Oh yes he did, he did do it.
   Gunnar Larson raped him. He had to ...

   On Fri, Apr 8, 2022, 8:32 PM <[1]cypherpunks-request@lists.cpunks.org>
   wrote:

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        1. Re: cypherpunks Digest, Vol 106, Issue 94 (Gunnar Larson)
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     Message: 1
     Date: Fri, 8 Apr 2022 20:29:43 -0400
     From: Gunnar Larson <[6]g@xny.io>
     To: cypherpunks <[7]cypherpunks@lists.cpunks.org>
     Subject: Re: cypherpunks Digest, Vol 106, Issue 94
     Message-ID:

     <[8]CAPc8xwO4+uLbaR52tWvdyRckPtLWO49uxSHk-boT0HwUkMmUVw@mail.gmail.c
     om>
     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
     <[9]cypherpunks-request@lists.cpunks.org> wrote:
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     >    1. Re: cypherpunks Digest, Vol 106, Issue 93 (Gunnar Larson)
     >
     >
     >
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     >
     > Message: 1
     > Date: Fri, 8 Apr 2022 19:54:15 -0400
     > From: Gunnar Larson <[14]g@xny.io>
     > To: cypherpunks <[15]cypherpunks@lists.cpunks.org>
     > Subject: Re: cypherpunks Digest, Vol 106, Issue 93
     > Message-ID:
     >         <CAPc8xwPsCK2cA3tT1U-wjuV09T5kc=
     > [16]TBMcrzLz46uyNHJXV9cg@mail.gmail.com>
     > Content-Type: text/plain; charset="utf-8"
     >
     > At first glance, this was a great article.
     >
     > On Fri, Apr 8, 2022, 7:52 PM
     <[17]cypherpunks-request@lists.cpunks.org> wrote:
     >
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     > >
     > >
     > > Today's Topics:
     > >
     > >    1. Re: DALL-E (coderman)
     > >
     > >
     > >
     --------------------------------------------------------------------
     --
     > >
     > > Message: 1
     > > Date: Fri, 08 Apr 2022 23:50:53 +0000
     > > From: coderman <[22]coderman@protonmail.com>
     > > To: coderman <[23]coderman@protonmail.com>
     > > Cc: "cy\"Cypherpunks" <[24]cypherpunks@cpunks.org>
     > > Subject: Re: DALL-E
     > > Message-ID:
     > >
     > >
     >
     <a9WeFGpr9g422W0Uym9aQZyxT6mqWNzNwLsG6yKqqlD4BLpH6NxuARXLOMvBY8IdZF9
     HMetBKZYGjdH--qJRFZDIWnXdMRQVqr3pmMYVo5I=@
     > > [25]protonmail.com>
     > >
     > > Content-Type: text/plain; charset="utf-8"
     > >
     > > DALL·E[1]([26]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](
     > > [27]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](
     > > [28]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]([29]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](
     > >
     >
     [30]https://openai.com/blog/dall-e/#rf14)[15](https://openai.com/blo
     g/dall-e/#rf15
     > )
     > > each image is compressed to a 32x32 grid of discrete latent
     codes using a
     > > discrete VAE[10](
     > >
     >
     [31]https://openai.com/blog/dall-e/#rf10)[11](https://openai.com/blo
     g/dall-e/#rf11
     > )
     > > that we pretrained using a continuous relaxation.[12](
     > >
     >
     [32]https://openai.com/blog/dall-e/#rf12)[13](https://openai.com/blo
     g/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](
     > > [33]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]([34]https://openai.com/blog/dall-e/#fn3)
     > >
     > > Further details provided in [a later section](
     > > [35]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]([36]https://openai.com/blog/dall-e/#fn4)
     > >
     > > This task is called variable binding, and has been extensively
     studied in
     > > the literature.[17](
     > >
     >
     [37]https://openai.com/blog/dall-e/#rf17)[18](https://openai.com/blo
     g/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]([38]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]([39]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]([40]https://openai.com/blog/dall-e/#rf3) and
     > StackGAN++[4](
     > > [41]https://openai.com/blog/dall-e/#rf4) use multi-scale GANs to
     scale up
     > the
     > > image resolution and improve visual fidelity. AttnGAN[5](
     > > [42]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](
     > >
     >
     [43]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](
     > > [44]https://openai.com/blog/dall-e/#rf8) and Cho et. al[9](
     > > [45]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](
     > > [46]https://arxiv.org/abs/1906.00446), we use [CLIP](
     > > [47]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](
     > > [48]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.
     [↩︎]([49]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](
     > >
     >
     [50]https://openai.com/blog/dall-e/#rf14)[15](https://openai.com/blo
     g/dall-e/#rf15
     > )
     > > each image is compressed to a 32x32 grid of discrete latent
     codes using a
     > > discrete VAE[10](
     > >
     >
     [51]https://openai.com/blog/dall-e/#rf10)[11](https://openai.com/blo
     g/dall-e/#rf11
     > )
     > > that we pretrained using a continuous relaxation.[12](
     > >
     >
     [52]https://openai.com/blog/dall-e/#rf12)[13](https://openai.com/blo
     g/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. [↩︎](
     > > [53]https://openai.com/blog/dall-e/#fnref2)
     > >
     > > -
     > >
     > > Further details provided in [a later section](
     > > [54]https://openai.com/blog/dall-e/#summary). [↩︎](
     > > [55]https://openai.com/blog/dall-e/#fnref3)
     > >
     > > -
     > >
     > > This task is called variable binding, and has been extensively
     studied in
     > > the literature.[17](
     > >
     >
     [56]https://openai.com/blog/dall-e/#rf17)[18](https://openai.com/blo
     g/dall-e/#rf18)[19](https://openai.com/blog/dall-e/#rf19)[20](https:
     //openai.com/blog/dall-e/#rf20
     > )
     > > [↩︎]([57]https://openai.com/blog/dall-e/#fnref4)
     > >
     > > ---------------------------------------------------------------
     > >
     > > References
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     > >
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     H. (2016).
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     > >
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     Metaxas, D.
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     stacked
     > > generative adversarial
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     > >
     > > - Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X.,
     Metaxas, D.
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     generative
     > > adversarial networks]([64]https://arxiv.org/abs/1710.10916)”. In
     IEEE TPAMI
     > > 2018. [↩︎]([65]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]([66]https://arxiv.org/abs/1711.10485).
     > [↩︎](
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     > >
     > > - Li, W., Zhang, P., Zhang, L., Huang, Q., He, X., Lyu, S., Gao,
     J.
     > > (2019). “[Object-driven text-to-image synthesis via adversarial
     > training](
     > > [68]https://arxiv.org/abs/1902.10740)”. In CVPR 2019. [↩︎](
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     > >
     > > - Koh, J. Y., Baldridge, J., Lee, H., Yang, Y. (2020).
     “[Text-to-image
     > > generation grounded by fine-grained user attention](
     > > [70]https://arxiv.org/abs/2011.03775)”. In WACV 2021. [↩︎](
     > > [71]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]([72]https://arxiv.org/abs/1612.00005).
     [↩︎](
     > > [73]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]([74]https://arxiv.org/abs/2009.11278)”. EMNLP
     2020. [↩︎](
     > > [75]https://openai.com/blog/dall-e/#rfref9)
     > >
     > > - Kingma, Diederik P., and Max Welling. “[Auto-encoding
     variational
     > bayes](
     > > [76]https://arxiv.org/abs/1312.6114).” arXiv preprint (2013).
     [↩︎](
     > > [77]https://openai.com/blog/dall-e/#rfref10a) [↩︎](
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     > >
     > > - Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra.
     > “[Stochastic
     > > backpropagation and approximate inference in deep generative
     models](
     > > [79]https://arxiv.org/abs/1401.4082).” arXiv preprint (2014).
     [↩︎](
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     > >
     > > - Jang, E., Gu, S., Poole, B. (2016). “[Categorical
     reparametrization
     > with
     > > Gumbel-softmax]([82]https://arxiv.org/abs/1611.01144)”. [↩︎](
     > > [83]https://openai.com/blog/dall-e/#rfref12a) [↩︎](
     > > [84]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](
     > > [85]https://arxiv.org/abs/1611.00712)”. [↩︎](
     > > [86]https://openai.com/blog/dall-e/#rfref13a) [↩︎](
     > > [87]https://openai.com/blog/dall-e/#rfref13b)
     > >
     > > - van den Oord, A., Vinyals, O., Kavukcuoglu, K. (2017).
     “[Neural
     > discrete
     > > representation learning]([88]https://arxiv.org/abs/1711.00937)”.
     [↩︎](
     > > [89]https://openai.com/blog/dall-e/#rfref14a) [↩︎](
     > > [90]https://openai.com/blog/dall-e/#rfref14b)
     > >
     > > - Razavi, A., van der Oord, A., Vinyals, O. (2019). “[Generating
     diverse
     > > high-fidelity images with
     VQ-VAE-2]([91]https://arxiv.org/abs/1906.00446)”.
     > > [↩︎]([92]https://openai.com/blog/dall-e/#rfref15a) [↩︎](
     > > [93]https://openai.com/blog/dall-e/#rfref15b)
     > >
     > > - Andreas, J., Klein, D., Levine, S. (2017). “[Learning with
     Latent
     > > Language]([94]https://arxiv.org/abs/1711.00482)”. [↩︎](
     > > [95]https://openai.com/blog/dall-e/#rfref16)
     > >
     > > - Smolensky, P. (1990). “[Tensor product variable binding and
     the
     > > representation of symbolic structures in connectionist systems](
     > >
     >
     [96]http://www.lscp.net/persons/dupoux/teaching/AT1_2014/papers/Smol
     ensky_1990_TensorProductVariableBinding.AI.pdf)
     > ”.
     > > [↩︎]([97]https://openai.com/blog/dall-e/#rfref17a) [↩︎](
     > > [98]https://openai.com/blog/dall-e/#rfref17b)
     > >
     > > - Plate, T. (1995). “[Holographic reduced representations:
     convolution
     > > algebra for compositional distributed representations](
     > > [99]https://www.ijcai.org/Proceedings/91-1/Papers/006.pdf)”.
     [↩︎](
     > > [100]https://openai.com/blog/dall-e/#rfref18a) [↩︎](
     > > [101]https://openai.com/blog/dall-e/#rfref18b)
     > >
     > > - Gayler, R. (1998). “[Multiplicative binding, representation
     operators &
     > > analogy]([102]http://cogprints.org/502/)”. [↩︎](
     > > [103]https://openai.com/blog/dall-e/#rfref19a) [↩︎](
     > > [104]https://openai.com/blog/dall-e/#rfref19b)
     > >
     > > - Kanerva, P. (1997). “[Fully distributed representations](
     > > [105]http://www.cap-lore.com/RWC97-kanerva.pdf)”. [↩︎](
     > > [106]https://openai.com/blog/dall-e/#rfref20a) [↩︎](
     > > [107]https://openai.com/blog/dall-e/#rfref20b)
     > >
     > > ---------------------------------------------------------------
     > >
     > > Authors
     > > [Aditya
     Ramesh]([108]https://openai.com/blog/authors/aditya/)[Mikhail
     > Pavlov](
     > > [109]https://openai.com/blog/authors/mikhail/)[Gabriel Goh](
     > > [110]https://openai.com/blog/authors/gabriel/)[Scott Gray](
     > > [111]https://openai.com/blog/authors/scott/)
     > > (Primary Authors)
     > > [Mark Chen]([112]https://openai.com/blog/authors/mark/)[Rewon
     Child](
     > > [113]https://openai.com/blog/authors/rewon/)[Vedant Misra](
     > > [114]https://openai.com/blog/authors/vedant/)[Pamela Mishkin](
     > > [115]https://openai.com/blog/authors/pamela/)[Gretchen Krueger](
     > > [116]https://openai.com/blog/authors/gretchen/)[Sandhini
     Agarwal](
     > > [117]https://openai.com/blog/authors/sandhini/)[Ilya Sutskever](
     > > [118]https://openai.com/blog/authors/ilya/)
     > > (Supporting Authors)
     > > ---------------------------------------------------------------
     > >
     > > Filed Under
     > > [Research](
     > >
     >
     [119]https://openai.com/blog/tags/research/)[Milestones](https://ope
     nai.com/blog/tags/milestones/)[Multimodal](https://openai.com/blog/t
     ags/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 --------------
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     > > >
     > >
     > > ------------------------------
     > >
     > > Subject: Digest Footer
     > >
     > > _______________________________________________
     > > cypherpunks mailing list
     > > [121]cypherpunks@lists.cpunks.org
     > > [122]https://lists.cpunks.org/mailman/listinfo/cypherpunks
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     > > ------------------------------
     > >
     > > End of cypherpunks Digest, Vol 106, Issue 93
     > > ********************************************
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