Oh yes he did, he did do it. Gunnar Larson raped him. He had to ...
On Fri, Apr 8, 2022, 8:32 PM <[email protected]> wrote: > Send cypherpunks mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > https://lists.cpunks.org/mailman/listinfo/cypherpunks > or, via email, send a message with subject or body 'help' to > [email protected] > > You can reach the person managing the list at > [email protected] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of cypherpunks digest..." > > > Today's Topics: > > 1. Re: cypherpunks Digest, Vol 106, Issue 94 (Gunnar Larson) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Fri, 8 Apr 2022 20:29:43 -0400 > From: Gunnar Larson <[email protected]> > To: cypherpunks <[email protected]> > Subject: Re: cypherpunks Digest, Vol 106, Issue 94 > Message-ID: > < > capc8xwo4+ulbar52twvdyrckptlwo49uxshk-bot0hwukmm...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Did Gunnar Larson, rape Mr. Mark Zuckerburg? Or, was it fair game? > > Finders keepers? > > On Fri, Apr 8, 2022, 7:56 PM <[email protected]> wrote: > > > Send cypherpunks mailing list submissions to > > [email protected] > > > > To subscribe or unsubscribe via the World Wide Web, visit > > https://lists.cpunks.org/mailman/listinfo/cypherpunks > > or, via email, send a message with subject or body 'help' to > > [email protected] > > > > You can reach the person managing the list at > > [email protected] > > > > When replying, please edit your Subject line so it is more specific > > than "Re: Contents of cypherpunks digest..." > > > > > > Today's Topics: > > > > 1. Re: cypherpunks Digest, Vol 106, Issue 93 (Gunnar Larson) > > > > > > ---------------------------------------------------------------------- > > > > Message: 1 > > Date: Fri, 8 Apr 2022 19:54:15 -0400 > > From: Gunnar Larson <[email protected]> > > To: cypherpunks <[email protected]> > > Subject: Re: cypherpunks Digest, Vol 106, Issue 93 > > Message-ID: > > <CAPc8xwPsCK2cA3tT1U-wjuV09T5kc= > > [email protected]> > > Content-Type: text/plain; charset="utf-8" > > > > At first glance, this was a great article. > > > > On Fri, Apr 8, 2022, 7:52 PM <[email protected]> > wrote: > > > > > Send cypherpunks mailing list submissions to > > > [email protected] > > > > > > To subscribe or unsubscribe via the World Wide Web, visit > > > https://lists.cpunks.org/mailman/listinfo/cypherpunks > > > or, via email, send a message with subject or body 'help' to > > > [email protected] > > > > > > You can reach the person managing the list at > > > [email protected] > > > > > > When replying, please edit your Subject line so it is more specific > > > than "Re: Contents of cypherpunks digest..." > > > > > > > > > Today's Topics: > > > > > > 1. Re: DALL-E (coderman) > > > > > > > > > ---------------------------------------------------------------------- > > > > > > Message: 1 > > > Date: Fri, 08 Apr 2022 23:50:53 +0000 > > > From: coderman <[email protected]> > > > To: coderman <[email protected]> > > > Cc: "cy\"Cypherpunks" <[email protected]> > > > Subject: Re: DALL-E > > > Message-ID: > > > > > > > > > <a9WeFGpr9g422W0Uym9aQZyxT6mqWNzNwLsG6yKqqlD4BLpH6NxuARXLOMvBY8IdZF9HMetBKZYGjdH--qJRFZDIWnXdMRQVqr3pmMYVo5I=@ > > > protonmail.com> > > > > > > Content-Type: text/plain; charset="utf-8" > > > > > > DALL·E[1](https://openai.com/blog/dall-e/#fn1) > > > > > > We decided to name our model using a portmanteau of the artist Salvador > > > Dalí and Pixar’s WALL·E. > > > > > > is a 12-billion parameter version of[GPT-3]( > > > https://arxiv.org/abs/2005.14165) trained to generate images from text > > > descriptions, using a dataset of text–image pairs. We’ve found that it > > has > > > a diverse set of capabilities, including creating anthropomorphized > > > versions of animals and objects, combining unrelated concepts in > > plausible > > > ways, rendering text, and applying transformations to existing images. > > > > > > --------------------------------------------------------------- > > > > > > Text prompt > > > an illustration of a baby daikon radish in a tutu walking a dog > > > AI-generated > > > images > > > > > > Edit prompt or view more images > > > Text prompt > > > an armchair in the shape of an avocado. . . . > > > AI-generated > > > images > > > > > > Edit prompt or view more images > > > Text prompt > > > a store front that has the word ‘openai’ written on it. . . . > > > AI-generated > > > images > > > > > > Edit prompt or view more images > > > Text & image > > > prompt > > > the exact same cat on the top as a sketch on the bottom > > > AI-generated > > > images > > > > > > Edit prompt or view more images > > > --------------------------------------------------------------- > > > > > > GPT-3 showed that language can be used to instruct a large neural > network > > > to perform a variety of text generation tasks. [Image GPT]( > > > https://openai.com/blog/image-gpt) showed that the same type of neural > > > network can also be used to generate images with high fidelity. We > extend > > > these findings to show that manipulating visual concepts through > language > > > is now within reach. > > > > > > Overview > > > > > > Like GPT-3, DALL·E is a transformer language model. It receives both > the > > > text and the image as a single stream of data containing up to 1280 > > tokens, > > > and is trained using maximum likelihood to generate all of the tokens, > > one > > > after another.[2](https://openai.com/blog/dall-e/#fn2) > > > > > > A token is any symbol from a discrete vocabulary; for humans, each > > English > > > letter is a token from a 26-letter alphabet. DALL·E’s vocabulary has > > tokens > > > for both text and image concepts. Specifically, each image caption is > > > represented using a maximum of 256 BPE-encoded tokens with a vocabulary > > > size of 16384, and the image is represented using 1024 tokens with a > > > vocabulary size of 8192. > > > > > > The images are preprocessed to 256x256 resolution during training. > > Similar > > > to VQVAE,[14]( > > > > > > https://openai.com/blog/dall-e/#rf14)[15](https://openai.com/blog/dall-e/#rf15 > > ) > > > each image is compressed to a 32x32 grid of discrete latent codes > using a > > > discrete VAE[10]( > > > > > > https://openai.com/blog/dall-e/#rf10)[11](https://openai.com/blog/dall-e/#rf11 > > ) > > > that we pretrained using a continuous relaxation.[12]( > > > > > > https://openai.com/blog/dall-e/#rf12)[13](https://openai.com/blog/dall-e/#rf13 > > ) > > > We found that training using the relaxation obviates the need for an > > > explicit codebook, EMA loss, or tricks like dead code revival, and can > > > scale up to large vocabulary sizes. > > > > > > This training procedure allows DALL·E to not only generate an image > from > > > scratch, but also to regenerate any rectangular region of an existing > > image > > > that extends to the bottom-right corner, in a way that is consistent > with > > > the text prompt. > > > > > > We recognize that work involving generative models has the potential > for > > > significant, broad societal impacts. In the future, we plan to analyze > > how > > > models like DALL·E relate to societal issues like economic impact on > > > certain work processes and professions, the potential for bias in the > > model > > > outputs, and the longer term ethical challenges implied by this > > technology. > > > > > > Capabilities > > > > > > We find that DALL·E is able to create plausible images for a great > > variety > > > of sentences that explore the compositional structure of language. We > > > illustrate this using a series of interactive visuals in the next > > section. > > > The samples shown for each caption in the visuals are obtained by > taking > > > the top 32 of 512 after reranking with [CLIP]( > > > https://openai.com/blog/clip/), but we do not use any manual > > > cherry-picking, aside from the thumbnails and standalone images that > > appear > > > outside.[3](https://openai.com/blog/dall-e/#fn3) > > > > > > Further details provided in [a later section]( > > > https://openai.com/blog/dall-e/#summary). > > > > > > Controlling Attributes > > > > > > We test DALL·E’s ability to modify several of an object’s attributes, > as > > > well as the number of times that it appears. > > > > > > Click to edit text prompt or view more AI-generated images > > > a pentagonal green clock. a green clock in the shape of a pentagon. > > > > > > navigatedownwide > > > a cube made of porcupine. a cube with the texture of a porcupine. > > > > > > navigatedownwide > > > a collection of glasses is sitting on a table > > > > > > navigatedownwide > > > > > > Drawing Multiple Objects > > > > > > Simultaneously controlling multiple objects, their attributes, and > their > > > spatial relationships presents a new challenge. For example, consider > the > > > phrase “a hedgehog wearing a red hat, yellow gloves, blue shirt, and > > green > > > pants.” To correctly interpret this sentence, DALL·E must not only > > > correctly compose each piece of apparel with the animal, but also form > > the > > > associations (hat, red), (gloves, yellow), (shirt, blue), and (pants, > > > green) without mixing them up.[4](https://openai.com/blog/dall-e/#fn4) > > > > > > This task is called variable binding, and has been extensively studied > in > > > the literature.[17]( > > > > > > https://openai.com/blog/dall-e/#rf17)[18](https://openai.com/blog/dall-e/#rf18)[19](https://openai.com/blog/dall-e/#rf19)[20](https://openai.com/blog/dall-e/#rf20 > > > ) > > > > > > We test DALL·E’s ability to do this for relative positioning, stacking > > > objects, and controlling multiple attributes. > > > > > > a small red block sitting on a large green block > > > > > > navigatedownwide > > > a stack of 3 cubes. a red cube is on the top, sitting on a green cube. > > the > > > green cube is in the middle, sitting on a blue cube. the blue cube is > on > > > the bottom. > > > > > > navigatedownwide > > > an emoji of a baby penguin wearing a blue hat, red gloves, green shirt, > > > and yellow pants > > > > > > navigatedownwide > > > > > > While DALL·E does offer some level of controllability over the > attributes > > > and positions of a small number of objects, the success rate can depend > > on > > > how the caption is phrased. As more objects are introduced, DALL·E is > > prone > > > to confusing the associations between the objects and their colors, and > > the > > > success rate decreases sharply. We also note that DALL·E is brittle > with > > > respect to rephrasing of the caption in these scenarios: alternative, > > > semantically equivalent captions often yield no correct > interpretations. > > > > > > Visualizing Perspective and Three-Dimensionality > > > > > > We find that DALL·E also allows for control over the viewpoint of a > scene > > > and the 3D style in which a scene is rendered. > > > > > > an extreme close-up view of a capybara sitting in a field > > > > > > navigatedownwide > > > a capybara made of voxels sitting in a field > > > > > > navigatedownwide > > > > > > To push this further, we test DALL·E’s ability to repeatedly draw the > > head > > > of a well-known figure at each angle from a sequence of equally spaced > > > angles, and find that we can recover a smooth animation of the rotating > > > head. > > > > > > a photograph of a bust of homer > > > > > > navigatedownwide > > > > > > DALL·E appears to be able to apply some types of optical distortions to > > > scenes, as we see with the options “fisheye lens view” and “a spherical > > > panorama.” This motivated us to explore its ability to generate > > reflections. > > > > > > a plain white cube looking at its own reflection in a mirror. a plain > > > white cube gazing at itself in a mirror. > > > > > > navigatedownwide > > > > > > Visualizing Internal and External Structure > > > > > > The samples from the “extreme close-up view” and “x-ray” style led us > to > > > further explore DALL·E’s ability to render internal structure with > > > cross-sectional views, and external structure with macro photographs. > > > > > > a cross-section view of a walnut > > > > > > navigatedownwide > > > a macro photograph of brain coral > > > > > > navigatedownwide > > > > > > Inferring Contextual Details > > > > > > The task of translating text to images is underspecified: a single > > caption > > > generally corresponds to an infinitude of plausible images, so the > image > > is > > > not uniquely determined. For instance, consider the caption “a painting > > of > > > a capybara sitting on a field at sunrise.” Depending on the orientation > > of > > > the capybara, it may be necessary to draw a shadow, though this detail > is > > > never mentioned explicitly. We explore DALL·E’s ability to resolve > > > underspecification in three cases: changing style, setting, and time; > > > drawing the same object in a variety of different situations; and > > > generating an image of an object with specific text written on it. > > > > > > a painting of a capybara sitting in a field at sunrise > > > > > > navigatedownwide > > > a stained glass window with an image of a blue strawberry > > > > > > navigatedownwide > > > a store front that has the word ‘openai’ written on it. a store front > > that > > > has the word ‘openai’ written on it. a store front that has the word > > > ‘openai’ written on it. ‘openai’ store front. > > > > > > navigatedownwide > > > > > > With varying degrees of reliability, DALL·E provides access to a subset > > of > > > the capabilities of a 3D rendering engine via natural language. It can > > > independently control the attributes of a small number of objects, and > > to a > > > limited extent, how many there are, and how they are arranged with > > respect > > > to one another. It can also control the location and angle from which a > > > scene is rendered, and can generate known objects in compliance with > > > precise specifications of angle and lighting conditions. > > > > > > Unlike a 3D rendering engine, whose inputs must be specified > > unambiguously > > > and in complete detail, DALL·E is often able to “fill in the blanks” > when > > > the caption implies that the image must contain a certain detail that > is > > > not explicitly stated. > > > > > > Applications of Preceding Capabilities > > > > > > Next, we explore the use of the preceding capabilities for fashion and > > > interior design. > > > > > > a male mannequin dressed in an orange and black flannel shirt > > > > > > navigatedownwide > > > a female mannequin dressed in a black leather jacket and gold pleated > > skirt > > > > > > navigatedownwide > > > a living room with two white armchairs and a painting of the colosseum. > > > the painting is mounted above a modern fireplace. > > > > > > navigatedownwide > > > a loft bedroom with a white bed next to a nightstand. there is a fish > > tank > > > beside the bed. > > > > > > navigatedownwide > > > > > > Combining Unrelated Concepts > > > > > > The compositional nature of language allows us to put together concepts > > to > > > describe both real and imaginary things. We find that DALL·E also has > the > > > ability to combine disparate ideas to synthesize objects, some of which > > are > > > unlikely to exist in the real world. We explore this ability in two > > > instances: transferring qualities from various concepts to animals, and > > > designing products by taking inspiration from unrelated concepts. > > > > > > a snail made of harp. a snail with the texture of a harp. > > > > > > navigatedownwide > > > an armchair in the shape of an avocado. an armchair imitating an > avocado. > > > > > > navigatedownwide > > > > > > Animal Illustrations > > > > > > In the previous section, we explored DALL·E’s ability to combine > > unrelated > > > concepts when generating images of real-world objects. Here, we explore > > > this ability in the context of art, for three kinds of illustrations: > > > anthropomorphized versions of animals and objects, animal chimeras, and > > > emojis. > > > > > > an illustration of a baby daikon radish in a tutu walking a dog > > > > > > navigatedownwide > > > a professional high quality illustration of a giraffe turtle chimera. a > > > giraffe imitating a turtle. a giraffe made of turtle. > > > > > > navigatedownwide > > > a professional high quality emoji of a lovestruck cup of boba > > > > > > navigatedownwide > > > > > > Zero-Shot Visual Reasoning > > > > > > GPT-3 can be instructed to perform many kinds of tasks solely from a > > > description and a cue to generate the answer supplied in its prompt, > > > without any additional training. For example, when prompted with the > > phrase > > > “here is the sentence ‘a person walking his dog in the park’ translated > > > into French:”, GPT-3 answers “un homme qui promène son chien dans le > > parc.” > > > This capability is called zero-shot reasoning. We find that DALL·E > > extends > > > this capability to the visual domain, and is able to perform several > > kinds > > > of image-to-image translation tasks when prompted in the right way. > > > > > > the exact same cat on the top as a sketch on the bottom > > > > > > navigatedownwide > > > the exact same teapot on the top with ’gpt’ written on it on the bottom > > > > > > navigatedownwide > > > > > > We did not anticipate that this capability would emerge, and made no > > > modifications to the neural network or training procedure to encourage > > it. > > > Motivated by these results, we measure DALL·E’s aptitude for analogical > > > reasoning problems by testing it on Raven’s progressive matrices, a > > visual > > > IQ test that saw widespread use in the 20th century. > > > > > > a sequence of geometric shapes. > > > > > > navigatedownwide > > > > > > Geographic Knowledge > > > > > > We find that DALL·E has learned about geographic facts, landmarks, and > > > neighborhoods. Its knowledge of these concepts is surprisingly precise > in > > > some ways and flawed in others. > > > > > > a photo of the food of china > > > > > > navigatedownwide > > > a photo of alamo square, san francisco, from a street at night > > > > > > navigatedownwide > > > a photo of san francisco’s golden gate bridge > > > > > > navigatedownwide > > > > > > Temporal Knowledge > > > > > > In addition to exploring DALL·E’s knowledge of concepts that vary over > > > space, we also explore its knowledge of concepts that vary over time. > > > > > > a photo of a phone from the 20s > > > > > > navigatedownwide > > > > > > Summary of Approach and Prior Work > > > > > > DALL·E is a simple decoder-only transformer that receives both the text > > > and the image as a single stream of 1280 tokens—256 for the text and > 1024 > > > for the image—and models all of them autoregressively. The attention > mask > > > at each of its 64 self-attention layers allows each image token to > attend > > > to all text tokens. DALL·E uses the standard causal mask for the text > > > tokens, and sparse attention for the image tokens with either a row, > > > column, or convolutional attention pattern, depending on the layer. We > > > provide more details about the architecture and training procedure in > our > > > [paper](https://arxiv.org/abs/2102.12092). > > > > > > Text-to-image synthesis has been an active area of research since the > > > pioneering work of Reed et. al,[1](https://openai.com/blog/dall-e/#rf1 > ) > > > whose approach uses a GAN conditioned on text embeddings. The > embeddings > > > are produced by an encoder pretrained using a contrastive loss, not > > unlike > > > CLIP. StackGAN[3](https://openai.com/blog/dall-e/#rf3) and > > StackGAN++[4]( > > > https://openai.com/blog/dall-e/#rf4) use multi-scale GANs to scale up > > the > > > image resolution and improve visual fidelity. AttnGAN[5]( > > > https://openai.com/blog/dall-e/#rf5) incorporates attention between > the > > > text and image features, and proposes a contrastive text-image feature > > > matching loss as an auxiliary objective. This is interesting to compare > > to > > > our reranking with CLIP, which is done offline. Other work[2]( > > > > > > https://openai.com/blog/dall-e/#rf2)[6](https://openai.com/blog/dall-e/#rf6)[7](https://openai.com/blog/dall-e/#rf7 > > ) > > > incorporates additional sources of supervision during training to > improve > > > image quality. Finally, work by Nguyen et. al[8]( > > > https://openai.com/blog/dall-e/#rf8) and Cho et. al[9]( > > > https://openai.com/blog/dall-e/#rf9) explores sampling-based > strategies > > > for image generation that leverage pretrained multimodal discriminative > > > models. > > > > > > Similar to the rejection sampling used in [VQVAE-2]( > > > https://arxiv.org/abs/1906.00446), we use [CLIP]( > > > https://openai.com/blog/clip/) to rerank the top 32 of 512 samples for > > > each caption in all of the interactive visuals. This procedure can also > > be > > > seen as a kind of language-guided search[16]( > > > https://openai.com/blog/dall-e/#rf16), and can have a dramatic impact > on > > > sample quality. > > > > > > an illustration of a baby daikon radish in a tutu walking a dog > [caption > > > 1, best 8 of 2048] > > > > > > > > > > > > navigatedownwide--------------------------------------------------------------- > > > > > > Footnotes > > > > > > - > > > > > > We decided to name our model using a portmanteau of the artist Salvador > > > Dalí and Pixar’s WALL·E. [↩︎](https://openai.com/blog/dall-e/#fnref1) > > > > > > - > > > > > > A token is any symbol from a discrete vocabulary; for humans, each > > English > > > letter is a token from a 26-letter alphabet. DALL·E’s vocabulary has > > tokens > > > for both text and image concepts. Specifically, each image caption is > > > represented using a maximum of 256 BPE-encoded tokens with a vocabulary > > > size of 16384, and the image is represented using 1024 tokens with a > > > vocabulary size of 8192. > > > > > > The images are preprocessed to 256x256 resolution during training. > > Similar > > > to VQVAE,[14]( > > > > > > https://openai.com/blog/dall-e/#rf14)[15](https://openai.com/blog/dall-e/#rf15 > > ) > > > each image is compressed to a 32x32 grid of discrete latent codes > using a > > > discrete VAE[10]( > > > > > > https://openai.com/blog/dall-e/#rf10)[11](https://openai.com/blog/dall-e/#rf11 > > ) > > > that we pretrained using a continuous relaxation.[12]( > > > > > > https://openai.com/blog/dall-e/#rf12)[13](https://openai.com/blog/dall-e/#rf13 > > ) > > > We found that training using the relaxation obviates the need for an > > > explicit codebook, EMA loss, or tricks like dead code revival, and can > > > scale up to large vocabulary sizes. [↩︎]( > > > https://openai.com/blog/dall-e/#fnref2) > > > > > > - > > > > > > Further details provided in [a later section]( > > > https://openai.com/blog/dall-e/#summary). [↩︎]( > > > https://openai.com/blog/dall-e/#fnref3) > > > > > > - > > > > > > This task is called variable binding, and has been extensively studied > in > > > the literature.[17]( > > > > > > https://openai.com/blog/dall-e/#rf17)[18](https://openai.com/blog/dall-e/#rf18)[19](https://openai.com/blog/dall-e/#rf19)[20](https://openai.com/blog/dall-e/#rf20 > > ) > > > [↩︎](https://openai.com/blog/dall-e/#fnref4) > > > > > > --------------------------------------------------------------- > > > > > > References > > > > > > - Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H. > > > (2016). “[Generative adversarial text to image synthesis]( > > > https://arxiv.org/abs/1605.05396)”. In ICML 2016. [↩︎]( > > > https://openai.com/blog/dall-e/#rfref1) > > > > > > - Reed, S., Akata, Z., Mohan, S., Tenka, S., Schiele, B., Lee, H. > (2016). > > > “[Learning what and where to draw](https://arxiv.org/abs/1610.02454)”. > > In > > > NIPS 2016. [↩︎](https://openai.com/blog/dall-e/#rfref2) > > > > > > - Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang X., Metaxas, D. > > > (2016). “[StackGAN: Text to photo-realistic image synthesis with > stacked > > > generative adversarial networks](https://arxiv.org/abs/1612.03242)”. > In > > > ICCY 2017. [↩︎](https://openai.com/blog/dall-e/#rfref3) > > > > > > - Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, > D. > > > (2017). “[StackGAN++: realistic image synthesis with stacked generative > > > adversarial networks](https://arxiv.org/abs/1710.10916)”. In IEEE > TPAMI > > > 2018. [↩︎](https://openai.com/blog/dall-e/#rfref4) > > > > > > - Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X. > > > (2017). “[AttnGAN: Fine-grained text to image generation with > attentional > > > generative adversarial networks](https://arxiv.org/abs/1711.10485). > > [↩︎]( > > > https://openai.com/blog/dall-e/#rfref5) > > > > > > - Li, W., Zhang, P., Zhang, L., Huang, Q., He, X., Lyu, S., Gao, J. > > > (2019). “[Object-driven text-to-image synthesis via adversarial > > training]( > > > https://arxiv.org/abs/1902.10740)”. In CVPR 2019. [↩︎]( > > > https://openai.com/blog/dall-e/#rfref6) > > > > > > - Koh, J. Y., Baldridge, J., Lee, H., Yang, Y. (2020). “[Text-to-image > > > generation grounded by fine-grained user attention]( > > > https://arxiv.org/abs/2011.03775)”. In WACV 2021. [↩︎]( > > > https://openai.com/blog/dall-e/#rfref7) > > > > > > - Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., Yosinski, J. > > (2016). > > > “[Plug & play generative networks: conditional iterative generation of > > > images in latent space](https://arxiv.org/abs/1612.00005). [↩︎]( > > > https://openai.com/blog/dall-e/#rfref8) > > > > > > - Cho, J., Lu, J., Schwen, D., Hajishirzi, H., Kembhavi, A. (2020). > > > “[X-LXMERT: Paint, caption, and answer questions with multi-modal > > > transformers](https://arxiv.org/abs/2009.11278)”. EMNLP 2020. [↩︎]( > > > https://openai.com/blog/dall-e/#rfref9) > > > > > > - Kingma, Diederik P., and Max Welling. “[Auto-encoding variational > > bayes]( > > > https://arxiv.org/abs/1312.6114).” arXiv preprint (2013). [↩︎]( > > > https://openai.com/blog/dall-e/#rfref10a) [↩︎]( > > > https://openai.com/blog/dall-e/#rfref10b) > > > > > > - Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra. > > “[Stochastic > > > backpropagation and approximate inference in deep generative models]( > > > https://arxiv.org/abs/1401.4082).” arXiv preprint (2014). [↩︎]( > > > https://openai.com/blog/dall-e/#rfref11a) [↩︎]( > > > https://openai.com/blog/dall-e/#rfref11b) > > > > > > - Jang, E., Gu, S., Poole, B. (2016). “[Categorical reparametrization > > with > > > Gumbel-softmax](https://arxiv.org/abs/1611.01144)”. [↩︎]( > > > https://openai.com/blog/dall-e/#rfref12a) [↩︎]( > > > https://openai.com/blog/dall-e/#rfref12b) > > > > > > - Maddison, C., Mnih, A., Teh, Y. W. (2016). “[The Concrete > distribution: > > > a continuous relaxation of discrete random variables]( > > > https://arxiv.org/abs/1611.00712)”. 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(1997). “[Fully distributed representations]( > > > http://www.cap-lore.com/RWC97-kanerva.pdf)”. [↩︎]( > > > https://openai.com/blog/dall-e/#rfref20a) [↩︎]( > > > https://openai.com/blog/dall-e/#rfref20b) > > > > > > --------------------------------------------------------------- > > > > > > Authors > > > [Aditya Ramesh](https://openai.com/blog/authors/aditya/)[Mikhail > > Pavlov]( > > > https://openai.com/blog/authors/mikhail/)[Gabriel Goh]( > > > https://openai.com/blog/authors/gabriel/)[Scott Gray]( > > > https://openai.com/blog/authors/scott/) > > > (Primary Authors) > > > [Mark Chen](https://openai.com/blog/authors/mark/)[Rewon Child]( > > > https://openai.com/blog/authors/rewon/)[Vedant Misra]( > > > https://openai.com/blog/authors/vedant/)[Pamela Mishkin]( > > > https://openai.com/blog/authors/pamela/)[Gretchen Krueger]( > > > https://openai.com/blog/authors/gretchen/)[Sandhini Agarwal]( > > > https://openai.com/blog/authors/sandhini/)[Ilya Sutskever]( > > > https://openai.com/blog/authors/ilya/) > > > (Supporting Authors) > > > --------------------------------------------------------------- > > > > > > Filed Under > > > [Research]( > > > > > > https://openai.com/blog/tags/research/)[Milestones](https://openai.com/blog/tags/milestones/)[Multimodal](https://openai.com/blog/tags/multimodal/ > > > ) > > > --------------------------------------------------------------- > > > > > > Cover Artwork > > > > > > Justin Jay Wang > > > > > > --------------------------------------------------------------- > > > > > > Acknowledgments > > > > > > Thanks to the following for their feedback on this work and > contributions > > > to this release: Alec Radford, Andrew Mayne, Jeff Clune, Ashley > > Pilipiszyn, > > > Steve Dowling, Jong Wook Kim, Lei Pan, Heewoo Jun, John Schulman, > Michael > > > Tabatowski, Preetum Nakkiran, Jack Clark, Fraser Kelton, Jacob Jackson, > > > Greg Brockman, Wojciech Zaremba, Justin Mao-Jones, David Luan, Shantanu > > > Jain, Prafulla Dhariwal, Sam Altman, Pranav Shyam, Miles Brundage, > Jakub > > > Pachocki, and Ryan Lowe. > > > > > > --------------------------------------------------------------- > > > > > > Contributions > > > > > > Aditya Ramesh was the project lead: he developed the approach, trained > > the > > > models, and wrote most of the blog copy. > > > > > > Aditya Ramesh, Mikhail Pavlov, and Scott Gray worked together to scale > up > > > the model to 12 billion parameters, and designed the infrastructure > used > > to > > > draw samples from the model. > > > > > > Aditya Ramesh, Gabriel Goh, and Justin Jay Wang worked together to > create > > > the interactive visuals for the blog. > > > > > > Mark Chen and Aditya Ramesh created the images for Raven’s Progressives > > > Matrices. > > > > > > Rewon Child and Vedant Misra assisted in writing the blog. > > > > > > Pamela Mishkin, Gretchen Krueger, and Sandhini Agarwal advised on > broader > > > impacts of the work and assisted in writing the blog. > > > > > > Ilya Sutskever oversaw the project and assisted in writing the blog. > > > -------------- next part -------------- > > > A non-text attachment was scrubbed... > > > Name: not available > > > Type: text/html > > > Size: 45019 bytes > > > Desc: not available > > > URL: < > > > > > > https://lists.cpunks.org/pipermail/cypherpunks/attachments/20220408/96a4e98c/attachment.txt > > > > > > > > > > ------------------------------ > > > > > > Subject: Digest Footer > > > > > > _______________________________________________ > > > cypherpunks mailing list > > > [email protected] > > > https://lists.cpunks.org/mailman/listinfo/cypherpunks > > > > > > > > > ------------------------------ > > > > > > End of cypherpunks Digest, Vol 106, Issue 93 > > > ******************************************** > > > > > -------------- next part -------------- > > A non-text attachment was scrubbed... > > Name: not available > > Type: text/html > > Size: 39458 bytes > > Desc: not available > > URL: < > > > https://lists.cpunks.org/pipermail/cypherpunks/attachments/20220408/66867ca0/attachment.txt > > > > > > > ------------------------------ > > > > Subject: Digest Footer > > > > _______________________________________________ > > cypherpunks mailing list > > [email protected] > > https://lists.cpunks.org/mailman/listinfo/cypherpunks > > > > > > ------------------------------ > > > > End of cypherpunks Digest, Vol 106, Issue 94 > > ******************************************** > > > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: not available > Type: text/html > Size: 46836 bytes > Desc: not available > URL: < > https://lists.cpunks.org/pipermail/cypherpunks/attachments/20220408/eea82fed/attachment.txt > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > cypherpunks mailing list > [email protected] > https://lists.cpunks.org/mailman/listinfo/cypherpunks > > > ------------------------------ > > End of cypherpunks Digest, Vol 106, Issue 95 > ******************************************** >
