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> Better training, larger models, and larger datasets, will lead to models that Hypothetically, with enough information, one could predict the future (barring

> Better training, larger models, and larger datasets, will lead to models that Hypothetically, with enough information, one could predict the future (barring truly random events like radioactive decay). Generative AI is also constrained by economic forces - how much are GenAI companies willing to invest to get eyeball reflections right? Would they earn adequate revenue to cover the increase in costs to justify that feature? There are plenty of things that humanity can technically achieve, that don't get done because the incentives are not aligned- for instance, there is enough food grown to feed every human on earth and the technology to transport it, and yet we have hunger, malnutrition and famines.

stevenwalton 2y ago
> how much are GenAI companies willing to invest to get eyeball reflections right? Willing to? Probably not much. Should? A WHOLE LOT. It is the whole enchilada. While this might not seem like a big issue and truthfully most people don't notice, getting this right (consistently) requires getting a lot more right. It doesn't require the model knowing physics (because every training sample face will have realistic lighting). But what underlines this issue is the model understanding subtleties. No model to date accomplishes this. From image generators to language generators (LLMs). There is a pareto efficiency issue here too. Remember that it is magnitudes easier to get a model to be "80% correct" than to be "90% correct". But recall that the devil is in the details. We live in a complex world, and what that means is that the subtleties matter. The world is (mathematically) chaotic, so small things have big effects. You should start solving problems not worrying about these, but eventually you need to move into tackling these problems. If you don't, you'll just generate enshitification. In fact, I'd argue that the difference between an amateur and an expert is knowledge of subtleties and nuance. This is both why amateurs can trick themselves into thinking they're more expert than they are and why experts can recognize when talking to other experts (I remember a thread a while ago where many people were shocked about how most industries don't give tests or whiteboard problems when interviewing candidates and how hiring managers can identify good hires from bad ones).
dwaltrip 2y ago
Getting the eyeballs correct will correlate with other very useful improvements. They won’t train a better model just for that reason. It will just happen along the way as they seek to broadly improve performance and usefulness.
rowanG077 2y ago
Yeah every person is constantly predicting the future, often even scarely accurately. I don't see how this is a hot take at all.
bastawhiz 2y ago
> how much are GenAI companies willing to invest to get eyeball reflections right This isn't how it works. As the models are improved, they learn more about reality largely on their own. Except for glaringly obvious problems (like hands, deformed limbs, etc) the improvements are really just giving the models techniques for more accurately replicating features from reasoning data. There's nobody that's like "today we're working on fingernails" or "today we're making hair physics work better": it's about making the model understand and replicate the features already present in the training dataset.
- sangnoir 2y ago
  > This isn't how it works. As the models are improved, they learn more about reality largely on their own. AI models aren't complete blackboxes to the people who develop them: there is careful thought behind the architecture, dataset selection and model evaluation. Assuming that you can take an existing model and simply throw more compute at it will automatically result in higher fidelity illumination modeling takes almost religious levels of faith. If moar hardware is all you need, Nvidia would have the best models in every category right now. Perhaps someone ought to write the sequel to Fred Brooks' book amd name it "The Mythical GPU-Cluster-Month". FWIW, Google has AI-based illumination adjustment in Google Photos where one can add virtual lights - so specialized models for lighting already exist. However, I'm very cynical about a generic mixed model incidentally gaining those capabilities without specific training for it. When dealing with exponential requirements (training data, training time, GPUs, model weight size), you'll run out of resources in short order.
  - devbent 2y ago
    > If moar hardware is all you need, Nvidia would have the best models in every category right now. Nvidia is making boatloads of money right now selling GPUs to companies that think they will be making boatloads of money in the future. Nvidia has the better end of things at this very moment in time.
  - bastawhiz 2y ago
    What you're refuting isn't what I said. I'm making the point that nobody is encoding all of the individual features of the human form and reality into their models through code or model design. You build a model by making it capable of observing details and then letting it observe the details of your training data. Nobody is spending time getting the reflections in the eyeballs working well, that comes as an emergent property of a model that's able to identify and replicate that. That doesn't mean it's a black box, it means that it's built in a general way so the researchers don't need to care about every facet of reality.
kaba0 2y ago
I’m far from an expert on this, but these are often trained in conjunction with a model that recognizes deep fakes. Improving one will improve the other, and it’s an infinite recursion.
harimau777 2y ago
I could see state actors being willing to invest to be able to make better propaganda or counter intelligence.
KingOfCoders 2y ago
Popper disagrees https://en.wikipedia.org/wiki/The_Poverty_of_Historicism https://en.wikipedia.org/wiki/The_Poverty_of_Historicism "Individual human action or reaction can never be predicted with certainty, therefore neither can the future" See the death of Archduke Franz Ferdinand - perhaps could be predicted when it was known that he would go to Sarajevo. But before? If you look at SciFi, some things have been predicted, but many -obvious things- haven't. What if Trump had been killed? And Kennedy?

stevenwalton 2y ago

> how much are GenAI companies willing to invest to get eyeball reflections right? Willing to? Probably not much. Should? A WHOLE LOT. It is the whole enchilada. While this might not seem like a big issue and truthfully most people don't notice, getting this right (consistently) requires getting a lot more right. It doesn't require the model knowing physics (because every training sample face will have realistic lighting). But what underlines this issue is the model understanding subtleties. No model to date accomplishes this. From image generators to language generators (LLMs). There is a pareto efficiency issue here too. Remember that it is magnitudes easier to get a model to be "80% correct" than to be "90% correct". But recall that the devil is in the details. We live in a complex world, and what that means is that the subtleties matter. The world is (mathematically) chaotic, so small things have big effects. You should start solving problems not worrying about these, but eventually you need to move into tackling these problems. If you don't, you'll just generate enshitification. In fact, I'd argue that the difference between an amateur and an expert is knowledge of subtleties and nuance. This is both why amateurs can trick themselves into thinking they're more expert than they are and why experts can recognize when talking to other experts (I remember a thread a while ago where many people were shocked about how most industries don't give tests or whiteboard problems when interviewing candidates and how hiring managers can identify good hires from bad ones).

dwaltrip 2y ago

Getting the eyeballs correct will correlate with other very useful improvements. They won’t train a better model just for that reason. It will just happen along the way as they seek to broadly improve performance and usefulness.

rowanG077 2y ago

Yeah every person is constantly predicting the future, often even scarely accurately. I don't see how this is a hot take at all.

bastawhiz 2y ago

> how much are GenAI companies willing to invest to get eyeball reflections right This isn't how it works. As the models are improved, they learn more about reality largely on their own. Except for glaringly obvious problems (like hands, deformed limbs, etc) the improvements are really just giving the models techniques for more accurately replicating features from reasoning data. There's nobody that's like "today we're working on fingernails" or "today we're making hair physics work better": it's about making the model understand and replicate the features already present in the training dataset.

sangnoir 2y ago
> This isn't how it works. As the models are improved, they learn more about reality largely on their own. AI models aren't complete blackboxes to the people who develop them: there is careful thought behind the architecture, dataset selection and model evaluation. Assuming that you can take an existing model and simply throw more compute at it will automatically result in higher fidelity illumination modeling takes almost religious levels of faith. If moar hardware is all you need, Nvidia would have the best models in every category right now. Perhaps someone ought to write the sequel to Fred Brooks' book amd name it "The Mythical GPU-Cluster-Month". FWIW, Google has AI-based illumination adjustment in Google Photos where one can add virtual lights - so specialized models for lighting already exist. However, I'm very cynical about a generic mixed model incidentally gaining those capabilities without specific training for it. When dealing with exponential requirements (training data, training time, GPUs, model weight size), you'll run out of resources in short order.
- devbent 2y ago
  > If moar hardware is all you need, Nvidia would have the best models in every category right now. Nvidia is making boatloads of money right now selling GPUs to companies that think they will be making boatloads of money in the future. Nvidia has the better end of things at this very moment in time.
- bastawhiz 2y ago
  What you're refuting isn't what I said. I'm making the point that nobody is encoding all of the individual features of the human form and reality into their models through code or model design. You build a model by making it capable of observing details and then letting it observe the details of your training data. Nobody is spending time getting the reflections in the eyeballs working well, that comes as an emergent property of a model that's able to identify and replicate that. That doesn't mean it's a black box, it means that it's built in a general way so the researchers don't need to care about every facet of reality.

kaba0 2y ago

I’m far from an expert on this, but these are often trained in conjunction with a model that recognizes deep fakes. Improving one will improve the other, and it’s an infinite recursion.

harimau777 2y ago

I could see state actors being willing to invest to be able to make better propaganda or counter intelligence.

KingOfCoders 2y ago

Popper disagrees https://en.wikipedia.org/wiki/The_Poverty_of_Historicism https://en.wikipedia.org/wiki/The_Poverty_of_Historicism "Individual human action or reaction can never be predicted with certainty, therefore neither can the future" See the death of Archduke Franz Ferdinand - perhaps could be predicted when it was known that he would go to Sarajevo. But before? If you look at SciFi, some things have been predicted, but many -obvious things- haven't. What if Trump had been killed? And Kennedy?