Apologies for an open-ended question but: does anyone know if there is a term for something like Turing-completeness within AI, where a certain level of intelligence can simulate any other type of intelligence like our brains do? For example, using DeMorgan's theorem, we can build any logic circuit out of all NAND or NOR gates: https://www.electronics-tutorials.ws/boolean/demorgan.html https://en.wikipedia.org/wiki/N…
Dall-E 2
211–220 of 511 posts
Re: Dall-E 2
#212Earlier quoted context omitted.
Large chunks, yes, but all that means is that engineers will move up the abstraction stack and become more efficient, not that engineers will be replaced. Bytecode -> Assembly -> C -> higher level languages -> AI-assisted higher-level languages
At some point we will be "replaced". When you get AI to be able to navigate all user interfaces, communicate with other agents, plan long term and execute short term, we will no longer be the main drivers of economical growth. At some point AI will become as powerful as companies. And then AI will be able to sustain positive feedback loop of creating more powerful company like ecosystems that will create even more po…
> At some point we will be "replaced".
We will be united with the AI, we're already relying on it so much that it has become a part of our extended minds.
Re: Dall-E 2
#213Is there an 'explain it like I'm 15' for how this works? It seems like black magic. I've been a computer hobbyist since the late 1980's and this is the first time I cannot explain how a computer does what it does. Absolutely the most amazing thing I've ever seen, and I have zero clue how it works.
Re: Dall-E 2
#214What jobs will be there in 5~10 years when we consider all the progress done with Dall-E, GPT-3, Codex/GitHub Copilot, Alpha* and so on?
Most creative output is duplicated effort: consider how much code each person on HN has written that has been written before. Consider how, a decade ago, we were all writing html and styling it, element by element, and then Twitter bootstrap came along and revolutionised front-end development in what is, ultimately, a very small and low technology way. All it really did was reduce duplicate effort.
Nowadays there’s lots of great low/no code platforms, like Retool, that represent a far greater threat to the amount of code that needs to be produced than AI ever will.
To use a cliche: code is a bug, not a feature. Abstracting away the need for code is the future, not having a machine churn out the same code we need today.
Re: Dall-E 2
#215Apologies for an open-ended question but: does anyone know if there is a term for something like Turing-completeness within AI, where a certain level of intelligence can simulate any other type of intelligence like our brains do? For example, using DeMorgan's theorem, we can build any logic circuit out of all NAND or NOR gates: https://www.electronics-tutorials.ws/boolean/demorgan.html https://en.wikipedia.org/wiki/N…
Almost everything stated here is simply wrong or misinformed.
>For example, I went to an AI talk about 5 years ago where the guy said that any of a dozen algorithms like K-Nearest Neighbor, K-Means Clustering, Simulated Annealing, Neural Nets, Genetic Algorithms, etc can all be adapted to any use case. They just have different strengths and weaknesses. At that time, all that really mattered was how the data was prepared.
How do you suppose KNN is going to generate photorealistic images? I don't understand the question here
>I guess fundamentally my question is, when will AGI start to become prevalent, rather than these special-purpose tools like GPT-3 and Dall-E 2?
Actual AGI research is basically non-existant, and GPT-3/Dall-E 2 are not AGI-level tools.
>Personally I give it less than 10 years of actual work, maybe less
Lol...
>I just mean that to me, Dall-E 2 is already orders of magnitude more complex than what's required to run a basic automaton to free humans from labor.
Categorically incorrect
Re: Dall-E 2
#216Dall-E 2 seems to be incapable to catch the essence of the art. I'm not really surprised by it, I'd be surprised a lot if it could. But nevertheless: if you looked in the eye of a Girl With A Pearl Earring[1], you'd be forced to stop and to think what does she have on her mind right now. Or may be you had some other question in your mind, but it really stops people to think. But none of Dall-E interpretations have th…
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Re: Dall-E 2
#217It's becoming clear that efficient work in the future will hinge upon one's ability to accurately describe what one wants . Unpacking that -- a large piece is the ability to understand all the possible "pitfalls" and "misunderstandings" that could happen on the way to a shared understanding. While technical work will always have a place -- I think that much creative work will become more like the management of a team…
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Re: Dall-E 2
#218Earlier quoted context omitted.
At some point we will be "replaced". When you get AI to be able to navigate all user interfaces, communicate with other agents, plan long term and execute short term, we will no longer be the main drivers of economical growth. At some point AI will become as powerful as companies. And then AI will be able to sustain positive feedback loop of creating more powerful company like ecosystems that will create even more po…
> At some point we will be "replaced". We will be united with the AI, we're already relying on it so much that it has become a part of our extended minds.
> we're already relying on it so much that it has become a part of our extended minds.
What's this in reference to?
Re: Dall-E 2
#219Earlier quoted context omitted.
> It is compositing as final step. I might be misinterpeting your use of "compositing" here (and my own technical knowledge is fairly shallow) but I don't think there's any compositing of elements generally in AI image generation. (unless Dall-E 2 changes this. I haven't read the paper yet)
https://cdn.openai.com/papers/dall-e-2.pdf > Given an image x, we can obtain its CLIP image embedding zi and then use our decoder to “invert” zi, producing new images that we call variations of our input. .. It is also possible to combine two images for variations. To do so, we perform spherical interpolation of their CLIP embeddings zi and zj to obtain intermediate zθ = slerp(zi, zj , θ), and produce variations of z…
The first quote is talking about prompting the model with images instead of text. The second quote is using "mix up" in the sense that the model is confused about the prompt, not that it mixes up existing images.
ML models can output training data verbatim if they over-fit, but a well trained model does extrapolate to novel inputs. You could say that this model doesn't know that images are 2d representations of a larger 3d universe, but now we have NERF which kind of obsoletes this objection as well.
Re: Dall-E 2
#220I'm only part way through the paper, but what struck me as interesting so far is this: In other text-to-image algorithms I'm familiar with (the ones you'll typically see passed around as colab notebooks that people post outputs from on Twitter), the basic idea is to encode the text, and then try to make an image that maximally matches that text encoding. But this maximization often leads to artifacts - if you ask for…
What always bother me with this stuff is, well, you say one approach is more sensible than the other because the images happen to come out more pleasing. But there's no real rhyme or reason, it is a sort of alchemy. Is text encoding strictly worse or is it an artifact of the implementation? And if it is strictly worse, which is probably the case, why specifically? What is actually going on here? I can't argue that th…
> This is a fantastic and time saving photoshop plugin. But what do we have really?
Stock photography sales are in the many billions of dollars per year and custom commissioned photography is larger still. That's a pretty seriously sized ready-made market.
> But if doesn't spit up exactly what you want can't edit it further.
I suspect there's a big startup opportunity in pioneering an easy-to-use interface allowing users to provide fast iterative feedback to the model - including positional and relational constraints ("put this thing over there"). Perhaps even more valuable would be easy yet granular ways to unconstrain the model. For example, "keep the basketball hoop like that but make the basketball an unexpected color and have the panda's right paw doing something pandas don't do that human hands often do."