5 ms·

AI is moving so fast that we might be a couple of years prior to having a program capable of conversing with a dumb and indecisive person like this and outputti

by Bakary 4y ago

AI is moving so fast that we might be a couple of years prior to having a program capable of conversing with a dumb and indecisive person like this and outputting what they want

mjr00 4y ago
Dec 2016 - "These 20 companies are racing to build self-driving cars in the next 5 years." [0] Oct 2022 - "Even after $100 billion, self-driving cars are going nowhere." [1] People have been historically notoriously bad at predicting how good AI/technology will be in 5-10 years time. If the predictions from 2015 were right, the roads would have been filled with level 4 and 5 autonomous vehicles for years now. [0] https://www.businessinsider.com/companies-making-driverless-cars-by-2020-2016-11 https://www.businessinsider.com/companies-making-driverless-... [1] https://www.bloomberg.com/news/features/2022-10-06/even-after-100-billion-self-driving-cars-are-going-nowhere https://www.bloomberg.com/news/features/2022-10-06/even-afte...
- TaylorAlexander 4y ago
  I remember watching a panel discussion with some CEOs and some industry engineering veterans, I think hosted by NVIDIA, in 2016. The CEOs were saying we'd have self driving cars all over the roads by 2019, and the engineering veterans were saying we'd maybe have partial deployments by 2023. It's interesting that the veterans seem to have made accurate predictions. I think what we see are CEOs looking to raise funds, and news organizations looking to sell an interesting story that will say "revolutionary tech is just around the corner", but this is motivated reasoning. You're right that this is the same with AI technology, where some people say AGI is just around the corner, whereas some veterans say it may well be decades still, and the truth is we don't know. So anyway I guess I agree with what you are saying, which is that AI development is difficult to predict and many people make bad predictions. I just wanted to point out that it tends to be people with a motivation to predict rapid growth that tend to produce a lot of these errors. These errors get propagated widely because technology press is one of those groups with this bias. However not everyone makes such bad predictions.
- TulliusCicero 4y ago
  > People have been historically notoriously bad at predicting how good AI/technology will be in 5-10 years time. Taking the people who are most incentivized to overhype things to get clicks and/or funding as the consensus view is maybe not the best take here. If you looked at people in general or engineers in general and looked at the median predicted timeframe, it would've probably been much more conservative.
- Bakary 4y ago
  With Stable Diffusion & co. I've had the opposite sensation. I was completely floored as to how it blew past all of my expectations.
  - mjr00 4y ago
    Don't get me wrong, Stable Diffusion & co are incredibly impressive. I'm using NovelAI image generation for a project I'm working on, so it's already useful to me as more than just a toy, even. It is absolutely a massive technological step change. But NovelAI and Stable Diffusion both have limitations. It's nearly impossible to generate two different specified characters, much less specify two characters interacting in a certain way. For NovelAI, common/popular art styles are available, but you can't use the style of an artist with ~200 pictures. (Understandable, given how the AI works technically, but still a shortcoming from a user's perspective.) Both are awful at anything that requires precision, like a website design or charts (as shown in the article). And, as most people know by now, human hands and feet are more miss than hit. People are extrapolating the initial, enormous step change as a consistent rate of change of improvement, just like what was done with self-driving cars. People are handwaving SD's current limitations away; "it just needs more training data" or "it just needs different training data." That's what people said about autonomous vehicles; it just needed more training data, and then it would be able to drive in snow and rain, or be able to navigate construction zones. Except $100 billion of training data later, these issues still haven't been resolved. It'd be awesome if I were wrong and these issues were resolved. Maybe a version of SD or similar that lets me describe multiple characters in a scene performing different actions is right around the corner. But until I actually see it, I'm not assuming that its capabilities are going to move by leaps and bounds.
    - saberience 4y ago
      I think you're wrong here. My partner works in design and her design teams have jumped all in on using Stable Diffusion in their workflows, something that is effectively in "version 1." For concept art especially it is incredibly useful. They can easily generate hundreds to thousands of images per hour and yes, while SD is not great at hands and faces, if you generate hundreds or thousands of images, you get MANY which have perfect hands and faces. Additionally it's possible to chain together Stable Diffusion with other models like GFPGAN and ERSGAN, for up-ressing, fixing faces, etc. Self driving cars are completely different, no one was using "version 1" of self driving cars within weeks of the software existing. Stable Diffusion and similar models are commercially viable right now and are only getting better in combination with other models and improved training sets. I think you're shifting the goalposts to what success is here to be quite frank. "The model needs me to be able to specify multiple characters in a scene all performing different actions." The truth is, if I had to ask art professionals on Fiverr for "beautiful art photography of multiple characters doing different actions", it would be difficult and expensive for them too! And worse, you would get one set of pictures for your money and if you weren't satisfied, you're shit out of luck! On my PC, Stable Diffusion can crank out > 1000 unique pictures per hour until I'm satisfied.
- namrog84 4y ago
  Perhaps unpopular opinion but I think the tech is more than good enough that I think most cars should be autonomous already. However the reason I think there isn't is because public perception, regulation, changing tradition is hard, and peoples acceptable safety. It seems like most would rather wait until autonomous cars are way better than human drivers while not truly acknowledging most human drivers are awful. Sure I dont want people hurt or killed but I think it could have made more progress in prod so to speak.
  - mola 4y ago
    Your assumptions are just wrong. Currently, self driving cars are much worse than humans. Invest some time and do the research. It's appalling how misleading sources like Tesla PR are.
  - fuckstick 4y ago
    > However the reason I think there isn't is because public perception, regulation, changing tradition is hard, and peoples acceptable safety. No, the reason is that for city driving there is no system that is even close to navigating typical driving problems that humans encounter multiple times on a daily basis. There are plenty of videos of self driving cars flummoxed by basic road obstacles. What people like you call “edge cases” are actually common occurrences. If you think any non geofenced system is close to average human level competence you are simply deluded.
    - tluyben2 4y ago
      I don’t agree with the gp, but humans, in my tiny village, drive into shit every single day. We just don’t accept that from self driving cars, but we do from humans because it’s normal.
      - ClumsyPilot 4y ago
        Do they make catastrophuc errors like mistake back of a semi for an underpass? Do they stop in front of a cardboard box and just stand there for minutes?
- p1esk 4y ago
  People tend to overestimate progress in the near future, and underestimate progress in the long term future.
- squarefoot 4y ago
  The self driving car analogy isn't applicable here as the contexts are way different: operating conditions aside (roads not built for self driving cars, random unexpected situations, etc.) a single accident can result in one or more fatalities, which calls for extreme caution before wider adoption.

by Bakary 4y ago

AI is moving so fast that we might be a couple of years prior to having a program capable of conversing with a dumb and indecisive person like this and outputting what they want

mjr00 4y ago

Dec 2016 - "These 20 companies are racing to build self-driving cars in the next 5 years." [0] Oct 2022 - "Even after $100 billion, self-driving cars are going nowhere." [1] People have been historically notoriously bad at predicting how good AI/technology will be in 5-10 years time. If the predictions from 2015 were right, the roads would have been filled with level 4 and 5 autonomous vehicles for years now. [0] https://www.businessinsider.com/companies-making-driverless-cars-by-2020-2016-11 https://www.businessinsider.com/companies-making-driverless-... [1] https://www.bloomberg.com/news/features/2022-10-06/even-after-100-billion-self-driving-cars-are-going-nowhere https://www.bloomberg.com/news/features/2022-10-06/even-afte...

TaylorAlexander 4y ago
I remember watching a panel discussion with some CEOs and some industry engineering veterans, I think hosted by NVIDIA, in 2016. The CEOs were saying we'd have self driving cars all over the roads by 2019, and the engineering veterans were saying we'd maybe have partial deployments by 2023. It's interesting that the veterans seem to have made accurate predictions. I think what we see are CEOs looking to raise funds, and news organizations looking to sell an interesting story that will say "revolutionary tech is just around the corner", but this is motivated reasoning. You're right that this is the same with AI technology, where some people say AGI is just around the corner, whereas some veterans say it may well be decades still, and the truth is we don't know. So anyway I guess I agree with what you are saying, which is that AI development is difficult to predict and many people make bad predictions. I just wanted to point out that it tends to be people with a motivation to predict rapid growth that tend to produce a lot of these errors. These errors get propagated widely because technology press is one of those groups with this bias. However not everyone makes such bad predictions.
TulliusCicero 4y ago
> People have been historically notoriously bad at predicting how good AI/technology will be in 5-10 years time. Taking the people who are most incentivized to overhype things to get clicks and/or funding as the consensus view is maybe not the best take here. If you looked at people in general or engineers in general and looked at the median predicted timeframe, it would've probably been much more conservative.
Bakary 4y ago
With Stable Diffusion & co. I've had the opposite sensation. I was completely floored as to how it blew past all of my expectations.
- mjr00 4y ago
  Don't get me wrong, Stable Diffusion & co are incredibly impressive. I'm using NovelAI image generation for a project I'm working on, so it's already useful to me as more than just a toy, even. It is absolutely a massive technological step change. But NovelAI and Stable Diffusion both have limitations. It's nearly impossible to generate two different specified characters, much less specify two characters interacting in a certain way. For NovelAI, common/popular art styles are available, but you can't use the style of an artist with ~200 pictures. (Understandable, given how the AI works technically, but still a shortcoming from a user's perspective.) Both are awful at anything that requires precision, like a website design or charts (as shown in the article). And, as most people know by now, human hands and feet are more miss than hit. People are extrapolating the initial, enormous step change as a consistent rate of change of improvement, just like what was done with self-driving cars. People are handwaving SD's current limitations away; "it just needs more training data" or "it just needs different training data." That's what people said about autonomous vehicles; it just needed more training data, and then it would be able to drive in snow and rain, or be able to navigate construction zones. Except $100 billion of training data later, these issues still haven't been resolved. It'd be awesome if I were wrong and these issues were resolved. Maybe a version of SD or similar that lets me describe multiple characters in a scene performing different actions is right around the corner. But until I actually see it, I'm not assuming that its capabilities are going to move by leaps and bounds.
  - saberience 4y ago
    I think you're wrong here. My partner works in design and her design teams have jumped all in on using Stable Diffusion in their workflows, something that is effectively in "version 1." For concept art especially it is incredibly useful. They can easily generate hundreds to thousands of images per hour and yes, while SD is not great at hands and faces, if you generate hundreds or thousands of images, you get MANY which have perfect hands and faces. Additionally it's possible to chain together Stable Diffusion with other models like GFPGAN and ERSGAN, for up-ressing, fixing faces, etc. Self driving cars are completely different, no one was using "version 1" of self driving cars within weeks of the software existing. Stable Diffusion and similar models are commercially viable right now and are only getting better in combination with other models and improved training sets. I think you're shifting the goalposts to what success is here to be quite frank. "The model needs me to be able to specify multiple characters in a scene all performing different actions." The truth is, if I had to ask art professionals on Fiverr for "beautiful art photography of multiple characters doing different actions", it would be difficult and expensive for them too! And worse, you would get one set of pictures for your money and if you weren't satisfied, you're shit out of luck! On my PC, Stable Diffusion can crank out > 1000 unique pictures per hour until I'm satisfied.
namrog84 4y ago
Perhaps unpopular opinion but I think the tech is more than good enough that I think most cars should be autonomous already. However the reason I think there isn't is because public perception, regulation, changing tradition is hard, and peoples acceptable safety. It seems like most would rather wait until autonomous cars are way better than human drivers while not truly acknowledging most human drivers are awful. Sure I dont want people hurt or killed but I think it could have made more progress in prod so to speak.
- mola 4y ago
  Your assumptions are just wrong. Currently, self driving cars are much worse than humans. Invest some time and do the research. It's appalling how misleading sources like Tesla PR are.
- fuckstick 4y ago
  > However the reason I think there isn't is because public perception, regulation, changing tradition is hard, and peoples acceptable safety. No, the reason is that for city driving there is no system that is even close to navigating typical driving problems that humans encounter multiple times on a daily basis. There are plenty of videos of self driving cars flummoxed by basic road obstacles. What people like you call “edge cases” are actually common occurrences. If you think any non geofenced system is close to average human level competence you are simply deluded.
  - tluyben2 4y ago
    I don’t agree with the gp, but humans, in my tiny village, drive into shit every single day. We just don’t accept that from self driving cars, but we do from humans because it’s normal.
    - ClumsyPilot 4y ago
      Do they make catastrophuc errors like mistake back of a semi for an underpass? Do they stop in front of a cardboard box and just stand there for minutes?
p1esk 4y ago
People tend to overestimate progress in the near future, and underestimate progress in the long term future.
squarefoot 4y ago
The self driving car analogy isn't applicable here as the contexts are way different: operating conditions aside (roads not built for self driving cars, random unexpected situations, etc.) a single accident can result in one or more fatalities, which calls for extreme caution before wider adoption.