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> They all have a role, and we have to incorporate them in sensible ways. I'm sure that these AI tools will have some use, but these language models don't know

> They all have a role, and we have to incorporate them in sensible ways. I'm sure that these AI tools will have some use, but these language models don't know anything. I find it worrying that I see so many techies not understanding this. Especially, because many bigger players in the area benefit from this lack of knowledge. This means that people who don't have time to procrastinate on the orange website are even less likely to understand the implications. LLMs are excellent plausible bullshit generators, which has many uses, but being factually correct is not their strength. The issue is that even if the factual mistakes are rare, they're really hard to spot. I'm looking for a good, terse, catchy metaphor I could use to explain that to some of my family members and friends. > We can spend billions on deploying AI all over Africa, or we pay for [...] Looking back at the conversations I had with founders even I pre ChatGPT bonanza, I'm 100% sure someone's already pitching that idea to someone. I wouldn't be surprised if that conversation started in a marketing department of an ad tech business.

SuoDuanDao 4y ago
So long as the LLM teaching the kids and whoever is testing the kid's learning are rivalrous entities, that seems like an easy problem to get around.
- rpastuszak 4y ago
  Why do you think so and how would that work?
  - SuoDuanDao 4y ago
    If the test-making entity writes the test so that the weaknesses of LLMs are brought to the fore, students taking the test will need to know how to compensate for those weaknesses when they're using them as a tool post-graduation.
deleted 4y ago
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unshavedyak 4y ago
> LLMs are excellent plausible bullshit generators, which has many uses, but being factually correct is not their strength. The issue is that even if the factual mistakes are rare, they're really hard to spot. I'm looking for a good, terse, catchy metaphor I could use to explain that to some of my family members and friends. To me the bigger question is how far can we take them? Ie i get that they just pick likely next words. They're autocomplete on steroids. Yet that simple primitive is shockingly good. Way, way way beyond that i would have predicted is possible for an "autocomplete". So where is the limit of this autocomplete? Would it be possible to eliminate ~90% of the current errors? Fake math, but would that put us at a more correct version than students typically graduate with? If so yea, it can be wrong at times - but assuming as don't abandon standardized testing/etc, it could still be an amazing tool for interactively learning something. My hope is that even if it never goes beyond being an autocomplete; if we can improve the training dataset, help it not conflict with itself, etc - that maybe the autocomplete will be insanely useful.
- krainboltgreene 4y ago
  > Would it be possible to eliminate ~90% of the current errors? They aren't errors. When the machine generates the text: "Next you'll tell me 2 + 2 = 5" there isn't an error in math, because no math was attempted. It's a success in predictable wording structure. In fact, that example alone is a super success since we english speaking humans can get a broader context from that statement: You're going to tell me something nonsensical. I don't get how you say the first part of your comment and then follow up with completely misunderstanding in the second half. Is GPT that enthralling to our brains?
  - unshavedyak 4y ago
    Well, if my phone's autocomplete took a very sane prediction and made it insane, i'd call that a failure. There's obvious success and failure conditions even in the most basic auto completion. Same goes for 2+2. Which is the meat of what we're discussing, right? We steer the auto completion into favorable results. We fine tune how it predicts. All of this is with the goal of a higher success rate and lower failure rate given some conditions. No?
deleted 4y ago
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maxdoop 4y ago
I find it equally worrying how so many techies continue to dismiss LLMs as “bullshit generators “. I struggle to see how even the current GPT-4 is any worse than your average human. How this is dismissed because it’s not 100% perfect (might at add, “yet”) is beyond me.
- rpastuszak 4y ago
  I don't dismiss them. I think there's a huge potential in LLMs, but they _also_ happen to be really good at generating plausible, difficult to detect bullshit. Now, people seem to miss or ignore that fact, and I think is a very risky path. I'd say that ca. 8/10 founders who pitched to me an idea leveraging LLMs completely missed that limitation. An example would be something like using LLMs as a replacement for therapy. > How this is dismissed because it’s not 100% perfect (might at add, “yet”) is beyond me. Again, I'm not dismissing them, but the current tech behind GPT or LLaMA has no concept of "correctness". These models don't understand what they're saying and this is not a trivial issue to fix. > I struggle to see how even the current GPT-4 is any worse than your average human. Where, how, and what do you mean by worse? I'm pretty sure there are cases where I'd agree with you, but this is a very broad statement.
- klyrs 4y ago
  I don't want "your average human" tutoring my child. I don't "dismiss" gpt4, I give it a failing grade. Just like Tesla's so called full self driving: it is not ready for prime time, and embracing it before it is has has legitimately concerning risk.
- krainboltgreene 4y ago
  > I struggle to see how even the current GPT-4 is any worse than your average human. In addition to techies believing LLMs ar emore than bullshit generators they also appear to think humans are incredibly simple machines. I'm so glad the tech industry is given so much money and power over the rest of us without actually being forced to understand us.
- version_five 4y ago
  You may want to read the AI Mirror article recently posted. They can appear very "human-like" but they are just generating patterns. I'm more worried that either demographics on HN have shifted or a lot of formerly smart and skeptical people now have been taken in so completely. https://news.ycombinator.com/item?id=34850890 https://news.ycombinator.com/item?id=34850890

SuoDuanDao 4y ago

So long as the LLM teaching the kids and whoever is testing the kid's learning are rivalrous entities, that seems like an easy problem to get around.

rpastuszak 4y ago
Why do you think so and how would that work?
- SuoDuanDao 4y ago
  If the test-making entity writes the test so that the weaknesses of LLMs are brought to the fore, students taking the test will need to know how to compensate for those weaknesses when they're using them as a tool post-graduation.

deleted 4y ago

[deleted]

unshavedyak 4y ago

> LLMs are excellent plausible bullshit generators, which has many uses, but being factually correct is not their strength. The issue is that even if the factual mistakes are rare, they're really hard to spot. I'm looking for a good, terse, catchy metaphor I could use to explain that to some of my family members and friends. To me the bigger question is how far can we take them? Ie i get that they just pick likely next words. They're autocomplete on steroids. Yet that simple primitive is shockingly good. Way, way way beyond that i would have predicted is possible for an "autocomplete". So where is the limit of this autocomplete? Would it be possible to eliminate ~90% of the current errors? Fake math, but would that put us at a more correct version than students typically graduate with? If so yea, it can be wrong at times - but assuming as don't abandon standardized testing/etc, it could still be an amazing tool for interactively learning something. My hope is that even if it never goes beyond being an autocomplete; if we can improve the training dataset, help it not conflict with itself, etc - that maybe the autocomplete will be insanely useful.

krainboltgreene 4y ago
> Would it be possible to eliminate ~90% of the current errors? They aren't errors. When the machine generates the text: "Next you'll tell me 2 + 2 = 5" there isn't an error in math, because no math was attempted. It's a success in predictable wording structure. In fact, that example alone is a super success since we english speaking humans can get a broader context from that statement: You're going to tell me something nonsensical. I don't get how you say the first part of your comment and then follow up with completely misunderstanding in the second half. Is GPT that enthralling to our brains?
- unshavedyak 4y ago
  Well, if my phone's autocomplete took a very sane prediction and made it insane, i'd call that a failure. There's obvious success and failure conditions even in the most basic auto completion. Same goes for 2+2. Which is the meat of what we're discussing, right? We steer the auto completion into favorable results. We fine tune how it predicts. All of this is with the goal of a higher success rate and lower failure rate given some conditions. No?

deleted 4y ago

[deleted]

maxdoop 4y ago

I find it equally worrying how so many techies continue to dismiss LLMs as “bullshit generators “. I struggle to see how even the current GPT-4 is any worse than your average human. How this is dismissed because it’s not 100% perfect (might at add, “yet”) is beyond me.

rpastuszak 4y ago
I don't dismiss them. I think there's a huge potential in LLMs, but they _also_ happen to be really good at generating plausible, difficult to detect bullshit. Now, people seem to miss or ignore that fact, and I think is a very risky path. I'd say that ca. 8/10 founders who pitched to me an idea leveraging LLMs completely missed that limitation. An example would be something like using LLMs as a replacement for therapy. > How this is dismissed because it’s not 100% perfect (might at add, “yet”) is beyond me. Again, I'm not dismissing them, but the current tech behind GPT or LLaMA has no concept of "correctness". These models don't understand what they're saying and this is not a trivial issue to fix. > I struggle to see how even the current GPT-4 is any worse than your average human. Where, how, and what do you mean by worse? I'm pretty sure there are cases where I'd agree with you, but this is a very broad statement.
klyrs 4y ago
I don't want "your average human" tutoring my child. I don't "dismiss" gpt4, I give it a failing grade. Just like Tesla's so called full self driving: it is not ready for prime time, and embracing it before it is has has legitimately concerning risk.
krainboltgreene 4y ago
> I struggle to see how even the current GPT-4 is any worse than your average human. In addition to techies believing LLMs ar emore than bullshit generators they also appear to think humans are incredibly simple machines. I'm so glad the tech industry is given so much money and power over the rest of us without actually being forced to understand us.
version_five 4y ago
You may want to read the AI Mirror article recently posted. They can appear very "human-like" but they are just generating patterns. I'm more worried that either demographics on HN have shifted or a lot of formerly smart and skeptical people now have been taken in so completely. https://news.ycombinator.com/item?id=34850890 https://news.ycombinator.com/item?id=34850890