’s going to save you tons of time. Where it gets incredibly frustrating is tech leadership seeing these trivial examples, and assuming it extrapolates to general software engineering at their companies. “Oh it writes code, or makes our engineers faster, or whatever. Get the managers mandating this, now! Also, we need to get started on the layoffs. Have them stack rank their reports by who uses AI the best, so that we are ready to pull the trigger.” But every real engineer who uses these tools on real (as in huge, poorly written) codebases, if they are being honest (they may not be, given the stack ranking), will tell you “on a good day it multiplies my productivity by, let’s say, 1.1-2x? On a bad day, I end up scrapping 10k lines of LLM code, reading some documentation on my own, and solving the problem with 5 lines of intentional code.” Please, PLEASE pay attention to this details that I added: Huge, poorly written codebases. This is just the reality at most software companies that have graduated from series A startup. What my colleagues and I are trying to tell you, leadership, is that these “it made a script” and “it made a html form with a backend” examples ARE NOT cleanly extrapolating to the flaming dumpster fire codebases we actually work with. Sometimes the tools help! Sometimes, they don’t. It’s as if LLM is just another tool we use sometimes. This is why I am annoyed. It’s incredibly frustrating to be told by your boss “use tool or get fired” when that tool doesn’t always fit the task at hand. It DOES NOT mean I see zero value in LLMs.

evilfred 1y ago

most work in software jobs is not making one-off scripts like in your example. a lot of the job is about modifying existing codebases which include in-house approachs to style and services along with various third party frameworks like Spring driven by annotations, and requirements around how to write tests and how many. AI is just not very helpful here, you spend more time spinning wheels trying to craft the absolute perfect script than just making code changes directly.

dvfjsdhgfv 1y ago

There is no single reason. Nobody will argue that LLMs are already quite useful at some tasks if used properly. As for the opposing view, there are so many reasons. * Founders and other people who bet their money on AI try to pump up the hype in spite of problems with delivery * We know some of them are plainly lying, but the general public doesn't * They repeat their assumptions as facts ("AI will replace most X and Y jobs by year Z") * We clearly see that the enormous development of LLMs has plateaued but they try to convince the general public it's the contrary * We see the difference on how a single individual (Aaron Swartz) is treated when making a small copyright infringement, and how the consequences for AI companies like OpenAI or Meta who copied the whole contents of Libgen are non-existent. * Some people like me just hate AI slop - in writing and imaging. It just puts me off and I stop reading/watching etc. There are many more points like this.