8 ms·

There are quite a few "ai apologists" in the comments but I think the title is fair when these models are marketed towards low vision people ("Be my eyes" https

There are quite a few "ai apologists" in the comments but I think the title is fair when these models are marketed towards low vision people ("Be my eyes" https://www.youtube.com/watch?v=Zq710AKC1gg https://www.youtube.com/watch?v=Zq710AKC1gg) as the equivalent to human vision. These models are implied to be human level equivalents when they are not. This paper demonstrates that there are still some major gaps where simple problems confound the models in unexpected ways. These is important work to elevate otherwise people may start to believe that these models are suitable for general application when they still need safeguards and copious warnings.

sweezyjeezy 2y ago
The paper I linked should hopefully mark me out as far from an AI apologist, it's actually really bad news for GenAI if correct. All I mean to say is the clickbait conclusion and the evidence do not match up.
- Melomololotolo 2y ago
  We have started the ara of ai. It really doesn't matter how good current llms are. They have been good enough to start this ara. And no it's not and never has been just llms. Look what Nvidia is doing with ml. Whisper huge advantage, segment anything again huge. Alpha fold 2 again huge. All the robot announcements -> huge I doubt we will reach agi just through llms. We will reach agi through multi modal, mix of experts, some kind of feedback loop, etc. But the stone started to roll. And you know I prefer to hear about ai advantages for the next 10-30 years. That's a lot better than the crypto shit we had the last 5 years.
  - zwnow 2y ago
    We won't reach agi in our lifetimes.
pixl97 2y ago
Yea, really if you look at human learning/seeing/acting there is a feedback loop that LLM for example isn't able to complete and train on. You see an object. First you have to learn how to control all your body functions to move toward it and grasp it. This teaches you about the 3 dimensional world and things like gravity. You may not know the terms, but it is baked in your learning model. After you get an object you start building a classification list "hot", "sharp", "soft and fuzzy", "tasty", "slick". Your learning model builds up a list of properties of objects and "expected" properties of objects. Once you have this 'database' you create as a human, you can apply the logic to achieve tasks. "Walk 10 feet forward, but avoid the sharp glass just to the left". You have to have spatial awareness, object awareness, and prediction ability. Models 'kind of' have this, but its seemingly haphazard, kind of like a child that doesn't know how to put all the pieces together yet. I think a lot of embodied robot testing where the embodied model feeds back training to the LLM/vision model will have to occur before this is even somewhat close to reliable.
- TeMPOraL 2y ago
  Embodied is useful, but I think not necessary even if you need learning in a 3D environment. Synthesized embodiment should be enough. While in some cases[0] it may have problems with fidelity, simulating embodied experience in silico scales much better, and more importantly, we have control over time flow. Humans always learn in real-time, while with simulated embodiment, we could cram years of subjective-time experiences into a model in seconds, and then for novel scenarios, spend an hour per each second of subjective time running a high-fidelity physics simulation[1]. -- [0] - Like if you plugged a 3D game engine into the training loop. [1] - Results of which we could hopefully reuse in training later. And yes, a simulation could itself be a recording of carefully executed experiment in real world.
  - pegasus 2y ago
    > Like if you plugged a 3D game engine into the training loop Isn't this what synthesized embodiment basically always is? As long as the application of the resulting technology is in a restricted, well controlled environment, as is the case for example for an assembly-line robot, this is a great strategy. But I expect fidelity problems will make this technique ultimately a bad idea for anything that's supposed to interact with humans. Like self-driving cars, for example. Unless, again, those self-driving cars are segregated in dedicated lanes.
Melomololotolo 2y ago
Ah yes the blind person who constantly needs to know if two lines intersect. Let's just ignore what a blind person normally needs to know. You know what blind people ask? Sometimes there daily routine is broken because there is some type of construction and models can tell you this. Sometimes they need to read a basic sign and models can do this. Those models help people already and they will continue to get better. I'm not sure if I'm more frustrated how condescending the authors are or your ignorance. Valid criticism doesn't need to be shitty
- shagie 2y ago
  As an aside... from 2016 this is what was a valid use case for a blind person with an app. Seeing AI 2016 Prototype - A Microsoft research project - https://youtu.be/R2mC-NUAmMk https://youtu.be/R2mC-NUAmMk https://www.seeingai.com https://www.seeingai.com are the actual working apps. The version from 2016 I recall showing (pun not intended) to a coworker who had some significant vision impairments and he was really excited about what it could do back then. --- I still remain quite impressed with its ability to parse the picture and likely reason behind it https://imgur.com/a/JZBTk2t https://imgur.com/a/JZBTk2t
benreesman 2y ago
If we're throwing "citation needed" tags on stuff, how about the first sentence? "Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 Pro are powering countless image-text processing applications" I don't know how many a "countless" is, but I think we've gotten really sloppy in terms of what counts for LLMs as a demonstrated, durable win in a concrete task attached to well-measured outcomes and holding up over even modest periods of time. This stuff is really promising and lots of builders are making lots of nifty things, so if that counts as an application then maybe we're at countless, but in the enterprise and in government and in refereed academic literature we seem to be at the proof-of-concept phase. Impressive chat bots as a use case are pretty dialed in, enough people claim that they help with coding that I tend to believe it's a real thing (I never seem to come out ahead of going directly to the source, StackOverflow). The amount of breathless press on this seems "countless", so maybe I missed the totally rigorous case study on how X company became Y percent more profitable by doing Z thing with LLMs (or similar), and if so I'd be grateful for citations, but neither Google nor any of the big models seem to know about it.
- dsr_ 2y ago
  "maybe I missed the totally rigorous case study on how X company became Y percent more profitable by doing Z thing with LLMs (or similar), and if so I'd be grateful for citations, but neither Google nor any of the big models seem to know about it." Goldman Sachs recently issued a report. https://www.goldmansachs.com/intelligence/pages/gs-research/gen-ai-too-much-spend-too-little-benefit/report.pdf https://www.goldmansachs.com/intelligence/pages/gs-research/... "We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low- wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my thirty years of closely following the tech industry"
- deleted 2y ago
  [deleted]
Lerc 2y ago
I disagree. I think the title, abstract, and conclusion not only misrepresents the state of the models but it misrepresents Thier own findings. They have identified a class of problems that the models perform poorly at and have given a good description of the failure. They portray this as a representative example of the behaviour in general. This has not been shown and is probably not true. I don't think that models have been portrayed as equivalent to humans. Like most AI in it has been shown as vastly superior in some areas and profoundly ignorant in others. Media can overblow things and enthusiasts can talk about future advances as if they have already arrived, but I don't think these are typical portayals by the AI Field in general.
- youssefabdelm 2y ago
  Exactly... I've found GPT-4o to be good at OCR for instance... doesn't seem "blind" to me.
  - deleted 2y ago
    [deleted]
  - spookie 2y ago
    You don't really need a LLM for OCR. Hell, I suppose they just run a python script in its VM and rephrase the output. At least that's what I would do. Perhaps the script would be a "specialist model" in a sense.
    - Spivak 2y ago
      It's not that you need an LLM for OCR but the fact that an LLM can do OCR (and handwriting recognition which is much harder) despite not being made specifically for that purpose is indicative of something. The jump from knowing "this is a picture of a paper with writing on it" like what you get with CLIP to being able to reproduce what's on the paper is, to me, close enough to seeing that the difference isn't meaningful anymore.
      - _lvbh 2y ago
kenjackson 2y ago
Simple is a relative statement. There are vision problems where monkeys are far better than humans. Some may look at human vision and memory and think that we lack basic skills. With AINwe are creating intelligence but with different strengths and weaknesses. I think we will continue to be surprised at how well they work on some problems and how poor they do at some “simple” ones.
lynx23 2y ago
Be My Eyes user here. I disagree with your uninformed opinion. Be My Eyes is more often than not more useful then a human. And I am reporting from personal experience. What experience do you have?
brookst 2y ago
I don’t see Be My Eyes or other similar efforts as “implied” to be equivalent to humans at all. They’re just new tools which can be very useful for some people. “These new tools aren’t perfect” is the dog bites man story of technology. It’s certainly true, but it’s no different than GPS (“family drives car off cliff because GPS said to”).
- dartos 2y ago
  Based take

sweezyjeezy 2y ago

The paper I linked should hopefully mark me out as far from an AI apologist, it's actually really bad news for GenAI if correct. All I mean to say is the clickbait conclusion and the evidence do not match up.

Melomololotolo 2y ago
We have started the ara of ai. It really doesn't matter how good current llms are. They have been good enough to start this ara. And no it's not and never has been just llms. Look what Nvidia is doing with ml. Whisper huge advantage, segment anything again huge. Alpha fold 2 again huge. All the robot announcements -> huge I doubt we will reach agi just through llms. We will reach agi through multi modal, mix of experts, some kind of feedback loop, etc. But the stone started to roll. And you know I prefer to hear about ai advantages for the next 10-30 years. That's a lot better than the crypto shit we had the last 5 years.
- zwnow 2y ago
  We won't reach agi in our lifetimes.

pixl97 2y ago

Yea, really if you look at human learning/seeing/acting there is a feedback loop that LLM for example isn't able to complete and train on. You see an object. First you have to learn how to control all your body functions to move toward it and grasp it. This teaches you about the 3 dimensional world and things like gravity. You may not know the terms, but it is baked in your learning model. After you get an object you start building a classification list "hot", "sharp", "soft and fuzzy", "tasty", "slick". Your learning model builds up a list of properties of objects and "expected" properties of objects. Once you have this 'database' you create as a human, you can apply the logic to achieve tasks. "Walk 10 feet forward, but avoid the sharp glass just to the left". You have to have spatial awareness, object awareness, and prediction ability. Models 'kind of' have this, but its seemingly haphazard, kind of like a child that doesn't know how to put all the pieces together yet. I think a lot of embodied robot testing where the embodied model feeds back training to the LLM/vision model will have to occur before this is even somewhat close to reliable.

TeMPOraL 2y ago
Embodied is useful, but I think not necessary even if you need learning in a 3D environment. Synthesized embodiment should be enough. While in some cases[0] it may have problems with fidelity, simulating embodied experience in silico scales much better, and more importantly, we have control over time flow. Humans always learn in real-time, while with simulated embodiment, we could cram years of subjective-time experiences into a model in seconds, and then for novel scenarios, spend an hour per each second of subjective time running a high-fidelity physics simulation[1]. -- [0] - Like if you plugged a 3D game engine into the training loop. [1] - Results of which we could hopefully reuse in training later. And yes, a simulation could itself be a recording of carefully executed experiment in real world.
- pegasus 2y ago
  > Like if you plugged a 3D game engine into the training loop Isn't this what synthesized embodiment basically always is? As long as the application of the resulting technology is in a restricted, well controlled environment, as is the case for example for an assembly-line robot, this is a great strategy. But I expect fidelity problems will make this technique ultimately a bad idea for anything that's supposed to interact with humans. Like self-driving cars, for example. Unless, again, those self-driving cars are segregated in dedicated lanes.

Melomololotolo 2y ago

Ah yes the blind person who constantly needs to know if two lines intersect. Let's just ignore what a blind person normally needs to know. You know what blind people ask? Sometimes there daily routine is broken because there is some type of construction and models can tell you this. Sometimes they need to read a basic sign and models can do this. Those models help people already and they will continue to get better. I'm not sure if I'm more frustrated how condescending the authors are or your ignorance. Valid criticism doesn't need to be shitty

shagie 2y ago
As an aside... from 2016 this is what was a valid use case for a blind person with an app. Seeing AI 2016 Prototype - A Microsoft research project - https://youtu.be/R2mC-NUAmMk https://youtu.be/R2mC-NUAmMk https://www.seeingai.com https://www.seeingai.com are the actual working apps. The version from 2016 I recall showing (pun not intended) to a coworker who had some significant vision impairments and he was really excited about what it could do back then. --- I still remain quite impressed with its ability to parse the picture and likely reason behind it https://imgur.com/a/JZBTk2t https://imgur.com/a/JZBTk2t

benreesman 2y ago

If we're throwing "citation needed" tags on stuff, how about the first sentence? "Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 Pro are powering countless image-text processing applications" I don't know how many a "countless" is, but I think we've gotten really sloppy in terms of what counts for LLMs as a demonstrated, durable win in a concrete task attached to well-measured outcomes and holding up over even modest periods of time. This stuff is really promising and lots of builders are making lots of nifty things, so if that counts as an application then maybe we're at countless, but in the enterprise and in government and in refereed academic literature we seem to be at the proof-of-concept phase. Impressive chat bots as a use case are pretty dialed in, enough people claim that they help with coding that I tend to believe it's a real thing (I never seem to come out ahead of going directly to the source, StackOverflow). The amount of breathless press on this seems "countless", so maybe I missed the totally rigorous case study on how X company became Y percent more profitable by doing Z thing with LLMs (or similar), and if so I'd be grateful for citations, but neither Google nor any of the big models seem to know about it.

dsr_ 2y ago
"maybe I missed the totally rigorous case study on how X company became Y percent more profitable by doing Z thing with LLMs (or similar), and if so I'd be grateful for citations, but neither Google nor any of the big models seem to know about it." Goldman Sachs recently issued a report. https://www.goldmansachs.com/intelligence/pages/gs-research/gen-ai-too-much-spend-too-little-benefit/report.pdf https://www.goldmansachs.com/intelligence/pages/gs-research/... "We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low- wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my thirty years of closely following the tech industry"
deleted 2y ago
[deleted]

Lerc 2y ago

I disagree. I think the title, abstract, and conclusion not only misrepresents the state of the models but it misrepresents Thier own findings. They have identified a class of problems that the models perform poorly at and have given a good description of the failure. They portray this as a representative example of the behaviour in general. This has not been shown and is probably not true. I don't think that models have been portrayed as equivalent to humans. Like most AI in it has been shown as vastly superior in some areas and profoundly ignorant in others. Media can overblow things and enthusiasts can talk about future advances as if they have already arrived, but I don't think these are typical portayals by the AI Field in general.

youssefabdelm 2y ago
Exactly... I've found GPT-4o to be good at OCR for instance... doesn't seem "blind" to me.
- deleted 2y ago
  [deleted]
- spookie 2y ago
  You don't really need a LLM for OCR. Hell, I suppose they just run a python script in its VM and rephrase the output. At least that's what I would do. Perhaps the script would be a "specialist model" in a sense.
  - Spivak 2y ago
    It's not that you need an LLM for OCR but the fact that an LLM can do OCR (and handwriting recognition which is much harder) despite not being made specifically for that purpose is indicative of something. The jump from knowing "this is a picture of a paper with writing on it" like what you get with CLIP to being able to reproduce what's on the paper is, to me, close enough to seeing that the difference isn't meaningful anymore.
    - _lvbh 2y ago

kenjackson 2y ago

Simple is a relative statement. There are vision problems where monkeys are far better than humans. Some may look at human vision and memory and think that we lack basic skills. With AINwe are creating intelligence but with different strengths and weaknesses. I think we will continue to be surprised at how well they work on some problems and how poor they do at some “simple” ones.

lynx23 2y ago

Be My Eyes user here. I disagree with your uninformed opinion. Be My Eyes is more often than not more useful then a human. And I am reporting from personal experience. What experience do you have?

brookst 2y ago

I don’t see Be My Eyes or other similar efforts as “implied” to be equivalent to humans at all. They’re just new tools which can be very useful for some people. “These new tools aren’t perfect” is the dog bites man story of technology. It’s certainly true, but it’s no different than GPS (“family drives car off cliff because GPS said to”).

dartos 2y ago
Based take