6 ms·

It's also a good lesson for the new AI cycle we're in now. Often inserting ML subsystems into your broader system just makes it go from "deterministically but f

It's also a good lesson for the new AI cycle we're in now. Often inserting ML subsystems into your broader system just makes it go from "deterministically but fixably bad" to "mysteriously and unfixably bad".

ytdytvhxgydvhh 2y ago
I think that’ll define the industry for the coming decades. I used to work in machine translation and it was the same. The older rules-based engines that were carefully crafted by humans worked well on the test suite and if a new case was found, a human could fix it. When machine learning came on the scene, more “impressive” models that were built quicker came out - but when a translation was bad no one knew how to fix it other than retraining and crossing one’s fingers.
- space_fountain 2y ago
  Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable? It sucks when things don't always work, but that is also kind of life and if the AI version worked more often that is usually ok (as long as the occasional failures aren't so catastrophic as to ruin everything)
  - ytdytvhxgydvhh 2y ago
    Can’t help but read that and think of Tesla’s Autopilot and “Full Self Driving”. For some comparisons they claim to be safer per mile than human drivers … just don’t think too much about the error modes where the occasional stationary object isn’t detected and you plow into it at highway speed.
    - space_fountain 2y ago
      Well Tesla might be the single worst actor in the entire AI space, but I do somewhat understand your point. The lake of predictable failures is a huge problem with AI, I'm not sure that understandability is by itself. I will never understand the brain of an Uber driver for example
    - Terr_ 2y ago
      Or in some cases, the Tesla slows down, then changes its mind and starts accelerating again to run over child-like obstructions. Ex: https://www.youtube.com/watch?v=URpTJ1Xpjuk&t=293s https://www.youtube.com/watch?v=URpTJ1Xpjuk&t=293s
      - friendzis 2y ago
        Tesla's driver assist since the very beginning to now seems to not posses object/decision permanence. Here you can see it detected an obstacle (as evidenced by info on screen), made a decision to stop, however it failed to detect existence of the object right in front of the car, promptly forgot about the object and decision to stop and happily accelerated over the obstacle. When tackling a more complex intersection it can happily change its mind with regards to exit lane multiple times, e.g. it will plan to exit on one side of a divider, replan to exit onto upcoming traffic, replan again.
    - someguydave 2y ago
      relevant to the grandparent’s point: I am demoing FSD in my Tesla and what I find really annoying is that the old Autopilot allowed you to select a maximum speed that the car will drive. Well, on “FSD” apparently you have no choice but to hand full longitudinal control over to the model. I am probably the 0.01% of Tesla drivers who have the computer chime when I exceed the speed limit by some offset. Very regularly, even when FSD is in “chill” mode, the model will speed by +7-9 mph on most roads. (I gotta think that the young 20 somethings who make up Tesla's audience also contributed their poor driving habits to Tesla's training data set) This results in constant beeps, even as the FSD software violates my own criteria for speed warning. So somehow the FSD feature becomes "more capable" while becoming much less legible to the human controller. I think this is a bad thing generally but it seems to be the fad today.
      - throwaway2037 2y ago
        I have no experience with Tesla and their self-driving features. When you wrote "chill" mode, I assume it means the lowest level of aggressiveness. Did you contact Tesla to complain the car is still too aggressive? There should be a mode that tries to drive exactly the speed limit, where reasonable -- not over or under.
        someguydave 2y ago
        Yes there is a “chill” mode that refers to maximum allowed acceleration and “chill mode” that refers to the level if aggressiveness with autopilot. With both turned on the car still exceeds the speed limit by quite a bit. I am sure Tesla is aware.
    - simion314 2y ago
      > For some comparisons they claim to be safer per mile than human drivers They are lying with statistics, for the more challenging locations and conditions the AI will give up and let the human take over or the human notices something bad and takes over. So Tesla miles are miles are cherry picked and their data is not open so a third party can make real statistics and compare apples to apples.
  - ethbr1 2y ago
    > Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable? The key difference is how tolerant the specific use case is of a probably-correct answer. The things recent-AI excels at now (generative, translation, etc.) are very tolerant of "usually correct." If a model can do more, and is right most of the time, then it's more valuable. There are many other types of use cases, though.
    - nojs 2y ago
      A case in point is the ubiquity of Pleco in the Chinese/English space. It’s a dictionary, not a translator, and pretty much every non-native speaker who learns or needs to speak Chinese uses it. It has no ML features and hasn’t changed much in the past decade (or even two). People love it because it does one specific task extremely well. On the other hand ML has absolutely revolutionised translation (of longer text), where having a model containing prior knowledge about the world is essential.
- satvikpendem 2y ago
  As someone who worked in rules-based ML before the recent transformers (and unsupervised learning in general) hype, rules-based approaches were laughably bad. Only now are nondeterministic approaches to ML surpassing human level tasks, something which would not have been feasible, perhaps not even possible in a finite amount of human development time, via human-created rules.
  - Anotheroneagain 2y ago
    The thing is that AI is completely unpredictable without human curated results. Stable diffusion made me relent and admit that AI is here now for real, but I no longer think so. It's more like artificial schizophrenia. It does have some results, often plausible seeming results, but it's not real.
- beefnugs 2y ago
  yes, who exactly looked at the 70% accuracy of "live automatic closed captioning" and decided Great! ship it boys!
  - throwaway2037 2y ago
    My guess: They are hoping user feedback will help them to fix the bugs later -- iterate to 99%. Plus, they are probably under unrealistic deadlines to delivery _something_.
- raincole 2y ago
  But rule-based machine translation, from what I've seen, is just so bad. ChatGPT (and other LLM) is miles ahead. After seeing what ChatGPT does, I can't even call rule-based machine translation "tranlation". *Disclaimer: as someone who's not an AI researcher but did quite some human translation works before.
- otikik 2y ago
  Perhaps using a ML to craft the deterministic rules and then have a human go over them is the sweet spot.
  - astrange 2y ago
    Rules could never work for translation unless the incoming text was formatted in a specific way. Eg, you just couldn't translate a conversation transcript in a pro-drop language like Japanese into English sentence-by-sentence, because the original text just wouldn't have sentences in it. So you need some "intelligence" to know who is saying what.
- rugina 2y ago
  I think NM translation was broken all along. Not in the neural network part but in choosing the right answer. https://aclanthology.org/2020.coling-main.398.pdf https://aclanthology.org/2020.coling-main.398.pdf
  - astrange 2y ago
    Since LLMs are loosely based on NM models, it seems research on newer sampling methods like Mirostat might help here.
  - earngurus234 2y ago
    [dead]
chrisweekly 2y ago
I've heard AI described as the payday loan (or "high-interest credit card") of technical debt.
x0x0 2y ago
mysteriously with a helping of random too!
__loam 2y ago
This is why hallucinations will never be fixed in language models. That's just how they work.
munk-a 2y ago
I think - I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations... but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in "Write me a story about a bunny" and get twelve paragraphs of text out. As someone working in a healthcare adjacent field I've seen the glint in executive's eyes when talking about AI and it can provide real benefits in data summarization and annotation assistance... but there are limits to what you should trust it with and if it's something big-i Important then you'll always want to have a human vetting step.
- munificent 2y ago
  > I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations. The people I see who are most excited about ML are business types who just see it as a black boxes that makes stock valuation go vroom. The people that deeply love building things, really enjoy the process of making itself, are profoundly sceptical. I look at generative AI as sort of like an army of free interns. If your idea of a fun way to make a thing is to dictate orders to a horde of well-meaning but untrained highly-caffienated interns, then using generative AI to make your thing is probably thrilling. You get to feel like an executive producer who can make a lot of stuff happen by simply prompting someone/something to do your bidding. But if you actually care about the grit and texture of actual creation, then that workflow isn't exactly appealing.
  - spacemadness 2y ago
    They wouldn’t think this way if stock investors weren’t so often such naive lemmings ready to jump off yet another cliff with each other.
  - fragmede 2y ago
    We get it, you're skeptical of the current hype bubble. But that's one helluva no true Scotsman you've got going on there. Because a true builder, one that deeply loves building things wouldn't want to use text to create an image. Anyone who does is a business type or an executive producer. A true builder wouldn't think about what they want to do in such nasty thing as words. Creation comes from the soul, which we all know machines, and business people, don't have. Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does. Only a person that truly loves building things, far deeper than you'll ever know, someone that's never programmed in a compiled language, would get that.

ytdytvhxgydvhh 2y ago

I think that’ll define the industry for the coming decades. I used to work in machine translation and it was the same. The older rules-based engines that were carefully crafted by humans worked well on the test suite and if a new case was found, a human could fix it. When machine learning came on the scene, more “impressive” models that were built quicker came out - but when a translation was bad no one knew how to fix it other than retraining and crossing one’s fingers.

space_fountain 2y ago
Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable? It sucks when things don't always work, but that is also kind of life and if the AI version worked more often that is usually ok (as long as the occasional failures aren't so catastrophic as to ruin everything)
- ytdytvhxgydvhh 2y ago
  Can’t help but read that and think of Tesla’s Autopilot and “Full Self Driving”. For some comparisons they claim to be safer per mile than human drivers … just don’t think too much about the error modes where the occasional stationary object isn’t detected and you plow into it at highway speed.
  - space_fountain 2y ago
    Well Tesla might be the single worst actor in the entire AI space, but I do somewhat understand your point. The lake of predictable failures is a huge problem with AI, I'm not sure that understandability is by itself. I will never understand the brain of an Uber driver for example
  - Terr_ 2y ago
    Or in some cases, the Tesla slows down, then changes its mind and starts accelerating again to run over child-like obstructions. Ex: https://www.youtube.com/watch?v=URpTJ1Xpjuk&t=293s https://www.youtube.com/watch?v=URpTJ1Xpjuk&t=293s
    - friendzis 2y ago
      Tesla's driver assist since the very beginning to now seems to not posses object/decision permanence. Here you can see it detected an obstacle (as evidenced by info on screen), made a decision to stop, however it failed to detect existence of the object right in front of the car, promptly forgot about the object and decision to stop and happily accelerated over the obstacle. When tackling a more complex intersection it can happily change its mind with regards to exit lane multiple times, e.g. it will plan to exit on one side of a divider, replan to exit onto upcoming traffic, replan again.
  - someguydave 2y ago
    relevant to the grandparent’s point: I am demoing FSD in my Tesla and what I find really annoying is that the old Autopilot allowed you to select a maximum speed that the car will drive. Well, on “FSD” apparently you have no choice but to hand full longitudinal control over to the model. I am probably the 0.01% of Tesla drivers who have the computer chime when I exceed the speed limit by some offset. Very regularly, even when FSD is in “chill” mode, the model will speed by +7-9 mph on most roads. (I gotta think that the young 20 somethings who make up Tesla's audience also contributed their poor driving habits to Tesla's training data set) This results in constant beeps, even as the FSD software violates my own criteria for speed warning. So somehow the FSD feature becomes "more capable" while becoming much less legible to the human controller. I think this is a bad thing generally but it seems to be the fad today.
    - throwaway2037 2y ago
      I have no experience with Tesla and their self-driving features. When you wrote "chill" mode, I assume it means the lowest level of aggressiveness. Did you contact Tesla to complain the car is still too aggressive? There should be a mode that tries to drive exactly the speed limit, where reasonable -- not over or under.
      - someguydave 2y ago
        Yes there is a “chill” mode that refers to maximum allowed acceleration and “chill mode” that refers to the level if aggressiveness with autopilot. With both turned on the car still exceeds the speed limit by quite a bit. I am sure Tesla is aware.
  - simion314 2y ago
    > For some comparisons they claim to be safer per mile than human drivers They are lying with statistics, for the more challenging locations and conditions the AI will give up and let the human take over or the human notices something bad and takes over. So Tesla miles are miles are cherry picked and their data is not open so a third party can make real statistics and compare apples to apples.
- ethbr1 2y ago
  > Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable? The key difference is how tolerant the specific use case is of a probably-correct answer. The things recent-AI excels at now (generative, translation, etc.) are very tolerant of "usually correct." If a model can do more, and is right most of the time, then it's more valuable. There are many other types of use cases, though.
  - nojs 2y ago
    A case in point is the ubiquity of Pleco in the Chinese/English space. It’s a dictionary, not a translator, and pretty much every non-native speaker who learns or needs to speak Chinese uses it. It has no ML features and hasn’t changed much in the past decade (or even two). People love it because it does one specific task extremely well. On the other hand ML has absolutely revolutionised translation (of longer text), where having a model containing prior knowledge about the world is essential.
satvikpendem 2y ago
As someone who worked in rules-based ML before the recent transformers (and unsupervised learning in general) hype, rules-based approaches were laughably bad. Only now are nondeterministic approaches to ML surpassing human level tasks, something which would not have been feasible, perhaps not even possible in a finite amount of human development time, via human-created rules.
- Anotheroneagain 2y ago
  The thing is that AI is completely unpredictable without human curated results. Stable diffusion made me relent and admit that AI is here now for real, but I no longer think so. It's more like artificial schizophrenia. It does have some results, often plausible seeming results, but it's not real.
beefnugs 2y ago
yes, who exactly looked at the 70% accuracy of "live automatic closed captioning" and decided Great! ship it boys!
- throwaway2037 2y ago
  My guess: They are hoping user feedback will help them to fix the bugs later -- iterate to 99%. Plus, they are probably under unrealistic deadlines to delivery _something_.
raincole 2y ago
But rule-based machine translation, from what I've seen, is just so bad. ChatGPT (and other LLM) is miles ahead. After seeing what ChatGPT does, I can't even call rule-based machine translation "tranlation". *Disclaimer: as someone who's not an AI researcher but did quite some human translation works before.
otikik 2y ago
Perhaps using a ML to craft the deterministic rules and then have a human go over them is the sweet spot.
- astrange 2y ago
  Rules could never work for translation unless the incoming text was formatted in a specific way. Eg, you just couldn't translate a conversation transcript in a pro-drop language like Japanese into English sentence-by-sentence, because the original text just wouldn't have sentences in it. So you need some "intelligence" to know who is saying what.
rugina 2y ago
I think NM translation was broken all along. Not in the neural network part but in choosing the right answer. https://aclanthology.org/2020.coling-main.398.pdf https://aclanthology.org/2020.coling-main.398.pdf
- astrange 2y ago
  Since LLMs are loosely based on NM models, it seems research on newer sampling methods like Mirostat might help here.
- earngurus234 2y ago
  [dead]

chrisweekly 2y ago

I've heard AI described as the payday loan (or "high-interest credit card") of technical debt.

x0x0 2y ago

mysteriously with a helping of random too!

__loam 2y ago

This is why hallucinations will never be fixed in language models. That's just how they work.

munk-a 2y ago

I think - I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations... but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in "Write me a story about a bunny" and get twelve paragraphs of text out. As someone working in a healthcare adjacent field I've seen the glint in executive's eyes when talking about AI and it can provide real benefits in data summarization and annotation assistance... but there are limits to what you should trust it with and if it's something big-i Important then you'll always want to have a human vetting step.

munificent 2y ago
> I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations. The people I see who are most excited about ML are business types who just see it as a black boxes that makes stock valuation go vroom. The people that deeply love building things, really enjoy the process of making itself, are profoundly sceptical. I look at generative AI as sort of like an army of free interns. If your idea of a fun way to make a thing is to dictate orders to a horde of well-meaning but untrained highly-caffienated interns, then using generative AI to make your thing is probably thrilling. You get to feel like an executive producer who can make a lot of stuff happen by simply prompting someone/something to do your bidding. But if you actually care about the grit and texture of actual creation, then that workflow isn't exactly appealing.
- spacemadness 2y ago
  They wouldn’t think this way if stock investors weren’t so often such naive lemmings ready to jump off yet another cliff with each other.
- fragmede 2y ago
  We get it, you're skeptical of the current hype bubble. But that's one helluva no true Scotsman you've got going on there. Because a true builder, one that deeply loves building things wouldn't want to use text to create an image. Anyone who does is a business type or an executive producer. A true builder wouldn't think about what they want to do in such nasty thing as words. Creation comes from the soul, which we all know machines, and business people, don't have. Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does. Only a person that truly loves building things, far deeper than you'll ever know, someone that's never programmed in a compiled language, would get that.